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This work presents a soft continuum robot (SCR) that can be used as a soft continuum manipulator (SCM) and a soft snake robot (SSR). This is achieved using expanded polyethylene foam (EPE) modules as the soft material. In situations like post-earthquake search operations, these dual-purpose robots could play a vital role. The soft continuum manipulator with a camera attached to the tip can manually search for survivors in the debris. On the other hand, the soft snake robot can be made by attaching an active wheel to the soft continuum manipulator. This mobile robot can reach places humans cannot and gather information about survivors. This work presents the design, fabrication, and experimental validation of the dual soft continuum robot., Comment: 41 pages, 21 figures

Named Entity Recognition (NER) is a sequence classification Natural Language Processing task where entities are identified in the text and classified into predefined categories. It acts as a foundation for most information extraction systems. Dungeons and Dragons (D&D) is an open-ended tabletop fantasy game with its own diverse lore. DnD entities are domain-specific and are thus unrecognizable by even the state-of-the-art off-the-shelf NER systems as the NER systems are trained on general data for pre-defined categories such as: person (PERS), location (LOC), organization (ORG), and miscellaneous (MISC). For meaningful extraction of information from fantasy text, the entities need to be classified into domain-specific entity categories as well as the models be fine-tuned on a domain-relevant corpus. This work uses available lore of monsters in the D&D domain to fine-tune Trankit, which is a prolific NER framework that uses a pre-trained model for NER. Upon this training, the system acquires the ability to extract monster names from relevant domain documents under a novel NER tag. This work compares the accuracy of the monster name identification against; the zero-shot Trankit model and two FLAIR models. The fine-tuned Trankit model achieves an 87.86% F1 score surpassing all the other considered models.

Clustering is a powerful and extensively used data science tool. While clustering is generally thought of as an unsupervised learning technique, there are also supervised variations such as Spath's clusterwise regression that attempt to find clusters of data that yield low regression error on a supervised target. We believe that clusterwise regression is just a single vertex of a largely unexplored design space of supervised clustering models. In this article, we define a generalized optimization framework for predictive clustering that admits different cluster definitions (arbitrary point assignment, closest center, and bounding box) and both regression and classification objectives. We then present a joint optimization strategy that exploits mixed-integer linear programming (MILP) for global optimization in this generalized framework. To alleviate scalability concerns for large datasets, we also provide highly scalable greedy algorithms inspired by the Majorization-Minimization (MM) framework. Finally, we demonstrate the ability of our models to uncover different interpretable discrete cluster structures in data by experimenting with four real-world datasets., Comment: 23 pages, 5 figures

Face recognition algorithms, when used in the real world, can be very useful, but they can also be dangerous when biased toward certain demographics. So, it is essential to understand how these algorithms are trained and what factors affect their accuracy and fairness to build better ones. In this study, we shed some light on the effect of racial distribution in the training data on the performance of face recognition models. We conduct 16 different experiments with varying racial distributions of faces in the training data. We analyze these trained models using accuracy metrics, clustering metrics, UMAP projections, face quality, and decision thresholds. We show that a uniform distribution of races in the training datasets alone does not guarantee bias-free face recognition algorithms and how factors like face image quality play a crucial role. We also study the correlation between the clustering metrics and bias to understand whether clustering is a good indicator of bias. Finally, we introduce a metric called racial gradation to study the inter and intra race correlation in facial features and how they affect the learning ability of the face recognition models. With this study, we try to bring more understanding to an essential element of face recognition training, the data. A better understanding of the impact of training data on the bias of face recognition algorithms will aid in creating better datasets and, in turn, better face recognition systems., Comment: 10 pages, 5 figures, Accepted at the IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW), 2023

Quantum information processing has conceptually changed the way we process and transmit information. Quantum physics, which explains the strange behaviour of matter at the microscopic dimensions, has matured into a quantum technology that can harness this strange behaviour for technological applications with far-reaching consequences, which uses quantum bits (qubits) for information processing. Experiments suggest that photons are the most successful candidates for realising qubits, which indicates that integrated photonic platforms will play a crucial role in realising quantum technology. This paper surveys the various photonic platforms based on different materials for quantum information processing. The future of this technology depends on the successful materials that can be used to universally realise quantum devices, similar to silicon, which shaped the industry towards the end of the last century. Though a prediction is implausible at this point, we provide an overview of the current status of research on the platforms based on various materials., Comment: 48 pages, 3 figures

In the century following its discovery, applications for quantum physics are opening a new world of technological possibilities. With the current decade witnessing quantum supremacy, quantum technologies are already starting to change the ways information is generated, transmitted, stored and processed. The next major milestone in quantum technology is already rapidly emerging -- the quantum internet. Since light is the most logical candidate for quantum communication, quantum photonics is a critical enabling technology. This paper reviews the hardware aspects of the quantum internet, mainly from a photonics perspective. Though a plethora of quantum technologies and devices have emerged in recent years, we are more focused on devices or components that may enable the quantum internet. Our approach is primarily qualitative, providing a broad overview of the necessary technologies for a large-scale quantum internet., Comment: 38 pages, 1 table

We present the largest publicly available synthetic OCR benchmark dataset for Indic languages. The collection contains a total of 90k images and their ground truth for 23 Indic languages. OCR model validation in Indic languages require a good amount of diverse data to be processed in order to create a robust and reliable model. Generating such a huge amount of data would be difficult otherwise but with synthetic data, it becomes far easier. It can be of great importance to fields like Computer Vision or Image Processing where once an initial synthetic data is developed, model creation becomes easier. Generating synthetic data comes with the flexibility to adjust its nature and environment as and when required in order to improve the performance of the model. Accuracy for labeled real-time data is sometimes quite expensive while accuracy for synthetic data can be easily achieved with a good score.