Abstract

In the present study, a refined procedure for the seismic evaluation and retrofitting of reinforced concrete (RC) buildings based on the ‘quadrants assessment method’ and ‘material strain limit approach’ is proposed and numerically analysed. The quadrants assessment method involves the performance point, design base shear and threshold damage limit state. Herein, four existing RC buildings (models 1–4) are considered from the Koyna– Warna region, Maharashtra (zone-IV, India). These four buildings were studied using nonlinear static adaptive pushover analysis employing the SeismoStruct software. Based on the quadrants assessment method, the three-storey RC building (model-1) was retrofitted with RC jacketing, while the other three RC buildings did not need to be retrofitted. Also, significant seismic design parameters like ductility, over strength factor, response reduction factor, etc. were evaluated before and after retrofitting. The results depict that the combination of the ‘quadrants assessment method’ and ‘material strain limit approach’ is a rapid, reliable and refined procedure for seismic evaluation and retrofitting of RC buildings.

Abstract

Object Goal Navigation requires a robot to find and navigate to an instance of a target object class in a previously unseen environment. Our framework incrementally builds a semantic map of the environment over time, and then repeatedly selects a long-term goal (’where to go’) based on the semantic map to locate the target object instance. Long-term goal selection is formulated as a vision-based deep reinforcement learning problem. Specifically, an Encoder Network is trained to extract high-level features from a semantic map and select a long-term goal. In addition, we incorporate data augmentation and Q-function regularization to make the long-term goal selection more effective. We report experimental results using the photo-realistic Gibson benchmark dataset in the AI Habitat 3D simulation environment to demonstrate substantial performance improvement on standard measures in comparison with a state of the art data-driven baseline.

Abstract

This paper describes a framework for the object-goal navigation (ObjectNav) task, which requires a robot to find and move to an instance of a target object class from a random starting position. The framework uses a history of robot trajectories to learn a Spatial Relational Graph (SRG) and Graph Convolutional Network (GCN)-based embeddings for the likelihood of proximity of different semantically-labeled regions and the occurrence of different object classes in these regions. To locate a target object instance during evaluation, the robot uses Bayesian inference and the SRG to estimate the visible regions, and uses the learned GCN embeddings to rank visible regions and select the region to explore next. This approach is tested using the Matterport3D (MP3D) benchmark dataset of indoor scenes in AI Habitat, a visually realistic simulation environment, to report substantial performance improvement in comparison with state of the art baselines.

Abstract

Music sharing trends have been shown to change during times of socio-economic crises. Studies have also shown that music can act as a social surrogate, helping to significantly reduce loneliness by acting as an empathetic friend. We explored these phenomena through a novel study of online music sharing during the Covid-19 pandemic in India. We collected tweets from the popular social media platform Twitter during India’s first and second wave of the pandemic (n = 1,364). We examined the different ways in which music was able to accomplish the role of a social surrogate via analyzing tweet text using Natural Language Processing techniques. Additionally, we analyzed the emotional connotations of the music shared through the acoustic features and lyrical content and compared the results between pandemic and pre-pandemic times. It was observed that the role of music shifted to a more community focused function rather than tending to a more self-serving utility. Results demonstrated that people shared music during the Covid-19 pandemic which had lower valence and shared songs with topics that reflected turbulent times such as Hardship and Exclusion when compared to songs shared during pre-Covid times. The results are further discussed in the context of individualistic versus collectivistic cultures.

Abstract

A fire alarm system is mandatory for all nuclear and industrial establishments to protect the safety of the public, environment, and plant personnel from a plant fire and its potential effects on safe reactor operation. The system shall be provided for its rapid detection of fires and an adequate level of fire protection for structures, systems, and components important to safety. A fire that is not promptly extinguished will prevent essential plant safety functions from being performed. All the NPP(Nuclear power plants) and facilities are equipped with fire detection systems. Fire detection systems consist of thousands of fire sensors and associated electronics. Detectors need to be installed as per the design, selection, and installation criteria mentioned in the standards. This calls for procurement of thousands of various types of smoke detectors and associated electronics from private industry, which are met only from imports, as there is no established certified Indian manufacturer. Hence, there is a great need for the indigenous development of these detectors. Attempts are made, and feasibility prototypes are tested at ISS/EIG to develop smoke sensors, electronics, and communication protocols using simple techniques at throw-away costs

Abstract

—Stack Overflow is a Community Question Answering service that attracts millions of users to seek answers to their questions. Maintaining high-quality content is necessary for relevant question retrieval, question recommendation, and enhancing the user experience. Manually removing low-quality content from the platform is time-consuming and challenging for site moderators. Thus, it is imperative to assess the content quality by automatically detecting and ‘closing’ the low-quality questions. Previous works have explored lexical, communitybased, vote-based, and style-based features to detect low-quality questions. These approaches are limited to writing styles, textual, and handcrafted features. However, these features fall short in understanding semantic features and capturing the implicit relationships between tags and questions. In contrast, we propose LQuaD (Low-Quality Question Detection), a multi-tier hybrid framework that, a) incorporates semantic information of questions associated with each post using transformers, b) includes the question and tag information that enables learning via a graph convolutional network. LQuaD outperforms the state-of-the-art methods by a 21% higher F1-score on the dataset of 2.8 million questions. Furthermore, we apply survival analysis which acts as a proactive intervention to reduce the number of questions closed by informing users to take appropriate action. We find that the timeframe between the stages from the question’s creation till it gets ‘closed’ varies significantly for tags and different ‘closing’ reasons for these questions. Index Terms—Stack Overflow, Community Question Answering, Low-quality questions

Abstract

This work presents an ultra low phase noise 30 GHz oscillator topology in 14 nm technology for an active mode Micro-electro-mechanical systems (MEMS) based resonator that utilizes resonant-fin transistors (RFT). A novel oscillator architecture has been presented for the active mode RFT, which can be modelled as a voltage controlled current source with 270° phase shift between its output current and input voltage. The proposed oscillator has been designed in 14 nm GF technology and simulation results show that it achieves phase noise less than −144 dBc/Hz at 1 MHz offset for 30 GHz carrier frequency for active mode RFT with quality factor of 10,000, while consuming 5.5 mW power from 0.8 V supply.

Abstract

Progress in 3D object understanding has relied on manually canonicalized shape datasets that contain instances with consistent position and orientation (3D pose). This has made it hard to generalize these methods to in-the-wild shapes, eg., from internet model collections or depth sensors. ConDor is a self-supervised method that learns to Canonicalize the 3D orientation and position for full and partial 3D point clouds. We build on top of Tensor Field Networks (TFNs), a class of permutation- and rotation-equivariant, and translation-invariant 3D networks. During inference, our method takes an unseen full or partial 3D point cloud at an arbitrary pose and outputs an equivariant canonical pose. During training, this network uses self-supervision losses to learn the canonical pose from an un-canonicalized collection of full and partial 3D point clouds. ConDor can also learn to consistently co-segment object parts without any supervision. Extensive quantitative results on four new metrics show that our approach outperforms existing methods while enabling new applications such as operation on depth images and annotation transfer.

Abstract

The Hopfield network is an example of an artificial neural network used to implement associative memories. A binary digit represents the neuron’s state of a traditional Hopfield neural network. Inspired by the human brain’s ability to cope simultaneously with multiple sensorial inputs, this paper presents three multi-modal Hopfield-type neural networks that treat multi-dimensional data as a single entity. In the first model, called the vector-valued Hopfield neural network, the neuron’s state is a vector of binary digits. Synaptic weights are modeled as finite impulse response (FIR) filters in the second model, yielding the so-called convolutional associative memory. Finally, the synaptic weights are modeled by linear time-varying (LTV) filters in the third model. Besides their potential applications for multi-modal

Abstract

Long documents like contracts, financial documents, etc., are often tedious to read through. Linearly consuming (via scrolling or navigation through default table of content) these documents is time-consuming and challenging. These documents are also authored to be consumed by varied entities (referred to as persona in the paper) interested in only certain parts of the document. In this work, we describe DYNAMICTOC, a dynamic table of contentbased navigator, to aid in the task of non-linear, persona-based document consumption. DYNAMICTOC highlights sections of interest in the document as per the aspects relevant to different personas. DYNAMICTOC is augmented with short questions to assist the users in understanding underlying content. This uses a novel deep-reinforcement learning technique to generate questions on these persona-clustered paragraphs. Human and automatic evaluations suggest the efficacy of both end-to-end pipeline and different components of DYNAMICTOC.