Abstract

Text-to-speech (TTS) systems are modelled as mel-synthesizers followed by speech-vocoders since the era of statistical TTS that is carried forward into neural designs. We propose an alternative approach to TTS modelling referred to as ParrotTTS borrowing from self-supervised learning (SSL) methods. ParrotTTS takes a two-step approach by initially training a speech-to-speech model on unlabelled data that is abundantly available, followed by a text-to-embedding model that leverages speech with aligned transcriptions to extend it to TTS. ParrotTTS achieves competitive mean opinion scores on naturalness compared to traditional TTS models but significantly improves over the latter's data efficiency of transcribed pairs and speaker adaptation without transcriptions. This further paves the path to training TTS models on generically trained SSL speech models.

Abstract

Manuguru, a historic coal mining hub in India's Godavari Valley Coalfield, embodies the intricate connections between resource extraction and its wide-ranging impacts. For over a century, mining has carved into the land, leaving a legacy beyond mere geographic alterations. It has reshaped ecosystems, reshuffled communities, and fueled socio-economic changes, often along lines of caste and ethnicity. This study delves into this transformative process through a geospatial analysis of land use and land cover (LULC) changes from 1987-2020, utilizing multi-spectral/multi-temporal Landsat satellite imagery. With a focus on mining's impact, advanced band spatial indices which are coupled with visual interpretation, unveil eight distinct land cover categories: agricultural land, barren land, forest, mining sites, built-up areas, rivers, stagnant water bodies, and dried water bodies, which provide a quantitative understanding of temporal dynamics. Beyond the geographical, field work delves into the human story. We explore the socio-economic realities of Manuguru, examining how mining has intensified existing divisions and fostered caste-based tensions. We further investigate how public policies intended for development might have inadvertently contributed to spatial segregation. By integrating geospatial insights with social realities, this study underscores the complex interplay between extractive industries, environmental concerns, and the social fabric. Understanding these dynamics is crucial for informed decision-making toward a sustainable future for Manuguru and similar resource-rich landscapes, one that balances progress with social equity and environmental well-being.

Abstract

We show that there exists endotactic and strongly endotactic dynamical systems that are not weakly reversible and possess infinitely many steady states. We provide a few examples in two dimensions and an example in three dimensions that satisfy this property. In addition, we prove for some of these systems that there exist no weakly reversible mass-action systems that are dynamically equivalent to mass-action systems generated by these networks.

Abstract

We focus on the problem of LiDAR point cloud based loop detection (or Finding) and closure (LDC) for mobile robots. State-of-the-art (SOTA) methods directly generate learned embeddings from a given point cloud, require large data augmentation, and are not robust to wide viewpoint variations in 6 Degrees-of-Freedom (DOF). Moreover, the absence of strong priors in an unstructured point cloud leads to highly inaccurate LDC. In this original approach, we propose independent roll and pitch canonicalization of point clouds using a common dominant ground plane. We discretize the canonicalized point clouds along the axis perpendicular to the ground plane leads to images similar to digital elevation maps (DEMs), which expose strong spatial priors in the scene. Our experiments show that LDC based on learnt embeddings from such DEMs is not only data efficient but also significantly more robust, and generalizable than the current SOTA. We report an (average precision for loop detection, mean absolute translation/rotation error) improvement of (8.4, 16.7/5.43)% on the KITTI08 sequence, and (11.0, 34.0/25.4)% on GPR10 sequence, over the current SOTA. To further test the robustness of our technique on point clouds in 6-DOF motion we create and opensource a custom dataset called LidarUrbanFly Dataset (LUF) which consists of point clouds obtained from a LiDAR mounted on a quadrotor. More details on our website https://gsc2001.github.io/FinderNet/

Abstract

The current study aims to understand whether the constraints in the well-defined problem solving task impacts the ill- defined problem solving task of a different knowledge do- main. We chose variants of Raven’s advanced progressive matrices(APM) for well-defined problem-solving. For the ill- defined problem solving task we have chosen creative reason- ing tasks (CRT) which is of same knowledge domain and al- ternate uses task(Guilford, 1967) of a different knowledge do- main. We have collected a sample of 44 participants. We ob- served a significant effect of the variant of APM on CRT per- formance. Higher number of rules in the CRT was observed when it was preceded by cAPM (Mdn = 2) compared to the classic APM (Mdn = 1). Further, we observed a higher CRT Relationship Score under cAPM (Mdn = 78.5) compared to the classic APM (Mdn = 50.5) puzzle conditions. However, the variant of APM did not show a significant effect on AUT, fluency score(p = 0.8 for independent t-test) and elaboration score (p = 0.48 for Mann-Whitney U test). The current results indicate that creativity is more domain-specific than domain- general.

Abstract

We introduce an innovative approach to automated sleep stage classification using EOG signals, addressing the discomfort and impracticality associated with EEG data acquisition. In addition, it is important to note that this approach is untapped in the field, highlighting its potential for novel insights and contributions. Our proposed SE-Resnet-Transformer model provides an accurate classification of five distinct sleep stages from raw EOG signal. Extensive validation on publically available databases (SleepEDF-20, SleepEDF-78, and SHHS) reveals noteworthy performance, with macro-F1 scores of 74.72, 70.63, and 69.26, respectively. Our model excels in identifying REM sleep, a crucial aspect of sleep disorder investigations. We also provide insight into the internal mechanisms of our model using techniques such as 1D-GradCAM and t-SNE plots. Our method improves the accessibility of sleep stage classification while decreasing the need for EEG modalities. This development will have promising implications for healthcare and the incorporation of wearable technology into sleep studies, thereby advancing the field's potential for enhanced diagnostics and patient comfort.

Abstract

Learning embeddings of any data largely depends on the ability of the target space to capture semantic relations. The widely used Euclidean space, where embeddings are represented as point vectors, is known to be lacking in its potential to exploit complex structures and relations. Contrary to standard Euclidean embeddings, in this work, we embed point clouds as discrete probability distributions in Wasserstein space. We build a contrastive learning setup to learn Wasserstein embeddings that can be used as a pre-training method with or without supervision towards any downstream task. We show that the features captured by Wasserstein embeddings are better in preserving the point cloud geometry, including both global and local information, thus resulting in improved quality embeddings. We perform exhaustive experiments and demonstrate the effectiveness of our method for point cloud classification, transfer learning, segmentation, and interpolation tasks over multiple datasets including synthetic and realworld objects. We also compare against recent methods that use Wasserstein space and show that our method outperforms them in all downstream tasks. Additionally, our study reveals a promising interpretation of capturing critical points of point clouds that makes our proposed method self-explainable.

Abstract

Recently, the fundamental problem of unsupervised domain adaptation (UDA) on 3D point clouds has been motivated by a wide variety of applications in robotics, virtual reality, and scene understanding, to name a few. The point cloud data acquisition procedures manifest themselves as significant domain discrepancies and geometric variations among both similar and dissimilar classes. The standard domain adaptation methods developed for images do not directly translate to point cloud data because of their complex geometric nature. To address this challenge, we leverage the idea of multimodality and alignment between distributions. We propose a new UDA architecture for point cloud classification that benefits from multimodal contrastive learning to get better class separation in both domains individually. Further, the use of optimal transport (OT) aims at learning source and target data distributions jointly to reduce the cross-domain shift and provide a better alignment. We conduct a comprehensive empirical study on PointDA-10 and GraspNetPC-10 and show that our method achieves state-of-the-art performance on GraspNetPC-10 (with ≈ 4-12% margin) and best average performance on PointDA-10. Our ablation studies and decision boundary analysis also validate the significance of our contrastive learning module and OT alig

Abstract

Masonry is a traditional, extremely durable type of construction material, and many heritage masonry structures from different historical eras have endured adverse environmental conditions to varying degrees despite sometimes significantly losing their integrity, strength, and durability. Throughout history, the choice of masonry materials has largely been determined by availability, local geological formations, and environmental conditions. Although masonry has evolved over the centuries, the fundamentals of using stone, aggregate, or brick in combination with a binding material have not changed. It is not surprising that strong masonry materials were used to build some of the most notable historical buildings in human history that are still standing today. Resources nowadays are devoted to heritage architecture restoration and conservation because of its cultural significance. For the proper selection of construction techniques that could be applied to structural restoration or before taking any remedial action, a thorough understanding of masonry materials is required. Architectural surveys, the use of written evidence, laboratory investigations, and on-site tests can all be used to achieve this. Hence, the current study provides a thorough overview of the materials and their mechanical properties that have been developed over more than five thousand years for the construction of different structural components of masonry buildings that helped perform well during past seismic events.

Abstract

In the whole procedure of preservation, conservation, and restoration of monuments and historical buildings, structural restoration is considered an undesirable parameter, since it implies interventions on a large scale that might harm the authenticity of a culturally protected building. However, this type of intervention is an inevitable action, because it pertains to the safety of the building and, most importantly, to the safety of the users. In this respect, authenticity and safety are two concepts contradicting each other, and they are ensured by professionals of different origins and philosophies, namely archaeology & architecture on one side and science & technology on the other. In the case of structural restoration, these professionals are required to find a common space of co-existence so that the results of restoration satisfy both concepts, authenticity and safety. The present study gives an overview of thorough knowledge of construction techniques of masonry elements or systems or forms used in the past. In fact, in a period of more than five millennia, various techniques of construction have been developed as a result of big revolutions that have taken place in the history of human civilization. A deep knowledge of all the above is necessary for the suitable selection of present-day techniques and materials that might be used in structural restoration, where principles of reversible or irreversible techniques play a vital role together with the compatibility and durability of intervention materials. The main objective of this study is to guide the structural engineers involved in the structural repair of ancient monuments and buildings of national importance.