Ocean Circulation Velocities Over the Continents During Noah\u27s Flood

This paper focuses on numerical experiments that qualitatively explore the velocities and patterns of ocean circulations that could have risen when the earth’s continental surface was mostly flooded during the catastrophic event of Noah’s day. Velocities and flow patterns are observed resulting from the earth’s rotation and gravity and other pertinent parameters: change in latitude, water depth, supercontinent size, number of days, and mesh size. This parametric study can provide insight into the water velocities that carried Noah’s Ark and insight regarding the hydraulic mechanisms that transported millions of cubic kilometers of sediment during Noah’s Flood. The hydraulic sedimentation may explain many present-day geological formations, which reveal sudden and catastrophic formation. In many cases the sedimentary distributions horizontally extended hundreds and thousands of kilometers and accomplished a vast amount of geological work in a matter of months. The geological conditions were assumed to be similar to that of late Paleozoic and early Mesozoic era, when the Pangea supercontinent existed. The numerical calculations employ two codes, one written by National Center for Atmospheric Research (NCAR) and the other by Dr. Baumgardner (1994). Both codes solve the 2-D shallow water equations on a rotating sphere with surface topography. The calculations from Dr. Baumgardner’s code showed a surprising yet persistent result with high velocities of the ocean currents over the Pangean-like continental configurations. The magnitudes of these velocities were around 40–80 m/s at higher latitudes. Catastrophic cavitation occurs for water velocities around 20-30 m/s and for free stream conditions lead to vaporous cavitation (Brennen, 1995; 2005, p. 142; Brewer, 2002, p. 4). This depends on the cavitation number for the prevailing conditions. Around such velocities, one would expect severe and rapid erosion to be associated with any major transgression of the continents by the ocean currents. Such currents would be expected to arise in the context of the scripture “all the high mountains everywhere under the heavens were covered with water” (Genesis 7:19). The NCAR code results showed some slightly lower velocities ranging up to the mid-20 m/s range. Even with these velocities, which are lower than those of the Dr. Baumgardner’s code results, the velocities are still sufficiently large to induce a global movement of sedimentation. As such, these types of calculations strengthen the evidence for Noah’s Flood and the associated consequences on the geological history of sedimentary rocks

End-to-End Label Uncertainty Modeling in Speech Emotion Recognition using Bayesian Neural Networks and Label Distribution Learning

To train machine learning algorithms to predict emotional expressions in terms of arousal and valence, annotated datasets are needed. However, as different people perceive others' emotional expressions differently, their annotations are per se subjective. For this, annotations are typically collected from multiple annotators and averaged to obtain ground-truth labels. However, when exclusively trained on this averaged ground-truth, the trained network is agnostic to the inherent subjectivity in emotional expressions. In this work, we therefore propose an end-to-end Bayesian neural network capable of being trained on a distribution of labels to also capture the subjectivity-based label uncertainty. Instead of a Gaussian, we model the label distribution using Student's t-distribution, which also accounts for the number of annotations. We derive the corresponding Kullback-Leibler divergence loss and use it to train an estimator for the distribution of labels, from which the mean and uncertainty can be inferred. We validate the proposed method using two in-the-wild datasets. We show that the proposed t-distribution based approach achieves state-of-the-art uncertainty modeling results in speech emotion recognition, and also consistent results in cross-corpora evaluations. Furthermore, analyses reveal that the advantage of a t-distribution over a Gaussian grows with increasing inter-annotator correlation and a decreasing number of annotators.Comment: arXiv admin note: text overlap with arXiv:2207.1213

In-the-wild Speech Emotion Conversion Using Disentangled Self-Supervised Representations and Neural Vocoder-based Resynthesis

Speech emotion conversion aims to convert the expressed emotion of a spoken utterance to a target emotion while preserving the lexical information and the speaker's identity. In this work, we specifically focus on in-the-wild emotion conversion where parallel data does not exist, and the problem of disentangling lexical, speaker, and emotion information arises. In this paper, we introduce a methodology that uses self-supervised networks to disentangle the lexical, speaker, and emotional content of the utterance, and subsequently uses a HiFiGAN vocoder to resynthesise the disentangled representations to a speech signal of the targeted emotion. For better representation and to achieve emotion intensity control, we specifically focus on the aro\-usal dimension of continuous representations, as opposed to performing emotion conversion on categorical representations. We test our methodology on the large in-the-wild MSP-Podcast dataset. Results reveal that the proposed approach is aptly conditioned on the emotional content of input speech and is capable of synthesising natural-sounding speech for a target emotion. Results further reveal that the methodology better synthesises speech for mid-scale arousal (2 to 6) than for extreme arousal (1 and 7).Comment: Submitted to 15th ITG Conference on Speech Communicatio

Leveraging Semantic Information for Efficient Self-Supervised Emotion Recognition with Audio-Textual Distilled Models

In large part due to their implicit semantic modeling, self-supervised learning (SSL) methods have significantly increased the performance of valence recognition in speech emotion recognition (SER) systems. Yet, their large size may often hinder practical implementations. In this work, we take HuBERT as an example of an SSL model and analyze the relevance of each of its layers for SER. We show that shallow layers are more important for arousal recognition while deeper layers are more important for valence. This observation motivates the importance of additional textual information for accurate valence recognition, as the distilled framework lacks the depth of its large-scale SSL teacher. Thus, we propose an audio-textual distilled SSL framework that, while having only ~20% of the trainable parameters of a large SSL model, achieves on par performance across the three emotion dimensions (arousal, valence, dominance) on the MSP-Podcast v1.10 dataset.Comment: Accepted at Interspeech 202

End-To-End Label Uncertainty Modeling for Speech-based Arousal Recognition Using Bayesian Neural Networks

Emotions are subjective constructs. Recent end-to-end speech emotion recognition systems are typically agnostic to the subjective nature of emotions, despite their state-of-the-art performance. In this work, we introduce an end-to-end Bayesian neural network architecture to capture the inherent subjectivity in the arousal dimension of emotional expressions. To the best of our knowledge, this work is the first to use Bayesian neural networks for speech emotion recognition. At training, the network learns a distribution of weights to capture the inherent uncertainty related to subjective arousal annotations. To this end, we introduce a loss term that enables the model to be explicitly trained on a distribution of annotations, rather than training them exclusively on mean or gold-standard labels. We evaluate the proposed approach on the AVEC'16 dataset. Qualitative and quantitative analysis of the results reveals that the proposed model can aptly capture the distribution of subjective arousal annotations, with state-of-the-art results in mean and standard deviation estimations for uncertainty modeling.Comment: This paper is submitted to INTERSPEECH 202

EMOCONV-DIFF: Diffusion-based Speech Emotion Conversion for Non-parallel and In-the-wild Data

Speech emotion conversion is the task of converting the expressed emotion of a spoken utterance to a target emotion while preserving the lexical content and speaker identity. While most existing works in speech emotion conversion rely on acted-out datasets and parallel data samples, in this work we specifically focus on more challenging in-the-wild scenarios and do not rely on parallel data. To this end, we propose a diffusion-based generative model for speech emotion conversion, the EmoConv-Diff, that is trained to reconstruct an input utterance while also conditioning on its emotion. Subsequently, at inference, a target emotion embedding is employed to convert the emotion of the input utterance to the given target emotion. As opposed to performing emotion conversion on categorical representations, we use a continuous arousal dimension to represent emotions while also achieving intensity control. We validate the proposed methodology on a large in-the-wild dataset, the MSP-Podcast v1.10. Our results show that the proposed diffusion model is indeed capable of synthesizing speech with a controllable target emotion. Crucially, the proposed approach shows improved performance along the extreme values of arousal and thereby addresses a common challenge in the speech emotion conversion literature.Comment: Submitted to ICASSP 202

Interlinking Industry 4.0 and Academia through Robotics and Automation: An Indian Perspective

Robots and automation systems are growing rapidly in the society globally with an annual global sales value of 16.5 billion USD in 2018 according to the International Federation of Robotics (IFR). They have found a commonplace not only in industries and service sector but also in households. This has attracted heavy investment by industries globally in the research and development of robotics and its applications. Understanding this rising trend in the industry and society, there is an obvious need for expertise and future workforce in robotics. According to the IFR report, India has recorded a growth of 39% in 2018 compared to the previous year in terms of annual robot installations. The annual report of the Confederation of Indian Industry (CII) in 2019 recommends robotics and automation as one of the prime areas of focus toward the development of national policies on Industry 4.0. One such interlinking initiative in robotics research and innovation has started at the Centre for Product Design and Manufacturing (CPDM) in the Indian Institute of Science (IISc). The project is designated under India’s first Industry 4.0-compliant Smart Factory R&D platform in a unique academic set-up. It aligns with the policies of Govt. of India to boost vision Industry 4.0 for India’s technological and economic transformation

Inelastic Neutron scattering in CeSi_{2-x}Ga_x ferromagnetic Kondo lattice compounds

Inelastic neutron scattering investigation on ferromagnetic Kondo lattice compounds belonging to CeSi_{2-x}Ga_{x}, x = 0.7, 1.0 and 1.3, system is reported. The thermal evolution of the quasielastic response shows that the Kondo interactions dominate over the RKKY interactions with increase in Ga concentration from 0.7 to 1.3. This is related to the increase in k-f hybridization with increasing Ga concentration. The high energy response indicates the ground state to be split by crystal field in all three compounds. Using the experimental results we have calculated the crystal field parameters in all three compounds studied here.Comment: 12 Pages Revtex, 2 eps figures