Abstract

Quantum walks [1–6] are quantum analogs of random walks on graphs and have widespread applications in several areas of quantum information processing [7]. In particular, they are a universal model for quantum computation and are crucial to the design of quantum algorithms for a plethora of problems such as element distinctness [8,9], searching for a marked state in graphs [10–16], matrix product verification [17], triangle finding [18], group commutativity [19], and many others [20–24]. In fact, for some oracular problems, quantum walks exhibit an exponential speedup over their classical counterparts [21,22]. For several of these algorithms, the runtime strongly depends on the time required for the quantum walk on a graph to localize at a vertex inside a marked set of vertices of interest. In the context of classical random walks, the hitting time is defined as the expected time required to hit a marked vertex. As such, the quantum hitting time characterizes the running time of the underlying quantum algorithm [14,15,20,21,25,26]. For continuous-time quantum walk (CTQW), the Hamiltonian governing the dynamics of the walk encodes the (vertex or edge) connectivity of the underlying graph. The dynamics involves evolving some initial state according to this Hamiltonian for time T , followed by a measurement in the basis spanned by the vertices of the graph, thus finding a target state with a high probability. However, Hamiltonian evolutions are unitary, and therefore, unlike classical random walks, quantum walks never converge to a fixed state with time. One method to address this is to evolve the system by H

Abstract

Few-shot learners aim to recognize new categories given only a small number of training samples. The core challenge is to avoid overfitting to the limited data while ensuring good generalization to novel classes. Existing literature makes use of vast amounts of annotated data by simply shifting the label requirement from novel classes to base classes. Since data annotation is time-consuming and costly, reducing the label requirement even further is an important goal. To that end, our paper presents a more challenging few-shot setting with almost no class label access. By leveraging self-supervision to learn image representations and similarity for classification at test time, we achieve competitive baselines while using almost zero (0-5) class labels. Compared to existing state-of-the-art approaches which use 60,000 labels, this is a four orders of magnitude (10,000 times) difference. This work is a step towards developing few-shot learning methods that do not depend on annotated data. Our code is publicly released at https: //github.com/adbugger/FewShot. 3

Abstract

Advances in mobile computing have paved the way for new types of distributed applications that can be executed solely by mobile devices on device-to-device (D2D) ecosystems (eg, crowdsensing). Sophisticated applications, like cryptocurrencies, need distributed ledgers to function. Distributed ledgers, such as blockchains and directed acyclic graphs (DAGs), employ consensus protocols to add data in the form of blocks. However, such protocols are designed for resourceful devices that are interconnected via the Internet. Moreover, existing distributed ledgers are not deployable to D2D ecosystems since their storage needs are continuously increasing. In this work, we introduce and analyse Mneme, a DAG-based distributed ledger that can be maintained solely by mobile devices. Mneme utilizes two novel consensus protocols: Proof-of-Context (PoC) and Proof-of-Equivalence (PoE). PoC employs users' context to add data on Mneme. PoE is executed periodically to summarize data and produce equivalent blocks that require less storage. We analyze Mneme's security and justify the ability of PoC and PoE to guarantee the characteristics of distributed ledgers: persistence and liveness. Furthermore, we analyze potential attacks from malicious users and prove that the probability of a successful attack is inversely proportional to the square of the number of mobile users who maintain Mneme.

Abstract

The nationwide lockdown was imposed over In- dia from 25 March to 31 May 2020 with varied relax- ations from phase I to phase IV to contain the spread of COVID-19. Thus, emissions from industrial and transport sectors were halted during lockdown (LD), which has re-sulted in a significant reduction of anthropogenic pollutants. The first two lockdown phases were strictly implemented (phase I and phase II) and hence were considered to be total lockdown (TLD) in this study. Satellite-based tropospheric columnar nitrogen dioxide (TCN) from the years 2015 to 2020, tropospheric columnar carbon monoxide (TCC) dur-ing 2019/20, and aerosol optical depth (AOD550) from the years 2014 to 2020 during phase I and phase II LD and pre- LD periods were investigated with observations from Aura OMI, Sentinel-5P TROPOMI, and Aqua and Terra MODIS. To quantify lockdown-induced changes in TCN, TCC, and AOD550, detailed statistical analysis was performed on de-trended data using the Student paired statistical t test. Re-sults indicate that mean TCN levels over India showed a dipof 18 % compared to the previous year and also against the 5-year mean TCN levels during the phase I lockdown, which was found to be statistically significant (p value < 0.05) against the respective period. Furthermore, drastic changes in TCN levels were observed over hotspots, namely eastern region and urban cities. For example, there was a sharp de-crease of 62 % and 54 % in TCN levels compared to 2019 and against 5-year mean TCN levels over New Delhi with a p value of 0.0002 (which is statistically significant) dur-ing total LD. The TCC levels were high in the northeast (NE) region during the phase I LD period, which is mainly attributed to the active fire counts in this region. However, lower TCC levels are observed in the same region due to the diminished fire counts during phase II. Further, AOD550 is reduced over the country by ∼ 16 % (Aqua and Terra) from the 6-year (2014–2019) mean AOD550 levels, with a signifi-cant reduction (Aqua MODIS 28 %) observed over the Indo- Gangetic Plain (IGP) region with a p value of  0.05. How-ever, an increase in AOD550 levels (25 % for Terra MODIS, 15 % for Aqua MODIS) was also observed over central India during LD compared to the preceding year and found signif-icant with a p value of 0.03. This study also reports the rate of change of TCN levels and AOD550 along with statistical metrics during the LD period.

Abstract

Estimation of basic material properties of masonry and understanding its compressive behaviour is the initial crucial steps in analyzing the performance of masonry buildings. Masonry being a complex and inelastic material has a large variation in mechanical properties such as compressive strength and modulus of elasticity. The variation for Indian masonry is even higher, as most of the manufacturing and construction process is performed manually. Mechanical characterization of solid clay bricks, mortar cubes (1:4 cement-sand proportion) and masonry prisms are performed using laboratory tests to determine the compressive properties. The non-linear stress strain behaviour of masonry and its constituents is also presented. Based on the results of the present study and past literature, variation in mechanical properties of Indian masonry & its constituents is studied based on two different mortar grades (1:4 and 1:6). It is observed that these properties have a large portion of aleatory variability, due to variations in constituent materials and workmanship. The relationship between the compressive strength and elastic modulus of brick, mortar and masonry is also presented. Further, the relative performance of different empirical models in predicting the properties of masonry is compared and their efficacy is examined using the statistical and error-assessment parameters. Results showed that the compressive strength of the Indian masonry can be determined using most of the empirical relations available worldwide, with reasonable accuracy; however, the elastic modulus of masonry cannot be estimated with the same level of confidence.

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Abstract

Reducing electricity consumption has emerged as a key area of concern according to the National Mission of Sustainable Habitat of Government of India. Among the Indian cities, Delhi’s electricity consumption is the highest. This paper aims to evaluate the impact of UHI on electricity consumption of Delhi. The study utilizes the satellite land surface temperature (LST) data to compute the district-wise urban heat island intensity (UHII) in Delhi. The electricity consumption and LST data are used to examine the association between them. The scatter plots between percapita net electricity consumption and night-time LST of each district show strong association between them and polynomial curves best fit this association. The equations of these polynomial curves are then used to estimate the electricity consumption of each district assuming their LSTs to be equal to that of the surrounding rural strip (i.e. without UHI effect). The difference between the observed and estimated electricity consumption at observed and assumed (i.e. rural) LSTs provided the increase in electricity consumption due to UHI. The study shows that UHI formation caused an increased electricity consumption of 2600 GWh (i.e. 11.4%) annually on an average over eight districts of Delhi during the period April 2012 to March 2017.

Abstract

A Theory of Magnitude (ATOM) suggests that space, time, and quantities are processed through a generalized magnitude system. ATOM posits that task-irrelevant magnitudes interfere with the processing of task-relevant magnitudes as all the magnitudes are processed by a common system. Many behavioural and neuroimaging studies have found support in favour of a common magnitude processing system. However, it is largely unknown whether such cross-domain monotonic mapping arises from a change in accuracy of the magnitude judgments or results from changes in precision of the processing of magnitude. Therefore, in the present study, we examined whether large numerical magnitude affects temporal accuracy or temporal precision or both. In other words, whether numerical magnitudes change our temporal experience or simply bias our duration judgments. The temporal discrimination (between …

Abstract

his paper presents results of Document Visual Question Answering Challenge organized as part of" Text and Documents in the Deep Learning Era" workshop, in CVPR 2020. The challenge introduces a new problem-Visual Question Answering on document images. The challenge comprised two tasks. The first task concerns with asking questions on a single document image. On the other hand, the second task is set as a retrieval task where the question is posed over a collection of images. For the task 1 a new dataset is introduced comprising 50,000 questions-answer (s) pairs defined over 12,767 document images. For task 2 another dataset has been created comprising 20 questions over 14,362 document images which share the same document template.