Assertions about vocal learning capacities orthodoxly presumed absent in great apes cannot be taken for granted or blindly accepted without proper preceding research effort across all living great ape genera. Existing evidence suggests there was no neurological silver bullet or epiphany for the emergence of spoken language along the human lineage (Ghazanfar, 2008). Great apes exert voluntary control over the primary musculature actions involved in speech production: laryngeal (Lameira et al., 2016; e.g. 2015), supralaryngeal (e.g. Lameira et al., 2015; 2013b) and oscillatory (Lameira et al., 2015). Prospective research may attempt to zoom in what motivates great apes to invent and learn new calls and expand their call repertoire. Such dispositions probably prompted our ancestors to increasingly rely on vocal communication in the course of language evolution. The process of speechevolution involved advances in both the biological and cultural domains that ensued over millions of years (Lieberman, 2015). Using great ape vocal faculties as a time machine back to our last hominid common ancestor will permit recognizing the humble, and yet significant and steady first steps taken towards full-blown language along the phylogenetic branch that would see humans evolve one day.
Given this, processing of formulaic speech in the form of small clauses may provide a promising track to explore in neuroscience, one that can shed light on the distinction between what I postulate here to be (fossils of) proto-syntax and the more complex and more recent TP syntax. The production/perception of a TP may have to tap into two distinct neural mechanisms, with possibly some overlap: the one that supports the proto(-syntax) of small clauses, and another that supports the more recent TP syntax, necessarily activating the procedural memory. In other words, one may find neurobiological correlates of finiteness (TP expression) by comparing and contrasting the processing of small clauses (Problem solved; Stigla zima) with the processing of full finite clauses, such as The problem has been solved; Zima je stigla.) In addition, in the light of the discussion of transitivity in section 2.2., one may also expect to find neural correlates of transitivity by comparing and contrasting the processing of compounds such as rattlesnake with the compounds such as snake-rattler.
to map the transcriptions into some semantic form (Deoras et al., 2013). Such systems typically use different models for recognition and understanding. Since ASR yields recognition errors, parsing per- formance can degrade rapidly compared to pars- ing performance on written text. While the per- formance of ASR and parsing components are of- ten optimized independently of each other, in par- ticular in case of the ASR to minimize recognitions errors, research has shown that ASR transcriptions with a lower error rate can in fact yield worse un- derstanding performance (Wang et al., 2003; Bayer and Riccardi, 2012) and that joint approaches to recognition and understanding can yield improved performance (Wang and Acero, 2006b; Deoras et al., 2013). In particular, Wang and Acero (2006b) have shown that applying the same grammar for speech recognition and understanding can yield im- proved understanding performance compared to ap- plying a standard n-gram model with the ASR, since dependencies between acoustics and seman- tics can be captured. Their grammars are, however, learned in a supervised setting. In fact, while se- mantic grammars are often applied for speech recog- nition and/or understanding, they are often created manually or – as mentioned previously – learned from data containing semantic annotations, which are time-consuming to produce.
BG, which is one of the key brain structures in motor skill learning [43,44,46], has clear functional segregation in its sub-regions. Rostrodorsal (associative) putamen involves the early stage of motor skill learning, but at the later stage where the learning is progressed, caudoventral (sensorimotor) putamen plays a dominant role in motor control both in humans [47,48] and animals [2,49]. Thus, the latter is thought to be associated with speedy and automatic control of an acquired motor skill at a later stage of motor skill learning [43,46,47]. Hence, we may also assume in the present study that the cortico-striatal circuit played important roles when the present very-well trained participants performed the well-learned motor skill. If one considers the general notion that BG can play bootstrapping roles to up- and down-regulate M1 activity and may contribute to building a precise sequence of temporally ordered inhibition and activation of motor programs through its multiple-pathway organization in primates [50-52], the present plastic change in this circuit might affect the style of motor control, although this could also be due to the possible change in the influence of BG on the brainstem that may regulate muscle tone .
In the context of the community, Filipino is more preferred than Sorsoganon for inter-ethnic communication, especially when speaking with persons deemed as possessing high social status. Although most Maranao children learn the latter, their parents prefer not to although many of them can understand it and even Bikolano which is not included in this study. This was confirmed in an interview with one of the parents of the subjects, the eldest sister of Ustadz Alibasa and Nurus. She has resided in the city for more than 23 years now and has not learned the dominant native language except common words and phrases, but her
The paper proposed a comprehensive symbolic matrix for characterizing the topology of a metamorphic mechanism that involved information on the variations of links and the axial orientations of the kinematic joints. In addition, oper- ations on the matrices of the adjacent configuration mech- anisms are defined to construct an origin matrix and joint variation matrices. In particular, the construction and evolu- tion of the matrix representation for an original metamorphic mechanism show how it can be transformed into any config- uration matrix. The relationship between the original meta- morphic mechanism and all of its possible configurations and methods of moving between them were presented. Examples illustrate the effectiveness of this approach in characterizing metamorphic mechanisms. The configuration representation of metamorphic mechanisms provides a foundation for the analysis and synthesis of novel metamorphic mechanisms.
Synthesis of Spoken Messages from Semantic Representations (Semantic Representation to Speech System) Synthesis of Spoken Messages from Semantic Representations (Semantlc Representat Ion to Speech Sys[.]
A second possible limitation is that, because the experimental pain apparatus elicited a transient pain experience over which the participants had control, this might have affected the communication of the experience. In particular, participants may have been less motivated to communicate this sensation because they did not require the researcher to help in easing the pain or to provide any real emotional support. Thus, when communicating about a more ‘natural’ p ain sensation (such as toothache or back pain) to doctors and other professionals who are able to provide help and support the patterns identified in the present findings may be even more pronounced in terms of the complementarity and specificity of the information in gestures. Thus, future work should aim to extend the present coding system to examine the interplay between speech and gestures in the representation of pain sensation when
Instead of learning to compose a sentence rep- resentation from the word representations, the skip-thought model Kiros et al. (2015) utilizes the structure and relationship of the adjacent sen- tences in the large unlabelled corpus. Inspired by the skip-gram model (Mikolov et al., 2013a), and the sentence-level distributional hypothesis (Har- ris, 1954), the skip-thought model encodes the cur- rent sentence as a fixed-dimension vector, and in- stead of predicting the input sentence itself, the decoders predict the previous sentence and the next sentence independently. The skip-thought model provides an alternative way for unsuper- vised sentence representation learning, and has shown great success. The learned sentence repre- sentation encoder outperforms previous unsuper- vised pretrained models on 8 evaluation tasks with no finetuning, and the results are comparable to supervised trained models. In Triantafillou et al. (2016), they finetuned the skip-thought models on the Stanford Natural Language Inference (SNLI)
Evolutionary processes that involve learning are difficult to study. To our knowledge, no fully satisfactory model of learning exists today. We have cho- sen arrificial neural networks as models of receivers because they do not constrain signal form and are known to generalize realistically when novel signals appear (Ghirlanda & Enquist, 1998). Nevertheless, available learning algorithms for neu- ral networks, including the back-propagation one used here, are difficult to relate to real learning events (learning models such as those proposed by Rescorla & Wagner (1972) and Blough (1975) have similar advantages and disadvantages). Our simulations involved several simplifications of the actual learning sequence, but they capture the main difference between learned and inherited recognition: that receivers are born naive and will thus make mistakes while learning the ap- propriate behaviour.
The flexibility of input representation of our model enables us to further explore the properties of the input in learning abstract knowledge, fol- lowing psycholinguistic studies. Our results repli- cate the findings of Wonnacott et al. on the role of the distributional properties over the alternat- ing syntactic forms, but in naturalistic settings of many constructions. In future, we plan to extend this analysis by manipulating the distributions of our input data to replicate the exact settings of the artificial language used by Wonnacott et al.. More- over, in this study, we followed the settings of pre- vious computational and psycholinguistic studies that focused on the syntactic properties of the in- put (Perfors et al., 2010; Parisien and Stevenson, 2010; Wonnacott et al., 2008; Conwell and De- muth, 2007). However, we can further our anal- ysis by incorporating semantic features in the in- put to study syntactic bootstrapping effects (Scott and Fisher, 2009) as well as the role of seman- tic properties in constraining the generalizations across the alternating forms.
Table 2: Representation learning on test datasets. From Table 2 we can see that the F-measures are increased on both of the testing datasets when we use the learned representations to measure simi- larities between classes and properties compared with using term vectors, but the amount of im- provements are less than that on the development dataset. This is because we estimate the parame- ters of the representation learning model on the de- velopment dataset and then apply them on the test tasks directly. The precision is reduced when we use URL method, this may be due to the learned representations of entities are too general. In ad- dition, in the parameter adjustment process, we try to make the F value maximization, but not to care about mapping precision. This is because we usually compare the performance of the systems based on their matching F values.
Give the measure of the information a chance to be M × N ; in the suggested framework, every segment SDAE is one-fourth the span of its past segment. Layer-by-layer eager approach  with stochastic angle plunge is used to prepare the SDAE took after by adjusting with back-engendering technique. Middle of the road portrayals got utilizing the 2- concealed layer SDAE are additionally consolidated to get a joint portrayal as delineated in Fig. 5. The two layers with size M 2 × N 2 and M 4 × N 4 are used as info and a joint layer of size 2 × M 4 × N 4 is found out. Give f1 a chance to be the portrayal learned by the principal layer of SDAE and f2 be the component learned by the next layer of SDAE, the joint portrayal J can be gotten the hang of utilizing Eq. (17).𝐽 = 𝐺(𝑓1, 𝑓2) (18)
differences in the mantle secretomes of even closely-related molluscs; these typically exceed expected differences based on characteristics of the external shell. All mantle secretomes surveyed to date include novel genes encoding lineage-restricted proteins and unique combinations of co-opted ancient genes. A surprisingly large proportion of both ancient and novel secreted proteins containing simple repetitive motifs or domains that are often modular in construction. These repetitive low complexity domains (RLCDs) appear to further promote the evolvability of the mantle secretome, resulting in domain shuffling, expansion and loss. RLCD families further evolve via slippage and other mechanisms associated with repetitive sequences. As analogous types of secreted proteins are expressed in biomineralizing tissues in other animals, insights into the evolution of the genes underlying molluscan shell formation may be applied more broadly to understanding the evolution of metazoan biomineralization.
Even though the representation of speech appears to be “unmediated,” there is always the narrator who “quotes” the characters’ speech (Rimmon-Kenan 1983: 108). Moreover, since conversations between fictional characters are always more or less framed by the narrator’s discourse, the narrator’s selective actions determine the course of the storytelling. The narrator decides which “facts” are represented through the characters’ speech and which through the narration. While following the characters’ dialogues and the narrator’s commentary on them, the reader can make inferences about the relationships between the characters and the storyworld they inhabit. In general, the interpretation of dialogue is affected by the overall rhetorical structure of any given narrative text. Dialogue is among the devices that authors use to communicate their ideas, attitudes, beliefs, and values (through the implied author’s perspective) to the authorial audience (see “Rhetorical Approaches to Dialogue in Narrative” in this Introduction).
Network-distributed representation learning can be viewed as a problem using low-dimensional vectors to represent nodes in a network. Most net- work representation methods are based on a net- work structure. The traditional representation is based on matrix decomposition and uses eigenvec- tors as representation (Belkin and Niyogi, 2003; Roweis and Saul, 2000; Tenenbaum et al., 2001). Furthermore, they extend to high-order informa- tion (Cao et al., 2015). However, these meth- ods are not applicable to large-scale networks, and although many approximate approaches have been developed to solve this problem, they are not effective enough. Some methods are based on optimization objective functions (Tang et al., 2015; Pan et al., 2016; Yang et al., 2015). Al- though they are suitable for large-scale network data, they adopt shallow models that are limited in terms of performance and are difficult to use to obtain highly non-linear relationships that are vital to the preservation of network structure. In- spired by deep learning techniques in natural lan- guage processing, (Perozzi et al., 2014; Grover and Leskovec, 2016) adopted several stunted ran- dom walks in networks to generate node se- quences serving as sentence corpus and then ap- plied the skip-gram model to these sequences to learn node representation. However, they cannot easily handle additional information during ran- dom walks in a network.
But while this is the context within which, and the materials out of which, Millikan fashions her theory of representation, the driving force behind the theory is the problem of error. For it is one of the key features of representations that they can be both faithful and unfaithful to the entity they purport to represent, and in her view no other theory of representation has adequately accounted for this. Millikan divides the options into three: picture theories, causal/informational theories, and “PMese theories” (so named after Sellars’s term for symbolic logic). As we saw above, picture theories, which ground the representational relationship in “likeness”, appear to have trouble with determinately fixing the object of representation. This can be easily put into the context of error as follows: insofar as resemblance is what makes a representation represent a particular entity, then any failure of resemblance, any unfaithfulness on the part of the representation, rather than simply reducing the quality of the representation (making it a poor likeness of its object), instead actually undermines its very connection to that object. It may turn out to represent something else that it resembles more closely, or nothing at all, but insofar as it does not resemble a given object, it therefore cannot represent it. According to the picture theory, it does not appear that representational error is possible, for a representation cannot be simultaneously unfaithful to, and yet still represent, a given entity. Thus, if we wish to acknowledge that likeness plays at least some role in representational content (and this is surely the case), it appears that the story must be supplemented somehow.