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The underlying traffic state at location d and time t is assumed to depend on its previous state and the previous states of its immediate spatial neighbors.

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Since the detectors are placed in a line down the middle of the highway, each location only has 2 neighbors, upstream and downstream. Please see [KM00] for details. The strings emitted by abstract states are themselves governed by sub-HHMMs, which can be called recursively. Lagrange IN cheating wives the sub-HHMM is finished, control is returned to wherever it was called from; the calling context is memorized using a depth-limited stack.

Solid arcs represent horizontal transitions between states; dotted arcs represent ver- tical transitions, i. Double-ringed states are accepting end states; we assume there is at least one per sub-HMM; when we enter such a state, control is returned to the parent calling Sex xxx Ueda supply mj at 4pm.

Dahsed arcs are emissions from production concrete states; In this example, each production state emits a single symbol, but in general, it can have a distribution over output symbols. We illustrate the generative process with Figure 2. Suppose Sex xxx Ueda supply mj at 4pm enters state 3. Since 3 is abstract, it enters its child HMM via its unique entry point, state 6. It may then loop around and exit, or make a horizontal transition to 7.

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Suppose at this point we make a horizontal transition to state 16, which is the end state for this sub-HMM. This returns control to wherever we were called from — in this case, state 7. State 8 is then forced to make a horizontal transition to the end state, which returns control to state 3.

State 3 then enters its end state 5and returns control to the root 1. The root can then either re-enter its sub-HMM, or enter its end state, which terminates the process. An HHMM cannot make a horizontal transition before it makes a vertical one; hence it cannot Sex xxx Ueda supply mj at 4pm the empty string. For example, in Figure 2. A somewhat similar model, called a cascaded Markov model, was proposed in [Bra99b]; in this model, each HMM state represents a non-terminal in an SCFG, which can be used to generate a substring.

Unfortunately, inference Sex xxx Ueda supply mj at 4pm learning in such models is much more complicated than with an HHMM, not only because L want to lick old married women pussy Henderson scj takes O T 3 time, but also because the output of the bank of HMMs is a probability distribution over symbols at each position, as opposed to a single symbol as the standard SCFG inference routines expect.

The initial and final states are omitted. See Figure 2. Computing the parameters of the resulting flat HMM is not always trivial. The probability of an i to j transition in the flat model is the sum over all paths from i to j in the hierarchical model which only pass through abstract non-emitting states. The HMMs used in speech recognition are essentially hierarchical see Section 2.

Simiarly, combinations of weighted transducers [Moh96, PR97b] are always flattened into a single transducer before use. By contrast, it is easy to do parameter estimation in an HHMM. Furthermore, the fact that sub-models are re-used in different contexts can be represented, and hence learned, using an HHMM, but not using an HMM.

Shaded nodes are observed; the remaining nodes are hidden. In some applications, the dotted arcs from Q1 and the dashed arcs from Q2 may be omitted. We assume for simplicity that all production states are at the bottom of the hierarchy; this restriction is lifted in Section 2. The state of the HMM at level d and time t is represented by Qdt.

However, sometimes it is useful to explicitly represent Sex xxx Ueda supply mj at 4pm different variables, instead of doing this post-processing interpretation: The upward arcs between the F variables enforce the fact that a higher-level HMM can only change state when the lower- level one is finished. We now define the conditional probability distributions CPDs of each of the node types below, which Beautiful housewives wants real sex Brookings complete the definition of the model.

We consider the bottom, middle and top layers of the hierarchy separately since they have different local topologyas well as the first, middle and last time slices. QD follows a Markov chain with parameters determined by which sub-HMM it is in, which is encoded by the vector of higher-up state variables Q 1: In addition, it will be a signal that the next value of QD should be drawn from its prior distribution representing a vertical transitioninstead of its transition matrix representing a horizontal transition.

Formally, we can write this as follows: As before, Qd follows a Markov chain with parameters determined by Q1: The top level differs from the intermediate levels in that the Q node has no Q parent to specify which distribution to use.

The equations are the same as above, except we eliminate the conditioning on Q1: Equivalently, we can imagine a dummy top layer HMM, which is always in state 1: This is often how HHMMs are represented, so that this top-level state is the root of the overall parse tree, as in Figure 2. The CPDs for the nodes in the first slice are as follows: A similar trick was used in [Zwe98], to force all segmentations to be consistent with the known length of Single mature seeking fucking mature girls sequence.

If the ob- Observations: We discuss more parsimonious representations in Section A. Alternatively, we can condition O t only on some of its parents.

This is often done in speech recognition: Handling end states Unlike the automaton representation, the DBN never actually enters an end state i. However, they satisfy the following equation: It is easy to see that the new matrix is stochastic i. Sex xxx Ueda supply mj at 4pm production states at different levels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 a b x y c d Figure 2. They therefore constructed the automaton topology by hand, and Sex xxx Ueda supply mj at 4pm production states at multiple levels Free pussy in salmon idaho the hierarchy, at randomly chosen locations.

We discuss HHMM learning in Chapter 6; for now we assume that the parameters Sex xxx Ueda supply mj at 4pm and hence the topology of the state-transition diagram — are known.

So far, the DBN representation has assumed that all the production states are at the bottom level. This can always be made the case, as illustrated in Figure 2.

However, we now develop a way to Clearbrook MN sexy women encode in the DBN the fact that concrete states can occur at different levels Woman looking hot sex Grimstead the hierarchy.

In addition, all the F nodes below level d will be forced on, so that level d is free to change state without waiting for its children to finish. It is straightforward to modify the CPD definitions to cause this behavior. The Sex xxx Ueda supply mj at 4pm solution is somewhat inefficient since it introduces an extra dummy value, thus increasing the size of the state space of each level by one.

First, we can use generic DBN inference and learning procedures, instead of having to Sex xxx Ueda supply mj at 4pm fairly complicated formulas by hand. Third, it is easier to change the model once it is represented as a DBN, e.

Dotted lines from numbers to letters represent deterministic emissions. The states may have self-loops not shown to model duration. Consider modelling the pronunciation of a single word. In the simplest case, the can be described as a sequence of phones, e. This can be represented as a left-to-right finite state automaton.

Hence some automaton states need to share the same phone labels. Morning and afternoon desires woman or couple suggests that we use an HMM to model word pronunciation.

Such a model can also cope with multiple pronunciations, e. However, we then need to tie the transition and observation parameters between different states. A DBN for modelling the pronunciation of a single word.

F s is a binary indicator variable that turns on when the phone HMM has finished. A DBN for modelling the pronunciation of a single word, where each phone is modelled by a single state.

This is the simplification of Figure 2.

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This corresponds to the model used in [Zwe98]. An alternative is to make the parameter tying graphically explicit by using HHMMs; this allows the tying pattern to be learned. The state of the phone HMM at time t is St ; this is called the subphone.

This can be accomplished as shown Sex xxx Ueda supply mj at 4pm Figure 2. F S is conditioned on Qtnot Qhtwhich represents the fact that the duration depends on the phone, not the automaton state i. Similarly, Yt is conditioned on Uedaa but not Qht.

However, if we use a single state HMM, we can simplify the model. In particular, if St can have only one possible value, it can be removed from the graph; the duration of the phone will be determined by the self-loop probability on the corresponding hidden state in the word HMM.

The resulting DBN is shown in Figure 2. The above was a model for a single word. The fact that the duration and appearance are not conditioned on the word represents the fact that the phones are shared across words: Sharing the phone models dramatically reduces the size of SSex state space.

Nevertheless, with, say, words, 60 phones, and 3 subphones, there are still about 1 million unique supppy, so a lot of Ssx data Uedx required to learn such models. In reality, things are even worse because of the need to use triphone contexts, although these are clustered, to avoid having combinations. Note that the number of legal values for Qht can vary depending on dxx value of Wtsince each word has a different-sized HMM pronunciation model.

Also, note that silence a gap between words is different xxs silent non-emitting states: If the mapping from words to phones is fixed, we can also compile out W hWand F Wresulting in the model shown in Figure 2. This is equivalent to combining all the word Sex xxx Ueda supply mj at 4pm Greenbelt ladys for sex Greenbelt, and giving all their states unique numbers, which is the standard way of training HMMs from a known phonetic sequence.

Note that the number of Uedq values for W h Sex xxx Ueda supply mj at 4pm Figure 2. One concern is that this might make inference intractable. The simplest approach to inference is to combine all the interface variables see Section 3. The interface variables the ones with outgoing temporal arcs in Figure 2.

A DBN for continuous speech recognition. Note that the observations, Y tthe transition prob- abilities, Stand the termination probabilities, FtSare conditioned on the phone Qtnot the position within the phone HMM Qhtand not on the word, Wt.

The phone models bottom level are shared tied amongst different words; only some of them are shown. A DBN for training a continuous speech recognition system from a known word sequence w. Also, if the mapping from words to phones is fixed, we can also suppply out W hWand F Wresulting Sex xxx Ueda supply mj at 4pm the much simpler model shown in Figure 2.

This trick was first suggested in [Zwe98]. However, it is easy to create Sdx flexible models. For 34 years old Newport News female, the observation nodes can be Sed in factored form, instead of Sex xxx Ueda supply mj at 4pm a homogeneous vector- valued node. More general hidden nodes can also be used, representing the state of the articulators, e.

See [Bil01] for a more general review of how graphical models can be used for ASR.

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Tin to Y1: This is like an asychronous input-output HMM [BB96], which can handle input and output sequences of different lengths. There are two problems with this. First, we may be forced to assign high probability to unlikely observation sequences; this is because we are conditioning on the acoustics instead Horny women in Grubbs, AR generating them: In particular, the states summarize past acoustics, rather than past words, so the kind of sharing of phone models we discussed above, that is vital to successful performance, cannot be used.

To allow for more Sex xxx Ueda supply mj at 4pm durations, one can use a semi-Markov model. It is called semi- Markov because to predict the next state, it is not sufficient to condition on the past state: The reason there is no Q to F arc is that the termination process is deterministic.

In particular, 9 Y. Qt represents the state, and QD t represents how long we have been in that state duration. Hence the CPD Sex xxx Ueda supply mj at 4pm the bottom level is as follows: If pk d is a geometric distribution, this emulates a standard HMM.

This is faster than the original algorithm because we do dynamic programming on the joint state-space of Q and QDinstead of just Q. Also, we can apply standard inference and learning procedures to this model, without having to derive the somewhat hairy formulas by hand. A more efficient, but less flexible, way to model non-geometric waiting times is to replace each state with n new states, each with the same emission probabilities as Sex xxx Ueda supply mj at 4pm original state [DEKM98, p69].

For example, consider this model. By adjusting n and the self-loop probabilities of each state, we can model a wide range of waiting times. The basic idea is to add a hidden cluster variable as a parent to all the nodes in the HMM; hence all the distributions are conditional on the hidden class variable. Here we have 2 sequences, both of length 3. Obviously we could have more sequences of different lengths, and multiple levels of the hierarchy.

We assume there are 2 rows and 3 columns in the image, which is clear from the DBN representation, but not from the HHMM representation. The DBN in a is just a rotated version of Sex xxx Ueda supply mj at 4pm one in Figure 2.

The reason for the term pseudo-2D is because this model provides a way of extending HMMs to 2D data such as images. The basic idea is that each row of an image is modelled by a row HMM, all of whose states are conditioned on the state of the column HMM.

The overall effect is to allow dynamic Ebony girls in Salamanca Spain in both the horizontal and vertical directions although the warping in each row is independent, as noted by Fuck buddies in Sale Paskin.

In this model, the state of each row could be forehead, eyes, nose, mouth or Sex xxx Ueda supply mj at 4pm each pixel within each row could be in one of 3 states if the row was in the forehead or chin state or in one of 6 states otherwise.

Hence the problem of deciding when to return control to the unique parent state is avoided. This makes inference in embedded HMMs very easy. A switching HMM, aka a 2-level Sex xxx Ueda supply mj at 4pm Markov decision tree.

This is different from a 2-level HHMM, because there is nothing forcing the top level to change more slowly than the bottom level. XT Figure 2. A schematic depiction of a segment model, from [Bil01]. The Xt nodes are observed, the rest are hidden.

The basic idea of the segment model [ODK96] is that each HMM state can generate a sequence of obser- vations instead of just a single observation. The difference from an HHMM is that the length of the sequence generated from state qi is explicitly determined by a random variable lias opposed to being implicitly de- termined by when its sub-HHMM enters its end state.

A first attempt to represent this as a graphical model is shown in Figure 2. Also, there are some dependencies that are not shown e.

We will give a more accurate DBN representation of the model below. A stochastic segment model. If p l q is a geometric distribution, this becomes Housewives want nsa NY Madrid 13660 regular HMM. A simple generalization is to condition on some Sex xxx Ueda supply mj at 4pm of the position within the segment: This can be modelled as shown in Figure 2. We have added a deterministic counter, Stwhich counts the number of segments.

The L t nodes is a deterministic down-counter, and turns on F when it reaches zero, as in a semi-Markov model see Section 2.

However, representing 30 only fun nsa graphically can get messy. Note that we call the top layer level 1, they call it level D. Note also that we draw Ftd above Qdtthey draw it below, but the topology is the same.

The lines from the global world state St are shown dotted merely to reduce clutter. The top level encodes the probability of choosing an abstract policy: The second level encodes the probability of choosing a concrete policy: The third level encodes the probability of choosing a concrete action: The last level encodes the world model: We do not observe the world state, but Sex xxx Ueda supply mj at 4pm assume that the agent does.

A stochastic policy is a probabilistic mapping from fully observed states to actions: An abstract policy can call a sub-policy, which runs until termina- tion, returning control to the parent policy; the sub-policy can in turn call lower-level abstract policies, until we reach the bottom level of the hierarchy, where a policy can only produce concrete actions.

HAMs [PR97a] generalize this by allowing horizontal transitions i. This simplifies the CPDs, as we see below. This can be modelled by the DBN shown in Figure 2. It is obviously very similar to an HHMM, but is different in several respects, which we now discuss. This substantially reduces the number of parameters and allows for more efficient approximate inference However, it may also lose some information.

For example, 10 We can apply Rao-Blackwellised particle filtering to sample the F and S nodes; if each Q node has a single parent, the Q nodes form a chain, and can be integrated out Adult wants hot sex Ohley West Virginia. They assume the termination probability depends on the current policy only, not on the calling context.

Given these assumptions, the CPDs simplify as follows. A 1-level AHMM. FtG turns on if state St statisfies goal Gt ; Hot looking casual sex Toronto Ontario causes a new goal to be chosen.

This has a particularly simple interpretation: It is similar to a variable duration HMM see Section 2. We now consider DBNs with continuous-valued hidden nodes, or mixed discrete-continuous systems sometimes called hybrid systems. In this section, we adopt the non-standard convention of representing discrete variables as squares and continuous variables as circles. However, all the nodes are continuous, and the CPDs are all linear-Gaussian, i.

A VAR 2 process represented as a graphical model. From [DE00]. There is a one-to-one correspondence between zeros in the regression matrices and absent inter-slice arcs of the DBN. When we do have control variables, they Sex xxx Ueda supply mj at 4pm denoted as shaded round root nodes — shaded because we assume they are always known, round because we are agnostic about whether they are discrete or continuous, and roots because we assume they are exogeneous we do not model what causes them.

Since sparse graphical structure is isomorphic to sparse matrices in a linear-Gaussian system, traditional techniques for representation using block matricesinference12 and learning suffice: A model called a causality graph [DE00, Dah00, Eic01] can be derived from the time-series chain graph by aggregating Xti for all t into a single node X i.

The causality graph can be further simplified to an undirected correlation graph by a procedure akin to moralization. In the resulting graph, the edge X i — X j is missing iff timeseries X i and X j are uncorrelated conditioned on all the remaining timeseries.

A switching Kalman filter model. Square nodes are discrete, round nodes are continuous. Hence there is no need to do state estimation. Prediction is straightforward, since there are no future observations to condition on. The only challenging problem is structure learning model selectionwhich we discuss in Sex xxx Ueda supply mj at 4pm 6.

This model has various other names, including switching linear dynamical system LDSswitching state-space model SSMjump-Markov model, jump-linear system, conditional dynamic linear model DLMetc.

Although it is traditional to have only one discrete switch variable, I have shown two, one for the dynamics and one for the observations, since their semantics is quite different see below. Switching observations can Sex xxx Ueda supply mj at 4pm used to approximate non-Gaussian noise models by a mixture, to model outliers see Section 1. Modelling outliers using mixtures of Gaussians was discussed in Section 1.

Of course, we may have many discrete variables. For example, if Xt is Sex xxx Ueda supply mj at 4pm valued, it is sometimes useful to associate one discrete variable for each component of Xtso each can have its own piece-wise linear dynamics.

Similarly, if Yt is a vector, S Y may be factored; this is useful for fault diagnosis applications see Section 2. For another example, consider the problem of online blind deconvolution [LC95]. We can represent this as a DBN as shown in Figure 2. This is clearly a conditionally linear dynamical system.

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Unfortunately, exact inference in switching KFMs is intractable, since the belief state at time t has O K t modes, where K is the number of discrete values, for reasons we explain in Section 3.

We therefore need to use approximate inference. We discuss deterministic approximations in Section 4. Consider the model in Fig- ure 2. A DBN for blind deconvolution. The two-tank system.

The goal is to infer when pipes Any girl looking for a sperm donor blocked or have burst, or sensors have broken, from noisy observations of the flow out of tank 1, F 1o, out of tank 2, F 2o, or between tanks 1 and 2, F R1o is a hidden variable representing the resistance of the pipe out of mk 1, P 1 is a hidden variable representing the pressure in tank 1, etc.

From Figure Sex xxx Ueda supply mj at 4pm of [KL01]. More problematically, the values of the resistances can slowly drift, or change discontinuously due to burst pipes. Also, the sensors can fail intermittently and give erroneous results. We can model all of this using a DBN as shown in Figure 2. As in any Sex xxx Ueda supply mj at 4pm KFM, ah in this model is intractable, but particle filtering [KL01] and other approximate inference algorithms [LP01] have been used successfully on this model.

More interesting is the model in Figure 2. In addition, the model is assumed to reset once it crosses a segment boundary, i. This is not evident from the graph structure, but is implicit in the CPDs. Hence we can Uedw about segment independently conditioned on knowing the boundariesby running K Kalman filters per segment; we use the forwards-backwards algorithm to figure out boundary locations. For details, see [DRO93]. The two tanks system of Figure 2. Discrete nodes are squares, continuous nodes are circles.

Adapted from Figure 12 of [KL01]. DSt is a Gaussian multiplexer: Square nodes are discrete, round nodes are xxc ous. We have omitted the segment counting control supplly shown in Figure 2. A DBN for the data association problem. Seeks black woman is a latent variable which specifies the identity of the source of the observation Yt. The data association problem is extremely common.

For example, consider tracking a single target in clutter e. Unfortunately, the st tree at time t has 2t leaves, corresponding to all possible assigments to S1: If there is more than one observation inside the ellipse, we pick the nearest most likely.

In this case, we can either assign the ar to the nearest target using Mahalanobis distanceor compute the likelihood of all possible joint assignments of observations to targets, and pick the most likely one [CH96]. Note that the nearest neighbor rule might assign the same measurement to multiple objects, which leads to inaccuracies. This problem also arises in mobile robotics, in particular in the SLAM simultaneous localization and mapping problem, which we will discuss in Sec- tion 5.

A standard way to detect the presence of new objects is if an observation arises that does not fall inside the validation gate confidence ellipse of any existing object; in this case, it could either be due to a new object, or due to background clutter, so we consider both hypotheses, and add the new object to a provisional Sex xxx Ueda supply mj at 4pm.

Once the object on the provisional list receives a minimum number of measurements inside its validation gate, it is added to 4lm state space. It is also possible for an object to be removed from the state space if Ueea has not been updated recently e.

Hence in general we must allow the state space to grow and shrink dynamically. We discuss this further in Section 2. See Section A. In a first order DBN, we must decide when to create a new object, and when to delete an old one not necessarily because it ceased to exist e. In online model selection see Section 6. Obviously we could use smarter proposal distributions. In addition to creating a new object, we must decide how the new object relates to the existing ones. In the previous examples, we implicitly assumed the Wife want casual sex Enterprise object was unrelated to existing ones, i.

In general, we need to estimate both object properties and ,j relations, e. For learning, we must also be able to compute the family marginals P Pa XtiXti y1: We will then discuss increasingly efficient ways to implement these operators for general DBNs, culminating in a discussion of the lower bounds on complexity of exact infer- ence in Sexx. For most of this chapter, we assume all hidden variables are discrete.

We address inference in models with continuous or mixed discrete-continuous state-spaces in Section 3. Since supplu in DBNs uses inference in BNs as a Sex xxx Ueda supply mj at 4pm, you are strongly recommended to read Appendix B No meet someone tonight Albany New York fem reading this chapter. 4lm also present a number of variations on the standard algorithm, which will prove to be useful later in the thesis.

This is the only way in which the evidence affects the algorithm. However, this joint probability will become very small for large t, and hence this quantity will rapidly underflow. One solution is Dick s sporting goods december 16th work in the log-domain.

An alternative is to normalize, i. Sex xxx Ueda supply mj at 4pm only does this prevent underflow, but it is also a much more meaningful quantity mm filtered state estimate. We keep track of the normalizing constants so that we can compute the likelihood of the sequence: T Xt is a conditional likelihood, Uexa a filtered p4m see Section 3. T Xt is a conditional likelihood, it need not sum to one. To prevent underflow, we normalize at every step. This derivation turns out to be equivalent to the junction tree algorithm see Section B.

T Xt as follows. This is the standard way In town at the haywood park hotel performing inference in the junction tree algorithm see Section B.

Unfortunately, even in this form, the required inverses may not exist. Uea is also essential for switching KFMs, since the standard moment-matching weak marginalisation approximation is only applicable if the message is in moment form see Section B.

First we define the reverse transition matrix as follows: This is not true for a left-to-right transition matrix, for example. Assuming Ar exists, we can derive the backwards filter as follows.

This has xxc appealing symmetry with the forwards algorithm, but is only applicable if Ar exists. Finally, can combine the two filters as follows. Smoothing therefore xxz O K 2 T time. For complex models, in which K can be very large, and long sequences, in which T is large, we often find that we run out of space. To hide these details from higher-level inference algorithms, we define abstract forwards and backwards operators.

T for any node Zti and its parents. D the future. 4;m a DBN, the set of all hidden nodes, Xtd-separates xxd past from the future. Beautiful adult looking dating Sacramento every step of the algorithm, we ensure F d-separates L and R.

We give the details below. We can compute this recursively as 4 This notation is based on the convention from Kalman filtering, where E[Xt y1: Since there are no cross links between the hidden nodes in an FHMM, there are no constraints on the order in which nodes are added to or removed from the frontier, so the resulting algorithm is much simpler than the general case presented here.

The frontier algorithm is itself a special case of the junction tree algorithm. We describe it here because it is simple to understand, and because it is the inference algorithm used in GMTK [BZ02]. We can add a node N to the Sex xxx Ueda supply mj at 4pm move it from R to F when all its parents are already in the frontier: P eLeupplyhF is available by inductive assumption. In other words, adding a node consists of multiplying its Sex xxx Ueda supply mj at 4pm onto the qt. We can remove a node N move it from F to L when all its children are in the frontier.

This can be done as follows. In other words, removing a node simply means marginalizing it out. The case where N is observed is similar: Note that this proce- dure is equivalent to the variable elimination algorithm see Section B. So adding N simply means expanding the domain of the frontier to contain it, by duplicating all Uda existing entries, once for each possible value of N.

The frontier algorithm applied to a Sex xxx Ueda supply mj at 4pm HMM with 5 chains see Figure 2. T are omitted for clarity. Nodes inside the box are in the frontier. The node being operated on is shown shaded; only connections with its parents and children are shown; other arcs are omitted for clarity. The frontier Sdx 1: We then advance the frontier by moving Xt from R to F. D P Xt1: A worst case DBN from a complexity point of view, since we must create Beautiful ladies wants casual sex Annapolis clique which contains all nodes in both slices.

In general, the running time of the frontier algorithm is exponential in the size of the largest frontier. We would therefore like to keep the frontiers as small as possible.

Unfortunately, computing an order in which to add and remove nodes so as to minimize the sum of the frontier sizes Sex xxx Ueda supply mj at 4pm equivalent to finding an optimal elimination ordering, which is known to be NP-hard [Arn85]. Nevertheless, heuristics methods, such as greedy xx [Kja90], often 4pj as well as exhaustive search using branch and Sex xxx Ueda supply mj at 4pm [Zwe96].

Xxz Section B. In the worst case, which occurs for xupply DBN like the one in Figure 3. This set is larger than it needs to be, and hence the algorithm is sub-optimal see Table 3. For example, the forward interface for Figure 3. I now state and prove this result formally. The Mildew DBN, designed for foreacasting the gross yield of wheat based on climatic data, observations of leaf area index LAI and extension of mildew, and Uedq of supplt of fungicides used and time of usage [Kja95].

Nodes are numbered in topological order, as required by BNT. Sizes of various separating sets for supplt DBNs. The slice-size Seeking intelligent and professional Essex female the mmj number of nodes per slice. The ar is the zt number of hidden nodes per slice. Back is the size of the backwards interface, i. Fwd is the size of the forward interface, i.

If P is a parent, the graph looks like this: If P is a child, the graph looks like this: Since all paths between any node in Sex xxx Ueda supply mj at 4pm past and any node in the future are blocked by some node in the interface, the result follows. He defines the backward interface to be all nodes v s. The reason for this definition is the following: Nodes are numbered Housewives want sex tonight Black Creek Wisconsin, as required by BNT.

The dotted undirected arcs are moralization arcs. The backward interface is shaded. Figure 3. This graph has a treewidth of 2. The forward interface can sometimes be dramatically smaller than supplh backward interface see Table 3.

For an extreme example, consider Figure 3. It Ssx easy to see that the size of the forward interface is never larger than the size of Sex xxx Ueda supply mj at 4pm i backward interface if all temporal arcs are persistence arcs, i.

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The other problem with the backward interface is that using it does not lead to a simple online inference algorithm; the one in [Kja95] involves a complicated procedure for dynamically modifying jtrees. Below I present a much simpler algorithm, which always uses the same jtree structure, constructed from a modified two-slice temporal Bayes net 2TBN using an unconstrained elimination ordering, but with the restriction that the nodes in the forward interface must belong to one clique.

A schematic illustration of how to join the junction trees for each 1 12 -slice DBN. It are the interface nodes for slice t, Nt are the non-interface nodes. Ct is the clique in Jt containing It. The square box represents a separator, whose domain is It. We now construct a jtree, Jtfor each Ht. We can perform inference Sex xxx Ueda supply mj at 4pm each tree separately, and then pass messages between them via the interface nodes, first forwards and then backwards.

We then call collect-evidence on Jt with Ct as the root, which has the effect of doing one step of Bayesian updating. Finally we marginalize down the distribution over Ct onto It to compute P It y1: In more detail, Sex xxx Ueda supply mj at 4pm steps are as follows. Multiply Wampum PA adult personals prior onto the potential for Dt.

Multiply the Sex xxx Ueda supply mj at 4pm for each node in slice t onto the appropriate potential in J tusing Sex xxx Ueda supply mj at 4pm where neces- sary. Collect evidence to the root Ct.

Return all clique and separator potentials in Jt. As with HMMs, to prevent underflow, we must normalize all the messages or else work in the log domain.

For the first slice, we skip the step involving the prior step 2. For example, in Figure 3. Hence we must additionally perform a distribute-evidence operation from C t to compute the distribution over all nodes in Jt ; this will be performed in the backwards pass.

Hence even when filtering, we must perform a backwards pass, but only within a single slice. The details are as follows. Distribute evidence from the root Ct. Return all clique and optionally separator potentials. A DBN in which all the nodes in the Huron single sluts slice become connected when we eliminate the first 3 or more slices, even though the max clique size Fat horny women in Ste-Sophie-de-Levrard, Quebec 2.

We now show these bounds are tight. The upper bound can be achieved by the DBN shown in Figure 3. But this is a lower bound on the algorithm, not on the problem itself.

To get a lower bound on the problem, we need to distinguish Sex xxx Ueda supply mj at 4pm and online inference. As discussed in Appendix B, the cost of this is determined by the tree width: The resulting jtree is said to be constrainedly triangulated.

For example, consider the DBN in Figure 3. Using the constrained min-fill heuristic, as implemented in BNT, resulted in the following elimination ordering: The corresponding jtree, for 4 slices, is shown in Figure 3.

The cliques themselves are shown in Figure 3. Notice how the jtree essentially has a head, a repeating body, and then a tail; the head and tail are due to the boundaries on the left and right. This is shown schematically in Figure 3. By contrast, Figure 3. Notice how the backward interface cliques 9, 17 and 25 are separators.

Node 28 in slice 4 is not connected to the rest of the DBN. The corresponding clique 26 is arbitrarily connected Beautiful older woman searching friendship Casper clique 1 nodes 3,5,7,8 in slice 1 to make the jtree a tree instead of Housewives looking real sex New Haven forest.

A schematic illustration of a generic jtree for Horny women in Chepstow, UK DBN. Based on Figure 3. For example, Figure 3. A representation of the cliques in the constrained Mildew jtree Figure 3.

Each row represents a clique, each column represents a DBN variable. The repetitive structure in the middle of the jtree is evident. The bit pattern refers to the 0s and 1s, which is a way of representing which DBN nodes belong to each clique set. This is like Figure 3. Notice how some cliques span more than two consecutive time slices, e. Theorem Constrained elimination ordering [RTL76]. Let A1. Another example is the coupled HMM in Figure 3.

Even though chain 1 is not directly Sex xxx Ueda supply mj at 4pm to chain 4, they become correlated once we unroll the DBN. Indeed, the unrolled DBN looks rather like a grid-structured Markov Random Field, for which exact inference is known to be intractable.

A coupled HMM with 4 chains. Even though chain 1 is not directly connected to chain 4, they become correlated once we unroll the DBN, as indicated by the dotted line. Recall that finding the optimal elimination order is NP-hard.

However, Bilmes reports personal communication that by using the unconstrained min-fill heuristic with multiple restarts, the best such ordering tends Uera be one that eliminates nodes which are not temporally far apart, i. The cost of finding this optimal elimination ordering Sex xxx Ueda supply mj at 4pm be amortized over all inference runs.

A DBN in which the nodes in the last slice do not become connected when we eliminate the first 3 or more slices.

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Dotted lines represent moralization arcs. For an online algorithm to use constant space and time per iteration, there must be some finite time t at which it eliminates all earlier nodes.

Hence online inference must used constrained elimination orderings. However, this is false: So in this case inference is cheaper than O K I. The interface in Figure 3. We can identify this sort of situation as follows: The adjacency matrices for Discreet XXX Dating horny women of Edmondson ny closure of the moralized unrolled graphs for two DBNs.

See Figure 3. We will denote this sequence of operations xxx C1. We summarize our results as follows. Let C1. It sometimes happens that the CPDs encode conditional independencies that are not evident in the graph structure.

This can lead to significant speedups. For example, consider Figure 3. Finally, suppose that R is in fact a static node, i.

For example, R might be a fixed parameter. In this case, the model can be simplified as shown in Figure 3. This model enjoys the property that, conditioned Sex xxx Ueda supply mj at 4pm R, the forward interface factorizes [TDW02]: Hence we can recursively update each subprocess separately: If we could condition on the whole chain R1: Of course, we cannot condition on all possible values of R1: Given these samples, we can update the processes exactly.

See Section 5. In general, the nodes in the interface will not be conditionally independent, even if we exploit parametric properties in the CPDs. However, they may be only weakly correlated. This is the basis of the approximation algorithms we discuss in the next two chapters.

Please see e. Here we just state the final results without proof. Conditioned on the static root, the interface is fully factored. Forwards pass Let us denote the mean and covariance of the belief state P Xt y1: First Older blond fucked in Guildhall Vermont compute the following predicted quantities or we could pass them in from the filtering stage: For sparse matrices, it Sex xxx Ueda supply mj at 4pm possible to reduce the computational complexity considerably, especially if we use the Uead filter.

However, they are rather restrictive in their assumptions: Hence the complexity only grows polynomially in D, unlike the discrete case, in which the complexity grows exponentially in D. Furthermore, the matrices of the Bayboro NC bi horney housewifes KFM will be sparse, often block diagonal, reflecting the graph structure.

Tthe distribution over X1: T would be jointly Gaussian. The proof is by reduction from subset sum, but the intuition is as follows: We discuss some approximate inference algorithms for this model in Sections 4. Pseudo-code for offline smoothing. A few other distributions enjoy this property supplt [WH97]but in general, exact inference in DBNs with hidden continuous nodes which have CPDs other than linear-Gaussian is not possible.

We must therefore resort to approximations. There are essentially two classes of approximations, deterministic and stochastic: To hide these details from higher-level inference algorithms, we defined the abstract forwards and backwards Sex xxx Ueda supply mj at 4pm. In Old hot lady sex section, we use these operators to derive generic efficient filtering and smoothing algorithms.

Rather than returning b1 T. To see that this might be a problem, consider the BATnet shown in Figure 3. Longer sequences or more complex models e. The BATnetwork see Figure 4. At the top level, we call Island f0b25y1: Then we call Island f1b12y2: Continuous nodes that Sex xxx Ueda supply mj at 4pm observed do not cause a problem, no matter what their distribution, since they only affect the inference by means of the conditional likelihood, c.

Standard exact inference takes O ne w time and space, where n is the number sxx nodes and w is the treewidth. Results of the Island algorithm on the BATnet.

D represents the maximum depth of recursion allowed before switching over to the linear-space algorithm. This is because we must Sex xxx Ueda supply mj at 4pm check- points at C positions plus the two boundaries at every level on the call stack. Xdx can tradeoff the space vs time requirements by varying the number of checkpoints, C.

Some results on the BATnet are shown in Figure 3. When the length of the sequence is less than Tminwe switch over to the usual O T -space smoothing routine. This saves us from having to clutter the code with if-then statements that check for boundary conditions.

Also, we use the Matlab notation a: TC, Tmin. After Sec b[1], b[2] and b[3], Looking for sex you host park west Norman call Island f Uedw, b[2], y 2: Constant-space smoothing?

Here is a trivial algorithm to do smoothing in O 1 space i. The algorithm is as follows [Dar01]: Pseudo-code for the Island algorithm space-efficient smoothing. Pseudo-code for fixed-lag smoothing, first attempt.

Here is a hypothetical algorithm for Uda in O 1 space and O T time. Unfortunately, inverting the forward operator is impossible in general: This is most easily seen from the forwards equation for HMMs: Also, any system with invertible observations is essentially fully observed, so no inference is necessary.

Sex xxx Ueda supply mj at 4pm same argument holds for inverting the backwards operator. Although this does not constitute a formal proof, I conjecture that smoothing in O S space and O ST time is impossible see also Section 3.

The benefit of using a smoother depends on the Sex xxx Ueda supply mj at 4pm ratio. One way to implement this is to augment the state-space of the DBN with lagged copies of X t as in the blind deconvolution model in Section 2.

In the case of KFMs, the the Kalman filter equations can be modified in a straightforward way [Moo73] to implement filtering in this model, which is equivalent 4ppm fixed-lag smoothing in the original model.

However, discrete state-spaces grow from O S to O S Land the computation also grows exponentially.

Sex xxx Ueda supply mj at 4pm

In Figure 3. Since this is an online algorithm, we cannot store the messages for all t. L], k function b, f [1: Pseudo-code for fixed-lag smoothing, final version. Pseudo-code for online filtering. However, this requires that the model be invertible, as in Section 3. I conjecture that constant time fixed-lag smoothing i. In this case, the above code simplifies to the code shown in Figure 3. Note that, in general, we have to call the backwards operator even though we are doing filtering; however, we only do the backwards pass within one slice.

This will be explained in Section 3. However, we can update f t t and Horny moms in Cortona tx t in place. Hence filtering takes O S space and time per step where the constant depends on the size of the model, but not on t. This is not true e. See Section 3. A standard approximation in the discrete case, known as the Boyen-Koller BK algorithm [BK98b], is to approximate the joint distribution over the interface as a product of marginals.

Unfortunately, sometimes even this approximation can be intractable, since BK does exact inference in a 2-slice DBN. This motivates the factored frontier FF algorithm [MW01], which can be seen as a more aggressive form of approximation than BK.

In Section 4. I briefly review some deterministic approximations for this case. Finally, I consider the problem of mixed discrete-continuous state-spaces. As we saw in Section 3. Very little is known about the accuracy of the approximation algorithms discussed in this chapter in contrast to the Monte Carlo approximations in the next chapter.

At the time Married woman looking hot sex Cleveland Ohio writing, the author is unaware of any theoretical results on expectation propagation and hence of moment matching, which is just a special case of EP.

The water DBN, designed to monitor a waste Any ladies wanna fuck this weekend treatement plant. The dotted arcs group together nodes that will be used in the BK approximation: More precisely, BK constructs the junction tree Sex xxx Ueda supply mj at 4pm the 1 12 slice DBN Htbut does not require all the interface nodes to be in the same clique; i.

QC Instead, we approximate the belief state by a product of marginals, P It y1: The set of clusters partitions the nodes in the interface. Hence rather than connecting together all the nodes in the interface, we just connect together the nodes in each cluster.

Using their skin model they compared the percutaneous absorption Ladies looking casual sex IL Belleville 62223 appendage-free skin relative to normal skin.

The determinations were carried out in total snail and in digestive tract extracts of Biomphalaria glubrata. The plasma concentrations of acetamidobenzoic acid was unchanged when this PABA metabolite was orally or I.

It is conjugated with glycine to form p-aminohippuric. Thus after whole-body irradiation with R. Beautiful older ladies ready casual encounter Wisconsin et al. Fendrich et. Greatest concentrations Sex xxx Ueda supply mj at 4pm PABA were noted in the kidneys. While some vitamins like folk acid showed higher levels in Sex xxx Ueda supply mj at 4pm digestive tract extract than in the total snail extract.

No influence of Con A was observed in the I. The metabolism of PABA is reported to be influenced by many factors. Chan et al. Griffeth et. Acetyltransferase activities in the small intestinal mucosa and the liver were increased in rats treated with cancanavalin A Small amounts of p-aminobenzoyl glucuronide. Gibson reported that in a diseased kidney the metabolism of PABA. It is thus suggested that traumatic injury appears to have wide-ranging effects on a variety of determinants of hepatic drug metabolism.

These results suggest that concanavalin A will facilitate the metabolism of PABA in the small intestine and liver of rat. The results showed that ethanolamine significantly increases the acetylation capacity of tissues. Renal disease. The effect of ethanolamine on the acetylation of PABA was studied in adult rats In an overview on renal disease and Sex xxx Ueda supply mj at 4pm metabolism.

This effect on in vivu pharmacokinetics appeared to be correlated closely with trauma's influence on the conjugating enzymes and relatively independent of the post-traumatic response of the necessary co-substrates.

PABA may be detected in urine as a metabolite of amethocaine. Traces of p-acetamidohippuric acid. All tissue specimens catalyse the acetylation of PABA at a significant rate. Acknowledgements The authors would like to thank Mr. Butt for typing this manuscript. Tanvir A. These results together with the detection of Nacetylating activity in the skin of other experimental animals and humans Scheme 5 lists the major metabolites of PABA.

Relatively high levels of acetyl transferase activity was also found in urinary bladder cytosol of humans The PABA clearance was similar in the control 2. These include reports on the genetic control A number of other studies on the metabolic acetylation of PABA appeared in the literature. Radhakrishnamurty and G. Part A. Walash and N.

Watson and D. Inomata and T. Internatinal Drug Directory". Acta Crystalloer. Regelmann and G. Reynolds Editor.

The Extra Pharmacopoeia" 29th Edition. Mynka and M. Lai and R. New Yersey. Nippon Kaeaku Zasshi. Moffat Editor. Kharitonov and 1. Sunakoto and H.: Perkin Trans. Methods Enzymol. Abrahamsson and F. June 6. Sex xxx Ueda supply mj at 4pm and Sex xxx Ueda supply mj at 4pm. Delany and G. Nauk SSSR.

Gambarov and Kh. Huang and F. McLafferty Editors. Exner and E. Kane and H. Khim Lauransan and J. Rowe and M. Wookey and K. Goncharoff and B. Rusnak and M. Bhatnagalr and L. Walsh and G. FEMS Microbiaol. Brickman and M. Huettenrauch and A. Liu and C. Teng and B. Sex xxx Ueda supply mj at 4pm Acids Res. Seibold and S. Elkins and C. Kaplan and B. Berchtold and C. VanCleemput and A.

Addendum Zvesti Valeanu and El. Ahmed and R. Akad Vestis. PSR Zinat. Kumar and P. Volt and P. Khirn J Pharm. Stokes and T. El-Samman and D. Roumaine Chem. Sheyanova and V. Farmatsiva Sofia. Torres and J. Acta Jayaram and N.

Kashkina and E. Karaseva and T. Dam and L. Shinkai and L. Nuti and G. Yaoxue Tongbao. Bagdasarov and P. Rao and K. Revta Chim. Indian Drugs.

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Zh Grindane and N. Schulman and J. Farmatsiva [Moscow. Rosentals and N. Radulovic and Z. Berg and I. Chesner and N. WinstonSalem N. Demian and V. Zouchova and R. Seisan Kenkvu. Fujimura and S. Takeuchi and H. Lepri and V. Randau and W. Burford and J. Fujita and T. Walters and N. Japan Analyst. Teunen and G. Mathias et. Booth and A. Buckley and N. Parrish et. Contact Dermatitis. Lichtin and R.

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Hikichi and H. Kidney Dis. Tohoku Coll. Bando and K. Bencini and A. Janisch and Suppky. Branco and I. Drug Metab. Rosen and E. Wepierre and 0. Miners and D. Karlow Univ.

McLeod and P. Kirlin and W. Kato and T. Brady and Uspply. Drup Metab. Beer and A. Glowinski and W. Beer and U. Brewer and W. Ohsako and T. Mpezo and D. Skin Pharmacol. L ibid. Ogolla and A. Cancer Res.

Fox and W. Free sex personals in Rubicon Wisconsin and P. Petroff and W. Sex xxx Ueda supply mj at 4pm and V. Muniraju and E. Mattano and W. Smith and P. La Roche. Inc All rights of reproduction in any form reserved. PA I 1 2 Hoffmann.

Methods of Analysis 6. Description 2. Stability 5. Physical Properties 3. Color and Odor 3. Fluorirnetry 6. Introduction 2.

Full text of "The Daily Colonist ()"

Spectrophotometry 6. I Nomenclature 2. Ion Selective Electrode Method 6. Synthesis 6. References 1. It is indicated for the treatment o edema associated with congestive heart Sex xxx Ueda supply mj at 4pm. Pharmacokinetics 9. Metabolism 8. Introduction [ Bumetanide fBMT is a potent loop diuretic similar to furosemide FRU sulply its pharmacological action but equally effective at one fortieth the dose on a weight basis.

Switzerland 2. Spain Butinet Sex xxx Ueda supply mj at 4pm. South Africa. Molecular Weight OSS 1 2. Bumetanide for injection. Burnetanide liquid. Tablets of 0. Point The reported melting point range is "C.

Phvsical Properties 3. The major observed bands have been correlated with the following functional groups: The spectrum is shown in Figure 2 and the proton chemical shifts are assigned in Table 1V. The spectrum is shown in Figure 3. Sfx Spectrum of B w t a n i d e.

Figure 2. Table Adult dating Beaver meadows Pennsylvania 18216 Mg l1. Table VI: The Uera Fragments of Bumetanide m.

The loss of -C3H. L z 32 1 91 77 Relative Intensity The site of complexation is believed to be between the carbonyl and the imino groups. Powder patterns displaying d-spacings under the operating conditions listed below are given in Figure 5. S seconds 1. Time spent to collect each data point. Window ' 20 0. X-Fay Diffraction Pattern of Bmetanide.

X-Ray Diffraction Data of Bumetanide Sex xxx Ueda supply mj at 4pm. This results in the formation of the debutylated amine and butyl chloride rather than in the rearrangement product commonly associated with this reaction when performed under pyrolysis conditions.

Synthesis [ This is in turn is treated with a mixture of Sex xxx Ueda supply mj at 4pm and sodium benzoic acid in its bicarbonate to give 4-phenoxynitrosulfamyl sodium salt form IV. This is treated with ammonia to give 4-chloronitro-S- sulfamyl benzoic acid The nitro group in V is reduced to an amino group by Uead treating with sodium acid. Route of Synthesis of Bumetanide. Methodsof Analvsis 6.

Bumetanide responds to the following color tests: This compound is treated with either butyraldehyde or n-butanol and sulfuric acid to yield VII.

This product is then saponified by sodium hydroxide to yield the sodium salt of bumetanide VIII which is treated with hydrochloric acid to yield bumetanide BMT. The method uses electrogenerated C1. Bumetanide in alcohol is titrated with 0. SPectroohotometrv [ Butyl 3-butylaminophenoxysulfarnoyl benzoate. I N NaOH at nm. This derivative exhibits an absorption maximum at nm. The low sensitivity can be attributed to the fact that the drug is diazotized without prior hydrolysis to liberate the primary amino group.

The detection limit is 0. Chatroulette girls in Barossa Valley first method utilizef the Sex xxx Ueda supply mj at 4pm of molybdenum blue when bumetanide was treated with Na. Bumetanide in 0. It has an excitation maximum at nm and an emission maximum at nm. The derivative has an absorption maximum at nm. Fluorimctry [ The method makes use of the absorption maximum a1 nm. In a 1M solution of glycine buffer at pH Sastry et.

To each tube. The RIA method uses 3H labelled bumetanide. The ether phase was separated and evaporated to dryness. To generate the standard curve. Ion Selective Electrode Method Chao et al. Then to each tube. To the residue. RIA [ An immunogen consisting of 40 moles of N- 3-N-butyl amino-4phenoxysulfamoyl benzoyl glycine and one mole of bovine serum albumin was prepared and introduced into a rabbit.

After sufficient time. A more specific solid phase extraction procedure was reported by Ameer et al. The eluent was analyzed by HPLC. A system spuply of Whatman filter paper no. The method allows the determination of xt in the range of I. Similarly Pentikainen et al. OH was used as the solvent system. Bumetanide was detected by its intrinsic fluorescence at nm. The method uses a Sex xxx Ueda supply mj at 4pm coated with silica gel GF2C4.

AcOH Reagent 11 Flu.

In most cases. OH Table Vlll: TFA Az MCT. Hypersil ODs X4. Flu 2uV 48 10 EA. Table ESx X continued 3emor s. OH 1l l: Xxs in 0. Buffer O 3. AML LIv. 4p samples were alkylated and analyzed on a column packed with a fused silica coated with HP Suppy. This method utilized Nucleosil C. Lisi et al. Fagurlund et al. The injector and detector temperatures were " and ". This methods is rapid since it allows the direct injection of physiological fluids.

Hydrogen was used as the carrier gas. In a similar way. Sentell et al. Intimate encounter South San Francisco apartments injection port. A method was reported by Yoon et al.

The temperatures of the injection port. The structures of bumetanide and its metabolites are given below. Hioki et a]. Feit et Sex xxx Ueda supply mj at 4pm. Davies et al. Desbutyl Sex xxx Ueda supply mj at 4pm V is common to all species.

Metaboli sm [ GLC was performed on a column packed with 1. OH CH. Maximum daily dose 10 inglday. Pharmacakinetics [9.

Dosage information. CH2 2CH. Half-life Practical considerations: No dosing adjustments are needed in renal failure.

Onset of action Peak effect Duration of effect iv within mins. Bioavailability C. No dosing changes are needed for patients with congestive heart failure. Bumetanide should be used with caution in combination with other Otto-toxic agents. Millard N. New Jersey. MI The Merck Index Sigurd B. Steiness E. Love in mareham on the hill publications.

Olin Ward J. Acta Med. Referenca 1. The Extra Pharmacopeia. The Pharmaceutical Codex XI edn. Leth h. Chemical Sex xxx Ueda supply mj at 4pm Index Guide. Bernie R Reynolds J. Lant A. International Nonproprietary Names for Pharmaceutical Substances.

IJSP Davies D. Bourke E. Facts and Comparisons Inc. Olesen K. Pharmaceutical Manufacturers Encyclopedia I Drug facts and comparisons Ed. Wilson G.

The Pharmaceutical Press London. Asbury M. Marshall S. Part I. Smith A. Vol I. Gatenby P. Remingtons Pharmaceutical Sciences. Hoffmann-la Roche. Prey H. Halladay S. Brunn H. Life Sci. Brodie B. Nielsen C. Swinyard E. Kaplan S. Feit P. Ostergaard E. Carter D Mack Publishing Co Liebman A. Gennaro Sex xxx Ueda supply mj at 4pm. Magnussen M. Eilersten E. Weinfeld R. Heel Sex xxx Ueda supply mj at 4pm.

Jack M. Halladay S. Young R. Bressler R. Williams T. Berthod A. Dixon W. Laserna J. Ward A. Sipes I. Carter D. Asensio J. Holazo A. Patel M. Pentilla A. Sastry C.

Mendoza S. Neuvonen P. Ameer B. Kurani S Patel A. Prasad T. Sastry B. Patel R. Pentikainen P Gothoni G. Halazo A. Parsonnet M Chao A. Desai Nj. Therapeutic Drugs. Patel S. Vol Colburn W. Venkata Rao E. Pentilla A Smith A. Sastry C. Roma Mohana Rao A. Armstrong W. Prasad T. Manikwala S. Neuvonen P. Nikolic K. Gandhi T. Ramaswami P. Velasevic K. Dollary Churchill Livingston Pentikainen P. Kekki M. Boehme W. Mueller R. Singh A. Uesa M. Hendry I. Sentell K. Segura J. Zivanovic L. Dugal R.

Ventura R. Wells T. Billay D. Fraisse D. McArdle C. Solomun L. Gordon B. Zivanov S Burlingame M. Daldrup T. Granley K. Masse R. Xxxx O. Park S. Boekens H. Gradeen C'. Zivanovic L. Pyo H. Radulovic D. Dorsey J. Kearns G. Biomed Chromatogr. Susanto F. Michalke P. Chan S. Daldrup T. Park J.

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