Tree transformations and dependencies

Size: px
Start display at page:

Download "Tree transformations and dependencies"

Transcription

1 Tree transformations and deendencies Andreas Maletti Universität Stuttgart Institute for Natural Language Processing Azenbergstraße 12, Stuttgart, Germany Abstract Several tree transformation devices that are relevant in natural language rocessing are resented with a focus on the deendencies that they are able to cature In many cases, the consideration of the deendencies alone can be used to rovide a high-level exlanation of the short-comings of tree transformation devices and allows surrising insights into their structure 1 Motivation In the subfield of machine translation [31], which is concerned with the automatic translation of natural language texts, it was recently realized that string-based systems [50] cannot easily comute certain imortant translations [48, 1, 49, 9] and that the structural information rovided by modern and reliable arsers [11, 28, 27, 6] actually hels the translation rocess [53] This develoment created renewed interest in tree automata [5, 8] and tree transducers [32, 14], which are finite-state devices that comute tree languages and tree transformations, resectively However, there does not exist a tree translation model that is universally acceted and used in the machine translation task On the contrary, many different models with different exressive ower are used The first formal tree transducer model was the to-down tree transducer investigated by Thatcher [46] and Rounds [41] Several other models such as bottom-u tree transducers [47], attributed tree transducers [20, 30] and ebble tree transducers [38], macro tree transducers [12, 18] and modular tree transducers [19], monadic second-order logic tree transducers [16, 7], and tree bimorhisms [3] and various models with synchronization [40] were introduced later and have been investigated in the theory of formal languages In general, tree transducers rocess an inut tree and nondeterministically generate an outut tree In the rocess, they can move comlete subtrees or decide to rocess subtrees differently based on an internal state Shieber [42] and others have argued that to-down tree transducers are generally inadeuate for linguistic tasks In this survey we will focus on three models that received attention from the machine translation community, which are: Suorted by the German Research Foundation (DFG) grant MA/4959/1-1

2 extended to-down tree transducers [13, 2, 29, 26], extended multi bottom-u tree transducers [33, 3, 21, 22, 15, 35], and synchronous tree-seuence substitution grammars [40, 51, 52, 45] We review these three models and investigate their exressive ower from a very abstract viewoint by looking only at the tye of deendencies that they create It turns out that the models, which indeed have successively more exressive ower, can already be distinguished easily based on the tyes of deendencies that they can comute Informally, a deendency records that a certain art of the outut tree was created in accordance with a articular art of the inut tree (or vice versa) This influence is informally called deendence, synchronization, or contribution [17] Formally, we establish deendencies using the derivation mechanism, which is tyically term rewriting [4] However, for our uroses synchronous substitution is much more suitable since the synchronization links are an exlicit reresentation of our deendencies Thus, we adjust the derivation rocess to use synchronous substitution keeing all synchronization links during the whole derivation We investigate the tye of deendencies each of our three mentioned tree transformation models can comute It shows that all of them enjoy a certain hierarchy roerty that can be used to uickly show that transformations that have crossing deendencies cannot be comuted by any of our models This allows us, for examle, to set all three models aart from synchronous tree-adjoining grammars [44, 42, 43], which can comute crossing deendencies Moreover, a stricter version of the hierarchy roerty also allows us to distinguish our three models, which we demonstrate with an examle transformation in each case These examle transformations are taken from the literature, but instead of resenting the full roof from the literature, we simly add natural deendencies to the transformation and then show that no deendency comutable by a certain model is comatible with these deendencies This aroach highlights the essential and illustrative art of the formal roof and avoids the technical art of the roof, which is still needed to justify the initial deendencies The interested reader can find these technical arts in the cited literature or can rove them via a case analysis Thus, the aroach ursued here does not offer full roofs, but it will be obvious from the examles that those deendencies should be resent The survey is structured as follows: Section 2 recalls basic notions and notation In each of the next three sections (Sections 3 5) we recall one of our three tree transformation models in order of increasing exressive ower (ie, the order in which they are mentioned above) Section 3 also contains the definitions of the hierarchy roerties of deendencies that we will investigate We conclude with a short summary, which is resented as a table showing the identified roerties of deendencies (including those for synchronous tree-adjoining grammars) 2 Notation The set of all nonnegative integers is IN A relation ρ from a set S to a set T is a subset ρ S T The set of all finite words over S is S, where ε is the

3 emty word The concatenation of the words v, w S is vw or simly vw The length of a word w S is denoted by w An alhabet Σ is a nonemty and finite set, of which the elements are called symbols A ranked alhabet is a air (Σ, rk) consisting of an alhabet Σ and a rank maing rk: Σ IN For every k IN, let Σ k = rk 1 (k) We tyically write (k) to indicate that rk() = k A doubly ranked alhabet simly has a rank maing rk: Σ IN 2 We use the same notations for ranked and doubly ranked alhabets Moreover, we tyically assume that the maing rk is clear from the context The set T Σ (S) of Σ-trees with leaf labels S is the smallest set T such that S T and (t 1,, t k ) T for every Σ k and t 1,, t k T We generally assume that Σ S =, and thus we write () simly as for every Σ 0 Moreover, we write k (t) for ( (t) ) containing k occurrences of in the abbreviated list We write T Σ for T Σ ( ) The set os(t) IN of ositions of t T Σ (S) is inductively defined by os(s) = {ε} for every s S and os((t 1,, t k )) = {ε} k {iw w os(t i )} for every Σ k and t 1,, t k T Σ (S) The ositions os(t) are totally ordered by the lexicograhic order on IN and the refix order on IN Let t, t T Σ (S) and w os(t) The label of t at w is t(w), and the w-rooted subtree of t is t w Formally, s(ε) = s ε = s for every s S and t(w) = i=1 { if w = ε t i (v) if w = iv and i IN and t w = { t t i v if w = ε if w = iv and i IN where t = (t 1,, t k ) for every Σ k and t 1,, t k T Σ (S) For every L S, we let os L (t) = {w os(t) t(w) L} and os s (t) = os {s} (t) for every s S The tree t is linear in L if os l (t) 1 for every l L Moreover, var(t) = {s S os s (t) } The exression t[u] w denotes the tree that is obtained from t T Σ (S) by relacing the subtree t w at w by u T Σ (S) We extend this notation to seuences u = u 1,, u n of trees and ositions w = w 1,, w n of t that are airwise incomarable with resect to the refix order Thus, t[u] w denotes the tree obtained from t by relacing the subtree t wi at w i by u i for all 1 i n 3 Extended to-down tree transducer Our first model is the (linear and nondeleting) extended to-down tree transducer [13, 2, 29, 26] (xto), which is based on the classical to-down tree transducer [41, 46] A to-down tree transducer is a secial xto, in which all lefthand sides of rules contain exactly one inut symbol In general, the left-hand side of an xto can contain any number of inut symbols [34, 37] We resent a syntactic version here that is closer to synchronized grammars [10], but eually exressive as the classical version [41, 46, 29, 26] with term

4 rewrite rules In general, eual states in the left and right-hand side of a rule are linked In a derivation, they will be relaced at the same time In a rule of an xto, these links are bijective Definition 1 (see [37, Sect 22]) A (linear and nondeleting) extended tree transducer ( xto) is a tule (Q, Σ,, I, R), where Q is a finite set of states, Σ and are ranked alhabets of inut and outut symbols, I Q is a set of initial states, and R T Σ (Q) Q T (Q) is a finite set of rules such that l and r are linear in Q and var(l) = var(r) for every (l,, r) R In the following, let M = (Q, Σ,, I, R) be an xto As already mentioned, M is a to-down tree transducer if for every (l,, r) R there exist Σ k and 1,, k Q such that l = ( 1,, k ) To simlify the notation, we often write rules as l r instead of (l,, r) Examle 2 (see [3, Sect 34]) Let M bin = (Q, Σ, Σ, {}, R) and M debin = (Q, Σ, Σ, {}, R ) be the xto with Q = {,,, r}, Σ = { (3), (2), (1), (0) }, R, which contains the following rules for all x {,, r}: (, ) (, ) (,, ) (, (, )) (,, r) (, (, r)) (x) x (x) x, and R, which contains the following rules for all x {,, r}: (, ) (, ) (, (, )) (,, ) (, ()) (, ()) (x) x (x) x Clearly, M bin is even a to-down tree transducer, whereas M debin is not a todown tree transducer The rules of M bin are illustrated in Fig 1 Next, we move to the semantics of an xto M, which is given by synchronous substitution While the links in an xto rule are imlicit and established due to occurrences of eual states, we need an exlicit linking structure for our sentential forms In addition, these links will form the deendencies that we are interested in To this end, we store a relation between ositions of the inut and outut tree, which encodes the links Let L = P(IN IN ) = {S S IN IN } be the set of all link structures First, we define general sentential forms Roughly seaking, we have an inut tree and an outut tree, in which ositions are linked

5 r r x x x x Fig 1 Examle rules of the xto M bin of Ex 2 Definition 3 (see [23, Sect 3]) An element ξ, D, ζ T Σ (Q) L T (Q) is a sentential form if v os(ξ) and w os(ζ) for every (v, w) D Now we lift the imlicit link structure in an xto rule into an exlicit link relation This link relation will then be used in the derivation rocess once the rule is alied to determine the next links in the obtained sentential form Definition 4 Let l r R be a rule, and let v, w IN The rule s link structure links v,w (l r) L is links v,w (l r) = {(vv, ww ) v os (l), w os (r)} Q Note that links v,w (l r) is a bijective relation on the state occurrences The derivation rocess is started with a simle sentential form, {(ε, ε)}, consisting of the inut tree and the outut tree for some initial state I and the trivial link relating both states This is clearly a link structure that is bijective between state occurrences Next, we (nondeterministically) aly a rule l r to a air of linked occurrences of the state Such an alication relaces the linked occurrences of by the left and right-hand side of the rule The imlicit links in the rule are added to the (exlicit) link structure to obtain a new sentential form This yields another link structure that is bijective between state occurrences Since we are interested in the deendencies created during derivation, we reserve all links and never remove a link from the linking structure Note that this reservation causes that the link structure need not be functional on all ositions because we kee the links that were used in the relacement rocess This relacement rocess is reeated until no linked occurrences of states remain Definition 5 (see [23, Sect 3]) Given two sentential forms ξ, D, ζ and ξ, D, ζ such that D and D are bijective on state occurrences, we write ξ, D, ζ ξ, D, ζ

6 if there exists a rule l r R and an inut osition v os (ξ) such that ξ = ξ[l] v and ζ = ζ[r] w, where w os (ζ) is the uniue -labelled osition such that (v, w) D, and D = D links v,w (l r) As usual M is the reflexive and transitive closure of The xto M comutes the deendencies de(m) T Σ L T, which are given by de(m) = { t, D, u T Σ L T I :, {(ε, ε)}, M t, D, u } Moreover, the xto M comutes the tree transformation M T Σ T, which is given by M = {(t, u) (t, D, u) de(m)} r r r r 3 M Fig 2 Examle derivation where M = M bin (see Ex 6)

7 Examle 6 Let M = M bin be the xto of Ex 2, and let (, ((),, (,, ))) be the inut tree Selecting the only initial state, we can obtain the derivation that is dislayed in Fig 2 Overall (, ((), (, (, (, ))))) is a translation of the inut tree The translations of M bin and M debin of Ex 2 are illustrated in Fig 3 t 1 t 2 t 3 t n 4 t n 3 t n 2 t n 1 t n M bin t 1 t 2 t 3 t 4 t n 3 t n 2 M debin t 2 t 1 t 4 t 3 t n 2 t n 3 t n 1 t n t n 1 t n Fig 3 Translations of [3] that are individually comuted by the xto M bin and M debin Since every translation (t, u) M is ultimately created by (at least) one successful derivation, we can insect the links in the derivation rocess to exhibit the deendencies Roughly seaking, the links establish which arts of the outut tree were generated due to a articular art of the inut tree This corresondence is called contribution in [17] Examle 7 Recall the xto M bin of Ex 2 and the derivation in Ex 6, which is dislayed in Fig 2 Looking at the last sentential form, which is dislayed in Fig 4 for easier reference, its linking structure is D = {(ε, ε), (1, 1), (2, 2), (21, 21), (211, 211), (22, 221), (23, 222), (231, 2221), (232, 22221), (233, 22222)}, which reresents the deendencies introduced by the rule alications Next, let us observe some imortant roerties of the comuted deendencies To this end, we disregard the actual inut and outut trees and say that a linking structure D L is comuted by M if there exist an initial state I and trees t T Σ and u T such that, {(ε, ε)}, M t, D, u The set of all linking structures comuted by M is links(m) Definition 8 A linking structure D L is inut hierarchical if for every (v 1, w 1 ), (v 2, w 2 ) D with v 1 < v 2 we have w 2 w 1 and there exists (v 1, w 1) D such that w 1 w 2 It is strictly inut hierarchical if additionally

8 Fig 4 Deendencies comuted during the derivation of Fig 2 w w or w w for all (v, w), (v, w ) D and v 1 v 2 for all (v 1, w 1 ), (v 2, w 2 ) D with w 1 w 2 Roughly seaking, inut hierarchical linking structures have no crossing links (or deendencies) More formally, let (v, w), (v, w ) D be such that v < v and w < w Then (v, w) and (v, w ) are crossing links (or deendencies) Clearly, such links cannot exist in an inut hierarchical linking structure The same notions can be defined for the outut side by reuiring the corresonding roerties for the linking structure D 1 For examle, D is strictly outut hierarchical if D 1 is strictly inut hierarchical Moreover, it is strictly hierarchical if it is both strictly inut hierarchical and strictly outut hierarchical Finally, a set D L of linking structures has a certain hierarchical roerty if each element has it Examle 9 The linking structure D of Ex 7 is strictly hierarchical In addition, we also need a roerty that guarantees that there are enough links Roughly seaking, there should be an integer that limits the distance between links Definition 10 A set D L of link structures has bounded distance if there exists an integer k IN such that for every D D we have that for all (v, w), (vv, w ) D with v > k there exist v 1, v 2 v and w 1, w 2 such that v 1 k v v 2 and (vv 1, w 1 ), (vv 2, w 2 ) D, and for all (v, w), (v, ww ) D with w > k there exist v 1, v 2 and w 1, w 2 w such that w 1 k w w 2 and (v 1, ww 1 ), (v 2, ww 2 ) D In other words, between any two source- or target-nested links of large distance, there should exist links whose distance to the original links is small This yields that the distance to the next nested link (if such a link does exist) can be at most k Note however, that the above roerty does not reuire a link every k symbols This roerty would also be true for all xto, but it would no longer be true for all mbot, which are discussed in the next section To kee the resentation simle, we only discuss bounded distance as introduced

9 Examle 11 The set links(m bin ), where M bin is the xto of Ex 2, has bounded distance For the inut side, the distance is bounded by 1, and for the outut side, it is bounded by 2 Lemma 12 The set links(m) comuted by an xto M is strictly hierarchical with bounded distance Proof This lemma follows trivially from Definition 5 t 1 t 2 t 1 t 2 t 3 t n 4 t n 3 t 4 t 3 t n 2 t n 3 t n 2 t n 1 t n t n 1 t n Fig 5 Examle translation of [3] with deendencies, where the inverse arrow heads indicate that the deendencies oint to any node (not necessarily the root) inside the subtrees In addition, for a (linear and nondeleting) to-down tree transducer every inut osition has exactly one link (ie, the linking structures encountered with to-down tree transducers are functional) Next, we define the notion of comatibility of linking structures (or deendencies) This notion will allow us to rescribe a semantic deendency and then analyze whether a certain class of tree transformation devices can handle such linking structures Naturally, the imlementation in a tree transformation device can add more deendencies, which are created by the articular choice of rules Conseuently, comatibility only reuires that the given deendencies are a subset of the realized links in the linking structure Moreover, given a set of deendencies for a given inut and outut tree, it is sufficient to be comatible to at least one deendency because already one comatible deendency would render the translation lausible Definition 13 Let ξ, D, ζ and ξ, D, ζ be sentential forms with the same inut and outut trees Then ξ, D, ζ is comatible with ξ, D, ζ if D D Given sets L and L of sentential forms, L is comatible with L if for every ξ, D, ζ L there exist ξ, D, ζ L and ξ, D, ζ L such that ξ, D, ζ is comatible with ξ, D, ζ

10 Figure 5 shows the comosition of the tree transformations that are comuted by the xto M bin and M debin of Ex 2 This examle was used in [3] to show that the class of transformations comuted by xto is not closed under comosition In fact, assuming the deendencies indicated in Fig 5, we can rove this statement by observing that this set of deendencies is not comatible to a strictly hierarchical deendence with bounded distance Conseuently, these deendencies cannot be comuted by an xto Lemma 14 The deendencies deicted in Fig 5 are not comatible with the deendencies comuted by any xto Proof Suose that there is an xto that comutes deendencies that are comatible with the deendencies deicted in Fig 5 Then there exits a bound n such that all inut and outut tree airs whose -sine is longer than n must have a link on this -sine However, such a link together with the existing deendencies makes it incomatible to any strictly hierarchical deendency The revious lemma also yields that the tree transformation of Fig 5 cannot be comuted by an xto Actually, the difficult, but not very illustrative art of the full roof establishes that the deendencies deicted in Fig 5 are really necessary This art remains and is roved in [3] Theorem 15 (see [3, Sect 34]) The tree transformation illustrated in Fig 5 cannot be comuted by any xto 4 Extended multi bottom-u tree transducer In this section, we recall the (linear and nondeleting) extended multi bottom-u tree transducer (mbot), which was introduced in [33, 3] in the shae of a articular bimorhism The name multi bottom-u tree transducer seems to originate from [21, 22], where the deterministic variant of the model was rediscovered A more detailed resentation of various multi bottom-u tree transducers can be found in [15], and [35] reorts some results for the weighted model Definition 16 (see [35, Def 2]) A (linear and nondeleting) extended multi bottom-u tree transducer ( mbot) is a system (Q, Σ,, I, R) where Q, Σ, and are ranked alhabets of states, inut symbols, and outut symbols, resectively, I Q 1 is a subset of initial states, all of which are unary, and R T Σ (Q) Q T (Q) is a finite set of rules such that l is linear in Q, rk() = n, and n i=1 var(r i) var(l) for every (l,, r 1 r n ) R For all the remaining discussions, let M = (Q, Σ,, I, R) be an mbot Clearly, any xto is an mbot In addition, two items deserve exlicit mention First, the set Q of states is a ranked alhabet in contrast to xto or traditional to-down or bottom-u tree transducers [46, 41, 47] Roughly seaking, the rank rk() of a state Q coincides with the number r of trees in

11 f x x x x r r Fig 6 mbot rules of the mbot M com of Ex 17 the right-hand side of all rules (l,, r) R For examle, a nullary state has no outut trees at all and can be understood as a ure look-ahead [15] in the inut tree Second, all initial states are unary (ie, have exactly one outut tree) In this way, we obtain exactly one outut tree and ultimately a relation between inut and outut trees To simlify the discussion, we call l and r of a rule (l,, r) R the left- and right-hand side, resectively In accordance, we sometimes write l r instead of (l,, r) Examle 17 (see [3, Sect 34]) Let M com = (Q, Σ, Σ, {f}, R) be the mbot with Q = { (2), (1), (1), r (1), f (1) }, Σ = { (3), (2), (1), (0) }, and R, which contains the following rules for every x {,, r}: (, ) f (,, ) (,, ) (,, ) (,, r) (, r) (x) x (x) x, where we searate trees in a seuence by full stos The rules of M com are illustrated in Fig 6 Since our rules now have a more general structure, we again need to lift the imlicit link structure in an mbot rule into an exlicit link relation This time we rovide an inut osition and additionally as many outut ositions as reuired The reuired number is the rank of the state in a rule l r Definition 18 Let l r R be a rule Moreover, let v, w 1,, w n IN and w = w 1 w n where n = rk() The rule s link structure links v,w (l r) L is links v,w (l r) = Q i=1 n {(vv, w i w i) v os (l), w i os (r i )} Note that the inverse relation links v,w (l r) 1 is functional on the state occurrences However, in general, it is not bijective, and in articular, a state occurrence in the inut might be without any link

12 The semantics is again resented using synchronous substitution However, this time several states in the outut side of a sentential form can be linked to the state that is relaced in the inut side of the sentential side As before, the derivation rocess is started with a simle sentential form, {(ε, ε)}, Next, we (nondeterministically) aly a rule l r to an occurrence of a state in the inut side and all its linked occurrences on the outut side Those occurrences are relaced by the left and right-hand side of the rule, where the otentially several trees in the right-hand side relace the linked occurrences in lexicograhic order The final ste adds the imlicit links in the rule to the (exlicit) link structure to obtain a new sentential form Note that the functionality of the inverse imlicit linking structure of a rule is lost in the sentential forms due to the reservation of old links Definition 19 (see [36, Sect 3]) Given two sentential forms ξ, D, ζ and ξ, D, ζ, we write ξ, D, ζ ξ, D, ζ if there exists a rule l r R and an inut osition v os (ξ) such that rk() = n, ξ = ξ[l] v and ζ = ζ[r] w, where w = w 1 w n with (i) w 1,, w n os (ζ), (ii) w 1 w n, and (iii) {w 1,, w n } = {w (v, w) D}, and D = D links v,w (l r) As usual M is the reflexive and transitive closure of The mbot M comutes the deendencies de(m) T Σ L T, which are given by de(m) = { t, D, u T Σ L T I :, {(ε, ε)}, M t, D, u } Moreover, the mbot M comutes the relation M T Σ T, which is given by M = {(t, u) (t, D, u) de(m)}, and links(m) = {D (t, D, u) de(m)} Examle 20 It can easily be verified that M com of Ex 17 comutes the tree transformation deicted in Fig 5 An examle derivation using M com is shown in Fig 7 Conseuently, the tree transformation used in the revious section (see Fig 5) can be comuted by an mbot Figure 9 roughly sketches the deendencies created during the comutation of this transformation with the mbot M com of Ex 17 Next, let us look at the roerties of the deendencies reresented in Figs 8 and 9 Examle 21 Figure 8 reresents the sentential form t, D, u, where t = (, ((),, (,, ))) u = ((),, (,, (, ))) D = {(ε, ε), (1, 2), (2, 1), (2, 3), (21, 1), (211, 11), (22, 32), (23, 31), (23, 33), (231, 31), (232, 331), (233, 332)} The linking structure D is inut hierarchical and strictly outut hierarchical The same roerties also hold for the deendencies indicated in Fig 9

13 3 M r r Fig 7 Examle derivation where M = M com (see Ex 17) Fig 8 Examle deendency comuted by M com (see Ex 17)

14 t 1 t 2 t 1 t 2 t 3 t n 4 t n 3 t 4 t 3 t n 2 t n 3 t n 2 t n 1 t n t n 1 t n Fig 9 Examle translation of [3] with deendencies suitable for an mbot The roerties of the deendencies exhibited in Ex 21 are indicative for all deendencies comuted by mbot This is observed in the next lemma, which follows straightforwardly from Def 19 As usual, the finite size of the rules yields bounded distance Note that there can be unboundedly large arts of the inut tree without any link For examle, inut subtrees created by nullary states can have this roerty because the mbot does only check a regular roerty [24, 25] and does not roduce any corresonding outut Lemma 22 The set links(m) comuted by an mbot M is inut hierarchical and strictly outut hierarchical with bounded distance Next, we again use our knowledge about the tye of deendencies that are comutable by an mbot to illustrate a tree transformation that cannot be comuted by any mbot Examle 23 (see [39, Ex 45] and [40]) The mbot M sort = (Q, Σ,, {f}, R) and M sort2 = (Q, Σ,, {f}, R ) are given by Q = { (3), (3), r (3), f (1) }, Σ = { (0), (1), β (1), γ (1) } and = Σ { (3) }, the following rules in R: () () β() β() r r r r f (,, ) γ(r) r r r γ(r) r, and the following rules in R : () () β() β() r f (r, r, r) γ(r) r r r γ(r) r

15 Figure 10 dislays the rules R of the examle mbot M sort Since the rules R are very similar, we omitted a grahical reresentation The mbot M sort and M sort2 comute the tree transformations M sort = {( l (β m (γ n ())), ( l (), β m (), γ n ())) l, m, n IN} M sort2 = {(γ n (β m ( l ())), ( l (), β m (), γ n ())) l, m, n IN}, resectively In other words, M sort sorts all -symbols into the first outut subtree (below ), the β-symbols into the second subtree, and the γ-symbols into the third subtree f β β γ r γ r r r r r r r r r Fig 10 Examle rules of the mbot M sort of Ex 23 From the definition of the tree transformations of Ex 23 we can evidently conclude some deendencies, which we deict in Fig 11 Clearly, the shown deendencies are not strictly inut hierarchical However, they are inut hierarchical and strictly outut hierarchical Conseuently, the inverse deendencies are strictly inut hierarchical and outut hierarchical, but not strictly outut hierarchical In the same manner as for xto, we can conclude the following statement Lemma 24 The inverse deendencies deicted in Fig 11 are not comatible with the deendencies comuted by any mbot Theorem 25 (see [39, Ex 45]) The inverse of the tree transformation illustrated in Fig 11 cannot be comuted by any mbot 5 Synchronous tree-seuence substitution grammar In this final section before the summary, we recall the synchronous tree-seuence substitution grammar (stssg), which was introduced in [40, 51, 52, 45] We kee the resentation terse because most mechanisms have been exlained on the revious models

16 β β γ β γ β γ γ Fig 11 Some deendencies of the tree transformation of Ex 23 Definition 26 (see [45, Sect 2]) A synchronous tree-seuence substitution grammar ( stssg) is a system (Q, Σ,, I, R) where Q is a doubly ranked alhabet, Σ and are ranked alhabets of inut and outut symbols, resectively, I Q 1,1 is a subset of initial states, all of which are doubly unary, and R T Σ (Q) Q T (Q) is a finite set of rules such that rk() = (m, n) for every (l 1 l m,, r 1 r n ) R For the rest of this section, let M = (Q, Σ,, I, R) be an stssg Clearly, any mbot is an stssg, and moreover, any inverse transformation comuted by an mbot can be imlemented by an stssg The ranks rk() of a state Q coincide with numbers l and r of trees in the left- and right-hand side of all rules (l,, r) R As before all initial states are doubly unary (ie, have exactly one inut and exactly one outut tree) In this way, we again obtain a relation between inut and outut trees As before, we call l and r of a rule (l,, r) R the left- and right-hand side, resectively In accordance, we sometimes write l r instead of (l,, r) Definition 27 Let l r R be a rule, and let v 1,, v m, w 1,, w n IN, v = v 1 v m, and w = w 1 w n where rk() = (m, n) The rule s link structure links v,w (l r) L is links v,w (l r) = m n {(v j v j, w i w i) v j os (l j ), w i os (r i )} Q j=1 i=1 As before, the semantics is resented using synchronous substitution This time several states can be relaced in both the inut and the outut side of a sentential form

17 Definition 28 (see [45, Sect 2]) Given two sentential forms ξ, D, ζ and ξ, D, ζ, we write ξ, D, ζ ξ, D, ζ if there exists a rule l r R, inut ositions v 1,, v m os (ξ), and outut ositions w 1,, w n os (ζ) such that rk() = (m, n), ξ = ξ[l] v and ζ = ζ[r] w, where v = v 1 v m with v 1 v m and w = w 1 w n with w 1 w n, the ositions are linked; ie, m {w 1,, w n } = {w (v j, w) D} D = D links v,w (l r) {v 1,, v m } = j=1 n {v (v, w i ) D}, i=1 As usual M is the reflexive and transitive closure of The stssg M comutes the deendencies de(m) T Σ L T, which are given by de(m) = { t, D, u T Σ L T I :, {(ε, ε)}, M t, D, u } Moreover, the stssg M comutes the relation M T Σ T, which is given by M = {(t, u) (t, D, u) de(m)}, and links(m) = {D (t, D, u) de(m)} Examle 29 It can easily be verified that M 1 sort2 (ie, the inverse of M sort2 in which left- and right-hand side are exchanged) of Ex 23 is an stssg Lemma 30 The set links(m) comuted by an stssg M is hierarchical with bounded distance A final examle will use this knowledge about the tye of deendencies that are comutable by an stssg to show a tree transformation that cannot be comuted by any stssg Examle 31 (see [39, Ex 45] and [40]) The comosition of the tree transformations M sort and M 1 sort2 is which is shown in Fig 12 {( l (β m (γ n ())), γ n (β m ( l ()))) l, m, n IN}, Figure 12 already shows some evident deendencies, and we easily notice that they are crossing Conseuently, no stssg deendency is comatible with this deendence because they are all hierarchical by Lemma 30 Lemma 32 The deendencies deicted in Fig 12 are not comatible with the deendencies comuted by any stssg Theorem 33 (see [39, Ex 45]) The tree transformation illustrated in Fig 12 cannot be comuted by any stssg

18 γ β β γ γ γ β β Fig 12 Some deendencies of the tree transformation of Ex 31 6 Summary We resent the essential findings in the table below It additionally contains the synchronous tree-adjoining grammar (stag) [44, 42, 43], which has none of our hierarchy roerties References inut side outut side xto strictly hierarchical strictly hierarchical mbot hierarchical strictly hierarchical stssg hierarchical hierarchical stag 1 Alshawi, H, Bangalore, S, Douglas, S: Learning deendency translation models as collections of finite state head transducers Comut Linguist 26(1), (2000) 2 Arnold, A, Dauchet, M: Bi-transductions de forêts In: Michaelson, S, Milner, R (eds) Proc ICALP Edinburgh University Press (1976) 3 Arnold, A, Dauchet, M: Morhismes et bimorhismes d arbres Theoret Comut Sci 20(1), (1982) 4 Baader, F, Nikow, T: Term rewriting and all that Cambridge University Press (1998) 5 Berstel, J, Reutenauer, C: Recognizable formal ower series on trees Theoret Comut Sci 18(2), (1982) 6 Bikel, DM: On the Parameter Sace of Generative Lexicalized Statistical Parsing Models PhD thesis, University of Pennsylvania (2004) 7 Bloem, R, Engelfriet, J: A comarison of tree transductions defined by monadic second order logic and by attribute grammars J Comut System Sci 61(1), 1 50 (2000)

19 8 Borchardt, B: The Theory of Recognizable Tree Series PhD thesis, Technische Universität Dresden (2005) 9 Charniak, E, Knight, K, Yamada, K: Syntax-based language models for statistical machine translation In: Proc MT Summit IX (2003) 10 Chiang, D: An introduction to synchronous grammars In: Proc ACL Association for Comutational Linguistics (2006), art of a tutorial given with Kevin Knight 11 Collins, M: Head-Driven Statistical Models for Natural Language Parsing PhD thesis, University of Pennsylvania (1999) 12 Courcelle, B, Franchi-Zannettacci, P: Attribute grammars and recursive rogram schemes Theoret Comut Sci 17(2 3), , (1982) 13 Dauchet, M: Transductions inversibles de forêts Thèse 3ème cycle, Université de Lille (1975) 14 Engelfriet, J, Fülö, Z, Vogler, H: Bottom-u and to-down tree series transformations J Autom Lang Combin 7(1), (2002) 15 Engelfriet, J, Lilin, E, Maletti, A: Comosition and decomosition of extended multi bottom-u tree transducers Acta Inf 46(8), (2009) 16 Engelfriet, J, Maneth, S: Macro tree transducers, attribute grammars, and MSO definable tree translations Inform and Comut 154(1), (1999) 17 Engelfriet, J, Maneth, S: Macro tree translations of linear size increase are MSO definable SIAM J Comut 32(4), (2003) 18 Engelfriet, J, Vogler, H: Macro tree transducers J Comut System Sci 31(1), (1985) 19 Engelfriet, J, Vogler, H: Modular tree transducers Theoret Comut Sci 78(2), (1991) 20 Fülö, Z: On attributed tree transducers Acta Cybernet 5(3), (1981) 21 Fülö, Z, Kühnemann, A, Vogler, H: A bottom-u characterization of deterministic to-down tree transducers with regular look-ahead Inf Process Lett 91(2), (2004) 22 Fülö, Z, Kühnemann, A, Vogler, H: Linear deterministic multi bottom-u tree transducers Theoret Comut Sci 347(1 2), (2005) 23 Fülö, Z, Maletti, A, Vogler, H: Preservation of recognizability for synchronous tree substitution grammars In: Drewes, F, Kuhlmann, M (eds) Proc ATANLP 1 9 Association for Comutational Linguistics (2010) 24 Gécseg, F, Steinby, M: Tree Automata Akadémiai Kiadó, Budaest (1984) 25 Gécseg, F, Steinby, M: Tree languages In: Rozenberg, G, Salomaa, A (eds) Handbook of Formal Languages, vol 3, cha 1, 1 68 Sringer (1997) 26 Graehl, J, Knight, K, May, J: Training tree transducers Comut Linguist 34(3), (2008) 27 Klein, D, Manning, CD: Accurate unlexicalized arsing In: Proc ACL Association for Comutational Linguistics (2003) 28 Klein, D, Manning, CD: Fast exact inference with a factored model for natural language arsing In: Proc NIPS 3 10 MIT Press (2003) 29 Knight, K, Graehl, J: An overview of robabilistic tree transducers for natural language rocessing In: CICLing LNCS, vol 3406, 1 24 Sringer (2005) 30 Knuth, DE: Semantics of context-free languages Math Systems Theory 2(2), (1968) 31 Koehn, P: Statistical Machine Translation Cambridge University Press (2010) 32 Kuich, W: Tree transducers and formal tree series Acta Cybernet 14(1), (1999) 33 Lilin, E: Proriétés de clôture d une extension de transducteurs d arbres déterministes In: Proc CAAP LNCS, vol 112, Sringer (1981)

20 34 Maletti, A: Comositions of extended to-down tree transducers Inform and Comut 206(9 10), (2008) 35 Maletti, A: An alternative to synchronous tree substitution grammars J Natur Lang Engrg 17(2), (2011) 36 Maletti, A: How to train your multi bottom-u tree transducer In: Proc ACL Association for Comutational Linguistics (2011) 37 Maletti, A, Graehl, J, Hokins, M, Knight, K: The ower of extended to-down tree transducers SIAM J Comut 39(2), (2009) 38 Milo, T, Suciu, D, Vianu, V: Tyechecking for XML transformers J Comut System Sci 66(1), (2003) 39 Radmacher, FG: An automata theoretic aroach to the theory of rational tree relations Tech Re AIB , RWTH Aachen (2008) 40 Raoult, JC: Rational tree relations Bull Belg Math Soc 4, (1997) 41 Rounds, WC: Maings and grammars on trees Math Systems Theory 4(3), (1970) 42 Shieber, SM: Synchronous grammars as tree transducers In: Proc TAG (2004) 43 Shieber, SM: Probabilistic synchronous tree-adjoining grammars for machine translation: The argument from bilingual dictionaries In: Proc SSST Association for Comutational Linguistics (2007) 44 Shieber, SM, Schabes, Y: Synchronous tree-adjoining grammars In: Proc CoLing vol 3, (1990) 45 Sun, J, Zhang, M, Tan, CL: A non-contiguous tree seuence alignment-based model for statistical machine translation In: Proc ACL Association for Comutational Linguistics (2009) 46 Thatcher, JW: Generalized 2 seuential machine mas J Comut System Sci 4(4), (1970) 47 Thatcher, JW: Tree automata: An informal survey In: Aho, AV (ed) Currents in the Theory of Comuting, Prentice Hall (1973) 48 Wu, D: Stochastic inversion transduction grammars and bilingual arsing of arallel corora Comut Linguist 23(3), (1997) 49 Yamada, K, Knight, K: A decoder for syntax-based statistical MT In: Proc ACL Association for Comutational Linguistics (2002) 50 Yu, S: Regular languages In: Rozenberg, G, Salomaa, A (eds) Handbook of Formal Languages, vol 1, cha 2, Sringer (1997) 51 Zhang, M, Jiang, H, Aw, A, Li, H, Tan, CL, Li, S: A tree seuence alignmentbased tree-to-tree translation model In: Proc ACL Association for Comutational Linguistics (2008) 52 Zhang, M, Jiang, H, Li, H, Aw, A, Li, S: Grammar comarison study for translational euivalence modeling and statistical machine translation In: Proc CoLing Association for Comutational Linguistics (2008) 53 Zollmann, A, Venugoal, A, Och, F, Ponte, J: A systematic comarison of hrase-based, hierarchical and syntax-augmented statistical MT In: Proc CoLing Association for Comutational Linguistics (2008)

Composition Closure of ε-free Linear Extended Top-down Tree Transducers

Composition Closure of ε-free Linear Extended Top-down Tree Transducers Composition Closure of ε-free Linear Extended Top-down Tree Transducers Zoltán Fülöp 1, and Andreas Maletti 2, 1 Department of Foundations of Computer Science, University of Szeged Árpád tér 2, H-6720

More information

Lecture 7: Introduction to syntax-based MT

Lecture 7: Introduction to syntax-based MT Lecture 7: Introduction to syntax-based MT Andreas Maletti Statistical Machine Translation Stuttgart December 16, 2011 SMT VII A. Maletti 1 Lecture 7 Goals Overview Tree substitution grammars (tree automata)

More information

How to train your multi bottom-up tree transducer

How to train your multi bottom-up tree transducer How to train your multi bottom-up tree transducer Andreas Maletti Universität tuttgart Institute for Natural Language Processing tuttgart, Germany andreas.maletti@ims.uni-stuttgart.de Portland, OR June

More information

Statistical Machine Translation of Natural Languages

Statistical Machine Translation of Natural Languages 1/26 Statistical Machine Translation of Natural Languages Heiko Vogler Technische Universität Dresden Germany Graduiertenkolleg Quantitative Logics and Automata Dresden, November, 2012 1/26 Weighted Tree

More information

Applications of Tree Automata Theory Lecture VI: Back to Machine Translation

Applications of Tree Automata Theory Lecture VI: Back to Machine Translation Applications of Tree Automata Theory Lecture VI: Back to Machine Translation Andreas Maletti Institute of Computer Science Universität Leipzig, Germany on leave from: Institute for Natural Language Processing

More information

Applications of Tree Automata Theory Lecture V: Theory of Tree Transducers

Applications of Tree Automata Theory Lecture V: Theory of Tree Transducers Applications of Tree Automata Theory Lecture V: Theory of Tree Transducers Andreas Maletti Institute of Computer Science Universität Leipzig, Germany on leave from: Institute for Natural Language Processing

More information

Compositions of Top-down Tree Transducers with ε-rules

Compositions of Top-down Tree Transducers with ε-rules Compositions of Top-down Tree Transducers with ε-rules Andreas Maletti 1, and Heiko Vogler 2 1 Universitat Rovira i Virgili, Departament de Filologies Romàniques Avinguda de Catalunya 35, 43002 Tarragona,

More information

The Power of Tree Series Transducers

The Power of Tree Series Transducers The Power of Tree Series Transducers Andreas Maletti 1 Technische Universität Dresden Fakultät Informatik June 15, 2006 1 Research funded by German Research Foundation (DFG GK 334) Andreas Maletti (TU

More information

Compositions of Extended Top-down Tree Transducers

Compositions of Extended Top-down Tree Transducers Compositions of Extended Top-down Tree Transducers Andreas Maletti ;1 International Computer Science Institute 1947 Center Street, Suite 600, Berkeley, CA 94704, USA Abstract Unfortunately, the class of

More information

Topic 7: Using identity types

Topic 7: Using identity types Toic 7: Using identity tyes June 10, 2014 Now we would like to learn how to use identity tyes and how to do some actual mathematics with them. By now we have essentially introduced all inference rules

More information

MATH 2710: NOTES FOR ANALYSIS

MATH 2710: NOTES FOR ANALYSIS MATH 270: NOTES FOR ANALYSIS The main ideas we will learn from analysis center around the idea of a limit. Limits occurs in several settings. We will start with finite limits of sequences, then cover infinite

More information

State Estimation with ARMarkov Models

State Estimation with ARMarkov Models Deartment of Mechanical and Aerosace Engineering Technical Reort No. 3046, October 1998. Princeton University, Princeton, NJ. State Estimation with ARMarkov Models Ryoung K. Lim 1 Columbia University,

More information

The Graph Accessibility Problem and the Universality of the Collision CRCW Conflict Resolution Rule

The Graph Accessibility Problem and the Universality of the Collision CRCW Conflict Resolution Rule The Grah Accessibility Problem and the Universality of the Collision CRCW Conflict Resolution Rule STEFAN D. BRUDA Deartment of Comuter Science Bisho s University Lennoxville, Quebec J1M 1Z7 CANADA bruda@cs.ubishos.ca

More information

The Power of Weighted Regularity-Preserving Multi Bottom-up Tree Transducers

The Power of Weighted Regularity-Preserving Multi Bottom-up Tree Transducers International Journal of Foundations of Computer cience c World cientific Publishing Company The Power of Weighted Regularity-Preserving Multi Bottom-up Tree Transducers ANDREA MALETTI Universität Leipzig,

More information

Extended Multi Bottom-Up Tree Transducers

Extended Multi Bottom-Up Tree Transducers Extended Multi Bottom-Up Tree Transducers Joost Engelfriet 1, Eric Lilin 2, and Andreas Maletti 3;? 1 Leiden Instite of Advanced Comper Science Leiden University, P.O. Box 9512, 2300 RA Leiden, The Netherlands

More information

On the Toppling of a Sand Pile

On the Toppling of a Sand Pile Discrete Mathematics and Theoretical Comuter Science Proceedings AA (DM-CCG), 2001, 275 286 On the Toling of a Sand Pile Jean-Christohe Novelli 1 and Dominique Rossin 2 1 CNRS, LIFL, Bâtiment M3, Université

More information

Lilian Markenzon 1, Nair Maria Maia de Abreu 2* and Luciana Lee 3

Lilian Markenzon 1, Nair Maria Maia de Abreu 2* and Luciana Lee 3 Pesquisa Oeracional (2013) 33(1): 123-132 2013 Brazilian Oerations Research Society Printed version ISSN 0101-7438 / Online version ISSN 1678-5142 www.scielo.br/oe SOME RESULTS ABOUT THE CONNECTIVITY OF

More information

Pure and O-Substitution

Pure and O-Substitution International Journal of Foundations of Computer Science c World Scientific Publishing Company Pure and O-Substitution Andreas Maletti Department of Computer Science, Technische Universität Dresden 01062

More information

#A37 INTEGERS 15 (2015) NOTE ON A RESULT OF CHUNG ON WEIL TYPE SUMS

#A37 INTEGERS 15 (2015) NOTE ON A RESULT OF CHUNG ON WEIL TYPE SUMS #A37 INTEGERS 15 (2015) NOTE ON A RESULT OF CHUNG ON WEIL TYPE SUMS Norbert Hegyvári ELTE TTK, Eötvös University, Institute of Mathematics, Budaest, Hungary hegyvari@elte.hu François Hennecart Université

More information

Solved Problems. (a) (b) (c) Figure P4.1 Simple Classification Problems First we draw a line between each set of dark and light data points.

Solved Problems. (a) (b) (c) Figure P4.1 Simple Classification Problems First we draw a line between each set of dark and light data points. Solved Problems Solved Problems P Solve the three simle classification roblems shown in Figure P by drawing a decision boundary Find weight and bias values that result in single-neuron ercetrons with the

More information

Preservation of Recognizability for Weighted Linear Extended Top-Down Tree Transducers

Preservation of Recognizability for Weighted Linear Extended Top-Down Tree Transducers Preservation of Recognizability for Weighted Linear Extended Top-Down Tree Transducers Nina eemann and Daniel Quernheim and Fabienne Braune and Andreas Maletti University of tuttgart, Institute for Natural

More information

Convex Optimization methods for Computing Channel Capacity

Convex Optimization methods for Computing Channel Capacity Convex Otimization methods for Comuting Channel Caacity Abhishek Sinha Laboratory for Information and Decision Systems (LIDS), MIT sinhaa@mit.edu May 15, 2014 We consider a classical comutational roblem

More information

Blame, coercion, and threesomes: Together again for the first time

Blame, coercion, and threesomes: Together again for the first time Blame, coercion, and threesomes: Together again for the first time Draft, 19 October 2014 Jeremy Siek Indiana University jsiek@indiana.edu Peter Thiemann Universität Freiburg thiemann@informatik.uni-freiburg.de

More information

Model checking, verification of CTL. One must verify or expel... doubts, and convert them into the certainty of YES [Thomas Carlyle]

Model checking, verification of CTL. One must verify or expel... doubts, and convert them into the certainty of YES [Thomas Carlyle] Chater 5 Model checking, verification of CTL One must verify or exel... doubts, and convert them into the certainty of YES or NO. [Thomas Carlyle] 5. The verification setting Page 66 We introduce linear

More information

Hierarchies of Tree Series TransducersRevisited 1

Hierarchies of Tree Series TransducersRevisited 1 Hierarchies of Tree Series TransducersRevisited 1 Andreas Maletti 2 Technische Universität Dresden Fakultät Informatik June 27, 2006 1 Financially supported by the Friends and Benefactors of TU Dresden

More information

Topic: Lower Bounds on Randomized Algorithms Date: September 22, 2004 Scribe: Srinath Sridhar

Topic: Lower Bounds on Randomized Algorithms Date: September 22, 2004 Scribe: Srinath Sridhar 15-859(M): Randomized Algorithms Lecturer: Anuam Guta Toic: Lower Bounds on Randomized Algorithms Date: Setember 22, 2004 Scribe: Srinath Sridhar 4.1 Introduction In this lecture, we will first consider

More information

Linking Theorems for Tree Transducers

Linking Theorems for Tree Transducers Linking Theorems for Tree Transducers Andreas Maletti maletti@ims.uni-stuttgart.de peyer October 1, 2015 Andreas Maletti Linking Theorems for MBOT Theorietag 2015 1 / 32 tatistical Machine Translation

More information

IMPROVED BOUNDS IN THE SCALED ENFLO TYPE INEQUALITY FOR BANACH SPACES

IMPROVED BOUNDS IN THE SCALED ENFLO TYPE INEQUALITY FOR BANACH SPACES IMPROVED BOUNDS IN THE SCALED ENFLO TYPE INEQUALITY FOR BANACH SPACES OHAD GILADI AND ASSAF NAOR Abstract. It is shown that if (, ) is a Banach sace with Rademacher tye 1 then for every n N there exists

More information

Linear diophantine equations for discrete tomography

Linear diophantine equations for discrete tomography Journal of X-Ray Science and Technology 10 001 59 66 59 IOS Press Linear diohantine euations for discrete tomograhy Yangbo Ye a,gewang b and Jiehua Zhu a a Deartment of Mathematics, The University of Iowa,

More information

RANDOM WALKS AND PERCOLATION: AN ANALYSIS OF CURRENT RESEARCH ON MODELING NATURAL PROCESSES

RANDOM WALKS AND PERCOLATION: AN ANALYSIS OF CURRENT RESEARCH ON MODELING NATURAL PROCESSES RANDOM WALKS AND PERCOLATION: AN ANALYSIS OF CURRENT RESEARCH ON MODELING NATURAL PROCESSES AARON ZWIEBACH Abstract. In this aer we will analyze research that has been recently done in the field of discrete

More information

CMSC 425: Lecture 4 Geometry and Geometric Programming

CMSC 425: Lecture 4 Geometry and Geometric Programming CMSC 425: Lecture 4 Geometry and Geometric Programming Geometry for Game Programming and Grahics: For the next few lectures, we will discuss some of the basic elements of geometry. There are many areas

More information

Fibration of Toposes PSSL 101, Leeds

Fibration of Toposes PSSL 101, Leeds Fibration of Tooses PSSL 101, Leeds Sina Hazratour sinahazratour@gmail.com Setember 2017 AUs AUs as finitary aroximation of Grothendieck tooses Pretooses 1 finite limits 2 stable finite disjoint coroducts

More information

16. Binary Search Trees

16. Binary Search Trees Dictionary imlementation 16. Binary Search Trees [Ottman/Widmayer, Ka..1, Cormen et al, Ka. 12.1-12.] Hashing: imlementation of dictionaries with exected very fast access times. Disadvantages of hashing:

More information

Distributed Rule-Based Inference in the Presence of Redundant Information

Distributed Rule-Based Inference in the Presence of Redundant Information istribution Statement : roved for ublic release; distribution is unlimited. istributed Rule-ased Inference in the Presence of Redundant Information June 8, 004 William J. Farrell III Lockheed Martin dvanced

More information

DIFFERENTIAL GEOMETRY. LECTURES 9-10,

DIFFERENTIAL GEOMETRY. LECTURES 9-10, DIFFERENTIAL GEOMETRY. LECTURES 9-10, 23-26.06.08 Let us rovide some more details to the definintion of the de Rham differential. Let V, W be two vector bundles and assume we want to define an oerator

More information

16. Binary Search Trees

16. Binary Search Trees Dictionary imlementation 16. Binary Search Trees [Ottman/Widmayer, Ka..1, Cormen et al, Ka. 1.1-1.] Hashing: imlementation of dictionaries with exected very fast access times. Disadvantages of hashing:

More information

Elementary Analysis in Q p

Elementary Analysis in Q p Elementary Analysis in Q Hannah Hutter, May Szedlák, Phili Wirth November 17, 2011 This reort follows very closely the book of Svetlana Katok 1. 1 Sequences and Series In this section we will see some

More information

Compositions of Tree Series Transformations

Compositions of Tree Series Transformations Compositions of Tree Series Transformations Andreas Maletti a Technische Universität Dresden Fakultät Informatik D 01062 Dresden, Germany maletti@tcs.inf.tu-dresden.de December 03, 2004 1. Motivation 2.

More information

Various Proofs for the Decrease Monotonicity of the Schatten s Power Norm, Various Families of R n Norms and Some Open Problems

Various Proofs for the Decrease Monotonicity of the Schatten s Power Norm, Various Families of R n Norms and Some Open Problems Int. J. Oen Problems Comt. Math., Vol. 3, No. 2, June 2010 ISSN 1998-6262; Coyright c ICSRS Publication, 2010 www.i-csrs.org Various Proofs for the Decrease Monotonicity of the Schatten s Power Norm, Various

More information

Solution sheet ξi ξ < ξ i+1 0 otherwise ξ ξ i N i,p 1 (ξ) + where 0 0

Solution sheet ξi ξ < ξ i+1 0 otherwise ξ ξ i N i,p 1 (ξ) + where 0 0 Advanced Finite Elements MA5337 - WS7/8 Solution sheet This exercise sheets deals with B-slines and NURBS, which are the basis of isogeometric analysis as they will later relace the olynomial ansatz-functions

More information

Averaging sums of powers of integers and Faulhaber polynomials

Averaging sums of powers of integers and Faulhaber polynomials Annales Mathematicae et Informaticae 42 (20. 0 htt://ami.ektf.hu Averaging sums of owers of integers and Faulhaber olynomials José Luis Cereceda a a Distrito Telefónica Madrid Sain jl.cereceda@movistar.es

More information

Cryptanalysis of Pseudorandom Generators

Cryptanalysis of Pseudorandom Generators CSE 206A: Lattice Algorithms and Alications Fall 2017 Crytanalysis of Pseudorandom Generators Instructor: Daniele Micciancio UCSD CSE As a motivating alication for the study of lattice in crytograhy we

More information

Syntax-Directed Translations and Quasi-alphabetic Tree Bimorphisms Revisited

Syntax-Directed Translations and Quasi-alphabetic Tree Bimorphisms Revisited Syntax-Directed Translations and Quasi-alphabetic Tree Bimorphisms Revisited Andreas Maletti and C t lin Ionuµ Tîrn uc Universitat Rovira i Virgili Departament de Filologies Romàniques Av. Catalunya 35,

More information

ON FREIMAN S 2.4-THEOREM

ON FREIMAN S 2.4-THEOREM ON FREIMAN S 2.4-THEOREM ØYSTEIN J. RØDSETH Abstract. Gregory Freiman s celebrated 2.4-Theorem says that if A is a set of residue classes modulo a rime satisfying 2A 2.4 A 3 and A < /35, then A is contained

More information

MODELING THE RELIABILITY OF C4ISR SYSTEMS HARDWARE/SOFTWARE COMPONENTS USING AN IMPROVED MARKOV MODEL

MODELING THE RELIABILITY OF C4ISR SYSTEMS HARDWARE/SOFTWARE COMPONENTS USING AN IMPROVED MARKOV MODEL Technical Sciences and Alied Mathematics MODELING THE RELIABILITY OF CISR SYSTEMS HARDWARE/SOFTWARE COMPONENTS USING AN IMPROVED MARKOV MODEL Cezar VASILESCU Regional Deartment of Defense Resources Management

More information

Section 0.10: Complex Numbers from Precalculus Prerequisites a.k.a. Chapter 0 by Carl Stitz, PhD, and Jeff Zeager, PhD, is available under a Creative

Section 0.10: Complex Numbers from Precalculus Prerequisites a.k.a. Chapter 0 by Carl Stitz, PhD, and Jeff Zeager, PhD, is available under a Creative Section 0.0: Comlex Numbers from Precalculus Prerequisites a.k.a. Chater 0 by Carl Stitz, PhD, and Jeff Zeager, PhD, is available under a Creative Commons Attribution-NonCommercial-ShareAlike.0 license.

More information

Dedekind sums and continued fractions

Dedekind sums and continued fractions ACTA ARITHMETICA LXIII.1 (1993 edekind sums and continued fractions by R. R. Hall (York and M. N. Huxley (Cardiff Let ϱ(t denote the row-of-teeth function ϱ(t = [t] t + 1/2. Let a b c... r be ositive integers.

More information

Eötvös Loránd University Faculty of Informatics. Distribution of additive arithmetical functions

Eötvös Loránd University Faculty of Informatics. Distribution of additive arithmetical functions Eötvös Loránd University Faculty of Informatics Distribution of additive arithmetical functions Theses of Ph.D. Dissertation by László Germán Suervisor Prof. Dr. Imre Kátai member of the Hungarian Academy

More information

16.2. Infinite Series. Introduction. Prerequisites. Learning Outcomes

16.2. Infinite Series. Introduction. Prerequisites. Learning Outcomes Infinite Series 6.2 Introduction We extend the concet of a finite series, met in Section 6., to the situation in which the number of terms increase without bound. We define what is meant by an infinite

More information

Feedback-error control

Feedback-error control Chater 4 Feedback-error control 4.1 Introduction This chater exlains the feedback-error (FBE) control scheme originally described by Kawato [, 87, 8]. FBE is a widely used neural network based controller

More information

Outline. CS21 Decidability and Tractability. Regular expressions and FA. Regular expressions and FA. Regular expressions and FA

Outline. CS21 Decidability and Tractability. Regular expressions and FA. Regular expressions and FA. Regular expressions and FA Outline CS21 Decidability and Tractability Lecture 4 January 14, 2019 FA and Regular Exressions Non-regular languages: Puming Lemma Pushdown Automata Context-Free Grammars and Languages January 14, 2019

More information

Proof Nets and Boolean Circuits

Proof Nets and Boolean Circuits Proof Nets and Boolean Circuits Kazushige Terui terui@nii.ac.j National Institute of Informatics, Tokyo 14/07/04, Turku.1/44 Motivation (1) Proofs-as-Programs (Curry-Howard) corresondence: Proofs = Programs

More information

HENSEL S LEMMA KEITH CONRAD

HENSEL S LEMMA KEITH CONRAD HENSEL S LEMMA KEITH CONRAD 1. Introduction In the -adic integers, congruences are aroximations: for a and b in Z, a b mod n is the same as a b 1/ n. Turning information modulo one ower of into similar

More information

Sets of Real Numbers

Sets of Real Numbers Chater 4 Sets of Real Numbers 4. The Integers Z and their Proerties In our revious discussions about sets and functions the set of integers Z served as a key examle. Its ubiquitousness comes from the fact

More information

DISCRIMINANTS IN TOWERS

DISCRIMINANTS IN TOWERS DISCRIMINANTS IN TOWERS JOSEPH RABINOFF Let A be a Dedekind domain with fraction field F, let K/F be a finite searable extension field, and let B be the integral closure of A in K. In this note, we will

More information

Lecture 9: Decoding. Andreas Maletti. Stuttgart January 20, Statistical Machine Translation. SMT VIII A. Maletti 1

Lecture 9: Decoding. Andreas Maletti. Stuttgart January 20, Statistical Machine Translation. SMT VIII A. Maletti 1 Lecture 9: Decoding Andreas Maletti Statistical Machine Translation Stuttgart January 20, 2012 SMT VIII A. Maletti 1 Lecture 9 Last time Synchronous grammars (tree transducers) Rule extraction Weight training

More information

Named Entity Recognition using Maximum Entropy Model SEEM5680

Named Entity Recognition using Maximum Entropy Model SEEM5680 Named Entity Recognition using Maximum Entroy Model SEEM5680 Named Entity Recognition System Named Entity Recognition (NER): Identifying certain hrases/word sequences in a free text. Generally it involves

More information

An Introduction To Range Searching

An Introduction To Range Searching An Introduction To Range Searching Jan Vahrenhold eartment of Comuter Science Westfälische Wilhelms-Universität Münster, Germany. Overview 1. Introduction: Problem Statement, Lower Bounds 2. Range Searching

More information

Examples from Elements of Theory of Computation. Abstract. Introduction

Examples from Elements of Theory of Computation. Abstract. Introduction Examles from Elements of Theory of Comutation Mostafa Ghandehari Samee Ullah Khan Deartment of Comuter Science and Engineering University of Texas at Arlington, TX-7609, USA Tel: +(87)7-5688, Fax: +(87)7-784

More information

16.2. Infinite Series. Introduction. Prerequisites. Learning Outcomes

16.2. Infinite Series. Introduction. Prerequisites. Learning Outcomes Infinite Series 6. Introduction We extend the concet of a finite series, met in section, to the situation in which the number of terms increase without bound. We define what is meant by an infinite series

More information

arxiv: v2 [math.co] 6 Jul 2017

arxiv: v2 [math.co] 6 Jul 2017 COUNTING WORDS SATISFYING THE RHYTHMIC ODDITY PROPERTY FRANCK JEDRZEJEWSKI arxiv:1607.07175v2 [math.co] 6 Jul 2017 Abstract. This aer describes an enumeration of all words having a combinatoric roerty

More information

Analysis of some entrance probabilities for killed birth-death processes

Analysis of some entrance probabilities for killed birth-death processes Analysis of some entrance robabilities for killed birth-death rocesses Master s Thesis O.J.G. van der Velde Suervisor: Dr. F.M. Sieksma July 5, 207 Mathematical Institute, Leiden University Contents Introduction

More information

FUGACITY. It is simply a measure of molar Gibbs energy of a real gas.

FUGACITY. It is simply a measure of molar Gibbs energy of a real gas. FUGACITY It is simly a measure of molar Gibbs energy of a real gas. Modifying the simle equation for the chemical otential of an ideal gas by introducing the concet of a fugacity (f). The fugacity is an

More information

PETER J. GRABNER AND ARNOLD KNOPFMACHER

PETER J. GRABNER AND ARNOLD KNOPFMACHER ARITHMETIC AND METRIC PROPERTIES OF -ADIC ENGEL SERIES EXPANSIONS PETER J. GRABNER AND ARNOLD KNOPFMACHER Abstract. We derive a characterization of rational numbers in terms of their unique -adic Engel

More information

8 STOCHASTIC PROCESSES

8 STOCHASTIC PROCESSES 8 STOCHASTIC PROCESSES The word stochastic is derived from the Greek στoχαστικoς, meaning to aim at a target. Stochastic rocesses involve state which changes in a random way. A Markov rocess is a articular

More information

#A45 INTEGERS 12 (2012) SUPERCONGRUENCES FOR A TRUNCATED HYPERGEOMETRIC SERIES

#A45 INTEGERS 12 (2012) SUPERCONGRUENCES FOR A TRUNCATED HYPERGEOMETRIC SERIES #A45 INTEGERS 2 (202) SUPERCONGRUENCES FOR A TRUNCATED HYPERGEOMETRIC SERIES Roberto Tauraso Diartimento di Matematica, Università di Roma Tor Vergata, Italy tauraso@mat.uniroma2.it Received: /7/, Acceted:

More information

Decision Problems of Tree Transducers with Origin

Decision Problems of Tree Transducers with Origin Decision Problems of Tree Transducers with Origin Emmanuel Filiot 1, Sebastian Maneth 2, Pierre-Alain Reynier 3, and Jean-Marc Talbot 3 1 Université Libre de Bruxelles 2 University of Edinburgh 3 Aix-Marseille

More information

Intrinsic Approximation on Cantor-like Sets, a Problem of Mahler

Intrinsic Approximation on Cantor-like Sets, a Problem of Mahler Intrinsic Aroximation on Cantor-like Sets, a Problem of Mahler Ryan Broderick, Lior Fishman, Asaf Reich and Barak Weiss July 200 Abstract In 984, Kurt Mahler osed the following fundamental question: How

More information

An introduction to forest-regular languages

An introduction to forest-regular languages An introduction to forest-regular languages Mika Raento Basic Research Unit, Helsinki Institute for Information Technology Deartment of Comuter Science, University of Helsinki Mika.Raento@cs.Helsinki.FI

More information

An Inverse Problem for Two Spectra of Complex Finite Jacobi Matrices

An Inverse Problem for Two Spectra of Complex Finite Jacobi Matrices Coyright 202 Tech Science Press CMES, vol.86, no.4,.30-39, 202 An Inverse Problem for Two Sectra of Comlex Finite Jacobi Matrices Gusein Sh. Guseinov Abstract: This aer deals with the inverse sectral roblem

More information

Minimization of Weighted Automata

Minimization of Weighted Automata Minimization of Weighted Automata Andreas Maletti Universitat Rovira i Virgili Tarragona, Spain Wrocław May 19, 2010 Minimization of Weighted Automata Andreas Maletti 1 In Collaboration with ZOLTÁN ÉSIK,

More information

Bond Computing Systems: a Biologically Inspired and High-level Dynamics Model for Pervasive Computing

Bond Computing Systems: a Biologically Inspired and High-level Dynamics Model for Pervasive Computing Bond Comuting Systems: a Biologically Insired and High-level Dynamics Model for Pervasive Comuting Linmin Yang 1, Zhe Dang 1, and Oscar H. Ibarra 2 1 School of Electrical Engineering and Comuter Science

More information

216 S. Chandrasearan and I.C.F. Isen Our results dier from those of Sun [14] in two asects: we assume that comuted eigenvalues or singular values are

216 S. Chandrasearan and I.C.F. Isen Our results dier from those of Sun [14] in two asects: we assume that comuted eigenvalues or singular values are Numer. Math. 68: 215{223 (1994) Numerische Mathemati c Sringer-Verlag 1994 Electronic Edition Bacward errors for eigenvalue and singular value decomositions? S. Chandrasearan??, I.C.F. Isen??? Deartment

More information

On generalizing happy numbers to fractional base number systems

On generalizing happy numbers to fractional base number systems On generalizing hay numbers to fractional base number systems Enriue Treviño, Mikita Zhylinski October 17, 018 Abstract Let n be a ositive integer and S (n) be the sum of the suares of its digits. It is

More information

System Reliability Estimation and Confidence Regions from Subsystem and Full System Tests

System Reliability Estimation and Confidence Regions from Subsystem and Full System Tests 009 American Control Conference Hyatt Regency Riverfront, St. Louis, MO, USA June 0-, 009 FrB4. System Reliability Estimation and Confidence Regions from Subsystem and Full System Tests James C. Sall Abstract

More information

POWERS OF RATIONALS MODULO 1 AND RATIONAL BASE NUMBER SYSTEMS

POWERS OF RATIONALS MODULO 1 AND RATIONAL BASE NUMBER SYSTEMS ISRAEL JOURNAL OF MATHEMATICS 68 (8), 53 9 DOI:.7/s856-8-56-4 POWERS OF RATIONALS MODULO AND RATIONAL BASE NUMBER SYSTEMS BY Shigeki Akiyama Deartment of Mathematics, Niigata University, Jaan e-mail: akiyama@math.sc.niigata-u.ac.j

More information

Chapter 1 Fundamentals

Chapter 1 Fundamentals Chater Fundamentals. Overview of Thermodynamics Industrial Revolution brought in large scale automation of many tedious tasks which were earlier being erformed through manual or animal labour. Inventors

More information

t 0 Xt sup X t p c p inf t 0

t 0 Xt sup X t p c p inf t 0 SHARP MAXIMAL L -ESTIMATES FOR MARTINGALES RODRIGO BAÑUELOS AND ADAM OSȨKOWSKI ABSTRACT. Let X be a suermartingale starting from 0 which has only nonnegative jums. For each 0 < < we determine the best

More information

On Isoperimetric Functions of Probability Measures Having Log-Concave Densities with Respect to the Standard Normal Law

On Isoperimetric Functions of Probability Measures Having Log-Concave Densities with Respect to the Standard Normal Law On Isoerimetric Functions of Probability Measures Having Log-Concave Densities with Resect to the Standard Normal Law Sergey G. Bobkov Abstract Isoerimetric inequalities are discussed for one-dimensional

More information

THE 2D CASE OF THE BOURGAIN-DEMETER-GUTH ARGUMENT

THE 2D CASE OF THE BOURGAIN-DEMETER-GUTH ARGUMENT THE 2D CASE OF THE BOURGAIN-DEMETER-GUTH ARGUMENT ZANE LI Let e(z) := e 2πiz and for g : [0, ] C and J [0, ], define the extension oerator E J g(x) := g(t)e(tx + t 2 x 2 ) dt. J For a ositive weight ν

More information

DRAFT - do not circulate

DRAFT - do not circulate An Introduction to Proofs about Concurrent Programs K. V. S. Prasad (for the course TDA383/DIT390) Deartment of Comuter Science Chalmers University Setember 26, 2016 Rough sketch of notes released since

More information

B8.1 Martingales Through Measure Theory. Concept of independence

B8.1 Martingales Through Measure Theory. Concept of independence B8.1 Martingales Through Measure Theory Concet of indeendence Motivated by the notion of indeendent events in relims robability, we have generalized the concet of indeendence to families of σ-algebras.

More information

Survey: Weighted Extended Top-down Tree Transducers Part III Composition

Survey: Weighted Extended Top-down Tree Transducers Part III Composition Survey: Weigted Extended To-down Tree Transducers Part III Comosition Aurélie Lagoutte 1 and Andreas aletti 2, 1 École normale suérieure de Cacan, Déartement Informatiue 61, avenue du Président Wilson,

More information

Brownian Motion and Random Prime Factorization

Brownian Motion and Random Prime Factorization Brownian Motion and Random Prime Factorization Kendrick Tang June 4, 202 Contents Introduction 2 2 Brownian Motion 2 2. Develoing Brownian Motion.................... 2 2.. Measure Saces and Borel Sigma-Algebras.........

More information

Lecture Notes on Inductive Definitions

Lecture Notes on Inductive Definitions Lecture Notes on Inductive Definitions 15-312: Foundations of Programming Languages Frank Pfenning Lecture 2 September 2, 2004 These supplementary notes review the notion of an inductive definition and

More information

ON THE NORM OF AN IDEMPOTENT SCHUR MULTIPLIER ON THE SCHATTEN CLASS

ON THE NORM OF AN IDEMPOTENT SCHUR MULTIPLIER ON THE SCHATTEN CLASS PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 00, Number 0, Pages 000 000 S 000-9939XX)0000-0 ON THE NORM OF AN IDEMPOTENT SCHUR MULTIPLIER ON THE SCHATTEN CLASS WILLIAM D. BANKS AND ASMA HARCHARRAS

More information

arxiv: v1 [physics.data-an] 26 Oct 2012

arxiv: v1 [physics.data-an] 26 Oct 2012 Constraints on Yield Parameters in Extended Maximum Likelihood Fits Till Moritz Karbach a, Maximilian Schlu b a TU Dortmund, Germany, moritz.karbach@cern.ch b TU Dortmund, Germany, maximilian.schlu@cern.ch

More information

ON THE LEAST SIGNIFICANT p ADIC DIGITS OF CERTAIN LUCAS NUMBERS

ON THE LEAST SIGNIFICANT p ADIC DIGITS OF CERTAIN LUCAS NUMBERS #A13 INTEGERS 14 (014) ON THE LEAST SIGNIFICANT ADIC DIGITS OF CERTAIN LUCAS NUMBERS Tamás Lengyel Deartment of Mathematics, Occidental College, Los Angeles, California lengyel@oxy.edu Received: 6/13/13,

More information

CHAPTER 2: SMOOTH MAPS. 1. Introduction In this chapter we introduce smooth maps between manifolds, and some important

CHAPTER 2: SMOOTH MAPS. 1. Introduction In this chapter we introduce smooth maps between manifolds, and some important CHAPTER 2: SMOOTH MAPS DAVID GLICKENSTEIN 1. Introduction In this chater we introduce smooth mas between manifolds, and some imortant concets. De nition 1. A function f : M! R k is a smooth function if

More information

The inverse Goldbach problem

The inverse Goldbach problem 1 The inverse Goldbach roblem by Christian Elsholtz Submission Setember 7, 2000 (this version includes galley corrections). Aeared in Mathematika 2001. Abstract We imrove the uer and lower bounds of the

More information

1 Riesz Potential and Enbeddings Theorems

1 Riesz Potential and Enbeddings Theorems Riesz Potential and Enbeddings Theorems Given 0 < < and a function u L loc R, the Riesz otential of u is defined by u y I u x := R x y dy, x R We begin by finding an exonent such that I u L R c u L R for

More information

Best approximation by linear combinations of characteristic functions of half-spaces

Best approximation by linear combinations of characteristic functions of half-spaces Best aroximation by linear combinations of characteristic functions of half-saces Paul C. Kainen Deartment of Mathematics Georgetown University Washington, D.C. 20057-1233, USA Věra Kůrková Institute of

More information

The Fekete Szegő theorem with splitting conditions: Part I

The Fekete Szegő theorem with splitting conditions: Part I ACTA ARITHMETICA XCIII.2 (2000) The Fekete Szegő theorem with slitting conditions: Part I by Robert Rumely (Athens, GA) A classical theorem of Fekete and Szegő [4] says that if E is a comact set in the

More information

The Knuth-Yao Quadrangle-Inequality Speedup is a Consequence of Total-Monotonicity

The Knuth-Yao Quadrangle-Inequality Speedup is a Consequence of Total-Monotonicity The Knuth-Yao Quadrangle-Ineuality Seedu is a Conseuence of Total-Monotonicity Wolfgang W. Bein Mordecai J. Golin Lawrence L. Larmore Yan Zhang Abstract There exist several general techniues in the literature

More information

Statics and dynamics: some elementary concepts

Statics and dynamics: some elementary concepts 1 Statics and dynamics: some elementary concets Dynamics is the study of the movement through time of variables such as heartbeat, temerature, secies oulation, voltage, roduction, emloyment, rices and

More information

RECIPROCITY LAWS JEREMY BOOHER

RECIPROCITY LAWS JEREMY BOOHER RECIPROCITY LAWS JEREMY BOOHER 1 Introduction The law of uadratic recirocity gives a beautiful descrition of which rimes are suares modulo Secial cases of this law going back to Fermat, and Euler and Legendre

More information

A Social Welfare Optimal Sequential Allocation Procedure

A Social Welfare Optimal Sequential Allocation Procedure A Social Welfare Otimal Sequential Allocation Procedure Thomas Kalinowsi Universität Rostoc, Germany Nina Narodytsa and Toby Walsh NICTA and UNSW, Australia May 2, 201 Abstract We consider a simle sequential

More information

Encoding Named Channels Communication by Behavioral Schemes

Encoding Named Channels Communication by Behavioral Schemes Acta olytechnica Hungarica Vol. 8, o. 2, 2011 Encoding amed Channels Communication by Behavioral Schemes Martin Tomášek Deartment of Comuters and Informatics, Faculty of Electrical Engineering and Informatics,

More information

Acceptance of!-languages by Communicating Deterministic Turing Machines

Acceptance of!-languages by Communicating Deterministic Turing Machines Acceptance of!-languages by Communicating Deterministic Turing Machines Rudolf Freund Institut für Computersprachen, Technische Universität Wien, Karlsplatz 13 A-1040 Wien, Austria Ludwig Staiger y Institut

More information

John Weatherwax. Analysis of Parallel Depth First Search Algorithms

John Weatherwax. Analysis of Parallel Depth First Search Algorithms Sulementary Discussions and Solutions to Selected Problems in: Introduction to Parallel Comuting by Viin Kumar, Ananth Grama, Anshul Guta, & George Karyis John Weatherwax Chater 8 Analysis of Parallel

More information