Analysis and Synthesis Prior Greedy Algorithms for Non-linear Sparse Recovery

In this work we address the problem of recovering sparse solutions to non linear inverse problems. We look at two variants of the basic problem, the synthesis prior problem when the solution is sparse and the analysis prior problem where the solution is cosparse in some linear basis. For the first problem, we propose non linear variants of the Orthogonal Matching Pursuit (OMP) and CoSamp algorithms; for the second problem we propose a non linear variant of the Greedy Analysis Pursuit (GAP) algorithm. We empirically test the success rates of our algorithms on exponential and logarithmic functions. We model speckle denoising as a non linear sparse recovery problem and apply our technique to solve it. Results show that our method outperforms state of the art methods in ultrasound speckle denoising

CertViT: Certified Robustness of Pre-Trained Vision Transformers

Lipschitz bounded neural networks are certifiably robust and have a good trade-off between clean and certified accuracy. Existing Lipschitz bounding methods train from scratch and are limited to moderately sized networks (< 6M parameters). They require a fair amount of hyper-parameter tuning and are computationally prohibitive for large networks like Vision Transformers (5M to 660M parameters). Obtaining certified robustness of transformers is not feasible due to the non-scalability and inflexibility of the current methods. This work presents CertViT, a two-step proximal-projection method to achieve certified robustness from pre-trained weights. The proximal step tries to lower the Lipschitz bound and the projection step tries to maintain the clean accuracy of pre-trained weights. We show that CertViT networks have better certified accuracy than state-of-the-art Lipschitz trained networks. We apply CertViT on several variants of pre-trained vision transformers and show adversarial robustness using standard attacks. Code : \url{https://github.com/sagarverma/transformer-lipschitz

Analysing the Differential Performances of Indian States in the Tourism Sector : (1947-early 2020)

Tourism is an ever evolving and a dynamic industry which can play a crucial role in increasing income and in providing employment opportunities in an economy. India especially with its rich heritage, culture and geographical landscapes has always had immense potential to become a leading tourist destination. Presently the major types of tourism prevalent in India are Medical Tourism, Rural/ Natural Tourism, Religious Tourism and Historical&amp; Educational Tourism. In 2018-19, the tourism sector contributed around 5% to India’s GDP. However with the health shock of Covid-19, the tourism sector has taken a major hit since early 2020, with several people losing their jobs in the tourism and hospitality sector when different states imposed lockdowns and took various measures to curb the pandemic. As restrictions in each state eased during the first wave of the pandemic, different states in India adopted various policies to revive the tourism industry. But to understand the effectiveness of these policies in each state/ UT, one needs to investigate the baseline at which the Tourism industry was before the pandemic hit the country. This paper attempts to look at the differential performances of states and UTs of India in tourism by categorizing them into various types of tourism between 1947 until March 2020.This paper aims to act as a base for further analysing the impact of this pandemic on Tourism across states in India

Array Diagnosis using Compressed Sensing in Near Field

This paper will present a technique for array diagnosis using a small number of measured data acquired by a near-field system by making use of the concepts of compressed sensing technique in image processing. Here, the high cost of large array diagnosis in near-field facilities is mainly caused by the time required for the data acquisition. So there is a need to decrease the measurement time and at the same time the reconstruction of an array must be satisfactory. The proposed technique uses less number of measurement points compared to other proposed techniques like back-propagation method and standard matrix inversion method. Keywords: Arrays, Compressed sensing, near fiel

Pretreatment of lignocelluloses for enhanced biogas production: A review on influencing mechanisms and the importance of microbial diversity

Received 13 August 2019; Received in revised form 10 July 2020; Accepted 28 July 2020, Available online 11 August 2020.As one of the most efficient methods for waste management and sustainable energy production, anaerobic digestion (AD) countenances difficulties in the hydrolysis of lignocelluloses biomass. Different pretreatment methods have been applied to make lignocelluloses readily biodegradable by microorganisms. These pretreatments can affect biogas yield by different mechanisms at molecular scale, including changes in chemical composition, cellulose crystallinity, degree of polymerization, enzyme adsorption/desorption, nutrient accessibility, deacetylation, and through the formation of inhibitors. The present article aims at critically reviewing the reported molecular mechanisms affecting biogas yield from lignocelluloses via different types of pretreatments. Then, a new hypothesis concerning the impact of pretreatment on the microbial community developed (throughout the AD process from an identical inoculum) was also put forth and was experimentally examined through a case study. Four different leading pretreatments, including sulfuric acid, sodium hydroxide, aqueous ammonia, and sodium carbonate, were performed on rice straw as model lignocellulosic feedstock. The results obtained revealed that the choice of pretreatment method also plays a pivotally positive or negative role on biogas yield obtained from lignocelluloses through alteration of the microbial community involved in the AD. Considerable changes were observed in the archaeal and bacterial communities developed in response to the pretreatment used. Sodium hydroxide, with the highest methane yield (338 mL/g volatile solid), led to a partial switch from acetoclastic to the hydrogenotrophic methane production pathway. The findings reported herein undermine the default hypothesis accepted by thousands of previously published papers, which is changes in substrate characteristics by pretreatments are the only mechanisms affecting biogas yield. Moreover, the results obtained could assist with the development of more efficient biogas production systems at industrial scale by offering more in-depth understanding of the interactions between microbial community structure, and process parameters and performance

Synthetic Nanoparticles for Vaccines and Immunotherapy

The immune system plays a critical role in our health. No other component of human physiology plays a decisive role in as diverse an array of maladies, from deadly diseases with which we are all familiar to equally terrible esoteric conditions: HIV, malaria, pneumococcal and influenza infections; cancer; atherosclerosis; autoimmune diseases such as lupus, diabetes, and multiple sclerosis. The importance of understanding the function of the immune system and learning how to modulate immunity to protect against or treat disease thus cannot be overstated. Fortunately, we are entering an exciting era where the science of immunology is defining pathways for the rational manipulation of the immune system at the cellular and molecular level, and this understanding is leading to dramatic advances in the clinic that are transforming the future of medicine.1,2 These initial advances are being made primarily through biologic drugs– recombinant proteins (especially antibodies) or patient-derived cell therapies– but exciting data from preclinical studies suggest that a marriage of approaches based in biotechnology with the materials science and chemistry of nanomaterials, especially nanoparticles, could enable more effective and safer immune engineering strategies. This review will examine these nanoparticle-based strategies to immune modulation in detail, and discuss the promise and outstanding challenges facing the field of immune engineering from a chemical biology/materials engineering perspectiveNational Institutes of Health (U.S.) (Grants AI111860, CA174795, CA172164, AI091693, and AI095109)United States. Department of Defense (W911NF-13-D-0001 and Awards W911NF-07-D-0004

Procalcitonin Is Not a Reliable Biomarker of Bacterial Coinfection in People With Coronavirus Disease 2019 Undergoing Microbiological Investigation at the Time of Hospital Admission

Abstract Admission procalcitonin measurements and microbiology results were available for 1040 hospitalized adults with coronavirus disease 2019 (from 48 902 included in the International Severe Acute Respiratory and Emerging Infections Consortium World Health Organization Clinical Characterisation Protocol UK study). Although procalcitonin was higher in bacterial coinfection, this was neither clinically significant (median [IQR], 0.33 [0.11–1.70] ng/mL vs 0.24 [0.10–0.90] ng/mL) nor diagnostically useful (area under the receiver operating characteristic curve, 0.56 [95% confidence interval, .51–.60]).</jats:p

Implementation of corticosteroids in treating COVID-19 in the ISARIC WHO Clinical Characterisation Protocol UK:prospective observational cohort study

BACKGROUND: Dexamethasone was the first intervention proven to reduce mortality in patients with COVID-19 being treated in hospital. We aimed to evaluate the adoption of corticosteroids in the treatment of COVID-19 in the UK after the RECOVERY trial publication on June 16, 2020, and to identify discrepancies in care. METHODS: We did an audit of clinical implementation of corticosteroids in a prospective, observational, cohort study in 237 UK acute care hospitals between March 16, 2020, and April 14, 2021, restricted to patients aged 18 years or older with proven or high likelihood of COVID-19, who received supplementary oxygen. The primary outcome was administration of dexamethasone, prednisolone, hydrocortisone, or methylprednisolone. This study is registered with ISRCTN, ISRCTN66726260. FINDINGS: Between June 17, 2020, and April 14, 2021, 47 795 (75·2%) of 63 525 of patients on supplementary oxygen received corticosteroids, higher among patients requiring critical care than in those who received ward care (11 185 [86·6%] of 12 909 vs 36 415 [72·4%] of 50 278). Patients 50 years or older were significantly less likely to receive corticosteroids than those younger than 50 years (adjusted odds ratio 0·79 [95% CI 0·70–0·89], p=0·0001, for 70–79 years; 0·52 [0·46–0·58], p80 years), independent of patient demographics and illness severity. 84 (54·2%) of 155 pregnant women received corticosteroids. Rates of corticosteroid administration increased from 27·5% in the week before June 16, 2020, to 75–80% in January, 2021. INTERPRETATION: Implementation of corticosteroids into clinical practice in the UK for patients with COVID-19 has been successful, but not universal. Patients older than 70 years, independent of illness severity, chronic neurological disease, and dementia, were less likely to receive corticosteroids than those who were younger, as were pregnant women. This could reflect appropriate clinical decision making, but the possibility of inequitable access to life-saving care should be considered. FUNDING: UK National Institute for Health Research and UK Medical Research Council