I'll speak in the central seminar that today is James Finley from the University of Oxford. And I'm going to take out the pseudo of the pseudo passive. Right. So yeah, OK, what I want to talk today about is what some people call the pseudo passive and some people call the repositional passive, which is essentially what you see in one. So it's this kind of construction where the subject corresponds to the object of a preposition in the active rather than the object of a verb. So you've got a verb like rely, which takes a PP complement. Scott relies on Logan. And you can pacifize this to Logan is relied on by Scott. Right. Now people call this the pseudo passive because, well, this has implications, right? It suggests that it's not really a good passive. It's defective in some way. It's kind of pretending. I don't think that's really the case. So I'm going to call it the prepositional passive. But terminology kind of waivers. So what I want to do is give you a little bit more background about what the construction is. Kind of look at some of the data. And then kind of an overview of what the properties of the regular passive are, in English at least, and how the prepositional passive looks a lot like that. Then we'll think about ways in which it might look different, or it might look special, and that have kind of troubled people, made them think it's not quite such a good example. I'm going to give a sort of brief LFG analysis, not getting into the nitty gritty too much. And then we'll finish. So that'll be good. Right, so starting off, if you think about verbs like rely on these kind of prepositional verbs, it's maybe not that surprising that they have this passive alternation, right? Because you have this idiosyncratically selected preposition that's semantically inert. So it's not really doing anything. The complex is operating as a whole. And as a whole, it has this kind of unified transitive meaning. So if you were to write the meaning of rely in some kind of logical language, you probably have a two-place predicate rely on or something, because it's just kind of a unified semantic whole. And given that it has a kind of transitive meaning, it's not surprising that it participates in the passive, because transitivity and passive go together. And if this was all there was to it, there maybe wouldn't be so much to say. But the preposition doesn't need to be specified by the verb. So we get very sort of semantically contentful prepositions. So like in two, we have sort of contentful argument PPs, where Scott spoke to Gene, it's OK, Scott spoke about Gene, and both have good prepositional passives, right? Gene was spoken to by Scott, Gene was spoken about. And OK, maybe these aren't contributing kind of full truth conditional value. They're just doing some kind of thematic role alignment or something. But the point is that they are contentful. They do do something. And if you change them one word, you change the meaning of the expression. So that word is meaningful in some sense. But what's more telling, or maybe worse, depending on your point of view, is that you get these with what look like adjunct PPs as well, where the preposition clearly seems to be contentful, and it clearly has some meaning. So there's some nice real world examples here. In three, you have, when I'm on the bus, I don't like being sat next to, right? Preferably good, passive, and it's got this locative preposition that the N stranded. Similarly, with Charles Dickens' quill pen has been written with by me, which is instrumental. And in five, which is from The Voice, which I'm assuming you're all desperately familiar with, this sort of talent show where the judges face away from the singers so that they can only judge them on their voice. And then if they like them, they turn around. There was a cadet who came on. Nobody turned around. She'd been on before. And one of the judges said, to come back and not get turned around for, she's trailed off into some tragedy. But is there some kind of benefactive for phrase? Maybe, you know, so there's all sorts of things that, look, or people have at various times thought of as adjuncts that seem to take part in this prepositional passive. So huddle, fill, and pull them in the Cambridge grammar of the English language, divide up the prepositional passive between these two kinds, basically, between the ones where the preposition is selected, so type one, and those where it's contentful. It has some kind of meaning, so type two. And if there is this kind of subtype that deals with adjuncts rather than arguments, then this is, you know, we might start to see where the pseudo is coming in, right? Because this looks problematic. If we think of the passive as an argument alternation, it shouldn't be involving adjuncts. Another thing about the prepositional passive is just, you know, as an aside, it's very productive. You know, you might think, oh, it's just been kind of lexicalized, or there's certain expressions that kind of come together quite often. But you get it with very recent neologisms. It seems like as soon as you have a verb, you can productively make prepositional passives out of it. So in six, we just have a few kind of tech examples, right? We can't bring you everything that's being blogged about. Sean was tweeted at by Molly Mesnick. I presume that's exciting if you know who Molly Mesnick is. This will definitely be Facebooked on. All these kind of things, clearly productive, clearly just part of the grammar. So it's my contention, really, that I agree with Alcina that the prepositional passive has all the features of a canonical passive construction except for one, namely the definitional one, that it involves alternation with the object of a preposition rather than the object of a verb. And so in this section, I kind of want to go over a few of the properties of the regular passive that it shares and just kind of try and convince you that these are really doing the same thing in some way, shape, or form. So the first thing, and it might seem kind of trivial, but it's worth mentioning, is that the Morpheus syntax is the same, right? We have the same morphology. So the passive in English, you have this perfect participle that gets used for the passive. And in the prepositional passive, it's exactly the same, right? You have the same perfect participle used. It's not a different form. Similarly, you have the same auxiliary choices, right? So in English, you can have B passives and you can have get passives. And you get this with the prepositional passive too, right? So a new experience I got spat on today, but this is OK with B as well. So this might seem trivial, but if they really were kind of different animals, there's no reason why you couldn't have different morphological constraints, different sort of syntactic selectional constraints with the prepositional passive. But you don't. More interesting, perhaps, are the kind of semantic pragmatic constraints that you get on the passive. So there's been a lot of literature about the passive and the prepositional passive, kind of trying to categorize properties that the subject has. And it's kind of not surprising because the subject is the privileged thing that we've promoted. The reason we're doing the passive is so we can do something with this subject. And it seems like certain properties are predicated of it. And basically, these are much the same between the two kinds of passive as well. So the first and the kind of big one that people talk about a lot is what Bollinger called the effectiveness condition, where he says the subject in a passive construction is conceived to be a true patient, i.e. to be genuinely affected by the action of the verb. So he has this nice minimal pair in 8 where we have I was approached by the train. It's supposed to be bad. Whereas I was approached by the stranger is OK. I think I share that judgment. And the claim is that I was approached by the train is bad because in this case, the subject is not actually a patient. It's some kind of, well, Bollinger calls it the terminus of a path or something. But the train comes towards me, but it doesn't interact with me in any way and not actually affected by this train. Whereas if I'm approached by a stranger, the implication is that they're talking to me. They're propositioning me. They're panhandling, whatever. And so I'm affected. I'm a patient in this interaction. And exactly the same thing is true of the propositional passive. So we have pairs like 9 where we can't say soul was slept in by the businessman last night. But we can say the bed was surely slept in by a huge guy last night. The argumentation goes along similar lines. Cities aren't affected by having one person sleep in them. They're relevant important properties are changed. But beds are affected by being slept in, especially by huge guys or sort of laboring the point a bit. But that's the idea. As a kind of criterion, though, this is not very formally explicit. And actually, it might end up being so broad as to be basically meaningless. So I was sort of pointing to a little back and forth in the literature about this. But I think a bigger problem is that it's not actually accurate. So it's not the whole story, at least. So Riddle and Chine took this to task and come up with a number of examples from literature and from elsewhere, which seem to A, not satisfy the effectiveness condition, and B, actually kind of go against it, go near the inverse. So in 10, we have the example, such a dress can't be sat down in, which seems fine to me. But in this case, it's not the dress that's being affected by the passive predicate. If anything, it's nothing doing the effecting. It's kind of stopping somebody sitting down. And similarly, this is sort of literary example. There, the mistakes were in their houses, pervading their lives, having to be sat with at every meal and slept with every night. Well, again, it's not the mistakes that are being affected. It's the people that they're tormenting, whatever. So the subject isn't affected. And if anything, it's affecting. So what Riddle and Chine took suggest is actually, if not effectiveness that's relevant here, it's role prominence. So they say that all and only NPs who's referenced the speaker views as being role prominent in the situation described by the passive clause occur as subjects of passive verbs. Okay? But this, okay, now we want to know what role prominent means. So they get the concept from Schachter, who says that role prominence belongs to the MP who's referenced the speaker views as being at the center of events. Okay, but again, this is pretty vague. It seems intuitively to go some way to explaining 10, right? So, okay, the dress and the mistakes aren't affected by these predicates, but the sentences are about them in some sense, right? They are kind of, maybe they're topical, I don't know, but something they are kind of centered in some way, prominent in some way, right? And as Riddle and Chine took point out, this might offer some explanation for behavior in the regular passive as well. So it seems that passes with first person by phrases are generally pretty odd. So the cake has been eaten is all right, but the cake has been eaten by me is distinctly peculiar. The argument being that, well, if I'm the agent, so I'm the one doing the action, it's unlikely for me to view something else as more prominent than me, because I'm always going to be quite prominent to me anyway. And if I'm agentive, then I'm even more sort of prominent. So it's going to be very odd for something else to take that position as the subject of the passive. So again, it looks like this kind of constraint applies to the regular passive as much as it does to the provisional passive. But again, this is kind of no less vague than the effectiveness condition. So if we want to kind of formalize this, we're going to run into trouble. And Riddle and Chine took point out that there's no, well, they say there's no algorithm for determining role prominence. And this is no problem, but it seems to me like that should be a problem. If you wanted to have any predictive power, then you need to have a way of deciding whether something's role prominent ahead of time, right? One final characteristic is what people are called characterization. So we get these contrasts in the prepositional passive like in 12 and 13, where again, you can't say Saul was walked around by his father, by somebody or whatever, presumably because of effectiveness, right? It's not going to affect the city. But you can say Saul can be walked around in a day. Similarly, you can't say this statue was stood beside by John, but you can say no statue should be stood beside in this park. And it seems problematic for the other two, because I mean, A, Saul doesn't seem to be affected in either case, right? And B, it seems pretty role prominent in both cases, or it seems like it could be role prominent in both cases. Similarly in 13, you know, the statue isn't affected, okay? But no statue isn't affected either. It's not really clear sort of how the role prominence thing would play in with kind of these negative existentials. So what Kim and others say is that actually, what matters is that the VP gives a kind of general or characteristic property as a subject, or characterizes it in some way. And you know, this is a property of Saul is that it can be walked around in a day. And again, this has a reflex in the regular passive. So I think this might go some way to explaining capacities like 14, which come from verbs where the active voice object is not a patient. So it can't be affected, right? So in spiders are feared by many people. Spiders aren't affected by being feared. I mean, they might be squished occasionally, but in the sort of abstract, they're not affected by being feared, but they are characterized by it, right? So this might go some way to explaining that, the existence of passives like this. So there's kind of, it's a whistle stop tour really, but of a few of the kind of semantic pragmatic constraints that people have talked about in terms of passives. And they seem to apply equally well to prepositional passive and the regular passive. It seems to be properties of both. So now let's look at kind of why we might think that the prepositional passive is actually a bit more special. It's from kind of the concerns that people have had that make them think, well, there's something else going on here, right? We can't just say this is a passive with a little bit of sort of syntactic sugar is actually something special is happening. Well, one thing that's going to be different, well, one thing that might well be different is that with the prepositional passive, we have a preposition to account for. And so we can ask questions about what's the relationship between this preposition and the verb. And one common observation is that the verb and the preposition have to be adjacent or at least they have some kind of closeness that they don't have in the active. So we get contrast like 15. We rely increasingly on David. That's fine. But David is relied increasingly on, it's supposed to be pretty bad. But this isn't as straightforward as it looks. So others have pointed out, this is inadequate. You can get other things intervening. So you can get things like PP specifiers, these kind of small little words like right, straight or clear that appear with certain prepositions. You can get these intervening. So you can have I stood there heavily pregnant and obviously so and then look straight through. That's okay. Well, you've been walked right by these cases where there's something intervening between the verb and the preposition. That's supposed to be bad, but it seems to be okay in these cases. And actually, if not just these kind of specified type things, you can get all sorts of adverbs here. So 17 is just a few examples with rely on. 17A, the other thing that he sees in wintertime is a lot of the services are relied hugely on by other people in need over the festive season. I mean, it seems all right. This is from an Irish newspaper. Maybe it's a feature of Irish English, but I don't think so. It just seems perfectly normal. 17B is from an academic conference about agriculture, I think. Yerba Mata is consumed in nearly 100% of Argentine households. It's considered a staple food and is relied especially on by poor Argentines when food is scarce. That seems all right to me. And 17C is from a peer-reviewed academic journal. It's a chemistry journal. It's called Combustion and Flame, which is a much cooler name than I think any linguistics journal has ever had, but there's gas turbines have rapid transient response capabilities and thus will be relied increasingly on in markets with large intermittent sources. And this is exactly what we said was bad in 15B. We had David as relied increasingly on, now we've got a pretty natural example that seems to be fine. I'm much happier with 17C than I am with 15B. So it's clearly not an outright ban on things appearing in between the verb and the preposition. One thing to note is that in 17, all of them have material after the preposition. So in the first two, it's the by-phrase. And in the third one, we have this sort of in markets. And I think that might sort of point towards something that's going on here. Maybe there's a prosodic sort of issue at hand. Maybe there's some kind of discourse, information structural thing happening. I don't really have a story for this, but the point is that it's not actually a sort of syntactic constraint on the prepositional passive as some people have proposed. And I think really the problem is that context is important. There's clearly something going on here and there's some need to get an acceptable context for these things to work.  But really, there are examples that make sense. So adverbs seem to be okay. We can get those. The question there is, well, what about direct objects? Because you can get these kind of, you ought to be able to get constructions where you have direct objects intervening. So these are generally pretty bad. Again, along the same lines as we've said before, yeah, along the lines of the adjacency constraint. You say they put some books on the table, that's okay, but the prepositional passive, the table was put some books on is, you know, garbage. But again, it's kind of leaky, this rule, right? So non-thematic things can appear there. So idiom chunks or bits of like verb constructions. So you can say you've been taken advantage of or Russia was declared war on or I've been made a fool of, these kind of things. These are perfectly acceptable prepositional passives with intervening objects. And again, it might be the case that really context is the key. So Bola just suggests that the only real restrictions are clarity and intent, which are definitely non-synaptic features. But, you know, you get examples like in 20, so that city has been fought many a battle over. We have quite a complex MP appearing between the verb and the preposition here. Or he's been burned, stuck pins in, beheaded, all in effigy of course. To be whispered such dirty innuendos about was enough to break any girl's heart. This one doesn't seem so good to me, but this tool has never been used for its main purpose. In fact, it's never been done anything with at all.  So your mileage may vary with some of these examples. There are definitely ones I think are better than others. And maybe some of them are more kind of light, verby, idiom-y than others. But I think the point is, it's kind of clear that there are things that kind of appear here, quite complex things sometimes, which seem to be real objects that seem to be okay. But again, it doesn't seem to be an outright ban. So I'm gonna assume that there's kind of no actual syntactic principle stopping things appearing here. There's clearly something kind of going on, but it's not ruled out sort of too cool. It's not a complete ban. But even if there were something syntactic going on, there's a kind of get out, which is that the same thing happens with the regular passive, it's just harder to see. So Trusswell pointed out that there's a certain parallel here with the fact that English is what people call an asymmetric language when it comes to double object constructions. So you have examples like Kim gave Sandy a book, 21a, and you can get the passive Sandy was given a book where you sort of promote the nearer argument, the closer object to the verb, but you can't have a book was given Sandy, at least in standard English, standard British English. And this is kind of parallel to the fact that you get this thing with the prepositional passive as well, where if you promote the nearer argument, so a book was given to Sandy, you're okay, but if you try and promote the further argument Sandy was given a book to, it's no good. So this is kind of suggestive, that maybe something similar is happening even in the non-prepositional passive. But what's, I think, more interesting is that in symmetric languages, where this contrast doesn't exist in the regular double object passives, the contrast doesn't exist in the prepositional passive as well, and the things that are bad in English are perfectly fine. So there are languages like Norwegian where you can promote either objects in a double object construction, right? So you can have John was given a fiddle or a fiddle was given John, both meaning that John's receiving the fiddle. And correspondingly, you get prepositional passives where you have an intervening direct object. So the letter was pasted stamps on or the answer was given a watch to yesterday. Well, so the various authors hedged their bets on these a little bit and some say some people find them awkward, some people don't like them so much, but they're certainly not as bad as the English versions seem to be. So even if there is something syntactic going on here, it seems to be the same syntactic thing as is going on in the regular passive. So it's not, again, not a special property of the prepositional passive, really it's something about the passive in general. Okay, so now we come to kind of what's maybe the more, well, troubling thing, I guess, for some people, is that you get these, suppose it adjuncts taking part in the argument alternation, in the passive. So we get, especially locatives and instrumentals, right? So he's had some examples earlier, there's a couple more examples here. This broken shovel has clearly been shoveled with rather too enthusiastically. The room had not been cleaned, the bed was slept in, but towels were everywhere, et cetera. So yeah, we definitely get these things that look like adjuncts and, you know, this is a problem. First of all though, note that it isn't all adjuncts, it's not that this is completely leaky and sort of open, it's not anything goes when it comes to these kind of things, there are still bad examples, so things like 27 and 28. The Jones is left after dinner is okay, but not dinner was left after. And Lincoln died in this theater, okay, but this theater was died in, that's just terrible. So part of this contrast seems to be a lexical thing, right? And this isn't surprising if we think that the passive is lexical, then, you know, there are gonna be lexical exceptions to it. And so people have pointed out that there's a difference between unaccusative and un-negative verbs in this respect. So this would explain why dye is bad. Unaccusative verbs are supposed to not like the passive, right? And dye is unaccusative, so, you know, this is just bad for those reasons. And this contrast is supposed to come up in little minimal pairs like 29, right, where the desk was sat on by the gorilla is okay, because it's sort of volitional, right? Whereas the desk was sat on by the lamp is bad. And this is supposed to kind of pick up the un-negative, unaccusative distinction, something approximating it. And there may be a sense in which this applies to regular passives as well. So things like Kim resembles Sandy is okay, but Sandy is resembled by Kim. It's not great. This might be an effectiveness thing or something else going on here as well. Not so sure this is quite so strongly the same effect. But I think a bigger problem here is the kind of issue of delimiting arguments from adjuncts in the first place. So the force of this objection is basically, well, there are these two categories, arguments and adjuncts. Passive targets, this group, arguments are not these. The prepositional passive seems to target both, therefore it's not the same thing, right? But we can take issue with the very first premise that there are these things called arguments and adjuncts.  There's been a lot of ink spilled on trying to find ways to successfully delimit the two, to find tests that reliably work for the two, and you can always pick out the right thing. And, you know, to no great success. So it seems really, I mean, the conclusion has always been that it's kind of gradient thing, but really it seems like there aren't good reasons for thinking of these as two separate categories. And it's been, well, there's been a lot of work on kind of, you know, either we end up inventing extra categories to kind of put in the middle or we just do away with the notion altogether. It's easy to say that there aren't kind of behaviors that correlate with these kind of properties, right? So there are clearly more argument-like things and more adjunct-like things.  So I ate the cake versus I ate happily or something. There's no one's going to be confused about whether happily is an argument or not. And these have syntactic reflexes because, you know, there's an ordering thing going on here. You can't say I ate happily the cake, but you can say I ate the cake happily, right? So the direct object has to appear. The argument has to appear before the adjunct. This is kind of what we're all supposed to be happy with. But, you know, particularly when it comes to obliques, particularly when it comes to PP kind of complements, the distinction is much more leaky, right? So the ordering thing doesn't work anymore, right? So you can speak happily to John or you can speak to John happily. And these kind of seem to permute quite readily. And, you know, there's been various proposals to kind of collapse certain kinds of obliques into adjuncts or vice versa, or to collapse everything into one kind of thing. So Shepiokovsky, the citation there, is suggesting that really we want an HPSG style thing where we have like a list of all the dependents and we don't really care too much which ones are arguments, which ones are adjuncts. There's just a kind of one type of thing. So if this is true, well, so this will affect how you look at the problem, right? If you're someone who's coming at this saying arguments and adjuncts are definitely different things and we need to respect that in our grammar and, you know, the passive is very clearly an argument targeting thing, then you're going to see the fact that the prepositional passive can kind of cross borders as a problem or as a reason to think of it as different from the passive at least, right? So not as the same kind of thing. But if you are coming at it from the perspective of, well, there's a kind of, you know, it's kind of messy to have this distinction in the first place and maybe it's better if we don't, then really this is kind of more grist to the mill. You're just saying, well, there's another example of why there's no distinction between arguments and adjuncts. The prepositional passive doesn't care either. This kind of operation doesn't distinguish between them. Yet another instance in the grammar of this distinction not mattering. So I think to some extent it depends on where you're coming from. You might object then, well, why don't we get regular passives from adjuncts, right? And I think probably it is a kind of categorical issue here where most of the, you want a nominal, right? You want an NP to take part in the passive and most of the adverbial MPs I can think of are sort of time things or maybe I ate yesterday or something. And that's going to be a bad passive subject for all sorts of reasons because it's not going to be affected by things. It's not going to characterize it very likely. It's not going to satisfy a lot of the other constraints in the passive. So I think probably the reason you don't get those not a lot of options and the ones that there are are just a bad passives. Whereas when you go into the world of obliques, you know, like I say, the boundaries between the obliques and adjuncts is much blurrier anyway. And there's a lot of kind of a lot more nominal type things in prepositional phrases, right? We all have this nominal object, which is available to kind of get used. So this is my kind of hand wavy way of saying that I guess it doesn't matter. If there's no such thing, if there's no distinction in arguments and adjuncts, then this kind of objection doesn't get off the ground, right? If it doesn't matter that the prepositional passive doesn't care because this distinction doesn't exist, so it shouldn't care. I have a slight story to tell later on about what it is that's kind of relevant. It's not really argument adjuncthood. I think it's sort of to do with the semantic pragmatic stuff and to do with kind of more meta-linguistic things. But I will come back to that. So for the moment, I'm sort of asking you to take it on faith that it's OK to collapse these things. Let's not worry too much about the fact that this seems to kind of cross borders here. So OK, in that case, I hope to have shown you that there's not that much going on in the prepositional passive, which is different. Really, the thing that's different is the mapping between what the syntax is doing and what the semantics is doing. So the difference is that the subject corresponds to the objective preposition and the active buses of verb. What that means for the analysis is that the subject of the clause has to correspond to the internal argument of a preposition in the semantics. So in the bed we're slept in, we want slept to go within in the semantics. So we want bed to go within in the semantics. And given that I'm claiming that there's not that much difference between the regular passive and the prepositional passive, what would be nice is if we had an analysis of the regular passive that we could just kind of tweak a little bit. That's what I'm sort of proposing to do. So I'm kind of taking a version of an LFG analysis of the passive and just adding a little bit of information. And I think this suffices. So I think LFG kind of has an easy time describing this kind of thing because of the kind of modularity. So I'm assuming everyone sort of knows a little bit about LFG, but I'll go over some kind of basic stuff. So because we have this kind of very modular grammar, it's kind of easy to get what it's easy to describe these kind of relationships between the different levels. So in figure one, you've got this kind of baby version of some part of the correspondence architecture. The kind of classic LFG is the constituent structure and the functional structure. You have the tree, the constituent structure, which tells you about hierarchy, tells you about constituency, linear order, this kind of stuff. And then you have the functional structure that tells you about more abstract things like grammatical functions, like long-distance dependencies, binding, that kind of thing. And they're connected by this function phi that kind of protects from the C structure to the F structure. Over the years, there's been all sorts of other structures added, proposed, whatever. And kind of one of the more venerable ones is the S structure, the semantic structure, which kind of changed shape various times over the years. But I'm kind of taking a modern version of it where it's kind of seen as this connected kind of attribute value matrix that describes kind of semantic relations. So figure two kind of gives you a little example of this. On the left, we have the S structure for the bed was slept in, where I just have, you know, this predicate sleep, the subject, the bed. It still has this kind of oblique hanging around within it, and it's in the passive. And then on the right-hand side, you have this semantic structure, where there's a sleep relation that has an event in an argument. And it has a first argument, an arg1, which is just the kind of the highest argument, whatever you want to call it. And then this location argument, which has its own kind of internal structure, where there's this in relation and its argument, which is called PRG, for the sake of kind of brevity. That's just the internal argument of the preposition. So really, what we want to describe is the mapping from the F structure to the semantic structure. And the way that I'm going to do this is based on some sort of recent work by Vasude and Giorgolo, and me, and some other people, where basically we have some version of a theory of lexical mapping theory, which is a family of theories, I guess, that basically describe these kinds of relations between grammatical functional things and semantic arguments, wherever they may be. And they provide equations a bit like 31. So 31, the arrow just refers to the F structure that you're in. The little sigma tells you to go follow the sigma function from wherever you are. So it says, OK, the F structure that I'm in, go to the semantic structure that corresponds to that. That arg1 is the same as the sigma projection from the subject or the oblique agent of the F structure that I'm in. So it basically just says, this will occur in a verb, lexical entry. And it'll just say, well, my arg1 is either a subject or it's an agentive byphrase. So it's underspecified in this way. And this is to allow for argument alternations. It's slightly more complicated than that, because in the short passage, for example, it doesn't get realized at all. So if you care about the sort of details, I've put some stuff in the footnote, but it all gets a bit weird and it's too many brackets. So I just wanted to simplify. But assume that 31 also accounts for the fact that you cannot realize this at all. Then what the passive has to do basically is constrain these possibilities. So the normal mapping thing says, OK, you can have one of two grammatical functions. And the passive says, no, actually, you can only have one. So 32 is a template, which is just a kind of named bundle of information which is supposed to correspond to basically what the passive does. So if a verb is in the passive, it has this information in it. So it says, OK, my voice is passive, sure. Then you have the mapping thing that basically constrain is 31. It's just a sort of non-open version of 31, which says, no, my arg1 is my agentive oblique. It's not a subject. And then we have this optional meaning constructor, which you don't need to worry about too much if you're not into glue semantics, but it's basically just a way of existentially closing the first argument. So in the short passive, you don't get anything that corresponds to the arg1, but you still need to do something with the meaning of the predicate in the semantics. So you just kind of existentially close it and say, there is some x that's doing whatever you want arg1 to do. That's fine. So this is kind of what the basic passive analysis is. So the question then is, well, what do we need to add to this to get the prepositional passive if it's really just the same, but with a little bit of a tweak? Well, really, we just need to add the mapping information. So it's just the question of, in Figure 2, the kind of bottom arrow. That's what we're worried about. The rest of it will follow from more general stuff, but we want to get the subject into the PR. It has to be the internal argument of the preposition and the meaning. So essentially, we just kind of stick this in. So 33 is just an equation that does exactly that. It says, OK, find the subject. Follow the sigma projection. You are at the PR of your oblique. Again, this is slightly more complicated because it's kind of ordering things that we have to figure out, but don't worry too much about that unless you really care about LFG architecture. So then, really, we just bung this in the passive template. We get what we see in 34. So we just have this as the third line. We put it in brackets because it's optional, because in the regular passive, you're not going to need it.  So basically, it appears when you need it and doesn't when you don't. So this is kind of what I'm suggesting is going on. So how does this kind of play out? Well, for the type ones, the things where we have this kind of esoterically selected preposition, what I suggest is that really, we just have that order. We have on page nine, wherever that may be. It's not where I thought it was. There we go. We have in 35, where we have the kind of excal entry for a rely. The meaning is the bottom line, just like any other transitive verb, basically. It's just some predicate rely on. But the second line just equates its second argument with the internal argument of on. This is OK. Well, there's some oval on. And that thing's PR, that thing's internal argument is my arg2. Now, in the normal case, that will be the object of the preposition. That's just how this kind of normally works. It just passes the meaning from the preposition to the main predicate. But in the prepositional passive case, we have this kind of mapping thing that says, OK, the subject is actually the PR. And that just ends up saying that the subject is the arg2, which is kind of what you want. And that's fine, basically. For the type twos, it's a little bit more complicated. Well, if the type twos are obliques or if we can collapse everything into one category or whatever it is I wanted us to do, then this is fine. This kind of carries over just the same. Because we're doing exactly what we need to do. We're just telling the semantics where to find the argument it needs. So if it needs it, it'll find it. The question is, well, if they're not obliques or if there is a distinction between obliques and adjuncts, then what's going on here? What actually is motivating what can take part in these things? And what it seems to me is that, well, the semantic pragmatic constraints really are the kind of ultimate arbiters of what's good and what's not. So 36 is a classic contrast that this bed has been slept in is OK, but this bed has been slept under is not OK in a kind of null context. Out of the blue, this contrast is supposed to hold because of effectiveness, presumably. But passes with slept under are fine. It's not that slept under can never express a good passive predicate or something because you can get things like 37, right? This heavy sheet is designed to be slept under where I know we've got some kind of characterization going on, perhaps. So given an appropriate context, you can rescue a lot of these things. And really, if it's about, well, even if it's about effectiveness, it's certainly about context. And it's really about kind of non-linguistic stuff or at least not just linguistic stuff. So the argument for 36 is that, OK, beds are affected by being slept in, but not by being slept under, right? So that's why 36A is OK, but 36B is not. But that's not really lexical knowledge, right? That's knowledge about the world. That's knowledge about what happens to beds when people sleep in them and what sleeping in a bed involves. But it seems to have grammatical effects, grammatical reflexes, right? It affects whether things are acceptable or not. And this is quite reminiscent of what James Pistoyovsky has talked about in relation to what he calls qualia structure, where he argues that there are lots of things like this, where there's kind of real world knowledge that has to be baked into the lexicon somehow in order for us to interpret certain things. So his classic example is he says, well, OK, the noun book has to include in its qualia structure the fact that what you normally do with books is read them or write them. And this is how we interpret sentences like John began the book, because really, we take that to mean John began to read or write the book, basically. The claim is that began kind of needs an event or some kind of thing, and you begin doing something. And so it looks inside the lexical entry for book and finds these kind of events, these reading and writing events. And so it must be one of those then that we're beginning. And that's how that works. And I recently found out about this nice example in German where a similar kind of thing happens where when a PP argument is separated from the noun that selects it, then the acceptability is determined on whether the meaning is kind of contextually unmarked or not. So in 38, I'm not going to attempt the German, because I'll embarrass myself, but we have about syntax. Has Hans a book borrowed? So it's the book about syntax that he's borrowed, but the about syntax has been sort of fronted. And this is OK. But if you have the same sentence, but with stolen instead, so about syntax has Hans a book stolen, this is supposed to be bad. I don't know if there are any German native speakers who can verify this judgment or not, but this is supposed to be the case. And again, this seems to be an example of real world type stuff affecting grammatical judgments. It's not that this is exactly the same syntactic structure. It's just that one meaning is less plausible or something. And therefore, this seems to rule it out grammatically. And the fact that context is important comes back to the contrast in 36, because it's not just that we have to have a linguistic context. If we get a good enough real world context, then 36B seems all right too. So if you imagine a situation where we're all sleeping in a dormitory, but it's really, really busy. And so some of us have started sleeping under the beds as well, because it's just not enough room. And then someone else comes in and looking for somewhere to bed down. They want to go sleep under a bed. You could quite happily say 39, I think. That bed is being slept under already, or that bed is already being slept under or something. And then it's fine. So really, it's about the context. But the point is that 39 requires a more marked context to come off successfully. You don't get it in the sort of null context. And so what it seems like is really that, well, relations that are available to the prepositional passive are simply those that are contextually relevant. The point is just that some of those relations are always relevant, or at least are available in the null context for want of a better expression. And so then you can use them without this richer background. And so what the, well, this is my more speculative Mickey think message, is that, well, actually, all sorts of things like, so all these sort of somatic-pragmatic conditions like effectiveness or prominence, and maybe even this argument adjunct distinction in some sense. So the things that are available in the null context are sort of more argument-like in that way, in that they are, if we go for a kind of qualia structure thing, then they are specified in the lexicon. Sleep does select for in, in some sense, because what we know about sleeping is that we usually do it in things. What we know about beds is that we normally sleep in them or something. So in some sense, they do select for each other. So it is more argument-like. And perhaps these kind of things are all actually just epiphenomena, and they may really be the result of some kind of Greithian-type inference interacting with knowledge about the world and its relationship with word meetings and things. And so actually, that's kind of the bigger explanation for what's going on with some of this stuff. So, OK, just take it, take us home. I hope to have convinced you, at least, that the propositional passive is only minimally different from the regular passive. And if that's the case, then a minimally satisfying analysis really is just the same analysis as we have for the passive, but tweet a little bit. And really, we can bake in these semantic pragmatic constraints if we want to. We can stick extra constraints into the passive template. We can put extra meanings in there that say things about the subject. So that's not a problem. But really, it would be nice if they were reducible to something more general or more basic. And I've tried to sort of gesture towards what that might be. But as ever, if it is to do with kind of relevance and more metalinguistic stuff, then a good formalism is going to be hard to come by. Thank you.


Thank you very much. Thank you, and I hope you've been enjoying our Marine Band. Well, please be seated and good morning. I Understand that there are some 150 principles with us here today. I'm delighted you're here, but I have to confess to some mixed emotions. I remember on a number of occasions when I was sent to see the principle. And now the principles are coming to see me. But I thank you, everyone, principles, superintendents, and school board presidents. Thanks for joining us on this day that has been set aside to honor outstanding American secondary schools. America's always had a love affair with learning from polished men of letters like Thomas Jefferson to humble, self-taught people like Abe Lincoln. Americans have put their faith in the profound power of education to enrich individual lives and to make our nation strong. But lately, many of our schools haven't been doing their job. Yes, there are many fine schools, dedicated principles, teachers, present company included. But since 1963, we all know that scholastic aptitude tests have shown a virtually unbroken decline. As of the 82-83 school year, half of our states required only one year of math and one year of science for a student to graduate. And 14 states imposed no requirements for graduation at all. We begun to realize that compared to students and other industrialized nations, many of ours performed badly. But let me make one thing clear. That is, if we take the overall student body across the nation and on the average, we don't compare favorably. On the other hand, when you take our top students, then they compare very favorably with the top students the best in those other countries. But I can't help but tell you about an experience that I had for eight years. Every year as governor of California, the exchange students from all over the world used to come for a visit with me at the Capitol in Sacramento. And every year for eight years, I asked the same question. I would ask these young people from these foreign lands how they, how it compared our education to theirs. Was it harder or more difficult? And there would be silence and then there would begin to be some smiles and then they would look at each other and pretty soon they would break into laughter. And that happened every time. And then they would begin volunteering the difference in what was required of them and what we're requiring of our students. The, I think the fact though that our top students and those from the schools such as you represent do compare favorably should be an answer to those who wonder whether American education can ever again become first rate. 152 secondary schools we're honoring today is answers that loud and clear. The answer is you bet. The panel of experts, people who know just what it takes to make a school good carefully reviewed the records of hundreds of schools under consideration for national honors. After the progress of rigorous selection, the panel recommended that your schools were those that best demonstrate the excellence that our sons and daughters deserve. Your schools provide proof of what the National Commission on Excellence in Education concluded in this report last April. America can do it. Now although each of your schools is unique, they all share a number of traits that the rest of us can learn from. Let me cite five that stand out. First, all of your schools know the meaning of team effort. You have school boards that skillfully translate community educational goals into sound policy. Superintendents, principals and teachers carry the policy to the students and armies of parents are supportive of your efforts. This summer I visited one of the schools that represented here today, Pioneer High School from Whittier, California. I met students, faculty and community representatives. I was impressed with the way they all were pulling together. Second, all of your schools share a few basic ideas about what goes into a good education. All of you happen to believe in leadership from superintendents and principals, dedication from teachers, homework, testing, using time efficiently and firm discipline. Third, you're resourceful. Whether it's buying new test tubes for the chemistry lab, finding transportation to send the band on tour, all of you've shown ingenuity and skill in meeting your school's needs. And you not only marshal resources, you share them. Secretary Bell has told me that a number of you have built good will in your communities by giving the public a chance to use the high school swimming pool, gymnasiums, classrooms after hours. Fourth, you've placed a strong emphasis on good teaching. You all recognize that good teachers are the heart and soul of any good school. And you promote good teaching. To do that, you've taken measures to reward it. Secretary Bell and I believe that schools across the country should learn this lesson and adopt policies and salary structures to reward outstanding teachers. And last, your schools have rejected contemporary academic standards in favor of your own much higher standards. You get more from your teachers and students because instead of accepting mediocrity, you demand excellence. And today, we're giving you flags to honor your efforts. By teaching your students the meaning of good education, you're teaching America as well. You know, I don't normally advocate busing, but right now, I wish I could bust this group to every high school in the country that isn't represented here. And with that said, you've probably noticed that we have a problem we haven't been able to solve in Washington, yet it has to do with the local airport. But God bless you all for being here and thank you very much.


What's up everyone it's your girl Kenzie here from Layton Sports Guards, and I'm ripping some personals for Matthew I we've got each 2022 Penini's and Zenith's first off the line football a 2022 Penini National Treasures baseball hobby and a 2022 tops finest baseball hobby best of luck here Matthew Thanks for hopping in See what we can get you Start with the finest Cornerstone Shohei Otani Refractor Aqua shimmer Max Scherzer to 175 Freeman Brinn and Marsh rookie Blackman devours we've got gold auto of Kyle Seeger 36 of 50 This gold auto there Don Matthew Mickey Cabrera Joey Vado Suzuki Cabrera Hayes refractive rookie Julio Rodriguez Very nice. I like Thomas Jose Miranda and Eddie Murray. We've got purple rookie Jose Miranda twins the 250 Yattie Bryson Stott rookie Springer CJ Abrams cornerstone Tim Anderson First mini box refractive rookie Mackenzie Gore Padres Detmer's incontruous Second half We've got blue rookie Gavin sheets white socks. Are they yeah 73 of 150 They like cruise tauti They game on a Sunday Kunia refractor rookie auto Riley Adams Kershaw a brave Cornerstone Freddie Freeman Trey Turner refractor Joe Maurer We've got Jonathan India on the rose gold to 75 love the rose gold look there Nice hit one Soto Kevin Smith and yeah burns. We've got die-cut 1947 Jackie Robinson That's sick Alonso and aeronado Last pack of the finest Bobby Whit Jr. Rookie base M. J. Melendez refractor rookie of Ida Bruja Jram and you're down up next Drift some Zenith photo First photo box that I'll be ripping there is your photo sticker. All right This bottom card is Pretty curved. We'll put it in a top loader. It should straighten out a bit behind the numbers of Joe mixing not numbered We've got how cool is this aloha of Ed Reed? That's so sick from when the Pro Bowl used to be in Hawaii. That is so cool This would be a really cool set to complete We've got Kyron Williams rookie for the Rams For the Steelers a Calvin Austin the third 44 out of 50 on the speckle RPA Two colored patch there Calvin Austin the third and last one here We have a Carson strong 42 a 50 on the speck on ice for colored patch Actually one more after this it was sticking to the this one very nice I'll play next year. This is on first off the line Devon. These are first off the line boxes and we've got Inscriptions 41 of 100 h a brown on card Otto Nice time to get this guy. Yeah, he's about to have a big game in Super Bowl. There you go Alrighty Matthew that will do it for the Zenith Now time for the NT. Oh The aloha's are the case hits sick But to say I've opened I've only opened one other Zena so far today. I have not seen that. That's really cool I like those The look and see who's on the checklist there All right, NT baseball time. Let's see a big. Let's see a car Oh, let's see a cut. I thought you're talking about the speckle my bad Devon My mistake It's pretty thick Carson strong magnet. Hey, you never know if Hertz goes down in the Super Bowl. He's a backup, right? Carson strong, who's the backup and Kelly the backup is We've got four colored patch here retro materials of Mitch Garver two of 25. Oh Yeah, that's right. It's Minshew and then he's third string. That's right Retro materials Man, I haven't heard that name in a while Corey seeker 49 of 49 of biography materials call me see We've got bat relic 12 of 25 Bobby Thompson Bobby Thompson. Yep Three touchdowns between those two you think so we've got acetate auto here of Edwin reels six of 63 on the legacy signatures Got auto 11 of 25 Taylor Trammell Mariners Tramel and she the mustache Uncle Rico, baby We got patch auto here pirates 22 of 99 Bubba Chandler little bubba Boba Chandler and we've got RPA 7 of 49 Connor Siebel Red Sox Connor Siebel. What's up Donald? How's it going? 7 of 49 Got 25 a 25 patch auto. Yeah, I'm Alina. Nice one there 25 a 25 bookend Game worn use material. Let's say For last and we've got shoeless Joe Jackson 94 of 99 on the treasured stat 356 average Joe Jackson doing pretty good Pretty good. Did you do your hamburger your fried hamburgers? Let me sit here doing yesterday and rookie signature jumbo material 87 of 99 Aaron Ashby for the brewers Alrighty that will do it for your boxes there Matthew congrats again on those hits. Thanks so much for your order That was a fun rip and we'll get these right out to you


1, i equal to 0 to n minus 1 right. So, for p 2 it is p 0 into i equal to 0 to 1 yes. So, pi 0 by epsilon 1 pi 1 by epsilon 2 right. So, this is my p n all my p n's in terms of this in terms of this one unknown single unknown p 0 and then how can I find this p 0 by using the normalization for probability. So, I know that the motor has to be in one of these states it must either have 0 motors bound sorry the cargo has to be in one of these states it can either have 0 motors bound or 1 motor bound or 2 motor bound or whatever, but ultimately it has to be in one of these states. So, p n n equal to 0 to n must be equal to 1 right. So, that is my normalization. So, if I do thatthen that gives me the solution for p 0 basically. So, so sum over n equal to 0 to n p n. So, let me actually writefrom n equal to 1. So, the first term is p 0 plus n equal to 1 p 0 product of i equal to 0 to n minus 1 pi i by epsilon i plus 1 this whole thing is equal to 1 which means that p 0 is equal to 1 divided by 1 plus sum over n equal to 1 to n product over i equal to 0 to n minus 1 pi i by epsilon minus 1 ok. So, in terms of these attachment and detachment rates pi is an epsilon's I can solve for this p 0 as well as for all of these. Once I know p 0 I know all of these p n's. So, I can solve for all of these in the steady state. Let me just say let me just change variables so that this is the most standard form ok. So, now that I know this I can calculate what is for example, the average number of bound mode. So, let me first calculate let us say yes generally that is not true. So, I have made like a somewhat stronger assumption in that sense of detail balance yes. Generically that is not true, but I just make that assumption and go ahead. So, now for example, I could ask that what is the probability that the cargo is bound to the filament, cargo is bound to the filament which is the sum of all these probabilities that p 1 plus p 2 plus dot dot plus t n or equivalently this is like 1 minus p naught. So, all of these states correspond to a cargo which is bound the p naught state corresponds to something like this where the cargo is completely unbound. So, I can define let me define a sort of new probability p n prime which is what is the probability to have n motors bound given that you know the cargo is bound to the filament. So, this is like a conditional probability and that is just this. So, I will just normalize by this 1 minus p 0 this is the probability of the cargo is bound to the filament. And in terms of this I can now calculate what is the average number of bound motors let us say average number of bound motors let us call it n b and that is just nothing, but n times this probability. So, n p n into 1 minus p 0 sum over n equal to 1 to capital. And I of course, I know what these p n's are because I just calculated that. Similarly, you could ask what is the average velocity you could ask what is the average velocity and that again is the velocity of a motor of a cargo which is being carried by n motors times this probability p n by 1 minus p 0. This I do not know yet I will have to make some sort of an answer as for this ok. I will come again I will come to that later. So, these epsilon's these pi's or these velocities these are separate ingredients that will go into the model and they will come into the model from experiments. From experiments we know how these unbinding rates binding rates velocities these all change and we will put that in at some point ok. But in principle you can calculate stuff like this the average number the average velocity or you could calculate for example, something like what is an effective unbinding rate of this cargo. You could calculate what is something like an effective unbinding rate and this I define by something like this that an effective unbinding rate from the bound state is equal to the effective binding rate from the unbound state. So, I define these new quantities epsilon effective and pi effective, but now instead of defining them in terms of this single p 0 p 1 p 2 I call this from this whole probability of the bound state and here the whole probability of the unbound state. The probability of the unbound state is of course, p 0 and the bound state is 1 minus p 0 right epsilon effective and you can go from this unbound to the bound state by the addition of a single motor right. The moment you have one motor bound you go to this bound state which means that this pi effective is nothing, but pi 0. So, now, you can substitute for this p 0 that we calculated which I have rubbed out, but you can substitute for that p 0 and you can find out what is this effective unbinding rate. So, I will just write that down. The effective unbinding rate comes out to be something like epsilon 1 1 plus sum over n equal to 1 to n minus 1 product of i equal to 1 to n pi i by epsilon by plus 1. This whole thing you could also for example, calculate you could get to this quantity the effective unbinding rate by using the formalism of mean first passage time. So, let us try to do that and that will should give you the same result. So, I could ask that well what is the mean time it takes for a cargo which started off with m bound motors given that the maximum number of possible bound motors is capital N. What is the mean time it takes for this state which started off with n bound motors to become unbound ok. So, I start off with this state m bound motors and I want to go to this absorbing state which is the unbound or the free cargo where I have no bound motors and this I call the mean time taken to do that given that I start off at this mth state. So, I can write down an equation for this T mn ok. So, I can write down an equation for this T mn for example. So, if I am an so, if I am in the state where my cargo is carried by n motor n m bound motors. So, there are m bound motors I can do two things I can either bind one more motor or I can unbind one motor right. So, if I bind one motor I will go to the state which is m plus 1 right and from that state it will take some time which I do not know, but it will take this time T m plus 1 comma n right. And with what probability will I go to this state? Yes Shogutha what is that? Pi, pi m that is the rate divided by pi m plus epsilon m right. The other thing I can do from this state is to go is to unbind one motor which means I will land up in the m minus 1 state and from that state it will take some time T m minus 1. In that state I will go with the probability which is this un proportional to this unbinding rate epsilon m again by the same pi m plus epsilon m ok. So, these are the two things that I could do I could bind a motor or I could unbind the motor or I could just do nothing I could just sit in that state for some time at least. And what is the typical time I will sit in that state for? Given that my two rates of escape from that state are pi m and epsilon m. So, for example, if I am if I attach a motor with a rate of 1 per second and if I detach a motor with another rate of 1 per second then typically in that state how long will I stay? 1 over the sum of these rates right because the rate of escape from that state is 2 per second which means on an average I will stay in that state for half a second which is 1 by pi m plus epsilon m is this clear? So, in this state I will stay for a typical time remember these are mean times ok. So, on an average I will stay in that state for this time with this probability I will go to that state and from that state I will take this time and with this probability I will go to the m minus 1 state and from that state I will take this time. So, what I have done is that I want to find out this quantities t m comma n what I have done is I have written down a set of recursion relations between these various states right. And I can write down the boundary the boundary times. So, for example, if I started off with the state where all these motors were bound right all capital N were bound then I have nothing more to bind because my maximum number of motors is capital N which means this sort of a term will not be there. The only thing I can do is that I can unbind the motor. So, I will typically I will stay in that state for the inverse of the unbinding rate and the only thing I can do is that I can go to the n minus 1 comma n state with probability 1. So, that is one boundary condition on the higher side. On the lower side what is the mean time to become unbound if you start with 0 motors that is simply 0 that is just an absorbing boundary right. If you start with no motors bound you take no time to reach the unbound state is clear. So, I now have a set of recursion relations which is a self consistent set of equations. So, I have these n unknowns this t m comma n n of capital N of these and I have n equations. So, I can solve for this. So, you can start off you know by doing it you can write down what is t 1 comma n for example, t 1 comma n is 1 by pi 1 plus epsilon 1 plus pi 1 by pi 1 plus epsilon 1 into t 2 comma n plus something into t 0 comma n which is 0. So, I do not care. So, I have written down t 2 comma n in terms of t 1 comma n. I put in the next time I can write t 3 comma n in terms of 1 comma n and so on. So, I can solve this whole set of equations ultimately. So, you should try to do that. So, if you do that I will just write down the answer what you get for t 1 comma n in terms of these in terms of these pi's and epsilon's. So, if you solve this set of equations what you get is t 1 comma n you can solve for all of course, I will just write down this t 1 comma n. This t 1 comma n is 1 by epsilon 1 1 plus sum over n equal to 1 to n minus 1 product of i equal to 1 to n pi i by epsilon i plus 1 which you will notice is nothing, but the inverse of this effective unbinding rate ok. Because the time it takes to go from the state with one motor bound to no motors bound is the effective time it takes for this motor to unbind complete sorry this cargo to unbind completely from the micro TV. So, this t 1 comma n is nothing, but the inverse of this effective unbinding rate and that comes out from this calculation. So, now, you need to put in the forms of if you wanted to actually see what all of this means you need to put in forms of these epsilon's and pi's and so on. And there we use sort of phenomenological estimates of what these rates look like. So, let me write here. So, let us say I want to write the unbind let me first do the case when there is no no force zero force. So, remember you could also like pull on this cargo let us say the this is a kinesin motors which want to go this way you could pull on it with an opposing load and that will change these rates. But let us first say what it will be for the zero force case. So, for example, if I want to know if the unbinding rate for a single motor is some epsilon I want to know what will be the unbinding rate when I have small n number of motors bound. And there what we say is that this is nothing, but n times epsilon because if any one of these n motors unbind you will go from the n state to the n minus 1 state ok. So, the effective unbinding rate is the sum of all of the unbinding rates of these individual motors n times epsilon. Similarly, you could write down what is going to be the attachment rate and that is something like write n minus n times some pi. So, when there are no motors bound when n is 0 you have some rate which is n times pi as more and more motors bind that that rate falls because it becomes more difficult to bind the filament. So, that rate becomes smaller the more number of motors that are bound. And you could say that the velocity does not depend on the number of motors at least in the zero force limit. So, the velocity with n motors bound is the same as the velocity with a single motor bound.  So, what this says is that if you have a single motor bound then the moment you apply an opposing load which is equal to the stall force f s your velocity will go to 0. If you have two motors bound then you need to apply twice the stall force which is what we saw in those experimental curves earlier on that any which is what we saw in the experimental curves earlier on if you remember there were some peaks at 6. If you had one motor bound then to completely stall this cargo you needed 6 piconewtons if you had two motors bound you needed 12 and so on. So, the force that you need to apply to stop a cargo which is being carried by n motors is n times f s and it varies linearly which is sort of an experimental approximation it is true in some cases it is not true in some other cases. And here for example, it says that the unbinding rate also grows exponentially with force with some force scale which is f d. So, the harder you pull it back the harder you make it for these motors to unbind which as we discussed is true for kinescence, but not for dinanes, but let us say it is true for this case. So, let us say we are modeling kinescence. So, if you put in all of these now if you put in these forms of these epsilon the v and the pi's into all of these equations you can calculate what this model would predict for example, for these average number of bound motors, average velocity, distributions of walking distances, unbinding rates and so on. So, here is what it says. So, for example, if I want to find if I want to find this is the effect prediction for the effective velocity as a function of this opposing load for cargo that is carried by different maximum number of possible motors. So, this is capital N. So, this curve is capital N equal to 1. So, there is only one motor that is possible it is either 1 or 0, this one is N equal to 2, this one is N equal to 3, 5 and 10. So, what it says is that for example, if you have considered this single motor cargo which is being carried by at most one motor and capital N equal to 1, any force greater than 6 would mean that the velocity is 0 the motor the cargo has stopped because you have crossed the stall force of the motor. As you carry as you are attached to more and more number of motors, you can walk with an appreciable sort of velocity even when you have a large force. So, for example, at even a 10 piconewton this case with 10 motors walks with almost 0.8 times the maximum possible velocity. Similarly, if you look at the average number of bound motors in this case of course, it is 1 because your capital N is 1. Therefore, it can be nothing but 1. But in these other cases as you apply more force, let us look at for example, this case this capital N equal to 10 case. As you apply more force the number of motors that are bound on an average at a given time, it will sort of decrease with force ultimately at some point when you reach the stall force of these 10 motors no motor will be bound. Similarly, if you look at the average distance it is walked which I have not calculated, but you can calculate. Again that and plot that as a function of force, the more the number of motors that is carrying your cargo, the more the distance that you can walk on an average. And these are not linear functions. So, these do not grow these do not change linearly with force. These change non sort of non trivially depending on the number of motors that are bound. So, the behavior for a single motor can be very different from the behavior for multiple motors bound. Similarly, you can not only look at the average sort of distance that it walks, but you can also look at distributions of these walking distances. So, you can calculate from this formalism the full probability distribution again which I have not done, but which if you go to this paper you can take a look. And for example, if I look at this high force case when I am pulling back with a force which is around 10 piconewton, these cases where the cargo is being carried by very few motors 1 or 2 or 3. It is a very short probability distribution peaked around 0, which means most often it will walk very little distance before falling off. On the other hand if you have more number of motors that are carrying the cargo, it can walk an appreciable distance. So, even at 3, 4 microns you have a reasonable probability that the cargo will get that far. So, the cell can. So, the cell can use this multiple motors, this cooperative effects between multiple motors in order to carry cargo large distances you ask me something. Yes. Yes. Which is going to 1. Yeah, that is because I have normalized my probabilities with this conditional probability. So, I am only considering the subset of cases where at least the cargo is bound to the filament. So, this 1 minus p 0 is there. That is because once the motor unbound unbinds sorry once all motors have unbound, the cargo sort of drifts off. So, experimentally it is a little difficult to compare with that. On the other hand it is easier to compare with this conditional probability that given that it is bound how many are there, how many motors are bound. So, you can do of. So, this is of course, in some sense mean field estimate I neglect sort of fluctuations, I only calculate average quantities you can do better and better you can take into account what is the effect of fluctuations, you can take into account what is the effect of opposing motors and so on. But you can do it in a sort of similar spirit, you can write down these sort of master equations, you can solve them numerically if not analytically and then try to see what it predicts for this sort of cooperative behavior. So, multiple motors is not just one motor scaled up n times, the effects can be fairly non-trivial and that non-trivial effects comes because of these non-trivial force velocity or force unbinding rate sort of dependencies completely from the micro TV. So, this T 1 comma n is nothing, but the inverse of this effective unbinding rate and that comes out from this calculation. So, now you need to put in the forms of if you wanted to actually see what all of this means, you need to put in forms of these epsilon's and pi's and so on. And there we use sort of phenomenological estimates of what these rates look like. So, let me write here. So, let us say I want to write done by let me first do the case when there is no force 0 force. So, remember you could also like pull on this cargo let us say the this is a kinesin motors which want to go this way, you could pull on it with an opposing load and that will change these rates. But let us first say what it will be for the 0 force case. So, for example, if I want to know if the unbinding rate for a single motor is some epsilon, I want to know what will be the unbinding rate when I have small n number of motors mount. And there what we say is that this is nothing, but n times epsilon ok. Because if any one of these n motors unbind, you will go from the n state to the n minus 1 state ok. So, the effective unbinding rate is the sum of all of the unbinding rates of these individual motors n times epsilon. Similarly, you could write down what is going to be the attachment rate. And that is something like write n minus n times some pi. So, when there are no motors bound when n is 0 you have some rate which is n times pi as more and more motors bind that that rate falls because it becomes more difficult to bind the filament. So, that rate becomes smaller the more in the number of motors that are bound. And you could say that the velocity does not depend on the number of motors at least in the 0 force limit. So, the velocity with n motors bound is the same as the velocity with a single motor. The interesting thing is of course, what happens to these relations when you apply an opposing load you apply an opposing load.  So, what this says is that if you have a single motor bound then the moment you apply an opposing load which is equal to the stall force F s your velocity will go to 0. If you have 2 motors bound then you need to apply twice the stall force which is what we saw in those experimental curves earlier on that any which is what we saw in the experimental curves earlier on if you remember there were some peaks at 6 up. If you had one motor bound then to completely stall this cargo you needed 6 piconewtons if you had 2 motors bound you needed 12 and so on. So, the force that you need to apply to stop a cargo which is being carried by n motors is n times F s and it varies linearly which is sort of an experimental approximation it is true in some cases it is not true in some other cases. And here for example, it says that the unbinding rate also grows exponentially with force with some force scale which is F d. So, the harder you pull it back the harder you make it for these motors to unbind which as we discussed is true for kinescence, but not for dyneins, but let us say it is true for this case. So, let us say we are modeling kinescence. So, if you put in all of these now if you put in these forms of these epsilon the v and the pi's into all of these equations you can calculate what this model would predict for example, for these average number of bound motors, average velocity, distributions of working distances, unbinding rates and so on. So, here is what it says. So, for example, if I want to find if I want to find this is the effect prediction for the effective velocity as a function of this opposing load for for cargo that is carried by different different maximum number of possible motors. So, this is capital N. So, this curve is capital N equal to 1. So, there is only one motor that is possible it is either 1 or 0, this one is N equal to 2, this one is N equal to 3, 5 and 10. So, what it says is that for example, if you have considered this single motor cargo which is being carried by at most one motor and capital N equal to 1 any force greater than 6 would mean that the velocity is 0 the motor the cargo has stopped because you have crossed the stall force of the motor. As you carry as you are attached to more and more number of motors you can walk with an appreciable sort of velocity even when you have a large force. So, for example, at even a 10 piconewton this case with 10 motors walks with almost 0.8 times the maximum possible velocity. Similarly, if you look at the average number of bound motors in this case of course, it is 1 because your capital N is 1 therefore, it can be nothing but 1. But in these other cases as you apply more force let us look at for example, this case this capital N equal to 10 case as you apply more force the number of motors that are bound on an average at a given time it will sort of decrease with force and ultimately at some point when you reach the stall force of these 10 motors no motor will be bound. Similarly, if you look at the average distance it has walked which I have not calculated, but you can calculate again that and plot that as a function of force. The more the number of motors that is carrying your cargo the more the distance that you can walk on an average and these are not linear functions. So, these do not grow these do not change linearly with force these change non sort of non trivially depending on the number of motors that are bound ok. So, the behavior for a single motor can be very different from the behavior for multiple motors bound. Similarly, you can not only look at the average sort of distance that it walks, but you can also look at distributions of these walking distances you can calculate from this formalism the full probability distribution again which I have not done, but which if you go to this paper you can take a look. And for example, if I look at this high force case when I am pulling back with a force which is around 10 piconewton these these cases with the cargo is being carried by very few motors 1 or 2 or 3. It is a very sharp probability distribution peaked around 0 which means most often it will walk very little distance before falling off. On the other hand if you have more number of motors that are carrying the cargo it can walk an appreciable distance. So, even at 3, 4 microns you have a reasonable probability that the cargo will get that far. So, the cell can so, the cell can use this multiple motors that this cooperative effects between multiple motors in order to carry cargo large distances you ask me something yes yes which is going to 1. Yeah that is because I have normalized my probabilities with this conditional probability. So, I am only considering the subset of cases where at least the cargo is bound to the filament. So, this 1 minus p 0 is there that is because once the motor unbound unbinds sorry once all motors have unbound the cargo sort of drifts off. So, experimentally it is a little difficult to compare with that on the other hand it is easier to compare with this conditional probability that given that it is bound how many are there how many motors are bound. So, you can do of so, this is of course, in some sense mean field estimate I neglect sort of fluctuations I only calculate average quantities you can do better and better you can take into account what is the effect of fluctuations you can take into account what is the effect of opposing motors and so on, but you can do it in a sort of similar spirit you can write down these sort of master equations you can solve them numerically if not analytically and then try to see what it predicts for this sort of cooperative behavior. So, multiple motors is not just one motor scaled up n times the effects can be fairly non-trivial and that non-trivial effects comes because of these non-trivial force velocity or force unbinding rates sort of dependencies.


Pendo, Florida, extracting a signal from the noise. It's theCUBE, covering Pentaho World 2015. Now your host, Dave Vellante and George Gilbert. Welcome back to Pentaho World in Orlando, everybody. This is theCUBE. theCUBE goes out to the events. We extract the signal from the noise. Rosanne Secone is here as the CMO of Pentaho. Rosanne, welcome to theCUBE and congratulations. Second year, great event. Thank you so much, we're so delighted to be here. So let's give people the stats. You talked in your keynote about who's here and what the breakdown is. Five or 600 people here. Give us the rundown. Yeah, absolutely. I mean, one of the things we're so excited about is having such a nice breath in our audience this year. They're from 27 different countries around the globe. We're representing small companies to large enterprise companies. Pretty much every image is represented. One of the other things I love about the stats for this conference is about... That agenda for those different audiences. I wonder if you could talk about that a little bit. To set different agendas for companies, to enable them to engage with their customers in a much more intimate way than they've been historically able to do. And it's gone from pilot projects to business value-driven use cases. And so we really wanted to talk about the power of big data at work because it's about implementations. It's about doing things differently. It's about showcasing user examples of big data and analytics. Yeah, and you guys had a big theme in the key notes this morning about operationalizing analytics. And it's clear, Pentaho's not an out-of-the-box BI tool. That's right. I love the saying, a fool with a tool. It's a platform. And it's been built up over a decade plus. 11 years old, just a year older than Hadoop. Which is kind of ironic and interesting. So, okay, so you're not an out-of-the-box BI tool. What is Pentaho? So Pentaho is an end-to-end integrated platform to enable companies to manage their data. So take data from pretty much any source you can imagine, use that data, manage that data, and then expose the information for analytics. And a lot of our best use cases are embedded analytics. Because most companies want to embed analytics in a workflow, in another application, in a portal. They're really trying to create an analytic capability that enables a regular person, a business person, to make decisions for their business. And it's really, the data is 80% of the challenge for people is getting the data prepared in a way where you can present that data in various formats and enable that decision-making. And George, we saw a great example of that with FINRA this morning. Go ahead. Absolutely.  And some have taken the approach of trying to baloney-slice this. What do you get in return for providing that end-to-end experience? Yeah, I think our founders of the company rooted this company in reality and say, what happens out there? And I don't know too many companies that have nice, neat data all ready to go but all you need is an analytics tool on top. Most of the challenge is in trying to get the right kind of data sources prepared and into that flow. So, if you think about that end-to-end flow, the value isn't in just the analytic piece, but it's in the flow, the modeling, kind of the inner relationships of the data, the context of the data, and then bringing that sort of analytical data pipeline to bear on the other side. There's something, that's something in there you said that's really significant because in the data warehouse, it's already curated. So, you can focus just on the presentation and it sounds like when you're in the data warehouse, because it's so much more raw, you really need to sort of integrate backwards into making, into tools that'll make that experience of refinery, of refining the data more powerful. I think it depends on the use cases. So many customers we talk to, it's a blended data story. They don't just go to one place to access that data.  So, they're trying to figure out how to blend that data and provide context around that data, right? So then when they're providing meaning to that data, it's more useful to the person who at the end of the day is in charge of the decision. And so, to us that philosophical value chain was really important in what we built on our platform. And to go longer, the idea that you're going all the way back to the sources, which is specifically different because in a data warehouse, you don't need to as much. Right, well I think it depends on what a customer's trying to do with the data. So there's a lot of problems in this space. Thank you guys for that. You mentioned 70, 80%, whatever the number is, of the time it's spent on trying to do stuff with the data, get the data quality right, get the data in, cleaning it, et cetera. And what a lot of customers are going to just throw it into the data lake or data ocean or whatever term you want. No schema on right will figure it out later. The problem is figuring it out is hard. The other problem is, yeah, EDW, critical, tried and true, well understood, but it's insights for a few. So the challenge is how do you get insights in the hands of many that can actually act on it? Fundamentally, you're solving those two problems, correct? I think so. In fact, what I think we try to do on our analytics side is focus on what's the ultimate decision making power that's needed and what's the use cases really that's being driven by that. And so what you'll see a lot of times is you might have data analysts who want to go deep with the data, they want a tool that goes deep, that can provide deep context. They may be able to try to find meaning in the data, but once they find meaning in that data, they want to operationalize that to everybody else. And they want an easy consumption model for those types of reports or those types of applications. And so to us, the spectrum of how people use data is so broad to encompass all the different members of the audience that want to use that data. So you have your data analyst that might go really, really deep, you'll have a business analyst who might go a little differently and then you're going to have business users like myself. I'm very metrics and operational driven, but I don't want to look at every piece of data. I want to see something that I can make actionable. So this is the second Pentaho world. And subsequent to the first, you were acquired by Hitachi. How has that changed things? Any surprises that you see? Yeah, it's a great question. You know, when you, the first year you read a conference at a new company, you're just trying to think about all the basics, right? You want to make sure you get a good audience. You want to make sure you have great speakers. You want to have really good track sessions, really kind of the basics. And then in the second year you're looking at, what can you do a little different? Bring a life stream to the environment. Bring more people into that conversation that you're having. You know, we wanted to add an internet of things related sort of topic. And when, what's kind of cool is we, I got to know Hitachi and the HDS team based on the Pentaho world last year. So they were here last year. They were part of our advisory board. They got to see sort of Pentaho in action in terms of sort of the bringing big data to life. So we developed a lot of great relationships from that. And then the acquisition happened. So there was really a tight alignment in our vision from day one, which enabled us at this conference to create a whole other track session around internet of things related solutions that's related to big data, but then obviously related to other things, right? You have to have the right vertical knowledge from a consulting organization really to kind of lock down what those solutions might look like. There's hardware that's kind of associated with some of those appliances. So they really bring to the table the extensions around some of the core that Pentaho has. And so what you'll see at the conference is places where the Hitachi capabilities really complement that core analytic platform that we bring to the table. So it's had a direct impact on the brand. So it's interesting that the internet of things mean three, four years ago, nobody was talking about IoT. So it seems to me, I think Chris said, look, strategy shouldn't change. Strategy should be consistent. So the strategy is, look, there's all this data. Somebody's got to deal with it. That's us. The founders of Pentaho clearly had that vision and then whatever wave of data comes, you want to be able to ride it. So my specific question is, how has the brand and the brand message changed? And what do you want the brand to stand for? Moving forward. So to me, the Pentaho brand still stands for, we always talk about it as the future of analytics. So we want to accommodate in our brand the new waves of data that are coming in into play. What are the things our customers are trying to do that they've wanted to do for 30 years, but the data never caught up to the, and the technology never caught up to the use case. So people have been talking about 360 degree view of a customer for 20 years, but now they can do really personalized experiences with more real time related experiences for customers. And the data, the analytics are enabling that and the new technologies are enabling that. The decision support world, George, and the EDW world have been promising that for years. And let's face it, it did not live up to that promise. Now you're up. Yeah. And you think about it, there's commoditization of a lot of the costs of big data sources. Like Hadoop has created this amazing opportunity for people to more cost effectively leverage their data that they couldn't really access before because it's too expensive. And so it's bringing this whole new insight into how do you leverage data in a way that's more meaningful for that problem you're trying to solve, not for everybody, but for the problem you have, the use case you have. So this is critical, because Dave's saying, so what's new now? We've heard the customer 360 story going all the way back to the first data warehouses and maybe even before, to the mainframe reporting stuff. So, and you've identified one critical new ingredient, which is the price has dropped by more than an order of magnitude. We hear reports that some customers clock their, their data warehouse investments at 100,000 a terabyte, which is kind of hard to believe. We thought it was in the 35 range. Besides price, what else has happened that we can deliver on these? You know, use cases that we were stuck on for decades. Yeah, well I think it's price, I think it's the new emerging streaming technologies you see with things like Spark that enable that. I think it's cloud is a huge enabler because a lot of the data is actually being created in the cloud, right? And so you have this whole enabler of information that resides outside of the Four Walls of a company that people are using to do that. And that's different than it was 20 years ago when data warehouses were being built. They weren't really built with a very open sort of framework that said data is being created everywhere you could imagine, everywhere you could imagine. So how do you bring that to bear when you're trying to solve a business problem? And that paradigm is really different than I think the traditional paradigm from 20 years ago. Well, and Quentin said, you know, it's getting cloudy and he's told right on the theme back in 2010 was big data gives the cloud something to do.  And then you juxtapose that against what happened this week with Dell and EMC.  The innovation is very clear and the cloud and big data. And it's just interesting to see you guys and Hitachi, your vision around that, much different than what we're seeing with sort of old line technology companies. Talk about that a little bit. I think one of the things I love about the approach Hitachi is taking is they build all the machines where these sensors are. They build mining equipment and healthcare equipment. And so the concept of building machines with sensors and then wanting to use that information, right? To drive more analytics, better decision making for the people that actually use that equipment. That's part of the DNA of that company. And so it isn't like it's an IT company or software company all of a sudden that wants to grab these sensors coming from somewhere. They really understand those industries. They have Hitachi Consulting that's built vertical practices around those industries. So the ability to take know how physical, the physical world and the virtual world and bring that together. And then leveraging Pataho as that analytics platform in the middle to be the glue for that. It's really credible. I mean if you think about it, because of the know how they bring to the table and the software expertise we bring to the table and the use case driven sort of approach. We both I think are very pragmatic about bringing a user-based, a use-based approach to sort of solving these problems that happen to have an internet of things component in terms of the data flow that comes in. Right, because even in a Hitachi scenario they don't just talk about the sensor data. They talk about all the other types of data that really provide that full solution for those examples. Along the lines of this customer journey where many of us started or many customers started in the data lake, get their feet wet. Part of what limits progress is, skills and processes and product capabilities. How do you see that journey? How do we push back the boundaries? Yeah, I mean that's a great question and I'll talk about it from a business point of view and we have technologists that can address it from a technology point of view. But what I see on a business point of view is as technologies mature, one of the first things they do is get easier. And the reason they get easier is because the economics say as a business, if you make your tools easier, more people will use them. And so I actually think the maturity of these Hadoop systems and stuff like that are starting to simplify because they know the way to access more people to get that to be normal in a mainstream environment means you have to have the right tools that simplify the ability to leverage them. So what used to be hard, you know, really hard is still somewhat hard, but I think there's a lot of innovation going on to try to simplify that. And I think we'll see that scale of simplification continue to move forward and that's going to be the true innovation in my mind. Is that? We're out of time but I wanted to give you the final word. Yeah. You're entering a new decade of your life as Pentaho, Hitachi acquisition, et cetera. Where do you see this going? Where do you want to take it? What's the vision? For Pentaho? Oh yeah, my vision for Pentaho and it's what we always said. So we look at this big data blended capability as being fundamental for companies that want to take their data and use it a meaningful way. And to do that they have to be able to blend that data together, provide context around that data and then consume it however they want to. It may be a report, a dashboard, it may be an application, it may be a workflow that they want to stream into. So our vision is really to enable that whole value chain to occur. And I think that, as you said, that will be an ongoing vision for us as new data comes in, different types of technologies come in. And our goal is really to simplify that ability to really leverage the end user who ultimately makes the decision, you know, with these. Well Rosanne, thanks very much for coming to theCUBE. Thanks for having us here. It's my pleasure to be here. It's really great. Thank you guys so much. Pentaho taking all this data mess, making it actionable, not just out of the box tool. Excited to learn more about this. Stay with us. We got customers coming on. We got partners like Mike Olson at Cloudera. We got the analyst perspective from Forrester and two days of coverage here live. We're theCUBE. We're at Pentaho World, right back. What were they talking about last year at Big Data NYC?