Abstract

A limiting factor for readout fidelity for superconducting qubits is the relaxation of the qubit to the ground state before the time needed for the resonator to reach its final target state. A technique known as excited state promoted (ESP) readout was proposed to reduce this effect and further improve the readout contrast on superconducting hardware. In this work, we use readout data from IBM’s five-qubit quantum systems to measure the effectiveness of using deep neural networks, like feedforward neural networks, and various classification algorithms, like knearest neighbors, decision trees, and Gaussian naive Bayes, for single-qubit and multi-qubit discrimination. These methods were compared to standardly used linear and quadratic discriminant analysis algorithms based on their qubit-state-assignment fidelity performance, robustness to readout crosstalk, and training time. Index Terms—Quantum Computing, Qubit Readout, Machine Learning, Deep Learning

Abstract

India Meteorological Department (IMD) is delivering agromet advisories, i.e., weather-based crop risk management advisories based on the medium-range weather forecast (five days) across India. Based on the weather prediction, once in five days, agromet advisory is provided for major crops and livestock by considering the district/block as a unit. In the literature, a framework was proposed to improve the process of advisory preparation by employing the notion of reuse. In that framework, an approach was explored to reuse the advisory prepared for the given weather situation to prepare advisory for similar weather situations in the future. For this, a notion of category-based weather condition (CWC) was proposed to model a given

Abstract

Historians and economists working on the Indian railways refer to what they term the ‘railway mania’ of Britain in the 1840s, resulting ultimately in two principal achievements of the industrial revolution (the steamship and the steam engine) being brought to the colonies.1 Multiple rationales have been ofered to explain the spread of this ‘mania’ to India. Recounting the debate around the Indian railways, Bipan Chandra reminds us of the ‘strong political and economic pressure’ from textile manufacturers in Lancashire, who wished to access the vast supplies of cotton and wheat in India.2 Apart from being a source of opulent raw materials, India could also become a huge consumer of textile and other products manufactured in Britain, Daniel Thorner points out.3 In addition to the commercial and economic advantages, once put in place, the railways could be used to transport military personnel from one part of the vast colony to another in a relatively short time.4 These economic and political justifcations apart—many of which have been dealt with in great detail by a number of scholars of colonial India—we are left with the undeniable fact that railway construction remained a crucial endeavour of colonial rule in British India. The frst passenger railway line in India, covering 34 km between Bori Bunder (Bombay) and Thane, was opened to trafc on 16 April 1853, barely 28 years after the world’s frst successful train ran between Stockton and Darlington in England in 1825. This was one of the frst railway lines in the country. Before passenger trains were introduced, there were a few trains used to transport stone and other material, such as on the Red Hill line in Chennai in South India. The frst short lines were followed by a rapid escalation of railway construction.5 In Ian Kerr’s estimate, by 1870, ‘excluding Russia (and thus Russian Asia), ffty-fve per cent of all operating railway mileage in Africa, Asia, and Latin America combined was located in India’. The fact that neither China nor Japan had operating lines at the time stresses the unique nature of railway construction in India.6 During these initial tumultuous years, the railway building project was

Abstract

Decay heat removal(DHR) system plays a crucial role in maintaining the reactor safety with the ultimate cooling arrangement in case all the other intended systems are unavailable. As a special case, the system shall also ensure that the reactor is not overcooled so that the coolant is not frozen. In this regard, a passive device is to be developed to meet the requirement of monitoring DHR system temperature. Toward this, a self-sustained instrument has been designed and tested with all the necessary compensation and calibration for the entire reactor life. This paper explains the design of such a sensor and its future scope.

Abstract

Evaluation in machine learning is usually informed by past choices, for example which datasets or metrics to use. This standardization enables the comparison on equal footing using leaderboards, but the evaluation choices become sub-optimal as better alternatives arise. This problem is especially pertinent in natural language generation which requires everimproving suites of datasets, metrics, and human evaluation to make definitive claims. To make following best model evaluation practices easier, we introduce GEMv2. The new version of the Generation, Evaluation, and Metrics Benchmark introduces a modular infrastructure for dataset, model, and metric developers to benefit from each others work. GEMv2 supports 40 documented datasets in 51 languages. Models for all datasets can be evaluated online and our interactive data card creation and rendering tools make it easier to add new datasets to the living benchmark.

Abstract

Non-Fungible Tokens (NFTs) are a relatively unexplored class of assets. Designing strategies to forecast NFT trends is an intricate task due to its extremely volatile nature. The market is largely driven by public sentiment and "hype", which in turn has a high correlation with conversations taking place on social media platforms like Twitter. Prior work done for modelling stock market data does not take into account the extent of impact certain highly influential tweets and their authors can have on the market. Building on these limitations and the nature of the NFT market, we propose a novel reachaware temporal learning approach to make predictions for forecasting future trends in the NFT market. We perform experiments on a new dataset consisting of over 1.3 million tweets and 180 thousand NFT transactions spanning over 15 NFT collections curated by us. Our model (TA-NFT) outperforms other state-ofthe-art methods by an average of 36%. Through extensive quantitative and ablative analysis, we demonstrate the ability of our approach as a practical method for predicting NFT trends.

Abstract

The COVID-19 pandemic required efficient allocation of public resources and transforming existing ways of societal functions. To manage any crisis, governments and public health researchers ex-ploit the information available to them in order to make informed decisions, also defined as situational awareness. Gathering situational awareness using so-cial media, has been functional to manage epidemics. Previous research focused on using discussions during periods of epidemic crises on social media platforms like Twitter, Reddit, or Facebook and developing NLP techniques to filter out important/relevant discussions from a huge corpus of messages and posts. Social media usage varies with internet penetration and other socio-economic factors, which might induce disparity in an-alyzing discussions across different geographies. How-ever, print media is a ubiquitous information source, irrespective of geography. Further, topics discussed in news articles are already ‘newsworthy’, while on social media ‘newsworthiness' is a product of techno-social processes. Developing this fundamental difference, we study Twitter data during the second wave in India focused on six high-population cities with varied macro-economic factors. Through a mixture of qualitative and quantitative methods, we further analyze two Indian newspapers during the same period

Abstract

Generating domain-specific content such as legal clauses based on minimal user-provided information can be of significant benefit in automating legal contract generation. In this paper, we propose a controllable graph-based mechanism that can generate legal clauses using only the topic or type of the legal clauses. Our pipeline consists of two stages involving a graph-based planner followed by a clause generator. The planner outlines the content of a legal clause as a sequence of keywords in the order of generic to more specific clause information based on the input topic using a controllable graph-based mechanism. The generation stage takes in a given plan and generates a clause. The pipeline consists of a graph-based planner followed by text generation. We illustrate the effectiveness of our proposed two-stage approach on a broad set of clause topics in contracts.

Abstract

Premenstrual syndrome (PMS) and premenstrual dysphoric disorder (PMDD) are medically unexplained disorders that occur with somatic or psychiatric symptoms presenting during the luteal phase of the menstrual cycle, ending within a few days after the onset of menstruation. Worldwide, the prevalence of PMS is remarkably high, and its causes are still unclear and are multifactorial. This chapter aims to understand better the etiopathogenesis, clinical features, diagnosis, contemporary, and integrative holistic approaches of PMS management. A thorough literature survey from various scientific databases such as Web of Science, ScienceDirect, PubMed, Google Scholar, Scopus, and other databases was retrieved for the

Abstract

Source code is usually accompanied by comments which help in improving code comprehension. However, not all comments are helpful in this respect, with some comments being redundant, some being unclear which results in a poorer code readability. Sifting through large volumes of code to identify such comments manually being tedious, the task of automatically evaluating the usefulness of a comment in context of the code it lies in can add value. In this work, we evaluate the performance of various pretrained transformer encoders to solve this task. We demonstrate decent performance of a few models alongside abnormally high performance obtained by a few other pretrained architectures