Márta Abrusán

Computing Perspective Shift in Narratives *

Introduction

Natural language allows changing the point of view in narrative texts without overt perspective shifting operators. A well-known example of such a perspective shift is free indirect discourse (FID). In this style, the hearer (reader) has to interpret certain elements of the sentence from the perspective of the narrator, and other elements from the perspective of a protagonist. But how do hearers (readers) know that they need to change the point of view in the first place? And when there are reasons to believe that the point of view is not that of the narrator, how do they know whose perspective is being developed? Importantly, sentences in isolation often do not carry any indication of whether they should be understood under a shifted perspective or not (cf. [START_REF] Wiebe | Tracking point of view in narrative[END_REF]. This is the case, for example, in (1a). When placed in a larger context, however, an interpretation of the same sentence with respect to some character's perspective might become plausible, as in (1b), or unlikely, as in (1c):

(1) a. It was raining.

b. Mary looked out the window anxiously. It was raining. c. The barbecue was canceled. It was raining.

Thus elements in the broader discourse context can license the shift in perspective, even in the absence of any indication of perspective shift inside the target sentence. Nevertheless, there are also sentence-internal markers that strongly signal that a subjective perspective is being expressed. In the example below, the exclamation mark, the evaluative adverb and the progressive all suggest that the sentence conveys some protagonist's opinion:

(2) Mary was being absolutely impossible! But note that even in these cases the identity of the perspectival center cannot be identified from the sentence alone. The best we can say about (2) is that the perspectival center is not Mary. To find out the identity of perspectival centres, the larger context is indispensable. The aim of this paper is to get a better understanding of the principles that tell us (a) Whether a perspective shift occurs and (b) When there is a perspective shift, who is the perspectival center? These questions are rarely asked in the literature, but there are two notable exceptions I am aware of: The pioneering work of [START_REF] Wiebe | Recognizing subjective sentences: A computational investigation of narrative text[END_REF][START_REF] Wiebe | Tracking point of view in narrative[END_REF] that addresses both questions from a computational perspective and [START_REF] Hinterwimmer | Prominent protagonists[END_REF] analysis examining the second question with the tools of pragmatics. After reviewing these two proposals, I add a few observations of my own that show the importance of discourse structure. Finally, I sketch a framework based on SDRT [START_REF] Asher | Logics of Conversation[END_REF] extended with MSDRT/ADT [START_REF] Kamp | Using proper names as intermediaries between labelled entity representations[END_REF][START_REF] Kamp | Epistemic specificity from a communication-theoretic perspective[END_REF], Maier 2015a, 2016) that allows us to express the insights made in [START_REF] Wiebe | Tracking point of view in narrative[END_REF] and [START_REF] Hinterwimmer | Prominent protagonists[END_REF] as well as the observations pertaining to discourse structure in a unified way.

A note on terminology: In this paper I understand perspective shift primarily as referring to FID. I assume that the mechanisms proposed could be extended to cases of what has been recently called protagonist projection (PP) or viewpoint shift, see [START_REF] Holton | Some telling examples: A reply to Tsohatzidis[END_REF], Stokke (2013, this volume), [START_REF] Hinterwimmer | Two kinds of perspective taking in narrative texts[END_REF], [START_REF] Abrusán | The spectrum of perspective shift: protagonist projection versus free indirect discourse[END_REF]. However, I will not discuss such examples in this paper. 1 Perspective shift is also often used in connection with the interpretation of certain lexical items that have a subjective aspect to their meaning, e.g. predicates of personal taste, speaker-oriented adverbs, etc. When these elements appear outside of direct or indirect discourse they are often markers of FID or PP. In such cases, at least, finding their perspectival center is the same question as finding the perspectival center of FID or PP.2 2 [START_REF] Wiebe | Recognizing subjective sentences: A computational investigation of narrative text[END_REF][START_REF] Wiebe | Tracking point of view in narrative[END_REF]) algorithm [START_REF] Wiebe | Recognizing subjective sentences: A computational investigation of narrative text[END_REF][START_REF] Wiebe | Tracking point of view in narrative[END_REF] groundbreaking work developed a computational algorithm that could recognise and track point of view in naturally occurring narrative discourse. 3 Point of view is important for understanding the meaning of what [START_REF] Wiebe | Tracking point of view in narrative[END_REF] calls subjective sentences, namely attitude reports and sentences in FID. While in the case of attitude reports the identity of the individual whose perspective is expressed is often given by the subject, finding the perspectival center of examples of FID is highly non-trivial.

The algorithm is based on regularities in the ways in which authors initiate, continue and resume a character's point of view. Based on a combination of the features of the target sentence (e.g. its tense, aspect, the presence of elements that express subjective meaning, etc.) and properties of the context (e.g. whether the previous sentence was subjective, the identity of a likely perspectival center, etc.), many point of view operations can be predicted by Wiebe's algorithm. Below I provide a summary of this algorithm, but note that this brief discussion cannot do justice to the richness of Wiebe's proposal.

The overall idea is captured by the Point of View (POV) function: This function maps the set of sentence features (Feature Set) and the current context (Context) into an Interpretation. The latter tells us whether or not the sentence is subjective and if yes, the identity of its perspectival center.

(3) Point of view (POV) function:

Feature Set × Context → Interpretation
In what follows I spell out the contents of the Feature Set, the Context and the Interpretation, respectively. I then illustrate the algorithm with an example.

The FeatureSet

The FeatureSet contains many diverse pieces of information. The most important are the potential subjective elements in the sentence (more about these below).

Another type of information contained in the FeatureSet is the type of state of affairs the sentence is about, for example whether it describes a private state action such as looking and sighing or some other types of action; or whether it is about a private state (i.e. an attitude, e.g. wondering, being afraid) or non-private state such as being being six feet tall. The FeatureSet also registers whether the head noun of the subject of the main clause is a private state noun (e.g. pain, astonishment, etc.) as well as the experiencers and actors of private states and private state actions. If the sentence contains a narrative parenthetical, the FeatureSet also registers the identity of the subject of the parenthetical. It further contains some other types of syntactic information as well.

The list of potential subjective elements in the sentence is largely based on [START_REF] Banfield | Unspeakable Sentences: Narration and Representation in the Language of Fiction[END_REF] list of subjective elements, with the difference that for Wiebe (1994) these items are not unambiguous marks of subjectivity, they only mark subjectivity given certain contexts and with varying degrees of strength. Here is the list of potential subjective elements for English:4 (NB: Similar lists of perspective sensitive items can be found in e.g. in Eckardt (2014, this volume) and [START_REF] Bylinina | The landscape of perspective shifting[END_REF]. See also Stojanovic (this volume) for a discussion of a subclass of evaluative nouns, viz. derogatory terms.) (4)

Potential subjective elements (PSEs) a. exclamations and direct questions b. elements that express evaluation or judgment (i) adjectives (awful, poor) (ii) nouns (old bag) (iii) adverbs (oddly, incredibly) (iv) modals that express judgment or obligation (had better, ought to, should, be supposed to) (v) adverbs such as scarcely and hardly (e.g. "She could hardly be expected to live there.") c. elements that express lack of knowledge (i) subordinators such as whoever, whatever (e.g. "Whatever it was, it has flown by quickly.") (ii) adjectival phrases such as some kind of (e.g. "The object in her hand was some kind of weapon.") d. sentence fragments e. kinship terms f. evidentials (that express certainty, or uncertainty, hedges, evidentials that address expectations, met or unmet) g. certain adverbial discourse connectives such as first, in addition, for instance, on the other hand, after all, anyway, yet, etc. (e.g. "Yet, they were the pride of the family.") h. conditional clauses i. comparative like (e.g. "They followed her like acolytes behind a goddess.") j. habitual sentences k. the past perfective but only in the main verb phrase l. the progressive, but only in the main verb phrase

The potential subjective elements have different strengths. The weakest ones, such as the past perfective and the progressive can typically only continue a character's POV and only within a paragraph. Stronger elements can continue or resume a POV after a paragraph break. Still stronger ones (e.g. evidentials and sentence fragments) can resume a last perspectival center's POV as long as they are expected perspectival centers. Finally, the strongest subjective elements, such as exclamations and questions, are always subjective, even when there is no expected character to whom we can attribute a sentence.

How do we know the target sentence's situation in the larger context and whether there is an expected perspectival center? This information is given by the Context, to which we turn below.

The Context

The Context consists of (a) the identity of the perspectival center (PC)5 of the last subjective sentence that appeared in the text, if there was one, (b) the identity of the last active character, if there was one (c) the identity of any characters whose point of view was taken earlier in the text and (d) the current Text Situation:6

(5) Context = LastPC, LastActiveCharacter, PreviousPCs, TextSituation

The Text Situation describes whether the current sentence has been preceded by a subjective sentence or a sentence with an active character or a paragraph break has preceded the current sentence. Accordingly, [START_REF] Wiebe | Tracking point of view in narrative[END_REF] The Text Situation interacts with the potential subjective elements in the following way. Expectations for a subjective sentence are strongest in continuingsubjective situations and weakest in the presubjective-nonactive situation. Even the weakest potential subjective elements are taken to be subjective in the first case, but only the strongest ones to be subjective in the last case. More precisely, each potential subjective element is associated with a set of text situations t such that the algorithm interprets the potential subjective elements to be subjective iff the current text situation is in t.

Finally, we can turn to the algorithm for identifying the perspectival center (PC): There are two cases: The first case is when the PC can be indentified on the basis of the target sentence alone, as in many attitude reports or examples of FID that contain parentheticals. The algorithm that summarises the procedure is given in (7) (adapted from Wiebe and Rappaport 1988): 7 (7)

Identifying the PC from the target sentence:

If the sentence contains a narrative parenthetical then PC is the subject of the parenthetical else if the sentence is a private-state sentence then if it has a non-subordinated subjective element 8 or the text situation is continuing-subjective then PC is identified from the tracking point of view, it is not sufficient. 7 NB: [START_REF] Wiebe | Tracking point of view in narrative[END_REF] primary aim is to identify FID in texts. The interpretation of unshiftable indexicals (e.g. first person indexicals) is (presumably) not influenced by the PC, but fixed by the global context. 8 This would be the case for example in Mary hardly wanted to leave, in which the subjective adjective hardly indicates that the PC is not Mary.

previous context else PC is the experiencer end if else PC is identified from the previous context end if

When the PC cannot be identified from the target sentence, it needs to be identified based on the previous context. ( 8)

Identifying the PC from the previous context: If there are two expected perspectival centers then if the sentence is about the last active character then PC is the last perspectival center else PC is the last active character end if else if there is an expected perspectival center then PC is the expected perspectival center else PC is unidentified end if

The expected perspectival centers (EPCs) are the Last PC and the last active character of the current context.

The Interpretation

The Interpretation functions tells us either that the sentence is a subjective sentence of a particular character, or that the sentence is objective9 and has a particular active character (ActiveCharacter might also be the empty set).

(9) The sentence in (e) is determined to be Sandy's subjective sentence, because it contains so used as a conjunct, which is subjective as long as there is an expected perspectival center.

Interpretation ∈ { subjective, PC | PC ⊆ Characters} ∪ { objective, ActiveCharacter | ActiveCharacter ⊆ Characters}

Tests of Wiebe's (1994) algorithm

The algorithm was tested on 450 sentential input items, which were complete sentences of every 5th page of two novels. The output of the POV function was judged against author's evaluations. Of the 450 input items, the algorithm made 27 primary errors, i.e. errors that were not dependent on other errors. Of these, 20 actual subjective sentences were interpreted as objective (13), or subjective but with wrong PC (7) and 7 actual objective sentences were interpreted as subjective.

A very impressive result, overall.

Later work on probabilistic classifiers for POV

Wiebe and Bruce ( 2001) present a probabilistic approach for tracking point of view. This work, however, is much more limited in scope than [START_REF] Wiebe | Recognizing subjective sentences: A computational investigation of narrative text[END_REF][START_REF] Wiebe | Tracking point of view in narrative[END_REF]. The underlying idea is that texts can be segmented into blocks such that all subjective sentences within the block express the point of view of the same character. The computational task that they define is segmenting the text into such blocks using a probabilistic classifier. The hypothesis for finding the subjective agent for each block is that these characters tend to enjoy a higher level of focus, i.e. they will appear more frequently. The subject noun phrases of the main clauses of each blocks are therefore extracted and the perspectival center of each block is predicted from these.

The idea that blocks of texts tend to have the same perspectival center finds an echo in the work of [START_REF] Hinterwimmer | Prominent protagonists[END_REF], to which I turn now.

3 Hinterwimmer (2019): Prominent protagonists Hinterwimmer (2019) asks a more modest question than Wiebe's extremely ambitious proposal. Assuming that we already know whether a given sentence is to be interpreted as FID, he asks what conditions need to be met for a protagonist in order to be identified as the perspectival center.

He points out that not all protagonists prominent enough to be taken up by personal pronouns are available as anchors for FID. (This point was also noted in [START_REF] Wiebe | Tracking point of view in narrative[END_REF] Observe the examples cited by Hinterwimmer ( 2019):

(11) FID a. Susan looked at George hatefully. The dumb jerk had managed to make her look like an idiot at the meeting yesterday. b. Susan looked at George hatefully. #The mean old hag had managed to make him look like an idiot at the meeting yesterday.

(12) Pronoun Resolution a. Susan looked at George hatefully. She quickly turned away when he returned the look. b. Susan looked at George hatefully. He returned the look, then turned his back on her and walked away.

The protagonist George is salient enough to be picked up by a personal pronoun in (12b), but not to be a perspectival center in (11b). 10 Hinterwimmer (2019) proposes that licit perspective takers are the protagonists that either are sentient with respect to the eventuality introduced by the verb of the immediately preceding sentence or function as topics with respect to larger text segments. He thus proposes two strategies for identifying a discourse referent as a perspectival center: one based on local prominence and one based on global prominence.

Local prominence: Protagonists that have the highest number of agentivity features in the preceding sentence are prominent. Agentivity thus is more important than subjecthood. This can be seen in the case of object experiencer verbs: In (13) below the experiencer Mary is salient for being the prespectival center of the following sentence, is but the subject John is not.

(13)

George bore Mary to death. a. Tomorrow she would definitely avoid sitting at a table with the bloated idiot again. b. #How sleepy she looked today! Thus the character who is sentient with respect to the eventuality introduced by the verb of the immediately preceding sentence is the preferred perspectival center of the event to be accommodated as FID.

Global prominence: [START_REF] Hinterwimmer | Prominent protagonists[END_REF] argues that in addition to local prominence, global prominence, i.e. topichood also matters for choosing the perspectival center. (Topichood is understood in the sense of QUD theories, cf. [START_REF] Roberts | Topics[END_REF][START_REF] Roberts | Information structure in discourse: Towards an integrated formal theory of pragmatics[END_REF] To illustrate the importance of topichood, he contrasts the two following examples: (14) George entered the room and looked around cautiously. Susan was sitting at the table in the corner with her best friend. Susan looked at George hatefully. The mean old hag had managed to make him look like an idiot at the meeting yesterday.

(15) Susan was sitting at a table in the corner with her best friend. George entered the room and looked around cautiously. Susan looked at George hatefully. #The mean old hag had managed to make him look like an idiot at the meeting yesterday.

The discourse in ( 14) is a story about George in the sense that George is felt to be its discourse topic. This is why George is available as the perspectival center for the last sentence of ( 14). The discourse in (15), on the other hand, seems to be about Susan, i.e. Susan is its discourse topic. This is why George is not available for being the perspectival center, despite of the fact that the sentence immediately preceding the FID passage is identical in both cases.

In sum, Hinterwimmer (2019) complements [START_REF] Wiebe | Recognizing subjective sentences: A computational investigation of narrative text[END_REF][START_REF] Wiebe | Tracking point of view in narrative[END_REF] with two very interesting observations. The first is that characters with the highest number of agentivity features are the (locally) expected likely candidates for being perspectival centers. 11 The observation about the effect of topicality allows a more refined characterisation than a simple chunking approach of [START_REF] Wiebe | Probabilistic classifiers for tracking point of view[END_REF] (though presumably it is much harder to track computationally).

Hinterwimmer's (2019) proposal was recently tested empirically by [START_REF] Bimpikou | Who perceives? who thinks? anchoring free reports of perception and thought in narratives[END_REF]. She found that locally prominent characters are more salient than globally prominent ones. At the same time, narrators are more salient than globally prominent characters but not more salient than locally prominent characters.

Rhetorical relations and FID

In this section I propose that rhetorical relations might be informative for inferring that perspective shift has taken place as well as for finding the perspectival center. In particular, certain types of discourse relations favour the interpretation of the target sentence from a shifted perspective, while others make it less likely or favour an unexpected perspective holder. The relevant division seems to be the distinction between subordinating and coordinating relations. A non-exhaustive list of each type of relation is given below, drawn from [START_REF] Asher | Subordinating and Coordinating Discourse Relations[END_REF]. A good approximation of the difference between the two major types of relations is that coordinating relations tend to move the story forward, while subordinating relations do not; rather, sentences linked with the latter relations tend to expand on a given issue.

Empirical observations

The first observation is that when an objective sentence is followed by a potentially perspective shifted (FID) sentence, subordinating relations make the interpretation as FID more likely, while coordinating relations favour a non-shifted interpretation. This is in accordance with [START_REF] Caenepeel | Aspect, temporal ordering and perspective in narrative fiction[END_REF] observation according to which perspectivally non-situated sentences typically move narrative time forward, while perspectivally situated sentences do not convey forward movement in time (see also Cumming this volume). This is because sentences connected with coordinating but not with subordinating relations tend to move the narrative time forward. Observe the example below:

(17) Mary started to cry. π1 She was fired again. π2 a. explanation(π 1 ,π 2 ): FID is salient b. result(π 1 ,π 2 ): no perspective shift.

The second observation is that when a sentence linked with a coordinating relation is nevertheless understood as FID, the perspectival center tends to be a character who is not the expected perspective taker (in the sense of Wiebe or Hinterwimmer).12 (18) Sam threatened Justin with a knife. π1 He had to defend himself! π2 a. Explanation(π 1 ,π 2 ): PC=Sam, he=Sam13 b. Result(π 1 ,π 2 ): PC=Justin, he=Justin

John handed a book to Bill. π1 He had to read this. π2 a. Explanation(π 1 ,π 2 ): PC=John, he=Bill b. Result(π 1 ,π 2 ): PC=Bill, he=Bill.

In both examples, the subject of the first sentence is the expected perspective center: It is the last active character [START_REF] Wiebe | Tracking point of view in narrative[END_REF]) and also the character with the most agentivity features [START_REF] Hinterwimmer | Prominent protagonists[END_REF]. When the target sentence is understood as being linked with the subordinating relation Explanation, it is the subject of the previous sentence that can function as the perspective holder. When the second sentence is understood as being linked with the co-ordinating relation Result, the preferred perspective center is the (indirect) object, which is not the expected perspective holder for either.

As for sentences in FID linked with a subordinating relation such as Explanation, their PC is usually the expected PC, but not always. Observe first two examples in which the character with the most agentive features is indeed the PC of the following sentence. ( 20 An interesting question is what happens when FID sentences are followed or preceded by a parenthetical (blabla..., he thought). One possibility is that it is the parenthetical that is linked to the preceding discourse with a discourse relation (cf. Bimpikou et al. 2020, Cumming this volume). The embedded clause is linked to the parenthetical clause with the subordinating Attribution relation. In such cases, it is the relation between the parenthetical and the previous discourse that influences the choice of PC, since the attitude holder is the PC:

(23)
Sam threatened Justin with a knife. π1 Next he would threaten Paul! π3 (,he thought π2 .) a. Elaboration(?)(π 1 ,π 2 ): PC=Sam, he in π 2 =Sam. Attribution (π 2 ,π 3 ), he in π 3 =Sam b. Result (π 1 ,π 2 ): PC=Justin, he in π 2 =Justin.

Attribution (π 2 ,π 3 ) he in π 3 =Sam.

But in some cases, as discussed by [START_REF] Hunter | Reports in discourse[END_REF], it is the embedded clause that is linked with a (possibly modalised) rhetorical relation to the previous discourse. For example:

( Further, as discussed by [START_REF] Hunter | Reports in discourse[END_REF] in connection with speech reports, we probably also need to allow cases in which the parenthetical and the embedded clause are linked to the preceding discourse with separate discourse relations. One might ask if there is an implicit parenthetical in the examples of FID discussed above in (17a), ( 18)-( 22), as suggested by [START_REF] Bimpikou | The discourse structure of free indirect discourse reports[END_REF]. Suppose that this is the case. In these particular examples, the relation Explanation/Result then holds between the first clause and the parenthetical. (Another possibility is that the relation holds between the first clause and the complex unit formed by the parenthetical and the embedded clause, cf. Cumming this volume for discussion.)

A final empirical observation concerns rhetorical relations between sentences in FID. In such cases coordinating relations such as Narration, Parallel or Result do not induce a change of PC:

(26)

Sam threatened Justin with a knife. π1 Next he would threaten Paul! π3 Then he would threaten Jane as well! π4 (,he thought π2 .) a. Elaboration(π 1 ,π 2 ): PC=Sam; or Result(π 1 ,π 2 ): PC=Justin, Parallel(π 3 ,π 4 )

In general, whoever is the PC of the FID passages, and whatever is the rhetorical relation between them, the PC has to be the same for the two sentences.

Summary of the empirical observations

Rhetorical relations between two continuous sentences in FID do not matter: the two sentences have the same PC. However, rhetorical relations between a narrative and a potentially FID sentence do matter for establishing perspective shift: Subordinating relations favour perspective shift, and the PC is typically the Expected PC. However, a change of PC is not excluded with subordinating relations. On the other hand, coordinating relations usually suggest the absence of perspective shift; or, if perspective shift still happens, the PC is most likely different from the expected PC.

Note that these observations are compatible with Hinterwimmer's topicality idea. This is because sentences linked with subordinating relations tend to elaborate on the same topic, while sentences liked with coordinating relations move the story forward, and introduce new topics.

5 Putting it all together: towards a framework Is there a way to incorporate the insights of the works discussed above as well as the observations about discourse relations in one unified theoretical framework? I propose that using SDRT [START_REF] Asher | Logics of Conversation[END_REF] extended with a representation of mental states (MSDRT/ADT, [START_REF] Kamp | Using proper names as intermediaries between labelled entity representations[END_REF], this volume, Kamp and Bende-Farkas 2018, Maier 2015a, 2016) allows us to do just this. Below I first provide a very brief introduction to these theories and then sketch a way to formulate the answers to our two leading questions.

Background on SDRT and MSDRT/ADT

SDRT [START_REF] Asher | Logics of Conversation[END_REF] is an extension of DRT [START_REF] Kamp | A theory of truth and semantic representation[END_REF] and subsequent work) that aims to express the semantics and structure of rhetorical relations within the discourse representation. One reason why this is important is to allow us to capture the constraints that govern anaphoric relations. That classic DRT is not sufficiently restrictive is shown by the following textbook example: (27) a. John had a great evening (π 1 ). He had a great meal (π 2 ). He ate salmon (π 3 ). He devoured lots of cheese (π 4 ). Then he won a dancing competition (π 5 ).

b. # It was a beautiful pink(π 6 ).

If we try to follow up the discourse in (27a) with the sentence in (27b), the pronoun it cannot refer back to the discourse referent introduced by salmon in the previous discourse. But this is not predicted by classic DRT, in which the structure of the above discourse is flat and the discourse referent for salmon should be available as an antecedent for the pronoun, as shown in Figure 1.14

x,y,z,v,w,k John(x) great evening(y) has(x,y) great meal(z) has(x,z) salmon(v) ate(x,v) lots-of-cheese(w) devoured(x,w) dancing competition(k) won(x,k)

Figure 1: DRS of (27a)

Rhetorical structure thus imposes constraints on anaphora resolution (among other domains). To capture this effect, SDRT extends the language of DRSs by introducing two new types of expressions expressions: (a) Speech act discourse referents and (b) rhetorical relations that relate speech act discourse referents. The resulting structures are called segmented DRSs (SDRSs). The SDRS of (27a) is given in Figure 2: π are speech act discourse referents, Ks stand for the DRS's associated with the sentences that introduce speech act discourse referents, and rhetorical relations (e.g. Narration, Elaboration) are given as relations between speech act discourse referents.

The hierarchical relations captured by the embedding structure of the SDRS can be seen more clearly in a graph format, cf. Figure 3.

The main constraint on anaphora resolution that predicts why (27a) cannot be followed by (27b) is the Right Frontier Constraint (RFC). This constraint says that referents in the constituent to which the new sentence is attached to (the current constituent) are accessible as well as those that dominate the current constituent. To find antecedents, this constraint thus allows to look left on the graph one step only or to look up. Since (27b) attaches to π 5 , the RFC limits the available antecedents to those that appear in π 5 or the node dominating it, π 1 . 15Another extension of DRT that is relevant for the present discussion is MS-DRT, proposed recently by [START_REF] Kamp | Using proper names as intermediaries between labelled entity representations[END_REF]; [START_REF] Kamp | Epistemic specificity from a communication-theoretic perspective[END_REF] and elaborated further in Maier (2015a[START_REF] Maier | Attitudes and mental files in discourse representation theory[END_REF] under the name ADT (see also Kamp this volume). Its aim is to provide an accurate description of the semantics of propositional attitudes and the referring expressions embedded in their scope. The theory gives simple and elegant solutions to various classic puzzles of reference, e.g. cases of mistaken identity, etc. 16 For the purposes of the present paper, I will only introduce some of its most basic aspects that are relevant for the present discussion.

π 0 π 0 : π 1 ,π 6 π 1 : K π1 π 6 : π 2 ,π 5 ,π 7 π 2 :K π2 , π 5 :K π5 N arration(π 2 ,π 5 ) π 7 : π 3 ,π 4 π 3 :K π3 , π 4 :K π4 N arration(π 3 ,π 4 ) Elaboration(π 2 ,π 7 ) Elaboration(π 1 ,π 6 )
In MSDRT/ADT the semantic representation of attitude reports are representations formed by the interpreter about the mental state of the attitude holder. Observe the example (28a) and its ADT representation in (28b). The mental state represented in the DRS describes John being in the mental state consisting of a belief and an anchored entity representation of the individual Sue. The latter consists of a discourse referent for the entity represented and a description of how the attitude holder is related to the entity.17

                     ANCH,
x Name(x, Sue) saw on the beach(j,x) BEL, pretty(x)

                    
As it was discussed in [START_REF] Wiebe | Tracking point of view in narrative[END_REF], when we are trying to identify the PC of FID sentence, we can distinguish two types of cases. Either the PC can be determined from the sentence (as in the cases with examples with parenthetical), or the PC cannot be determined from the sentence alone. In the first case, the ADT representation of the sentence can be determined exactly as in (28b) above.

(29) Sue was pretty, John thought.

In the second case, namely when perspective shift is signalled by the sentence features and the context but the PC cannot be determined from the sentence alone, an attitude representation needs to be projected with a variable standing for the attitude holder. The task of finding out the PC is then the task of finding the value for this projected variable.

(30) a. Sue was pretty! b.

y Att(y):

                 ANCH, x Name(x, Sue) BEL, pretty(x)                 
NB: In many cases, the identity of the projected attitude has to be figured out as well. However, we might assume that believe is a good default candidate.18

A sketch of a proposal

The advantage of building on an SDRT+MSDRT/ADT representation of the text is that a lot of what we need to implement Wiebe's ideas (as well as the observations made by Hinterwimmer and myself) is already contained in the discourse representation. This includes: (a) The available discourse referents on the Right Frontier, which can take over the function of the list of Last Active Character in Wiebe's system. (b) The attitude holders (perspectival centers) for subjective sentences whose PC can be recovered from the sentence alone (attitude reports, parentheticals), or whose PC has already been determined (past cases of FID or other PS). These are the subjects of attitude predicates in the MSDRT/ADT representation. (c) the type of predicate (private-state, active, etc), and the roles of their arguments (experiencers, e.g.) as these are part of the semantic analysis of the DRS of the sentence. (d) information about discourse structure, e.g. whether a scene break has occurred and some other information that is captured by Wiebe's TextSituation. (e) If needed, information about topics can be represented in SDRT as well, along the lines proposed in Hunter and Abrusán (2016) (I come back to this issue below).

Thus the question of tracking subjective perspective in the text boils down to two issues: First, we need to decide whether a sentence is subjective or objective (=Question 1). If it is determined likely subjective, an MSDRT/ADT structure needs to be projected and we need to find the identity of the attitude holder (=Question 2). (Note: unlike for Wiebe, for objective sentences nothing in particular needs to be done as its the discourse referents are encoded anyway in the representation of the sentence.) There is thus a two-step procedure: First, when adding a new sentence to the previous discourse, decide about Question 1. If the answer to this question is that the sentence is subjective, then we try to determine the answer to Question 2. 19Question 1: How do we know we have perspective shift? Literary scholars have identified various triggers for FID (e.g. [START_REF] Banfield | Unspeakable Sentences: Narration and Representation in the Language of Fiction[END_REF][START_REF] Fludernik | The fictions of language and the language of fiction. The Linguistic Representation of Speech and Consciousness[END_REF]. [START_REF] Banfield | Unspeakable Sentences: Narration and Representation in the Language of Fiction[END_REF] called these subjective elements. This list was the main inspiration for [START_REF] Wiebe | Tracking point of view in narrative[END_REF] list in her FeatureSet which she called potential subjective elements, realising that many linguistic features can contribute to signaling perspecitve shift but they rarely do so in an unambiguous and determinate way. Recent experimental results confirm the effect on perspective shifting for certain triggers, cf. [START_REF] Kaiser | Perspective-shifting and free indirect discourse: Experimental investigations[END_REF] concerning epithets and certain adverbs. As it is well known, tenses and aspect can be FID triggers in many languages, e.g. the imparfait in French, or-in the case of free indirect speech-the Konjunktiv in German.

In the framework that I am using in this paper, the question 'How do we know that we have perspective shift?' translates as 'How do we know that we need to project an attitude layer in the MSDRT/ADT representation?'. I propose to follow [START_REF] Wiebe | Tracking point of view in narrative[END_REF] by looking at her FeatureSet, together with the Text Situation, the latter now understood as the state of the SDRT representation. In addition to textual transitions, Text Situation now also represents rhetorical relations. This allows us to encode that perspective shift is more likely with subordinating relations.

The question we are posing can be thought of as a classification problem: Given various features of the target sentence S and the preceding text, is S objective or subjective (i.e. understood under a perspective shift)? Given the nature of the contextual cues and the interaction of the various features, the answer ideally should be given by probabilistic classifier rather than by a rule-based system (cf. [START_REF] Wiebe | Probabilistic classifiers for tracking point of view[END_REF]. However, developing such a system would go beyond the scope of this paper. We might note, still, that one crucial issue would be deciding the right level of interaction between the various sentential and textual features.

Question 2: How do we find the identity of the projected PC? If the sentence was found to be subjective, the second question is how to find its perspectival center. As discussed by [START_REF] Wiebe | Tracking point of view in narrative[END_REF], there are two basic cases. In the first case, the target sentence is an explicit attitude report (potentially in the parenthetical form). In this case the perspectival center can often be identified from the target sentence alone. In the second case, the target sentence itself does not allow us to identify the perspectival enter, it needs to be identified from the previous context.

Note that unlike the previous question, this question is not very natural to think of in terms of a simple probabilistic classification problem. This is because for a probabilistic classifier the possible values of the classification variable need to be set in advance and then each candidate is evaluated separately. However, we do not have the list of all potential perspectival centers before parsing the text (cf. Wiebe and Bruce 2001) and it is not natural to think of evaluating each candidate separately. It might make more sense to think in terms of a ranking algorithm that compares the most likely candidates for being the PC, similarly to some approaches for anaphora resolution (cf. [START_REF] Denis | A ranking approach to pronoun resolution[END_REF]. Nevertheless, in this paper I stick to assuming a rule-based model à la [START_REF] Wiebe | Tracking point of view in narrative[END_REF], but adding some information about discourse structure.

Let me review the first, easy case, the case of attitude reports. These are understood as providing information about the perspective of the attitude holder, except in cases where there are reasons to believe that the attitude report is itself the content of some other character's mental state. This might be the case e.g. in Mary hardly wanted to leave, in which the subjective adverb in the main clause might indicate that we need to infer someone else's perspective. Another exception is if the sentence continues a previously established subjective perspective. Here is then a slightly updated version of Wiebe's rule for identifying the PC from attitude reports:

(31)

Identifying the PC from the target sentence:

If the sentence contains a narrative parenthetical then PC is the subject of the parenthetical else if the sentence is an attitude report then if it has a non-subordinated subjective element, or the text situation is rhetorically linked to a subjective sentence then PC is identified from the previous context else PC is the attitude holder end if else PC is identified from the previous context end if

The second, harder case is finding the PC if it cannot be identified from the target sentence alone. I propose an updated version of Wiebe's rule that makes reference to discourse structure constraints, rhetorical relations and also incorporates Hinterwimmer's observation relating to experiencers.

Let us first assume a set of expected PCs consisting of the Last PC and the most recent discourse referent on the Right Frontier (RF) that is an experiencer.

Here is then a (highly tentative!) rule for finding the PC from the context: (32)

Updated rule for finding the PC from the previous context a. the sentence connects with a subordinating relation: If there are two expected PCs then if the sentence is about the most recent experiencer DR on the RF, PC is the Last PC else PC is the last experiencer DR on the RF else if there is one expected perspectival center then PC is the expected perspectival center else PC is unidentified b. the sentence connects with a coordinating relation to an objective sentence: PC is a DR on the RF that is not an expected PC.

Note that the above rule is not fully deterministic in the sense that it does not always give a unique output, e.g. in the case of sentences connecting with a coordinating relation there might be more suitable outputs. 20 Note also that the observation that coordinating relations indicate a shift away from the expected PC incorporates also the finding of [START_REF] Wiebe | Probabilistic classifiers for tracking point of view[END_REF] according to which natural 'blocks' of text tend to have the same perspectival center. This is because coordinating relations often introduce new 'blocks' of discourse. Potentially, the difference between co-ordinating and subordinating discourse relations can also explain Hinterwimmer's observation about topicality. I turn to this next.

Topicality vs. rhetorical structure

Hinterwimmer (2019) argued that topicality itself is a factor for determining the perspectival center. One way of incorporating this insight into the current system is to introduce a representation of topicality into SDRT. This could be done along the lines of [START_REF] Hunter | Rhetorical relations and QUDs[END_REF], who associate questions under discussions with subgraphs (CDUs) of the SDRT graph. Another possibility though is to explore whether the phenomenon can be reduced to something else. This is what I tentatively suggest below.

Recall the examples that motivated [START_REF] Hinterwimmer | Prominent protagonists[END_REF] to argue for the importance of topicality: (14) George entered the room and looked around cautiously 1 . Susan was sitting at the table in the corner with her best friend. 2 Susan looked at George hatefully. 3 The mean old hag had managed to make him look like an idiot at the meeting yesterday. 4 (15) Susan was sitting at a table in the corner with her best friend. 1 George entered the room and looked around cautiously. 2 Susan looked at George hatefully. 3 #The mean old hag had managed to make him look like an idiot at the meeting yesterday. 4

If we look closer, the Right Frontier constraint predicts the difference between the two examples as well. Notice that the rhetorical structure of the two examples is quite different: In (14) all the sentences following (1) elaborate on it and for this reason the Right Frontier is (1-3). George is the experiencer of (1), to which (4) connects. In (15), however, the right frontier is only (3), and George is not an experiencer of (3). This predicts that George will not be an available PC for (4).

If we change (15) in such a way that (3) is itself interpretable from the perspective of George, an interpretation of (4) with George as PC becomes marginally possible: (33) Susan was sitting at a table in the corner with her best friend. 1 George entered the room and looked around cautiously. 2 Susan looked at him hatefully. 3 ?The mean old hag had managed to make him look like an idiot at the meeting yesterday. 4 This is because in (33) now we have a continuing-subjective situation, and George is an expected PC when we get to sentence (4).

Conclusion

Readers (hearers) are able to figure out effortlessly if a perspective shift occurs in a discourse. They can also identify, in most cases, the likely perspective holder of the relevant text. How we are able to achieve this is a question that has been rarely addressed in the linguistic literature, with the notable exceptions of [START_REF] Wiebe | Recognizing subjective sentences: A computational investigation of narrative text[END_REF][START_REF] Wiebe | Tracking point of view in narrative[END_REF] and [START_REF] Hinterwimmer | Prominent protagonists[END_REF]. This paper reviewed these proposals, added a few observations of my own about the importance of rhetorical structure and proposed to incorporate all the previous insights into one unified framework.

  The eyes were an incredibly light blue, like the sea with sunlight touching the waves. (b) Lemech greeted him respectfully. (c) "Adnarel, we thank you."(d) Then he said to Sandy, "The seraph will be able to help you. Seraphim know much about healing." (e) So this was a seraph. (6) Tall, even taller than the twins. (L'Engle, Many waters, cited in[START_REF] Wiebe | Tracking point of view in narrative[END_REF], p.28)Assume that the situation is continuing-subjective at the beginning, and Sandy is the last perspectival center. The sentence in (a) continues Sandy's subjective context, because it contains the subjective elements incredibly and the comparative like. The sentences in (b)-(d) are objective (note: direct discourse is ignored by the algorithm). Since there is a paragraph break before(e), the Text Situation is set to postsubjective-nonactive, with the Last Perspectival Center being Sandy).

  Subordinating: Elaboration, Instance, Topic, Explanation, Precondition, Commentary. b. Coordinating: Narration, Background, Result, Continuation, Parallel, Contrast.

FigureFigure 3 :

 3 Figure 2: SDRS of (27a) π 1

I also do not look at cases of the historical present, but see Anand and Toosarvandani (to appear).

This does not mean that perspective shift is always PP or FID, see alsoStokke (this volume) and[START_REF] Abrusán | The spectrum of perspective shift: protagonist projection versus free indirect discourse[END_REF] for discussion.

Wiebe's early work on FID and other subjective sentences is unfortunately little known among linguists. One of the aims of this paper is to make this work more widely known among linguists and philosophers. Later, JanyceWiebe (1959Wiebe ( -2018) ) became one of the pioneers of the fields of sentiment analysis and opinion mining.

In languages other than English, tenses and aspect can be FID triggers, e.g. the imparfait in French, or-in the case of free indirect speech-the Konjunktiv in German.

Wiebe uses the terminology of subjective character for what I call perspectival center (PC). I have changed it to PC in this discussion in order to be consistent with the rest of the paper.

This approach of tracking potential subjective centers is inspired by[START_REF] Grosz | Attention, intentions, and the structure of discourse[END_REF] Centering Theory. As Wiebe points out however, although anaphora resolution is necessary for

[START_REF] Wiebe | Tracking point of view in narrative[END_REF] does not define what it means exactly for a sentence to be 'objective', but it is used in opposition with 'subjective', which is taken to represent the point of view of a character in the story. The narrator is not assumed to be character of the story (except in first person narration) and there is no PC associated with the narrator in objective sentences.

Intuitions about the availability of George as a perspectival center in (11b) are not completely clear, though. Andreas Stokke (pc.) finds that the example becomes acceptable if the context makes it probable that George hates Susan as well.

The preference for previous subjects and active characters was already noted in Wiebe 1994, but Hinterwimmer's 2017 suggestion is more explicit.

Many of the examples below are inspired by[START_REF] Kehler | Coherence and coreference revisited[END_REF].

Possibly, PC: Justin, if he is reasoning about Sam(=he)

NB: The DRSs are highly simplified in order to focus on the questions discussed in the paper.

The speech act discourse referent π 6 is constructed for the topic of the narration.

MSDRT/ADT is not the first approach to these issues within DRT, see for example[START_REF] Asher | Belief in discourse representation theory[END_REF][START_REF] Asher | A typology for attitude verbs and their anaphoric properties[END_REF].

The entity representation above is an internally anchored representation. In order to capture that there is indeed something that the attitude is about, an external anchor needs to be added to the representation. I omit this here for simplicity.

For a theory of FID that assumes mixed quotation(Maier 2015b), inferring an implicit verb of saying might be more natural. But even then the attitude believe might have to be projected in a 'parasitic' fashion (unless one has reasons to assume that the character is lying).

NB: sometimes the answer to these questions cannot be determined. For example, here is an example of a sentence in FID from[START_REF] Wiebe | Tracking point of view in narrative[END_REF] that appears as the first sentence of a novel: (i) Captain Scalawag's treasure! This was the first thing Pete thought of when he got up.The approach needs to be capable of handling such cases as well.

The rule above can only predict (22b) if Justin was the LastPC.

* Thanks to the editors for their helpful suggestions as well as the audience at the Workshop on the Language of Fiction in Uppsala for their questions, comments and ideas. The research reported here was supported by the institutional grants ANR-10-LABX-0087IEC and ANR-10-IDEX-0001-02PSL.


domain: phys sdu

text:
A New Cosmological Model:

Origin and Maintenance of the Universe.

Andreas Mandelis (1) and J Howard O Slater (2) (3)

(1) Center for Advanced Diffusion-Wave and Photoacoustic Technologies, University of Toronto, 5 King's College Road, Toronto, ON, M5S 3G8, Canada, mandelis@mie.utoronto.ca (2) Advanced Physics Initiatives, 57-58 Russell Square, London, WC1B 4HS, UK, jhowardo.slater@uclmail.net (3) Communicating Author

New Cosmology Model Dependent on the Canonical Thermon-PDW Mechanism.

We wish to suggest an alternative physical origin and organisation of the universe contrasting generated by interactions between a quantum field, and thermal and particle diffusion waves -the Thermon-PDW mechanism -a process which is observationally confirmed [ ]. An exact 3

"ensemble" of quantum fields responding to harmonic solutions of interacting thermal and particle diffusion waves [ , ] solves the problem of particle mass. However, this generates its own 4 5 complication: an obligatory requirement for a thermal source perfusing continuously throughout the universe. This constraint (and others) is accommodated by several properties of the principal hypothesis. Furthermore, if this interpretation is validated, it eliminates many difficulties due to the orthodox cosmological model, such as reverse engineering fundamental laws of physics, the initial singularity, initial high temperatures and densities and inflation; generally SMC as a whole should be discarded [1] together with other model possibilities, including string theory solutions, multiverse, quantum loop theories and physics beyond the Standard Model of Particles [ , ]. The 6 7

property of primary importance -overwhelming importance -for the new cosmological model is establishing a universe-wide flow of thermal energy, how this is done and its consequences.

Neglected Hidden Thermodynamics.

There is disputed history concerning the definition of a quantum system [ , ], some of which 8 9

is pertinent to a radically different understanding of the origin and operational maintenance of the universe [ , , , ], most particularly thermal energy which is assumed but not explicitly 10 11 12 13 modelled in other conjectures. Some twentieth century quantum revolution founders, including

Einstein and Schrödinger, were unsettled by the restrictive definitions of quantum theory. Others, especially De Broglie over many years, argued for missing factors, which he called the "hidden thermodynamics of particles". Later Bohm concluded that "it is not necessary for us to give up a precise, rational, and objective description of individual systems at a quantum level of accuracy".

Theoretical ideas were neglected for lack of experimental or observational evidence [ , , ] 14 15 16

which was an inevitable conclusion since, in De Broglie's era, at least 10 SM particles required to reveal the Thermon-PDW model were unknown. The physics world has erroneously consigned this debate to irrelevant ancient history, the more so now since we have identified a theoretical basis for missing thermodynamic factors.

Proto-Quantum System in Proto-Universe.

If we reverse the Thermon-PDW mechanism to an initiation point where t = 0, total mass = 0 and the fundamental thermal field = 0 (Eqs ( 17), ( 43) & ( 45) in [3]) with no particle diffusion momentum, then the residual core quantum field (the Proto-Quantum System, (PQS)) is entirely quiescent, even though the physical structure of PQS is unknown. This is the ab initio stage which we do not recognise as a "particle" in the accepted sense used for SM particles in Normal Matter, although there is satisfactory logic for this theoretical ab initio state (see Dark Matter and Energy).

This methodology is identical to the raison d'être used by Dicke et al. (indeed all cosmologists [ ]) 17

who speculated that the initial temperature of a singularity at t = 0 + a few billionths of a second was in the order of 10 10 o K, even though the physical nature of the singularity is unknown [ ]. 18

Unlike SMC, however, the Thermon-PDW model prescribes a precise sequence of events, with rational theory, to convert a PQS into a SM particle (Fig 1) crucially transitioning from mass = 0 to normal mass. This is a radical change in understanding the universe: that is, beyond an active surface boundary of the universe, there is the Proto-Universe which is composed only of massless PQS entities. Furthermore, as we justify later, the direct formation of SM particles, now with mass, is the central feature of the origin and operation of the universe.

Origin of Universe.

More significantly, identical origin events occur continuously to maintain the Thermon-PDW mechanism (Fig 1a Thermon-PDW values) but returns to background universe-wide values when the star dies. This is also important when it comes to explaining why CMB is so remarkably stable (see Cosmic

Microwave Background Radiation).

These events ensure the continuous flow of energy through previously formed particles, creating an interactive network (see Galaxy Arrangement in Universe). In one sense each PQS activation is a random quantum event, with the same meaning advocated by Lemaître [14].

However, its cosmological significance is only valuable when acting sequentially with multiple identical events. To us, applying Occam's Razor principles [ ], the controlled gradual release of 20 energy, notwithstanding the evidence of massive energy releases by a fully formed universe, is a satisfactory mechanism especially when complex conjectures, such as inflation or an initial singularity, have resisted theoretical and observational validation for decades. The Thermon-PDWdriven universe exhibits properties which are considered "steady state", misunderstanding the original use of a steady state universe in which total mass was fixed from the outset [ , , ]. In 21 22 23 the Thermon-PDW model for the universe, steady state refers to a constant flow of thermal energy throughout the universe which is maintained in a non-equilibrium state. It is this steady state meaning which determines the SM particle masses. It does not mean that the total mass of the universe is in a constant steady state.

Three-Dimensional Space.

The Thermon-PDW mechanism does not appear, after analysis to date, to include any properties or sub-models for the origin of three dimensional space but assumes that such a mechanism does exit. One obvious possibility is that pre-existing 3D-space forms within the proto-

Universe Time Mechanism Hidden within the Thermon-PDW model.

The Thermon-PDW model also explains universe time. Consider an ensemble containing two SM particles, or any other relationship of two or more particles, then coupled Thermon-PDW interactions establish a diffusive density leading to the onset of thermal oscillations and energy dissipation [ ]. This interaction is observed as a relative drift momentum between the two particles 26 (Fig 6 & Section 6.3 in [3]). Assuming suitable technology is available, the differential particle movements are measurable by an external observer. This interaction constitutes universe time described at the quantum level. Theoretically universe time is measurable within a single particle by an internal observer who can observe the evolution of diffusion (particle spreading) probability but not by an external observer. Under free time-domain diffusion there is a stationary locus of the maximum thermal energy but lateral diffusion depends on time. If the thermal activity is in the form of harmonic thermal waves (thermons), then both the maximum amplitude and the lateral diffusion become time-independent since both only depend on the thermal diffusion length 1/σt , (Eq 44b in [3]), which is fixed for fixed oscillation frequency. Therefore, time appears stationary to an external observer who is not entangled in the particle oscillation cycle. The impact of internal and external observer status must have a consequence for relativity equations.

Of greater significance is that universe time is defined explicitly, sequentially emerging as unstable Thermon-PDW waves transitioning to a steady state non-equilibrium phase (Fig 1a,b).

How this also impacts Einstein's equations is obviously an interesting question for consideration later. Another interesting facet of the Thermon-PDW hypothesis confirms that the passage of time, that is the so-called "direction of time", cannot be reversed. Consider that macroscopic irreversibility arises from time-reversible microscopic dynamics [ ]. It follows that SM particles 27 (analogous to a macroscopic particle) cannot be formed by reversing time (equivalent to microscopic dynamics). Putting this concept another way, the probability of reconstructing the exact same particle arrangement even in the simplest interaction of two ensembles is zero.

This model provides a solution to hidden (or missing) thermodynamics. So, it is a reasonable question to ask, for example, Kamenshchik et al. [25]: Within an ensemble, the SM particle residence time eventually reaches a maximum, τR,f , at a steady state corresponding to a particle density above which all particle diffusion across all regions of space occurs with the same probabilistic time constant. Under these conditions universe time is measurable by an external observer once steady state particle"to"particle diffusion, Dp > 0 (Eqs ( 17) & ( 18) in [3]). So, in summary, universe time emerges from momentum"mediated thermal energy dissipation, diffusion and oscillation (Eqs ( 29), ( 31) & ( 38) in [3]) concomitantly with particle mass and gravity mechanism. It is a function of the interactions occurring between entangled quantum systems (see Interim Summary Conclusions).

"

Gravity Mechanism Hidden within the Thermon-PDW Model.

Embedded in the Thermon-PDW model are several stages which generate the "thermophoresis" phenomenon. Firstly, directly from non-linear particle density diffusion solutions in response to diffusion wave harmonic spectra, the particle density is directly linked to the environment's background thermal energy, causing particle"to"particle attraction (Eqs 17-23a .

Since particle attraction occurs from the origin of Thermon-PDW functionality, this is crucial to maintaining the particles within an interacting ensemble's harmonic solutions, retaining the essential diffusion distances to yield the particle mass solutions (Fig. 1b). Secondly, from the analysis of the behaviour of two or more particles (when universe time is first encountered), then simultaneously particle diffusion and non"uniform random walks, in a thermal gradient, gives rise to thermophoresis (Fig 1b;Fig 6 in [3]). These observations support the notion that gravity, in the form of thermophoretic attraction, is concomitantly linked to universe time and mass.

Thermophoretic attraction is not necessarily what was expected previously from a quantum theory of gravity, for example, a new particle the "graviton". But nevertheless this mechanism provides an effective means to enable the formation of the universe's large scale structures right down at the quantum level.

Dark Matter and Energy.

Dark energy is not considered for now, but dark matter is qualitatively addressed. Two mechanisms generate sub-optimal SM particle forms not recognised by an external observer.

Thermon-PDW functionality has an obligatory requirement for a continuous thermal supply from an external source (see New Cosmology Model). This property is embedded in the source term N(x,t)

ħω (Eqs (29,34 & 44) in [3]). If the thermal source is switched off, the Thermon-PDW mechanism collapses, particle and mass formation cease, and the particles revert to a quiescent state equivalent to PQS in the proto-universe, but renamed dark matter. The magnitude and order of transitioning from normal matter to dark matter is unresolved because of the absence of quantitative information.

During the transition, does universe time function terminates (or becomes ineffective) before or after thermophoretic gravity effects (or vice versa)? Questions like this may answer the apparent observation that dark matter contributes to gravitational forces on a universe-wide basis. Another of importance is that the inability to detect dark matter (not observable by external observers) directly using methods involving normal matter (externally detectable).

The second mechanism is simpler. With the universe composed of 5% normal matter (products of the Thermon-PDW mechanism) the ca 40% dark matter is simply PQS material subsumed into 3D-space in an orientation which precludes formation of an embryonic ensemble. It is a matter of chance that, in a local environment, requisite PQS potential diffusion distances are spatially correct. Absenting these conditions then any 3D-space formation exacerbates the impossibility of forming ensembles to mature into complete stable non-equilibrium entities.

There is a rich opportunity to refine the impact of the canonical Thermon-PDW hypothesis. In particular the lack of external universe time is likely the reason for the inability to use normal matter based detection methods since any interactions are neutered by particle to particle interactions and/or the local environment removes the reliance on any external time component.

Cosmic Microwave Background Radiation (CMB).

CMB is prime evidence in favour of the SMC. This may be so, but it is not appropriate to critique either the evidence or the SMC theoretical rationale [18, ]. Indeed a direct theoretical 31 comparison is not possible, since the fundamentals of SMC and Thermon-PDW are different.

However, CMB as an accepted observation needs explanation in a Thermon-PDW driven universe.

CMB is an SM particle, so, as for all Thermon-PDW decomposition solutions, the final diffusion Thermon-PDW mechanism is important then it is reasonable to expect that a pre-formed thermal network might be implicated in quantum entanglement activity. The opportunity for thermal transfer, momentum interaction and particle interaction is obvious.

(b) Information loss from black holes. Thermal evaporation from black holes (Hawking radiation) is an unresolved difficulty [ ] but in a universe driven by the Thermon-PDW mechanism 36

this is a non-issue. since all forms of matter are formed without reference to any "information" contained within a black hole or anywhere else. Also unresolved, but of far greater importance is what form the matter residues within a black hole take, and how "thermally-depleted quantum systems" might be recycled.

Interim Summary Conclusion.

There is sufficient modelling, quantitative and qualitative evidence to submit the Thermon-PDW hypothesis for public scrutiny. Its fate now depends on its critical evaluation and the formation of mathematical and observational data sets where the interpretation is not overly influenced by preconceived ideas from the long history of SMC. The image we have is of a spherical globular universe in a surrounding sea of PQS, expanding at the surface in response to continuous thermal activity at the boundary. Randomly generated, assiduously maintained by continuous thermal flux and particle diffusion, is the concomitant formation of mass, time and gravity, in turn giving rise to large-scale bits and pieces in our universe. We see no need for any fundamentally new physics beyond the Standard Model of Particles. So, future progress depends on determining the physical and mathematical structure of the core quantum systems (PQS) devoid of Thermon-PDW activity.

  with the Standard Model of Cosmology [ ]. The masses of Standard Model (SM) particles [ ] are 1 2

[ 3 ]

 3 ) and is observationally supported(Section 3 in [3]). The continuous origin events to maintain thermal flow increases the local entropy in the boundary region, which is reflected in continuous universe expansion [ ]. The canonical feature is the direct formation of SM particles, eliminating 19 the speculative SMC singularity at high temperatures, high density and other associated difficulties requiring inventive guesses. Repeated origin events maintain the controlled release of thermal energy into the universe, the continued expansion of the universe and formation of SM particle mass. The theoretical model combines thermal and particle diffusion within an ensemble to generate harmonic solutions. When the thermal framework dynamics reach the predicted steady state, essentially when there is a constant thermal flux into and out of an ensemble, the particles are also in a steady state thereby defining the SM particle mass values (Fig 1c). One or more ensembles may occupy the same region in a local environment, the multiple harmonic waves oscillate together effectively creating a cluster of mass centres (Fig 1e), theoretically distributing stabilised SM particles with quantified mass throughout the universe. The gravitational implications are discussed later (see Gravity Mechanism Hidden within the Thermon-PDW Model). An important property of the thermal framework is that under constant environmental conditions all steady state values prescribed by the model are asymptotically reached. This is an inherent feature of the core equation (Eq (17) in [3]). So changing ambient temperature has the most important and far reaching effect on the asymptotic values reached since particle migration probabilities (Pij and Pji in Eqs (6) & (9 in [3]) are temperature dependant (Eqs (11), (14) & (14) in [3]) ultimately governing the non-linear differential equation and overall Thermon-PDW model equation (Eq (17) in [3]). Quantitative properties are observationally unknown, but theoretically include: particle diffusion distance; lag time; spatial separation of particles within the ensemble; particle hopping frequency between adjacent ensembles; diffusion coefficient and particle velocity. This enables any transient changes in temperature to eventually return to the governing thermal flux background. The presence of a star, for example, causes local changes (and different quantitative

  length temperature is determined by the local universe environment. The CMB radiation temperature is remarkably uniform, precisely what is expected for thermal steady state non-linear equilibrium conditions (see Origin of Universe) if the Thermon-PDW mechanism is uniform throughout the universe at least within the range of the present microwave detecting equipment [ ]. If photons do have mass, then the mechanism for energy transfer is the same as between any 32 other mass particle. If CMB has no mass contributing to on-going particle momentum and thermal oscillations, then the temperature uniformity might result from a Planck radiation energy continuum, which leaves a thermal signature in massless particles. The massless photon carries an effective mass through its momentum which, in an oscillating thermal field, behaves similarly to particles with mass [ ]. Either way CMB is an indicator that all SM particles on a universe-wide 33 basis behave in the same way and therefore is compatible with Thermon-PDW concepts. Other Phenomena. (a) Quantum entanglement. Quantum entanglement is a real phenomenon [ , ]. If the 34 35

( c )

 c Galaxy arrangement in universe. The large scale structure of the universe is based on galaxies resulting in the "cosmic web", a network of filaments and voids arranged as clusters, filaments and sheets [ ]. Since the universe-wide Thermon-PDW mechanism requires the 37 sequential and continuous transfer of thermal energy, creating mass centres which eventually evolved into stars and galaxy systems, then a linear relationship is expected. Cosmic filaments represent an historical, temporal record laid out spatially. These are fossil records of the past evolution of the universe where the different galaxy ages are embedded in the filament. Conversely voids are regions where for whatever reason the essential thermal flux has subsided below the threshold levels to sustain viable ensembles. It might be, for example, that the position of voids reveals much about the stability of filaments because the thermal flux originally destined for ensembles in the void area is sequestered by greater activity of the now fossilised record. (e) Gravity waves. Detection of Einstein-predicted gravitational waves as fluctuations or ripples in space-time [ ] can be interpreted as changes in the thermodynamic framework of the 38 universe, since transient oscillations of both thermal waves and particle waves are expected to fluctuate as the universe-wide non-equilibrium steady state is restored.

  How this "no boundary" solution [ ] links with the Thermon-25 PDW mechanism is unresolved, but we suggest that much of the previous no boundary debate is of limited value because we are advocating an obvious boundary. We expect, too,

	that theoretical developments will reorientate the spacetime concept, requiring reconciliation with
	general relativity.

universe, but that is a theoretical step too far for now. Alternatively, a 3D!space equivalent to Hartle and Hawking's proposed "ground!state wave function ..... in the quantum state of the universe that we live in because the matter wave function does not oscillate", termed a "minisuperspace" [ ]. 24

This theory describes a "space!like surface" structure but did not "specify time in these states". In the initial stages of minisuperspace formation, the absence of oscillating wave functions is not important, nor the lack of matter wave functions, since both are added in with Thermon-PDW activation (see Universe Time).

Thermon-PDW Universe

Proceedings of the National Academy ofScience, 15, 168!173, (1929).

Acknowledgments.

A. Mandelis gratefully acknowledges the Natural Sciences and Engineering Research Council (NSERC) Discovery Grants Program and the Canada Foundation for Innovation (CFI) Research Chairs Program. J.H.O. Slater acknowledges funding from Advanced Physics Initiatives.