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Page 00000001 Inductive Learning of General and Robust Local Expression Principles Gerhard Widmer Department of Medical Cybernetics and Artificial Intelligence, University of Vienna, and Austrian Research Institute for Artificial Intelligence, Vienna email: email@example.com Abstract The paper presents a new approach to inducing general rules of expressive performance from real performance data via inductive machine learning. A new learning algorithm is briefly presented, and then an experiment with a very large data set (performances of 13 Mozart piano sonatas) is described. The new learning algorithm succeeds in discovering some extremely simple and general principles of musical performance (at the level of individual notes), in the form of categorical prediction rules. These rules turn out to be very robust and general: when tested on performances by a different pianist and even on music of a different style (Chopin), they exhibit a surprisingly high degree of predictive accuracy. 1 Introduction The goal of the research described here is to use computational methods from machine learning and data mining to extract general quantitative models of expressive music performance (i.e., rules for tempo, timing, dynamics, articulation, etc.) from real performances by human musicians. We aim at using large amounts of 'real-world' performances in order to arrive at empirically valid models. In fact, for our investigations we have prepared what is most likely the largest set of precisely measured performances ever studied in performance research. Our motivation is to contribute both new computational analysis methods and new insights to expressive performance research. Previous work (Widmer 2000) has shown that it is indeed possible to find a certain amount of structure in such complex data. In an experimental study with an extended collection of piano performances (the same as used here), various machine learning algorithms learned to predict the performer's expressive choices (e.g., lengthen or shorten a particular note) at a local note-to-note level. It was shown that the learners were able to predict the performer's choices with better than chance probability. However, the learned models were extremely complex. For instance, a decision tree discriminating between accelerando (note shortening) and ritardando (note lengthening) with 58% accuracy had 3,037 leaves (which corresponds to 3,037 classification rules)! This is clearly not desirable as the purpose of the project is to discover intelligible rules that provide new insight. Also, it seems obvious that the level of individual notes is not sufficient as the sole basis for complete models of performance. Some of the performer's choices may be explainable by reference to single notes and their local context, but others may only become predictable if one relates them to higher levels of the musical structure of the piece (or explains them as part of a more abstract expression pattern such as a gradual crescendo). In other words, it is unreasonable to expect that one can find a note-level model that completely discriminates between different categories of performer actions. The new study presented here was motivated by these observations. We still remain at the note level, but develop a new approach to rule discovery. We abandon the idea of complete coverage/discrimination and instead aim at finding partial rule models that 'only explain what can be explained' at the note level. It will be shown that one can discover small numbers of very simple rules that still cover a substantial number (but by far not all) of the instances of a given category of expressive variation (e.g., note lengthening) and distinguish them quite well from the opposite classes (e.g., shortening). (For instance, we will show that 4 simple rules are sufficient to cover 22.89% of the instances of note lengthening in our large data set.) This is achieved via a new data mining approach to automated rule discovery. 2 Data and Target Concepts The data used in the present study consists of recordings of 13 complete piano sonatas by W.A. Mozart (K.279, 280, 281, 282, 283, 284, 330, 331, 332, 333, 457, 475, and 533), performed by a Viennese concert pianist (Roland Batik) on a Bisendorfer SE290 computer-monitored concert grand piano. The piano measurements (hammer impact times and
Page 00000002 pedal movements), together with the notated score in machinereadable form, provide us with all the information needed to compute the details of timing, dynamics, and articulation. The resulting dataset consists of more than 106,000 performed notes and represents some four hours of music. The experiments described here were performed on the melodies (mostly the soprano parts) only, which gives an effective training set of 41,116 notes. Each note is described by a number of attributes that represent both intrinsic properties (such as scale degree, duration, metrical position) and some aspects of the local context (e.g., melodic properties like the size and direction of the intervals between the note and its predecessor and successor notes, and rhythmic properties like the durations of surrounding notes and some abstractions thereof). With respect to expressive performance, the dimensions we are looking at are (local) timing, dynamics, and articulation. In trying to learn expression rules, we will not look at numeric prediction (i.e., exactly how long or loud is a note played), but rather at categorical decisions. The target classes we wish to predict are defined as follows: * in the timing dimension, a note N is assigned to class lengthen if the inter-onset interval (IOI) between the start of N and the next note is lengthened relative to (a) its predecessor and (b) the current tempo (computed over the last 20 events); class shorten is defined analogously; * in dynamics, a note N is considered an example of class louder if it was played louder (i.e, with higher MIDI velocity) than its predecessor, and also louder than the average level of the piece; class softer is defined analogously; * in articulation, three classes were defined: staccato if a note's ratio of performed vs. notated duration is less than 0.8, legato if the ratio is greater than 1, and portato otherwise; we will only try to learn rules for the classes staccato and legato. A performed note is considered a counter-example to a given class if it belongs to one of the competing classes. (Note that by the above definitions, there will be notes that are neither examples nor counter-examples of some concept.) 3 The Inductive Learning and Discovery Procedure From the perspective of machine learning and data mining, the problem is to find partial descriptive models of categories such as 'situations where a note is lengthened' vs. 'situations where a note is shortened'. Descriptive means that the models should characterize classes of situations that are treated in a similar way by the performer, and these descriptions should preferably be simple and musically meaningful. We will use rule-based models and algorithms for learning classification rules from data. Partial means that we do not expect the rules to be able to cover and describe all of (or even a large part of) the instances of a given category observed in the data. We are looking for rule sets that capture only a (possibly small) part of all observations, but describe these in meaningful terms. Moreover, given the nature of our data and target phenomena, we cannot expect to find rules with very high levels of discriminative accuracy - we cannot assume the artist to be perfectly consistent and predictable. The search for partial models necessitates a special approach to rule learning, since this is not the standard type of problem addressed in 'classical' classficiation settings. Consequently, we have developed a new machine learning algorithm named PLCG that is geared towards finding simple, robust classification rules in complex, noisy data. The algorithm and its properties are described and analyzed in more detail in (Widmer 2001a). Let us simply state the general approach here. For each expression dimension (timing, dynamics, articulation), PLCG does the following: 1. Separate the data into subsets according to tempo (slow/ fast) and time signatures (2/2, 2/4, 3/4, 4/4, 3/8, 6/8) of the pieces. 2. Learn partial rule models from each of these 2 x 6 subsets separately. This is done with a rule learning algorithm of the 'sequential set covering' variety (Firnkranz 1999) that was specially devised and implemented for this project. The resulting sets of rules will most likely be overly specific (specialized with respect to tempo and time signature). 3. Merge all these learned rule sets into one large set. 4. Perform a hierarchical clustering of the rules into a tree of clusters of similar rules, according to a syntactic/semantic rule similarity measure specially defined for our application. 5. For each of these clusters, compute the least general generalization of all the rules in the cluster (i.e., a generalization that subsumes all the rules and is no more general than necessary). The result is one rule per cluster. The resulting tree represents generalizations of various degrees of the original rules. 6. From this generalization tree, select those rules that optimize a given trade-off function between coverage (the number of cases covered by the rule) and precision (the
Page 00000003 percentage of covered cases that actually do belong to the rule's predicted class - in other words, the percentage of correct predictions made by the rule). The goal of the entire procedure is to arrive at rules that both cover a significant number of cases (and thus describe a significant 'sub-concept') and are still reasonably accurate in distinguishing positive examples from counter-examples. This is achieved via the process of learning many specialized rule sets (in step 2 above), finding rules in these sets that seem to describe similar sub-concepts (step 4), generalizing these to varying degrees (step 5), and selecting, from these alternative generalizations, those rules for the final model that optimize some criteria concerning coverage and accuracy. Again, more detail can be found in (Widmer 2001a). 4 Experimental Results This section briefly talks about some of the rules discovered in the experiments and gives a quantitative evaluation of the rules along three dimensions: (1) we quantify their degree of fit (coverage and precision) on the training data, and assess their generality by (2) testing them on some of the same pieces performed by a different pianist, and (3) testing them on pieces of a different style (Chopin). 4.1 Some Simple Principles Discovered The PLCG algorithm was run on the complete Mozart performance data set (41,116 notes), for each of the three expression dimensions timing, dynamics, and articulation. The final sets of rules selected (from a total of 383 specialized rules) consist of 6 rules for local timing, 6 rules for local dynamics, and 5 rules for articulation. These seem to represent very general (and mostly very simple) principles; some of them cover or "explain" a surprisingly large number of the pianist's expressive choices. We cannot discuss all 17 rules here; that is the topic of a forthcoming paper (Widmer 2001b). In the following, we will only give a brief description of the six rules for local timing (i.e., for categories lengthen and shorten). A summary of all the rules is given in Table 4. In the following, the rules are listed in the representation language used by the learning system and are briefly paraphrased. The numbers TP/FP following the paraphrase denote the number and percentage of positive examples in the training data correctly covered (true positives TP) and the number of cases where a rule makes an erroneous prediction, e.g., predicts a lengthening when the pianist actually shortened the note (false positives FP); the percentages are relative to the true numbers of positive examples and counterexamples, respectively. w is the rule's precision - the ratio of cases (out of the total number of cases in which the rule did make a prediction) where the rule's prediction was correct; in other words, 7 = TP/(TP + FP). We also give a separate evaluation of the rules on slow and fast pieces, as there seem to be some substantial differences sometimes. Rules predicting a note (IOI) lengthening: In the domain of local timing, there emerged two rules that appear to represent very strong, general principles, along with a third rule that describes an interesting class of situations. The most general discovered rule is RULE TL1: lengthen IF abstrdurcontext = equal-longer "Lengthen the middle note in a "cumulative" (Narmour 1977) 3-note rhythm situation (i.e., given two notes of equal duration followed by a longer note, lengthen the note that precedes the final, longer one)." slow: 1,020 (27.89 %) /153 (2.59 %), w =.870 fast: 1,645 (16.87 %) / 752 (5.14 %), w =.686 all: 2,665 (19.87 %) / 905 (4.40 %), w =.746 This is an extremely simple principle that is also surprisingly general and quite precise, especially in the slow pieces: there, TL1 covers 1,020 cases (27.89 % of all examples of lengthening in the data) correctly, while predicting a nonobserved lengthening in only 153 instances (2.59 % of the cases where the pianist did the opposite). For fast pieces, TL1 predicts a substantial number of positive instances correctly, but makes a higher number of wrong predictions (5.14 %). A second very simple rule that emerged very strongly and that is obviously related to TL1 is RULE TL2: lengthen IF next _durratio < 0.334 "Lengthen a note if it is followed by a substantially longer note (i.e., the ratio between its duration and that of the next note is < 1:3)." slow: 725 (19.82 %) /132 (2.23 %), w =.846 fast: 1,063 (10.90 %) / 439 (3.00 %), w =.708 all: 1,788 (13.33 %) / 571 (2.78 %), S w =.758 A variant of this rule, with substantially higher positive coverage but also more incorrectly predicted negative instances (particularly in the fast pieces), is RULE TL2a: lengthen IF next _durratio < 0.99 & metrstrength <2
Page 00000004 "Lengthen a note if it is followed by a longer note and if it is in a metrically weak position." slow: 1,121 (30.65 %) / 246 (4.16 %), 7r =.820 fast: 1,651 (16.93 %) / 918 (6.27 %), w =.643 all: 2,772 (20.67 %) / 1,164 (5.66 %), w =.704 (In the quantitative experiments to be described below, we will use TL2a for slow pieces only.) Clearly, rules TL1 and TL2/TL2a are strongly related, but they also partly complement each other; taken together, they cover 2,965 (22.11 %) of the positive examples, which is substantially more than either of them covers in isolation. It is remarkable that essentially one simple principle as embodied in rules TL1 and TL2 - lengthen a note if it is followed by a longer one - is sufficient to account for more than one fifth of all the significant lengthenings observed in a large body of performance data. We consider this a surprising discovery that merits some more detailed investigations. Of course, variants of this principle have been observed before. For instance, rule TL1 and rule TL2 cover all the cases of lengthening the last in a sequence of short notes before a (half) cadence that were observed by Palmer (1996) in a Mozart performance, and which were interpreted there as a strategy to draw attention to the upcoming cadence.' In addition to the apparently very strong principles embodied in TL1 and TL2, there was one weaker, less predictive rule that emerged from the learning process. RULE TL3: lengthen IF dir_next = up & int_next > p4 & metrstrength < 2 & int_prev < maj2 "Lengthen a note if itprecedes an upward melodic leap of more than a perfect fourth, if it is in a metrically weak position, and if it is preceded by (at most) stepwise motion (int_prev < maj2)." slow: 95 (2.60 %) / 38 (0.64 %), 7r =.714 fast: 164 (1.68 %) / 94 (0.64 %), 7r =.636 all: 259 (1.93 %) / 132 (0.64 %), 7r =.662 TL3 obviously represents a tendency rather than a strong rule. It is not as clear-cut as TL1 and TL2 and makes a relatively large number of wrong predictions, but it still distinguishes significantly between cases of lengthening and shortening. TL3 appears particularly noteworthy because it turns out to have an interesting parallel in the articulation rules that were discovered. ' The particular timing pattern was discussed by Palmer under the heading Performer-specific Expression. Our discovery shows that at least our pianist applies the same principle, and very consistently so. What TL3 describes is a tendency to slightly delay the target note of an upward leap, by lengthening the IOI occupied by the initial note of the leap. According to the articulation rule AS3 discovered in the same experiment, this seems to usually go along with a slight "staccato" (more appropriately: the insertion of a micropause before the target note of the leap), which amplifies the sense of delay and separation: RULE AS3: staccato IF int_next > p4 & dir_next = up & metr_strength < 2 "Insert a micropause after a note if it precedes an upward leap larger than a perfect fourth and is metrically weak." slow: 307 (6.27 %) / 161 (2.31 %), w =.656 fast: 930 (5.39 %) / 239 (1.99 %), w =.796 all: 1,237 (5.59 %) / 400 (2.11 %), w =.756 The preparation of leaps via timing and articulation has also been studied previously. For instance, the KTH rule set (Friberg 1995) features a pair of rules named Leap Tone Duration (LTD) and Leap Articulation (LA) that pertain to this type of situation: Leap Articulation inserts a micropause between the notes of a leap, and Leap Tone Duration shortens the first and lengthens the second note (IOI) of a leap for upward leaps, and does the opposite for downward leaps. Note that rule TL3 learned by our system predicts the opposite of Friberg's LTD rule - it calls for a lengthening of the first IOI in upward leaps (and this is supported by our performance data). This suggests that - at least in Mozart piano music - the Leap Tone Duration rule should be used with a negative k parameter. Regarding downward jumps, there does not seem to be a general trend in our set of performances. Rules predicting a note (IOI) shortening: Note shortening and local speedups seem much more difficult to predict. The learning algorithm did not find any really general rule that covers a substantial number of positive instances and is strongly discriminative. Rules with a reasonably large coverage also tend to be overly general, predicting a shortening in many cases where the pianist did not apply one. At best, these rules seem to represent general tendencies that would need to be made more specific in order to be useful as prescriptive rules. One rule with a reasonably positive TP/FP ratio (at least for slow pieces) is RULE TS1: shorten IF prev_dur_ratio < 0.67 & next _dur_ratio > 1. O
Page 00000005 Category #rules True Positives False Positives Precision lengthen 4 3069 (22.89%) 1234 (6.00%).713 shorten 2 397 (2.98%) 179 (0.87%).689 louder 3 1318 (11.33%) 591 (3.24%).690 softer 3 625 (6.63%) 230 (1.14%).731 staccato 4 6916 (31.25%) 1089 (5.74%).864 legato 1 687 (7.42%) 592 (1.86%).537 Table 1: Classification accuracy of learned rulesets on training data (13 Mozart sonatas). "Shorten a note (IOI) N in a sequence PN-N-NN ifN is longer than its predecessor PN and longer than its successor NN (more precisely, if the duration ratio PN:N < 2:3 and N:NN > 1:1)." slow: 354 (9.59 %) / 175 (2.94 %), wr =.669 fast: 489 (5.09 %) / 527 (3.61 %), wr =.481 all: 843 (6.34 %) / 702 (3.42 %), wr =.546 which expresses a tendency to shorten long notes between shorter ones - a sort of smoothing action. We use this rule only for slow pieces in the following experiments. Apart from such weak tendencies, the system was able to discover only rather specialized rules. The one with the clearest musical interpretation is RULE TS2: shorten IF tempo = fast & meter = 3/8 & prev_dur_ratio > 2.0 & dur < 0.5 & nextdur_ratio < 0.99 "Shorten a note (I01) in fast pieces in 3/8 time if the duration ratio between previous note and current note is larger than 2:1, the current note is at most a sixteenth (its duration is < 0.5 beat units), and it is again followed by a longer note." slow: 0 (0.00 %) / 0 (0.00 %) fast: 43 (0.45 %) / 4 (0.03 %), wT =.915 all: 43 (0.32 %) / 4 (0.02 %), wr =.915 This rule describes a well-known phenomenon that has been observed in empirical studies before, namely, the common shortening of sixteenth notes following a dotted eighth (and usually again followed by a longer note), as in the famous theme of the A major sonata K.331 (Gabrielsson 1987). Most of the cases covered by rule TS2 do indeed fall into this dotted-eigth-sixteenth note category. Note that in our case, the rule explicitly limits this prediction to fast pieces (as, e.g., in the third movement of the Sonata K.280). In the slow ones (e.g., the beginning of K.331) our pianist tended not to apply this shortening in a consistent manner (in only 16 out of 50 cases did the system find a significant shortening). Note also that rule TS2 calls for the duration ratio to be larger than 2:1. It has been observed by others that duration ratios of 2:1 are usually blurred by performers by lengthening the shorter (or shortening the longer) note (see also rule TS1 above). The difference in performing 2:1 vs. 3:1 duration ratios has been linked to considerations of categorical perception, i.e., the need to make these two rhythmic patterns more easily distiguishable for listeners (Sundberg 1993). It is nice to see this principle emerge automatically from real performance data via machine learning. Space limitations do not permit a detailed discussion of the other rules learned. Table 4 lists the entire rule set, along with information concerning individual rule coverage and precision. More details will be given in (Widmer 2001b). 4.2 Coverage and Precision on the Training Performances In trying to assess the quality of the rule sets as partial models, the first aspect we look at is the fit (coverage and precision) of the rule sets on the training performances they were learned from. Table 1 gives the overall coverage and precision of the three induced rule sets on the training data, separately for each prediction category. As can be seen, the categories for which we managed to find rules of high coverage (and still reasonably high discriminative power) are IOI lengthening and staccato or micropause insertion. The rules for dynamic attenuation (class softer) exhibit reasonable precision, but cover fewer cases. The other three categories turned out to be more difficult to predict at the note level. The remarkable result is that for the former three categories, such a high proportion of all observed occurrences can be predicted by so few (and simple) rules. 4.3 Generality I: Testing on a Different Pianist The purpose of the next experiment was to assess the degree of performer-specificity of the discovered rules, by testing them on performances of the same pieces, but by a differ
Page 00000006 Category #rules True Positives False Positives Precision lengthen 4 596 (29.27%) 242 (7.62%).711 shorten 2 90 (4.10%) 45 (1.49%).667 louder 3 210 (13.12%) 87 (2.85%).707 softer 3 53 (3.32%) 45 (1.65%).541 staccato 4 861 (39.28%) 228 (5.71%).791 legato 1 131 (4.63%) 57 (1.70%).697 Table 2: Classification accuracy of learned rulesets on test data (Mozart performances by P.Entremont). Category #rules True Positives False Positives Precision lengthen 4 1752 (69.06%) 327 (10.94%).843 shorten 2 1472 (53.20%) 110 (4.01%).930 louder 3 601 (25.13%) 285 (11.06%).678 softer 3 0 (0.00%) 0 (0.00%) staccato 4 950 (32.40%) 166 (5.92%).851 legato 1 17 (0.85%) 27 (0.73%).386 Table 3: Classification accuracy of learned rulesets on test data (performances of 2 Chopin pieces by 22 pianists). ent artist. The renowned pianist Philippe Entremont has also recorded some of Mozart's sonatas on a BWsendorfer SE290. We managed to obtain and process his renditions of the following pieces: Sonatas K.282 and K283 complete; plus second movements of K.279, K.280, K.281, K.284, and K.333. The resulting set of performed soprano notes comprises 8,105 notes. Table 2 summarizes the predictive accuracy of our learned rules on the Entremont performances. Comparing this to Table 1 we find no significant degradation in coverage and accuracy (except in category softer). On the contrary, for some categories (lengthen, louder, staccato) the coverage of positive examples is higher than on the original training set. The discriminative power of the rules (captured by the precision values) remains roughly at the same level. This (surprising?) result testifies to the generality of the discovered principles (and the merits of our rule discovery method). We are currently extending the Entremont data set with recordings of three additional complete sonatas that are not present in the original training set (K.309, 310, and 311). That will provide further insight into the true generality of the rules. 4.4 Generality II: Testing on a Different Style An additional experiment tested the generality of the discovered rules with respect to musical style. The rule sets were applied to two pieces by Fr6d6ric Chopin (the first 20 bars of the Etude Op. 10, No.3 in E major, and the first 45 bars of the Ballade Op.38, F major), and their predictions compared to performances (on a BWsendorfer SE290) of these pieces by 22 skilled pianists. The soprano parts ('melodies') of these 44 performances amount to 6,088 notes. The coverage and precision achieved by our simple rule sets are given in Table 3. This result is even more surprising. The categories softer and legato turn out to be basically unpredictable, and the rules for class louder cover a high percentage of positive examples, but also exhibit a rather high level of false predictions. But the results for the other classes (lengthen, shorten, and staccato) are extremely good, better in fact than on the original (Mozart) data which the rules had been learned from! The high coverage (TP) values, especially of the timing rules, are remarkable. A closer look at the Chopin test pieces shows that in the timing dimensions, three out of the six learned rules are sufficient to jointly produce these high TP rates of 69.06% and 53.20%. Both of these two test pieces have a rather regular rhythmic structure, and we plan to test the rules on a more diverse set of Chopin pieces. Remember also that the data represent a mixture of 22 different pianists. When looking at how well the rules fit individual pianists, we find that some of them are predicted extremely well (e.g., pianist #15: timing/lengthen: TP = 89/122 (72.95%), FP = 4/129 (3.10%), x =.957; timing/shorten: TP = 71/120 (59.17%), FP = 3/132 (2.27%), F =.959). In summary, we feel that these initial results are quite remarkable. They strongly indicate that it is possible to discover some basic performance principles (in complex, 'realworld' data) that are both fairly precise and general across a range of performers and musical styles.
Page 00000007 Rule Action Conditions pos. coverage Precision (slow+fast) slow fast total TL1 lengthen IF abstrdurcontext = equal-longer 2,665 (19.87%).870.686.746 TL2 lengthen IF next-dur-ratio < 0.334 1,788 (13.33 %).846.708.758 TL2a* lengthen IF next-durratio < 0.99 1,121 (8.36 %).820 -.820 & metr-strength < 2 TL3 lengthen IF dir-next = up 259 (1.93 %).714.636.662 & int-next > p4 & metr-strength < 2 & intprev < maj2 TS1* shorten IF prev-dur-ratio < 0.67 354 (2.66 %).669.669 & nextdurratio > 1.0 TS2** shorten IF tempo = fast 43 (0.32%).915.915 & meter = 3/8 & prev-dur-ratio > 2.0 & dur < 0.5 & nextdurratio < 0.99 DL1 louder IF dir-prev = up 747 (6.42%).847.761.782 & intprev > p4 & metr-strength > 2 DL2 louder IF mel-contour = updown 890 (7.65 %).734.731.731 & intprev > min3 & metr-strength > 2 DL3** louder IF prev-dur-ratio < 0.5 359 (3.09 %) -.709.709 & dir-prev = up & metr-strength > 3 DS1 softer IF prev-dur-ratio > 5.0 377 (4.00 %).764.675.710 DS2 softer IF dir-prev = down 173 (1.83 %).745.811.783 & intprev > maj3 & metr-strength < 1 & dur-prev > 0.33 DS3 softer IF dir-prev = down 169 (1.79%).840.797.813 & intprev > p5 & metr-strength < 1 AS1 staccato IF markedstaccato = yes 3,071 (13.88 %).916.938.934 AS2 staccato IF intnext = unison 2,929 (13.23 %).981.996.934 AS3 staccato IF intnext > p4 1,237 (5.59 %).656.796.756 & dir-next = up & metr-strength < 2 AS4 staccato IF nextdur-ratio < 0.4 1,215 (5.49 %).571.809.717 & dirprev = down AL1 legato IF staccato = no 687 (7.42 %).593.513.537 & mel-contour = updown Table 4: Summary of discovered rules and their individual coverage and precision (separately for slow and fast pieces) on the training data (*: used for slow pieces only; **: for fast pieces only).
Page 00000008 5 Conclusion The results presented here, though promising, are only a small first step towards the ambitious goal of a comprehensive computational model of expressive performance. What we have discovered to date are a few isolated rules that may indeed represent quite general and robust local expression principles. Their generality across performers and styles needs more empirical validation. If they do turn out to be sufficiently reliable, these rules may form the nucleus of a more complex, multi-level model of performance. Our next steps in this line of research are quite clear. First, we plan to perform additional experiments to evaluate the rules on different performers and different types of music. Second, we plan to extend the representation of the music with some important structural dimensions that are currently lacking, notably, harmony. There are some promising algorithms for automated harmony analysis (e.g., Temperley and Sleator 1999) that we are currently looking at. This may lead to the discovery of a few additional note-level principles. And thirdly, the next large step will be to go beyond the level of individual notes and look directly for structural performance regularities at higher levels of musical organization, in particular, phrase structure. We will take a more detailed look at the empirical validity of some published models of phraselevel timing and dynamics (Todd 1989; Todd 1992) vis-a-vis our complex performance data. The next step will then be to study ways of combining note-level and phrase-level models (of varying degrees of abstraction) into one comprehensive model that explains as much of the observed performance patterns as possible. 5.1 Acknowledgments This research is part of the START programme Y99-INF, financed by the Austrian Federal Ministry for Education, Science, and Culture. I would like to thank the pianists Roland Batik and Philippe Entremont for allowing us to use their performances, and the L. Bisendorfer company, Vienna, and in particular Fritz Lachnit for providing the data and technical help. References Friberg, A. (1995). A Quantitative Rule System for Musical Performance. Ph. D. thesis, Department of Speech Communication and Music Acoustics, Royal Institute of Technology (KTH), Stockholm, Sweden. Fiirnkranz, J. (1999). Separate-and-conquer rule learning. Artificial Intelligence Review 13(1). Gabrielsson, A. (1987). Once again: The theme from mozart's piano sonata in a major (k.331). In A. Gabrielsson (Ed.), Action and Perception in Rhythm and Music, Stockholm. Royal Swedish Academy of Music. Narmour, E. (1977). Beyond Schenkerism: The Needfor Alternatives in Music Analysis. Chicago, IL: University of Chicago Press. Palmer, C. (1996). Anatomy of a performance: Sources of musical expression. Music Perception 13(3), 433-453. Sundberg, J. (1993). How can music be expressive? Speech Communication 13, 239-253. Temperley, D. and D. Sleator (1999). Modeling meter and harmony: A preference rule approach. Computer Music Journal 23(1), 10-27. Todd, N. M. (1989). Towards a cognitive theory of expression: The performance and perception of rubato. Contemporary Music Review 4, 405-416. Todd, N. M. (1992). The dynamics of dynamics: A model of musical expression. Journal of the Acoustical Society ofAmerica 91, 3540-3550. Widmer, G. (2000). Large-scale induction of expressive performance rules: First quantitative results. In Proceedings of the International Computer Music Conference. International Computer Music Association. Widmer, G. (2001a). Discovering strong principles of expressive music performance with the PLCG rule learning strategy. In Proceedings of the 11th European Conference on Machine Learning (ECML'01), Berlin. Springer Verlag. Widmer, G. (2001b). Machine discoveries: Some simple, robust local expression principles. Forthcoming.


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Sinking under the weight of bank debt, the City of Tonawanda's funky Old Man River restaurant is sending out an SOS to potential investors.
Chuck Miano, owner of the popular glorified hot dog stand, said Thursday that he would close the 19-year-old establishment at the end of business today to concentrate his efforts on securing investment partners.
"It's do-or-die time for me," Miano said. "I've got to find investors by Nov. 13 or I lose it all. I'm working feverishly to avoid that."
Miano faces foreclosure by KeyBank, which has scheduled an auction for 10 a.m. Nov. 13 in an effort to recoup an undisclosed amount of debt.
The restaurateur finds himself in a situation that is all too common in the small-business world: The success of his original restaurant led him to expand. Unfortunately, as Miano now knows, it is not always possible to duplicate success at a new address.
In this case, Miano and family members opened additional Old Man River restaurants on Transit Road in Lockport and on Niagara Falls Boulevard in the Town of Tonawanda. Although both of those satellite locations are now closed, the costs associated with launching them have not gone away.
"We over-invested; we put a ton of money in and didn't get a ton of money back," Miano said. "The saddest part is that debt has been dumped at this doorstep."
The restaurateur had been negotiating with KeyBank for more than a year to find a way to repay an undisclosed amount of expansion debt, but the parties were unable to come to terms.
"They dropped the hammer on me," he said. "Now, my only hope of keeping Old Man River is to find some partners, and quick."
Miano said he has been in discussions with "some serious prospects" to help him bail out the business. Unfortunately, with the foreclosure auction just two weeks away, he is running out of time to find backers.
"It just kills me. I've got a line of customers out the door right now," he said. "I better be
able to pull a rabbit out the hat."
Chuck Anderson of Anderson Auction & Realty described the place as a "true landmark. Everybody's eaten at Old Man River. Unfortunately, he's saddled with the debt from his other locations."
The auctioneer said that if Miano is not able to save the business before the auction date, he expects that the sale will attract a number of serious bidders.
"He's got a tremendous location overlooking the Niagara River. There's a lot of restaurant people who will be giving it a look. It's a prime spot," he said.
Miano and his brother, Sam, opened Old Man River in 1978 with the idea of creating a carefree, lighthearted dining atmosphere reminiscent of the post-World War II years. Both the decor and menu reflect the whimsical attitude the Mianos intended.
One of the eye-catching decorations is what Miano calls the "Fallserator" -- a barrel-like contraption attached to canoes, which appears banged-up enough to actually have carried the restaurant's namesake character, "Old Man River," and his sidekick, "Sully," over Niagara Falls.
The menu is similarly unusual, a place where all-beef hot dogs share space with Old Man River's patented "Bunny Dog," a steamed and char-broiled carrot, served on a hot dog roll.
At Old Man River, customers order heaps of sweet potato fries at nearly the same frequency as do for their pale cousin, the french fry.
Another of the many longtime oddities is the "Liver Burger," a stack of char-broiled liver slabs piled on a hamburger bun. Just this year, the "Frickle" (a deep-fried, breaded wedge of dill pickle) made its debut.
"We've tried to keep things interesting," Miano said. "The ideas just keep coming."


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Selective induction of intestinal CYP3A23 by 1alpha,25-dihydroxyvitamin D3 in rats.
Yang Xu
Kazunori Iwanaga
Changcheng Zhou
Matthew J. Cheesman
Federico M. Farin
Kenneth E. Thummel
Biochemical pharmacology
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Enhancement of CYP3A transcription in both the small intestine and liver of the mouse by activation of a VDR signaling pathway was shown recently by Makishima et al. (Science, 2002). However, in humans and rats, hepatic VDR content is much lower than that found in small intestine, suggesting the possibility of tissue-selective responses to 1,25(OH)(2)D(3). The purpose of this study was to determine the effect of 1,25(OH)(2)D(3) on intestinal and hepatic CYP3A expression in the rat. We found that an acute intraperitoneal treatment (every 48 h) in adult male rats with 1,25(OH)(2)D(3) induced CYP3A transcription selectively in small intestine, but not in liver. At a dose of 100 ng, there was a 6.6-fold increase in intestinal CYP3A23 mRNA after the third treatment (p < 0.05). There were concordant effects of 1,25(OH)(2)D(3) treatment on intestinal CYP3A23 protein levels; 2.2-fold (p < 0.05), 3.5-fold (p < 0.05) and 4.8-fold (p < 0.01) increase following 1-3 doses of 100 ng 1,25(OH)(2)D(3), respectively. In contrast, there was no significant change of CYP3A23 protein content in liver at the 1,25(OH)(2)D(3) doses tested. In support of these findings, there was a 366-fold and 77-fold higher level of VDR mRNA expression in the respective rat and human jejunal mucosa, compared to the liver. These data suggest that the human liver will be less sensitive than the intestine to the transcriptional effects of 1,25(OH)(2)D(3) and that this regulatory pathway may contribute to inter-individual variability in constitutive intestinal CYP3A4 expression.
2 Figures and Tables
Table 1 – Western blot analysis of CYP3A23 and CYP3A9 in rat small intestine following different periods of treatment (24 h after 1, 2, or 3 doses; each dose was 48 h apart) with vehicle, 10 ng 1,25(OH)2D3 or 100 ng 1,25(OH)2D3
Table 2 – Testosterone 6b-hydroxylation activity in rat intestine and liver following different periods of treatment (24 h after 1, 2, or 3 doses; each dose was 48 h apart) with vehicle, 10 ng 1,25(OH)2D3 or 100 ng 1,25(OH)2D3
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(Non-Commercial Use)
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The Most Versatile, Easy-To-Build Tiny Cabin Anyone Can Afford. Starts at $18,049
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Job Jobse tickets and 2018 tour dates
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upcoming events
Lost Village Festival Lincolnshire in Lincolnshire
Thursday 23rd August
Messe Dusseldorf in Dusseldorf
Saturday 13th October
upcoming festivals
Lost Village
23rd - 26th Aug 2018
Lost Village
23rd - 26th Aug 2018
From £145 (+BF)
Minimum Age: 18
Get more information about Lost Village including festival tickets, news, reviews and lineups.
Awakenings & Time Warp present Connect
13th Oct 2018
Awakenings & Time Warp present Connect
13th Oct 2018
From 55.00 euros (+BF)
Minimum Age: 18
Get more information about Awakenings & Time Warp present Connect 2018 including festival tickets, news, reviews and lineups.
Photo: polshoogtemagazine.nl
Some people have no doubt about it: being a DJ is all they want to do. After learning the ropes at Amsterdam's legendary club 11, Job began his adventure into electronic music with an internship at the renowned Innervisions label in Berlin. After moving back to Amsterdam to work as the booker of Trouw, things went uphill very fast. Job currently holds his own successful residency at the famous club – Drukpers, the first party ever held at the venue – and is also active as the label manager for the popular Life and Death label. With all Job has achieved so far and a current heavy touring schedule, including appearances spanning from Burning Man to Panorama Bar, one can only wonder where he will go next.
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Found 282 tutors in Fort Worth, TX 76101 – page 1 of 29.
Fort Worth 76101 · 0 miles from downtown · $60/hour · teaches English as 2nd Language
I studied English and Spanish in Boston and in Madrid, Spain. I have a B2 level in French (High Intermediate). Beginner Level of Japanese. My knowledge of language learning is expert.Green Apple award, member for 3 years and 7 months
Fort Worth 76102 · 0 miles from downtown · $40/hour · teaches ACT English - ACT Reading
I am a certified teacher with degrees from Dartmouth, Harvard, and Middlebury. I love literature, history, and languages, and am happy to help you achieve your goals. Most of all, I believe that learning should be...certified
Fort Worth 76102 · 0 miles from downtown · $45/hour · teaches ACT English - Criminal
I have been tutoring students for over 10 years. I began tutoring at 12 until 18 for elementary students in math, reading/phonics, ESL, science, and social studies. I was a volunteer tutor during my time in college for...certified
Fort Worth 76107 · 3 miles from downtown · $30/hour · teaches ADHD - Autism Spectrum
My name is Nancy, and I am excited to help you polish your papers by checking content, citations, spelling, and grammar so that your paper, thesis, or dissertation is at its best! I have a Masters of Library Science...certified
Fort Worth 76101 · 0 miles from downtown · $45/hour · teaches Accounting, Algebra
B.A. Osmania University, India. Diploma in Accounting, Stratford Career Institute, Vermont, USA. Law, Special Programs, United States District Court, Los Angeles, California. Have over 26 years of experience...Green Apple award, member for 7 months
Fort Worth 76107 · 3 miles from downtown · $55/hour · teaches Algebra 1 - Algebra 2 -
I have served as a tutor for elementary, middle school, and high school students in mainly the areas of math and science. I have also helped my peers throughout my matriculation through high school and college.certified
Fort Worth 76111 · 2 miles from downtown · $40/hour · teaches Algebra 1 - Algebra 2 -
I am an experienced teacher in the Fort Worth Area looking to tutor students of all ages. I have worked with children from PK to 12th grade for over 10 years. It is such a joy to see the level of confidence my students...certified
Fort Worth 76110 · 4 miles from downtown · $55/hour · teaches Drawing - Drums - Painting
Drawing, painting, drums, percussion. These four words sum up what drives me and defines my mediums of expression. My father is a professional drummer and artist, so I was naturally exposed to the visual...certified
Fort Worth 76103 · 4 miles from downtown · $18/hour · teaches ACT English - ACT Math -
Hello, fellow life-long learners! I am a life-long learner who possesses a BA in English, a lifetime teaching certificate in English, an ESL teaching certificate that expires in 2019, and a Speech teaching certificate...certified
Fort Worth 76110 · 4 miles from downtown · $75/hour · teaches Algebra 1 - American
I have 3 degrees (Master's, Bachelor's & Associate's), and was an honor student all the way through my higher education. I am especially adept in English, writing, reading, grammar, math (though I do not tutor calculus...certified


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Personal Statement
I'm a caring, skilled professional, dedicated to simplifying what is often a very complicated and confusing area of health care....more
I'm a caring, skilled professional, dedicated to simplifying what is often a very complicated and confusing area of health care.
More about Dr. Akila
Dr. Akila is a popular Gynaecologist in Poonamallee High Road, Chennai. Doctor has been a practicing Gynaecologist for 20 years. Doctor has done MBBS, MS - Obstetrics & Gynaecology . You can meet Dr. Akila personally at The Guest Hospital in Poonamallee High Road, Chennai. Don't wait in a queue, book an instant appointment online with Dr. Akila on Lybrate.com.
Lybrate.com has an excellent community of Gynaecologists in India. You will find Gynaecologists with more than 41 years of experience on Lybrate.com. You can find Gynaecologists online in Chennai and from across India. View the profile of medical specialists and their reviews from other patients to make an informed decision.
Lybrate.com has an excellent community of Gynaecologists in India. You will find Gynaecologists with more than 41 years of experience on Lybrate.com. You can find Gynaecologists online in Chennai and from across India. View the profile of medical specialists and their reviews from other patients to make an informed decision.
MBBS - University Of Mysore, India - 1998
MS - Obstetrics & Gynaecology - Rajiv Gandhi University of Health Sciences, Bangalore, India - 2004
Tamilnadu Medical Council
Book Clinic Appointment with Dr. Akila
The Guest Hospital372 Poonamallee High Road, Kilpauk, Land Mark: Near Ega Cinema Hall, Chennai, Chennai Get Directions
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Asked for female, 20 years old from Dehradun
I am 20 years old. I always get periods cramps and pain that's is mostly unbearable and always need to consume medicines or injection. What should I do to make this cramp bearable? What should be taken in diet?
MPT - Orthopedic Physiotherapy, Fellowship in Orthopaedic Rehabilitation (FOR) Advanced Diploma in Nutrition and Diet
Physiotherapist, Kolkata
I need more details for your treatment through Nutritional Therapy. In the mean time, you can take Apple Cider Vinegar three teaspoon mixed in one full glass of lukewarm water empty stomach daily at morning. Among Nutraceuticals, many are found to be effective in hormonal balance thus regularising monthly cycles and reducing menstrual cramps and PMS To name the effective ones : Evening Primrose oil Chasteberry Capsules MACA Inositol Diet should be more of natural home made ones instead of Packaged and processed foods junk foods and carbonated drinks and refined Carbohydrates are to be kept at bay. Diet should include daily two servings of fresh seasonal locally grown fruits. Green leafy vegetables are a must . Fatty fish Tomatoes are a good source of Lycopene which is a strong antioxidant which helps to fight with your problems. Lose weight if found overweight. Daily 30 mins of brisk walking , gradually increasing it to 45mins Avoid sugar and excess salt . Consult for detailed protocol. Good luck !
Asked for female, 34 years old from Daman and Diu
Mere period ka 17 th day chal raha he aur humne bina dawai ka intercourse kiya he kya bachha run Sakta he?
International Academy of Classical Homeopathy, BHMS
Homeopath, Pune
i m not getting what exactly for which ur asking ? what complaints do u had? what is reason ? GUIDELINES FOR YOU overall everything is not for u but overall Hi According to classical homoeopathy we need detailed case taking how much time u had this problem? any major illness or hospitalisation? family history of major illness? what medication do u have now? is there any relation of weather dat affects ur complaints? is there any relation of food dat affects ur complaints?
Asked for Male, 13 years
My 11 and a half year old daughter is 5' in height. My height is 5'3" Her menstrual cycle has started last month. What medicine should I give to her to increase her height. I need diet suggestions for her.
Vaidya Visharad
Sexologist, Narnaul
Asked for male, 19 years old from Latur
I had unprotected sexual intercourse with guy and after 35 days I have tested for tridot test it was negative than 65 days tridot also it was negative and last test was 91 day both my and my partner tridot test is negative but symptoms likebodypain, mildfevar ,headache, sore thorat. It is possible after 6 months it turn positive thank you.
MBBS, Diploma In Ultrasound, Fellowship in Reproductive Medicine
IVF Specialist, Bangalore
Shared 2 years ago • Featured Tip
MD - Dermatology
Dermatologist, Pathanamthitta
Different veggies are sources of different nutrients. Green leafy vegetables and yellow colur fruits and vegetables are rich in vitamin a which is essential for skin, eyes and nails.
Asked for female, 36 years old from Faridabad
Is bed rest necessary after ivf pregnancy? Is light work like cooking. Allowed. I conceived by ivf in aug 16. But gone through pre term delivery in 5th month. I did job. So please suggest me for next attempt. What are the precautions?
Obstetrician, Pune
Bed rest along with mental peace is of utmost importance in pregnancy. In your next pregnancy you may need cervical stitch as a prophylactic measure. Your obstetrician will explain you the appropriate time to put the cervical stitch and procedure of cervical stitch and will guide you regarding the same. I will suggest you to take a leave from your job once you become pregnant. You should avoid strenuous work and long and tiring journey once you become pregnant. No sexual intercourse once you become pregnant.
Asked for female, 19 years old from Kolkata
I had sex with my boyfriend and we used condoms safely. Is there any more chances of getting pregnant?
Sexologist, Delhi
Health Query
My date was 26 August. But still having no periods. I had 3 pregnancy test, all r negative. Having headache, back pain, tiredness.
MD - Alternate Medicine, Doctor of Homeopathic Medicine (H.M.D.)
Homeopath, Delhi
Asked for male, 38 years old from Kolkata
Doctor, My Wife Has eaten Pills each month one pill (3 times). Her age is 16 years. How much pill can be eat for one year? Is it create any side effect? If yes, what are the side effect.
Diploma in Obstetrics & Gynaecology, MBBS
General Physician, Delhi
At 16, she is still a teenager, with developing hormones & you have given her more hormones in the form of the pill. Please do not give her more pills. Btw legal age for girls to get married is 18. If it is a child marriage, wait for her to turn 18 before you think of a baby. I personally feel that despite government making age as 18, girls do not get mature enough to handle a baby till age 21.
Asked for male, 27 years
I have had unprotected sex with a guy before a week, i m sure that his sperm is not came in mine, and now i have doubt that may be possible his sperm came in my? what can i do? is their any pill to block the pregnancy?
Doctor of Medicine, F.C.P.S.(Mid. & Gynae)
Gynaecologist, Mumbai
It is too late to block the pregnancy. If your period is delayed by a week, buy a Urine Pregnancy Test kit from a chemist and do the test.
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R | | Biography, Drama, History
In the antebellum United States, Solomon Northup, a free black man from upstate New York, is abducted and sold into slavery.
Along with Gravity (2013), this is the first film to tie for Best Picture at the Producer Guild Awards.
Alright now, y'all fresh niggers. Y'all gonna be in the cuttin' gang.
After Epps has been wallowing through the pig pen, in the next shot his clothes are clean with no evidence that he had fallen.
$923,715 (USA) (20 October 2013)
Check out our guide to horror, see what IMDb editors are watching, and more.


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KC Kua
14 Flips | 6 Magazines | 9 Likes | 2 Following | @kckua | Keep up with KC Kua on Flipboard, a place to see the stories, photos, and updates that matter to you. Flipboard creates a personalized magazine full of everything, from world news to life's great moments. Download Flipboard for free and search for "KC Kua"
微软 HoloLens 已正式向开发者开放预售
各位开发者,为你们准备的 HoloLens,刚刚已经正式开放了预售。如果你之前有登记过的话,应该会在近几天内收到来自微软的购买邀请。就算是现在才决定入手,也一样可以去官网上申请,等到 3 月 30 日之后,厂方便会陆续开始出货了(不过上来只会发往美、加两国)。价格方面跟先前公布的一样还是 3,000 美元,而微软也明确表示这并非一款为普通消费者准备的产品。在接受 Recode 采访时,工程师 Alex Kipman 表示自己的公司「并不急于」将 HoloLens 投入大众市场,之所以如此,是因为目前还没有足够好的应用生态能支撑起其高昂的售价。
出于这种考量,厂方也专门为购买了设备的开发者免费提供 …
A new gel-based 3-D printer might help print organs
One group in particular that could benefit is the medical community, thanks to a recent development by scientists that could make it easier to print organs from living tissue.
How? By printing structures inside special gel that provides support during the building process.
New Scientist reports that …
Formlabs' new desktop 3D printer targets creatives and entrepreneurs
Not so long ago, trying to integrate 3D printing into your small business took a huge hunk of time and cash. Not only were printers big and expensive, they also required skilled technicians to make prototypes work properly. The explosion of the Maker movement has changed that by bring 3D printing …Entrepreneurship


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Elige el idioma que prefieras. Hablamos español y otros 42 idiomas.
catedrales, compras y gente amable son algunas de las razones por las que te encantará Truro.
32 hoteles
El The Alverton es el único hotel de 4 estrellas que hay en la ciudad catedralicia de Truro. Está ubicado en un edificio protegido del siglo XIX que presenta ventanas con arcos y parteluz.
"Staff , facilities and location was just perfect for what we needed. We were attending a family funeral . We intend to recommend your hotel for a place to stay to see the area."
Puntuación media: 8,7
Set in an elegant Grade II listed Georgian townhouse, Mannings Hotel is just 150 metres from Truro Cathedral. There is a restaurant and free Wi-Fi in all areas. Parking is also available.
"Location, cleanliness, friendly breakfast staff."
Puntuación media: 8,8
El The County Arm es una posada tradicional de Cornualles que ofrece cervezas inglesas de malta galardonadas, cocina casera y hermosas vistas a la campiña.
"Walking distance to Truro. Good to have the pub with its food. There also was a quiz night whilst we were staying there."
Puntuación media: 8,5
Situated in Truro's lively city centre, the Merchant House boasts free Wi-Fi and free private parking.
"Lovely facility and hospitality. Pleasant and helpful staff. Quiet yet central location. And they gave us a room with direct outdoor access which was handy with the dog. Couldn't have been better."
Puntuación media: 8,4
Donnington Guesthouse is set in Truro, 5 km from Tregothnan. Free WiFi is featured and free private parking is available on site. A flat-screen TV is available.
"Great breakfast, people lovely and welcoming and will do all they can to help make your stay a great one. Would certainly recommend. Got lovely view of the Cathedral from back 2nd floor room"
Puntuación media: 8,4
Offering a sun terrace and views of the garden, Causilgey Manor is set in Truro, 10 km from Tregothnan. Free private parking is available on site. Each room includes a flat-screen TV.
"Everything was 100% perfect. I will go back for sure."
Puntuación media: 9,7
El Chycara ocupa 6,5 hectáreas de hermosos jardines y bosques y ofrece instalaciones amplias de ocio, como piscina cubierta, bañera de hidromasaje y pista de tenis.
"Staff very welcoming, immediately feeling like home."
Puntuación media: 9,2
Situated less than a 10-minute walk from the City Centre of Truro, Cliftons Guest House features free WiFi access and free private parking. Each room has a flat-screen TV.
"Just an absolutely lovely place with wonderful caring owners such that I was able to find my way easily to Fowey and Falmouth through travel tips. Easy walk to town, bus connections, and train. Great place and so quiet. Delicious breakfast with great choices on menu. Definitely would return!"
Puntuación media: 9,3
The Sleepery offers accommodation in Truro. Tregothnan is 4 km away. Free WiFi is available throughout the property.
"Muy bien situado y muy bonita la decoración"
Puntuación media: 8,8
Puntuación media: 8.9
El Garras Farm Bed & Breakfast ofrece alojamiento en una zona rural tranquila de Cornwall. Tiene aparcamiento gratuito. El alojamiento está ubicado en el corazón de una granja.
"Desayuno perfecto,muy limpio,un lugar muy agradable."
Puntuación media: 8.2
El The Fieldings es un bed and breakfast de estilo victoriano situado en Truro, Cornualles, y ofrece conexión WiFi gratuita en todas las habitaciones y aparcamiento gratuito.
"Español Un B&B muy acogedor y los dueños muy amables. El desayuno fue bueno y la decoración muy tradicional y desde nuestra habitación una buena panorámica sobre la ciudad. No está lejos del centro y se puede ir caminando tranquilamente (20 minutos) y de paso ves un poco la ciudad. Como ejemplo de su amabilidad, nos devolvieron con prontitud las llaves de nuestra casa y del coche que nos habíamos dejado en el B&B. Tuvieron un comportamiento EJEMPLAR. ------------------------------------------------------- English A very welcoming B&B with very friendly owners. A good breakfast and a very cozy atmosphere. From our room we had a very good view over the city. It is not far from the center, reacheable in around 20min by foot, a stroll that allows you to see a bit of the city. As an example of their friendliness, they shipped us the keys we forgot in the hotel. Fantastic customer service!!"
Puntuación media: 9.2
Featuring a garden, Cathedral Cottage is a semi-detached holiday home located in Truro in the Cornwall Region. Free WiFi is available throughout the property.
"A perfect place... everything was amazing"
Demelza is a holiday home located in Truro in the Cornwall Region and is 10 km from Tregothnan. It provides free private parking.
Puntuación media: 9.5
Featuring free WiFi throughout the property, Pecks B&B offers accommodation in a quiet residential area of Truro. Free private parking is available on site. Rooms come with a flat-screen TV.
Puntuación media: 9.6
Situated in 4.8 km from Truro, Cornwall, the chalets boasts a seasonal pool and barbecue facilities. Free WiFi and free private parking is available.
"Great location for seeing Cornwall"
Puntuación media: 9.9
Featuring free WiFi, Cedar Cottage B&B offers accommodation in Truro, 9 km from Tregothnan. Free private parking is available on site. A flat-screen TV, as well as a CD player are available.
"The property, owners, décor, beds and hospitality was a wonderful breath of fresh air. There were no typical B&B notices or instructions anywhere to be seen - it was like having a stay in a friends house! The welcome and overall hospitality was second to none. The room was total luxury - I felt very relaxed and at home there. I will definitely stay there again and absolutely would recommend this to all my friends and colleagues. I was travelling alone and the owners couldn't have been more helpful and welcoming. I'm now planning another trip there with my daughter. Just lovely."
Puntuación media: 9.6
La casa Polgwedhen Lodge se encuentra en una zona aislada, junto a un lago, y está rodeada de jardines extensos, terrenos privados y vida salvaje.
"Everything was absolutely perfect.. We have lived a dream."
Located 4.3 km from Tregothnan, The cabin offers pet-friendly accommodation in Truro. Guests benefit from free WiFi and private parking available on site.
The Snuggery is a holiday home featuring a garden with a barbecue, set in Truro, 11 km from Tregothnan. Guests benefit from free WiFi and private parking available on site.
Puntuación media: 8.8
Located 12 km from Tregothnan in Truro, Salem House B&B features free WiFi access and free private parking. All rooms include a flat-screen TV. You will find a kettle in the room.
"The breakfast and hospitality was fantastic and we had our own access to our room from outside and nice walk in shower wet room"
Seaforth Westcliff Porthtowan Cornwall is a holiday home with a barbecue, located in Truro. The unit is 18 km from Tregothnan, and guests benefit from free WiFi and private parking available on site.
Featuring a barbecue, Ardevora an Gylen is a holiday home situated in Truro. The holiday home is 2.7 km from Tregothnan. The kitchen features a dishwasher and an oven, as well as a kettle.
Set in Truro, The Old Dairy at Tregavethan 48555 is 9 km from Tregothnan. All units include a seating area. A TV and DVD player are available in some units.
Crowjy Ardevora offers accommodation in Truro. Tregothnan is 2.7 km away. All units include a TV. There is a seating and/or dining area in some units.
Situated in Truro, The Linhay 38692 is 9 km from Tregothnan. The accommodation comes with a TV and DVD player. There is also a kitchen, fitted with a dishwasher, oven and microwave.
Featuring free WiFi and a terrace, Trevannick House is a holiday home, set in Truro. The property is 4.3 km from Tregothnan and features views of the garden. Free private parking is available on site....
Featuring free WiFi and a terrace, Luxurious House in Cornwall is a holiday home located in Truro, 6 km from Tregothnan. It provides free private parking.
Located in Truro, this holiday home features a terrace and a garden with a barbecue. The holiday home is 1.5 km from Tregothnan. Free WiFi is provided .
Featuring free WiFi throughout the property and a swimming pool, South Penarth is a Grade II listed house located in Truro. Free private parking is available on site.


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The 5 Best Free Landline Phone Service for Seniors - Hotspot setup
Over the last century, copper wires were used in the transmission of phone calls between offices and households around the whole world. It was a system that was much more complicated and technical compared to the soup cans that could be attached to a string. These expensive complex systems and wires were owned by well-established …
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And Then Comes Christmas by Tom Brenner (Hardback, 2014)
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Brand new: lowest price
£9.74Free postage
- Get it by Thu, 30 Aug - Fri, 31 Aug from Oxford, Oxfordshire
- • New condition
- • 30 day returns - Buyer pays return postage | Returns policy
About Blackwell's. Don't forget to check with neighbours / sorting office / outhouses if you are out a lot!
Sold byblackwelloxford (74106)99.6% positive FeedbackContact seller


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What do you do if you feel your crush is losing interest in you :/?
Me and my crush told each we like each other. We dated twice , but broke up due to us going to different schools. I really like him and I don't want to drift away from him. What should I do?
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What Guys Said 1
If you two can overcome the issue of going to different schools then go for it and try dating again. See each other and try to make it work again.0
I'm worried because I don't want to be hurt again, and when we did date we rarely saw each other , but when we did it was as if I was learning something new every time we talk.
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Anytime! If you need any advice just ask
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