Abstract

Research and fabrication of micro devices hold the key to low power devices, sensors and lab-on-a-chip systems which are in much need today for diagnosis, IOT etc. Cost of setting up a fabrication laboratory and conducting photolithography is high due to need of expensive equipment. We present a design of low-cost UV exposure system which can be used for both high resolution and low resolution photolithography. In first part, we present the study of irradiance distribution patterns of different UV LED arrangements. We use uniformity as a criterion for selecting the arrangement that gives the best illumination uniformity in largest area. The maximum, minimum and average exposure energy and uniformity have been presented. Using these results, we present an optimal system design for UV photolithography. The novelty of our work is simulating the UV exposure system for different parameters for making an optimal choice for a versatile system (high energy & low energy), different uniformity for different resolution to save power and have better control.

Abstract

A TeV scale leptoquark (LQ) is one of the promising explanations of the recent anomalies in the semileptonic decays of 퐵 mesons. Among the various LQs, the vector U1 is capable of explaining the anomalies in both 푅퐷(∗) and 푅퐾 (∗) observables. We use the current LHC data to put bounds on the parameter space of U1 relevant for the anomalies. Precise bounds are drawn by recasting the latest 휏휏 and 휇휇 searches by the ATLAS and CMS collaborations. We find that it is imperative to include the resonant production modes for obtaining limits in the low mass regions. For higher mass points, the non-resonant production modes play a dominant role.

Abstract

One of the major challenges in the health care sector is that approximately 80% of generated data remains unstructured and unused. Since it is difficult to handle unstructured data from electronic medical record systems, it tends to be neglected for analyses in most hospitals and medical centers. Therefore, there is a need to analyze unstructured big data in health care systems so that we can optimally utilize and unearth all unexploited information from it. Objective: In this study, we aimed to extract a list of diseases and associated keywords along with the corresponding time durations from an indigenously developed electronic medical record system and describe the possibility of analytics from the acquired datasets. Methods: We propose a novel, finite-state machine to sequentially detect and cluster disease names from patients’ medical history. We defined 3 states in the finite-state machine and transition matrix, which depend on the identified keyword. In addition, we also defined a state-change action matrix, which is essentially an action associated with each transition. The dataset used in this study was obtained from an indigenously developed electronic medical record system called eyeSmart that was implemented across a large, multitier ophthalmology network in India. The dataset included patients’past medical history and contained records of 10,000 distinct patients. Results: We extracted disease names and associated keywords by using the finite-state machine with an accuracy of 95%, sensitivity of 94.9%, and positive predictive value of 100%. For the extraction of the duration of disease, the machine’s accuracy was 93%, sensitivity was 92.9%, and the positive predictive value was 100%. Conclusions: We demonstrated that the finite-state machine we developed in this study can be used to accurately identify disease names, associated keywords,

Abstract

Witness testimonies are important constituents of a court case description and play a significant role in the final decision. We propose two techniques to identify sentences representing witness testimonies. The first technique employs linguistic rules whereas the second technique applies distant supervision where training set is constructed automatically using the output of the first technique. We then represent the identified witness testimonies in a more meaningful structure – event verb (predicate) along with its arguments corresponding to semantic roles A0 and A1 [1]. We demonstrate effectiveness of such representation in retrieving semantically similar prior relevant cases. To the best of our knowledge, this is the first paper to apply NLP techniques to extract witness information from court judgements and use it for retrieving prior court cases.

Abstract

This paper presents an overview of Apertium, a free and open-source rule-based machine translation platform. Translation in Apertium happens through a pipeline of modular tools, and the platform continues to be improved as more language pairs are added. Several advances have been implemented since the last publication, including some new optional modules: a module that allows rules to process recursive structures at the structural transfer stage, a module that deals with contiguous and discontiguous multi-word expressions, and a module that resolves anaphora to aid translation. Also highlighted is the hybridisation of Apertium through statistical modules that augment the pipeline, and statistical methods that augment existing modules. This includes morphological disambiguation, weighted structural transfer, and lexical selection modules that learn from limited data. The paper also discusses how a platform like Apertium can be a critical part of access to language technology for so-called low-resource languages, which might be ignored or deemed unapproachable by popular corpus-based translation

Abstract

The Artificial Intelligence of Things (AIoT) is the amalgamation of Artificial Intelligence (AI) methods and the Internet of Things (IoT) infrastructure, which are deployed there to improve the overall performance of the system. AIoT can be deployed to achieve more efficient IoT operations; thereby can improve human-machine interactions and provide better data analysis. AI methods can be used to transform IoT data into useful information for the better decision-making processes, and it further increases the overall usability of the system. AIoT frameworks are very useful and applicable in a variety of applications, like security and surveillance system, smart home, intelligent transportation system, smart farming, secure and safe healthcare monitoring, industrial automation and control, eCommerce, logistics operations and control, and many more. However, AIoT frameworks may have issues related to data security and privacy as they are vulnerable to various types of information security-related attacks. These issues further cause the serious consequences, like the unauthorized data leakage and data update. Blockchain is a specific type of database. It is a digital ledger of transactions, which is duplicated and distributed across the entire network of computer systems. It stores data in the form of some blocks, which are then chained together. Blockchain is tamper proof and provides more security as compared to the traditional security mechanisms. Hence, blockchain can be integrated in various AIoT applications to provide more security. A generalized blockchain-envisioned secure authentication framework for AIoT has been proposed. The adversary model of blockchain-envisioned secure authentication framework for AIoT is also highlighted that covers most of the potential threats of a kind of communication environment. Various applications of the proposed framework are also discussed. Furthermore, different issues and challenges of the proposed framework are highlighted. In the end, we also provide some future research directions relevant to the proposed framework.

Abstract

COVID-19 infodemic has been spreading faster than the pandemic itself. The misinformation riding upon the infodemic wave poses a major threat to people’s health and governance systems. Managing this infodemic not only requires mitigating misinformation but also an early understanding of underlying psychological patterns. In this study, we present a novel epidemic response management strategy. We analyze the psychometric impact and coupling of COVID19 infodemic with official COVID-19 bulletins at the national and state level in India. We looked at them from the psycholinguistic lens of emotions and quantified the extent and coupling between them. We modified Empath, a deep skipgram-based lexicon builder, for effective capture of health-related emotions. Using this, we analyzed the lead-lag relationships between the time-evolution of these emotions in social media and official bulletins using Granger’s causality. It showed that state bulletins led the social media for some emotions such as Medical Emergency. In contrast, social media led the government bulletins for some topics such as hygiene, government, fun, and leisure. Further insights potentially relevant for policymakers and communicators engaged in mitigating misinformation are also discussed. We also introduce CoronaIndiaDataset, the first social-media-based Indian COVID-19 dataset at the national and state levels with over 5.6 million national and 2.6 million state-level tweets for the first wave of COVID-19 in India and 1.2 million national tweets for the second wave of COVID-19 in India.

Abstract

n this work, we revisit the theory of open quantum systems from the perspective of fermionic baths. Specifically, we concentrate on the dynamics of a central spin half particle interacting with a spin bath. We have calculated the exact reduced dynamics of the central spin and constructed the Kraus operators in relation to that. Further, the exact Lindblad type canonical master equation corresponding to the reduced dynamics is constructed. We have also briefly touched upon the aspect of non-Markovianity from the backdrop of the reduced dynamics of the central spin. Quanta 2021; 10: 55–64

Abstract

ccurate extraction of the cortical brain surface is critical for cortical thickness estimation and a key element to perform multimodal imaging analysis, where different metrics are integrated and compared in a common space. While brain surface extraction has become widespread practice in human studies, several challenges unique to neuroimaging of non-human primates (NHP) have hindered its adoption for the study of macaques. Although, some of these difficulties can be addressed at the acquisition stage, several common artifacts can be minimized through image preprocessing. Likewise, there are several image analysis pipelines for human MRIs, but very few automated methods for extraction of cortical surfaces have been reported for NHPs and none have been tested on data from diverse sources. We present PREEMACS, a pipeline that standardizes the preprocessing of structural MRI images (T1- and T2-weighted) and carries out an automatic surface extraction of the macaque brain. Building upon and extending pre-existing tools, the first module performs volume orientation, image cropping, intensity non-uniformity correction, and volume averaging, before skull-stripping through a convolutional neural network. The second module performs quality control using an adaptation of MRIqc method to extract objective quality metrics that are then used to determine the likelihood of accurate brain surface estimation. The third and final module estimates the white matter (wm) and pial surfaces from the T1-weighted volume (T1w) using an NHP customized version of FreeSurfer aided by the T2-weighted volumes (T2w). To evaluate the generalizability of PREEMACS, we tested the pipeline using 57 T1w/T2w NHP volumes acquired at 11 different sites from the PRIME-DE public dataset. Results showed an accurate and robust automatic brain surface extraction from images that passed the quality control segment of our pipeline. This work offers

Abstract

Extensive searches to probe the particle nature of dark matter (DM) have been going on for some decades now but, so far, no conclusive evidence has been found. Among various options, the Weakly Interacting Massive Particles (WIMP) remains one of the prime possibilities as candidates for DM near the TeV scale. Taking a phenomenological view, such null results may be explained for a generic WIMP in a Higgs-portal scenario if we allow the light-quark Yukawa couplings to assume non-Standard Model (non-SM)-like values. This follows from a cancellation among different terms in the DM-nucleon scattering which can, in turn, lead to a vanishingly small direct-detection cross section. It might also lead to isospin violation in the DM-nucleon scattering. Such non-SM values of light-quark Yukawa couplings may be probed in the high luminosity run of the LHC.