Abstract

Quantum thermodynamics can be naturally phrased as a theory of quantum state transformation and energy exchange for small-scale quantum systems undergoing thermodynamical processes, thereby making the resource theoretical approach very well suited. A key resource in thermodynamics is the extractable work, forming the backbone of thermal engines. Therefore it is of interest to characterize quantum states based on their ability to serve as a source of work. From a near-term perspective, quantum optical setups turn out to be ideal test beds for quantum thermodynamics; so it is important to assess work extraction from quantum optical states. Here, we show that Gaussian states are typically useless for Gaussian work extraction. More specifically, by exploiting the “concentration of measure” phenomenon, we prove that the probability that the Gaussian extractable work from a zero-mean energy-bounded multimode random Gaussian state is nonzero is exponentially small. This result can be thought of as an -no-go theorem for work extraction from Gaussian states under Gaussian unitaries, thereby revealing a fundamental limitation on the quantum thermodynamical usefulness of Gaussian compo

Abstract

Post-transcriptionally modified bases play vital roles in many biochemical processes involving RNA. Analysis of the non-covalent interactions associated with these bases in RNA is crucial for providing a more complete understanding of the RNA structure and function; however, the characterization of these interactions remains understudied. To address this limitation, we present a comprehensive analysis of base stacks involving all crystallographic occurrences of the most biologically relevant modified bases in a large dataset of high-resolution RNA crystal structures. This is accompanied by a geometrical classification of the stacking contacts using our established tools. Coupled with quantum chemical calculations and an analysis of the specific structural context of these stacks, this provides a map of the stacking conformations available to modified bases in RNA. Overall, our analysis is expected to facilitate structural research on altered RNA bases.

Abstract

Due to the lack of a large annotated speech corpus, many low-resource Indian languages struggle to utilize recent advancements in deep neural network architectures for Automatic Speech Recognition (ASR) tasks. Collecting large-scale databases is an expensive and time-consuming task. Current approaches lack extensive traditional expert-based data acquisition guidelines, as they are tedious and complex. In this work, we present the International Institute of Information Technology Hyderabad-Crowd Sourced Telugu Database (IIITH-CSTD), a Telugu corpus collected through crowdsourcing

Abstract

User authentication is a promising security solution in which an external user hav- ing his/her mobile device can securely access the real-time information directly from the deployed smart devices in an Internet of Things-enabled intelligent precision agri- cultural environment. To achieve this goal, we present a new signature-based three factor user authentication scheme. The established session key between a user and the accessed smart device is then used to make secure communication among them to fetch the real-time data of the device. A detailed security analysis including the random-oracle based formal security, formal security verification using the broadly recognized automated validation of internet security protocols and applications tool and nonmathematical informal security analysis show the robustness of the proposed scheme against a number of potential attacks. In addition, testbed experiments are performed for measuring computational time of various cryptographic primitives that are used for comparative study among the proposed scheme and other related exist- ing competing schemes. The detailed comparative analysis shows that the proposed scheme has a better trade-off among its offered security and functionality features, and communication and computational overheads as compared with those for other competing schemes.

Abstract

The paper presents a 0.5V supply, gate leakage-based current/voltage reference for ultra-low power IoT and biomedical applications. The references are generated by the proper addition of PTAT and CTAT curves, which are obtained by exploiting the traditional architecture of the beta multiplier and using the body biasing effect. Gate leakage transistors replace the resistors to ensure low power and low area. The circuit doesn't involve any Op-Amps avoiding the issues of offset that are prominent in these circuits. Implemented in CMOS 90nm technology, the proposed current (voltage) reference achieves a typical accuracy of 34.6ppm/∘C(29.68ppm/∘C) over a wide temperature range of −55∘C to 75∘C with typical value 63.32pA(0.35V). Excellent line sensitivity of 0.0318%N and 0.0576%N are observed for voltage and current reference, respectively, in a supply range of 0.5V - 2.3V. The area occupied by the total circuit is 0.0096mm2, while the power consumption is 415pW at the typical corner of 27∘C and 0.5V supply.

Abstract

Abstract—Initially designed to represent ownership of various assets, Non-Fungible Tokens (NFTs) have emerged as a new tool in the blockchain domain for investment and trading. The NFT markets are rapidly budding with significant growth in trading volumes over the last few years. While the NFT ecosystem is continuously evolving, users are exploring astute trading practices to gain financial profits. In this paper, we uncover shill looping, a novel NFT trade practice that NFT owners can exploit to artificially inflate the price of an NFT token. We investigate shill looping and its primary effects in a multi-billion dollar NFT collection called BAYC, showing that approximately 50% of these NFTs exhibit shill looping. Our empirical analysis shows that shill looping significantly boosts the average NFT values by over 45% in the best case. Our initial results highlight the severeness of the shill looping phenomenon

Abstract

This paper aims to explore the challenges faced by start-ups during COVID-19 and highlight solutions for catering to the new-normal consumer behaviour. The study accounts for 15 deep-tech start-ups sailing through the pandemic and their responsiveness. This study brings forth insights and experiences from the Indian start-up founders and CEOs during COVID-19.

Abstract

The sporadic large fluctuations seen in the stock market are due to different factors. These large fluctuations are termed extreme events (EE). We have identified fundamental, technical, and external factors and categorized positive or negative EE depending on the impact of these factors. During such events, the stock price time series is found to be nonstationary. Hence, the Hilbert–Huang transformation is used to identify EEs based on high instantaneous energy () concentration. The analysis shows that concentration in the stock price is very high during both positive and negative EE, surpassing a threshold of where and are the mean energy and standard deviation of energy, respectively. Further, support vector regression is used to predict the stock price during an EE, with the close price being found to be the most useful input than the open-high-low-close (OHLC) inputs. The maximum prediction accuracy for one step using close price and OHLC prices are 95.98% and 95.64%, respectively. Whereas, for the two step prediction, the accuracies are 94.09% and 93.58%, respectively. These results highlight that the accuracy of one-step predictions surpasses that of two-step predictions. Also, accuracy decreases when predicting stock prices closer to an EE. The EEs identified from predicted time series exhibit statistical characteristics similar to those obtained from the original data.

Abstract

It is generally known that in a mixture of coupled active and inactive nonlinear oscillators, the entire system may stop oscillating and become inactive if the fraction of active oscillators is reduced to a critical value. This emerging phenomenon, called the “aging transition,” can be analytically predicted from the view point of cluster synchronization. One can question whether a model-free, data-driven framework based on neural networks could be used to foretell when such a system will cease oscillation. Here, we demonstrate how a straightforward ESN with trained output weights can accurately forecast both the temporal evaluation and the onset of collapse in coupled paradigmatic limit-cycle oscillators. In particular, we have demonstrated that an ESN can identify the critical fraction of inactive oscillators in a large all-to-all, small-world, and scale-free network when it is trained only with two nodes (one active and the other inactive) selected from three different pre-collapse regimes. We further demonstrate that ESN can anticipate aging transition of the network when trained with the mean-field dynamics of active and inactive oscillators.

Abstract

One of the most significant recent advances in technology is the advent of unmanned aerial vehicles (UAVs), i.e., drones. They have widened the scope of possible applications and provided a platform for a wide range of creative responses to a variety of challenges. The Internet of Drones (IoD) is a relatively new concept that has arisen as a consequence of the combination of drones and the Internet. The fifth-generation (5G) and beyond cellular networks (i.e., drones in networks beyond 5G) are promising solutions for achieving safe drone operations and applications. They may have many applications, like surveillance or urban areas, security, surveillance, retaliation, delivering items, smart farming, film production, capturing nature videos, and many more. Due to the fact that it is susceptible to a wide variety of cyber-attacks, there are certain concerns regarding the privacy and security of IoD communications. In this paper, a secure blockchain-enabled authentication key management framework with the big data analytics feature for drones in networks beyond 5G applications is proposed (in short, SBBDA-IoD). The security of SBBDA-IoD against multiple attacks is demonstrated through a detailed security analysis. The Scyther tool is used to perform a formal security verification test on the SBBDA-IoD’s security, confirming the system’s resistance to various potential attacks. A detailed comparative analysis has identified that SBBDA-IoD outperforms the other schemes by a significant margin. Finally, a real-world implementation of SBBDA-IoD is shown to evaluate its effect on several measures of performance.