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Background The ability to learn from humans via observation was considered to be equally present across properly socialized dogs. We showed recently that cooperative working breeds learned from a human demonstrator more effectively. We hypothesized that functional breed selection could affect sensitivity to human attention-eliciting behavior. Accordingly, we ran the first ever study on dogs that compared the effect of ostensive and neutral verbal communication in a social learning scenario. We used the detour paradigm around a transparent V-shaped fence with either ostensive (addressing the receiver both with words and specific, attention-eliciting prosody) or neutral speech (monotonous reciting of a short poem) demonstration. The other features (gestures, movement) of the demonstration sequence were kept identical between the two conditions. We tested ( N  = 70) companion dogs from 17 cooperative and 16 independent breeds in three 1-min trials. Subjects had to obtain the reward by detouring around the fence. Results Detour latencies of the cooperative dogs improved after both ostensive and neutral speech demonstrations. The independent dogs did not improve their detour latency in either of the conditions. Remarkably, ostensive verbal utterances elicited longer relative looking time towards the demonstrator, cooperative dogs looked longer at the demonstrator, and longer looking time resulted in more successful detours. Conclusions Our study provides the first indication that functional breed selection had a significant impact on dogs’ sensitivity to ostensive human communication, which, apart from being crucially important for social learning from humans, until now was considered as a uniformly present heritage of domestication in dogs. Graphical Abstract

This chapter contains sections titled: Introduction The Spanish and Lengua of Palenque: a first characterization Palenquero creole: overview of some of its essential features 4 The vernacular Spanish of Palenque Palenque's ritual vernacular (lumbalú) and the special role of local Africanisms Palenque's language history: puzzling questions Conclusion and implications for further research References

Recent years have witnessed the great progress for speech emotion recognition using deep convolutional neural networks (DCNNs). In order to improve the performance of speech emotion recognition, a novel feature fusion method is proposed. With going deeper of the convolutional layers, the convolutional feature of traditional DCNNs gradually become more abstract, which may not be the best feature for speech emotion recognition. On the other hand, the shallow feature includes only global information without the detailed information extracted by deeper convolutional layers. According to these observations, we design a deep and shallow feature fusion convolutional network, which combines the feature from different levels of network for speech emotion recognition. The proposed network allows us to fully exploit deep and shallow feature. The popular Berlin data set is used in our experiments, the experimental results show that our proposed network can further improve speech emotion recognition rate which demonstrates the effectiveness of the proposed network.

Faces convey a wide range of information, including one’s identity, and emotional and mental states. Face perception is a major research topic in many research fields, such as cognitive science, social psychology, and neuroscience. Frequently, stimuli are selected from a range of available face databases. However, even though faces are highly dynamic, most databases consist of static face stimuli. Here, we introduce the Sabancı University Dynamic Face (SUDFace) database. The SUDFace database consists of 150 high-resolution audiovisual videos acquired in a controlled lab environment and stored with a resolution of 1920 × 1080 pixels at a frame rate of 60 Hz. The multimodal database consists of three videos of each human model in frontal view in three different conditions: vocalizing two scripted texts (conditions 1 and 2) and one Free Speech (condition 3). The main focus of the SUDFace database is to provide a large set of dynamic faces with neutral facial expressions and natural speech articulation. Variables such as face orientation, illumination, and accessories (piercings, earrings, facial hair, etc.) were kept constant across all stimuli. We provide detailed stimulus information, including facial features (pixel-wise calculations of face length, eye width, etc.) and speeches (e.g., duration of speech and repetitions). In two validation experiments, a total number of 227 participants rated each video on several psychological dimensions (e.g., neutralness and naturalness of expressions, valence, and the perceived mental states of the models) using Likert scales. The database is freely accessible for research purposes.

Object detection is one of the essential tasks in computer vision, with most detection methods relying on a limited number of sizes for anchor boxes. However, the boundaries of particular composite objects, such as ports, highways, and golf courses, are ambiguous in remote sensing images, and therefore, it is challenging for the anchor-based method to accommodate the substantial size variation of the objects. In addition, the dense placement of anchor boxes imbalances the positive and negative samples, which affects the end-to-end architecture of deep learning methods. Hence, this paper proposes a single-stage object detection model named Xnet to address this issue. The proposed method designs a deformable convolution backbone network used in the feature extraction stage. Compared to the standard convolution, it adds learnable parameters for dynamically analyzing the boundary and offset of the receptive field, rendering the model more adaptable to size variations within the same class. Moreover, this paper presents a novel anchor-free detector that classifies objects in feature images point-by-point, without relying on anchor boxes. Several experiments on the large remote sensing dataset DIOR challenging Xnet against other popular methods demonstrate that our method attains the best performance, surpassing by 4.7% on the mAP (mean average precision) metric.

Just like in human infants, ostensive verbal utterances can transform human actions into a natural teaching scenario for dogs. However, functional selection created ‘independent’ and ‘cooperative’ dog breeds with different dependence on human signals. We hypothesize that this could affect dogs’ sensitivity towards verbal communication. We tested independent and cooperative breeds in the two-choice ‘A-not-B paradigm’. The experimenter used either ostensive or neutral intonation speech while hiding the target. Based on the target’s position, the trial order was A-A-B-B-A. Perseverative ‘A-not-B’ errors in Trial 3 are interpreted as learning the rule to look for the reward at location ‘A’. From the near 100% success rate in Trials 1 and 2, each groups’ performance dropped to chance level in Trial 3, except for cooperative dogs in the neutral speech condition. Independent dogs in the neutral speech condition paid the least attention to the experimenter. We conclude that perseverative errors can be either the consequence of rule-learning elicited by ostensive intonation or reverting to the ‘win–stay’ strategy, when independent dogs lost interest in watching where the experimenter exactly hid the reward. Functional selection could influence dogs’ general attentiveness towards human communication; thus, neutral speech may have an underestimated relevance for cooperative dogs.

This paper focuses on the stability analysis of systems modeled as neutral delay differential equations (NDDEs). These systems include delays in both the state variables and their time derivatives. The proposed approach consists of a descriptor model transformation that constructs an equivalent set of delay differential algebraic equations (DDAEs) of the original NDDEs. We first rigorously prove the equivalency between the original set of NDDEs and the transformed set of DDAEs. Then, the effect on stability analysis is evaluated numerically through a delay-independent stability criterion and the Chebyshev discretization of the characteristic equations.

Weak syllables in Germanic and Romance languages have been reported to be challenging for young children, with syllable omission and/or incomplete reduction persisting till age five. In Mandarin Chinese, neutral tone (T0) involves a weak syllable with varied pitch realizations across (preceding) tonal contexts and short duration. The present study examined how and when T0 was acquired by 108 Beijing Mandarin-speaking children (3–5 years) relative to 33 adult controls. Lexicalized (familiar) and non-lexicalized (unfamiliar) T0 words were elicited in different preceding tonal contexts. Unlike previous reports, the present study revealed that children as young as three years have already developed a phonological category for T0, exhibiting contextually conditioned tonal realizations of T0 for both familiar and unfamiliar items. However, mastery of adult-like pitch and duration implementation of T0 is a protracted process not completed until age five. The implications for the acquisition of weak syllables more generally are discussed.

This work is devoted to capturing Emirati-accented speech database (Arabic United Arab Emirates database) in each of neutral and shouted talking environments in order to study and enhance text-independent Emirati-accented “speaker identification performance in shouted environment” based on each of “first-order circular suprasegmental hidden Markov models (CSPHMM1s), second-order circular suprasegmental hidden Markov models (CSPHMM2s), and third-order circular suprasegmental hidden Markov models (CSPHMM3s)” as classifiers. In this research, our database was collected from 50 Emirati native speakers (25 per gender) uttering eight common Emirati sentences in each of neutral and shouted talking environments. The extracted features of our collected database are called “Mel-Frequency Cepstral Coefficients (MFCCs)”. Our results show that average Emirati-accented speaker identification performance in neutral environment is 94.0, 95.2, and 95.9% based on CSPHMM1s, CSPHMM2s, and CSPHMM3s, respectively. On the other hand, the average performance in shouted environment is 51.3, 55.5, and 59.3% based, respectively, on “CSPHMM1s, CSPHMM2s, and CSPHMM3s”. The achieved “average speaker identification performance in shouted environment based on CSPHMM3s” is very similar to that obtained in “subjective assessment by human listeners”.

This explorative study examines intonation contours in neutral and non-neutral statements of Paraguayan Spanish, a variety shaped by extensive contact with Guarani, a co-official language of Paraguay. Paraguayan Spanish displays both lexical and syntactic borrowings from Guarani, along with innovative intonation patterns not found in other Spanish varieties. Previous but still limited research on yes/no and wh-questions in this variety suggests the emergence of a unique intonational system, possibly of a hybrid nature, in both Spanish monolinguals and Spanish–Guarani bilinguals. To date, no comprehensive description of intonation patterns in Paraguayan Spanish statements exists. The present study addresses this gap by analyzing data obtained through a Discourse Completion Task, covering broad-focus statements, contrastive focus, exclamatives, and statements of the obvious. Data were collected in 2014 from two monolingual speakers, eleven bilingual Spanish-dominant speakers, and eight bilingual Guarani-dominant speakers. The intonation is formalized using the Autosegmental–Metrical model of intonational phonology and the Spanish Tones and Break Indices labeling system. The findings reveal three main realizations of nuclear accents (L+H*, H+L*, and innovative >H+L*) in neutral and non-neutral declarative sentences, lengthening of syllables, diverse syntactical strategies, and lexical borrowings. The study contributes to the understanding of a lesser-studied Spanish variety and offers insights into theoretical aspects of contact linguistics.