86 research outputs found
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& 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
Recent technologies for transforming textile waste into value-added products:A review
The disposal of textile waste has become a growing issue worldwide. The rising consumption of clothing and textile materials has resulted in high waste generation. This could adversely impact environmental health, including humans, animals and plants. The three textile recycling methods can be divided into mechanical, chemical, and biological processes. There has been a focus on mechanical and chemical processes, pyrolysis, enzymatic hydrolysis, biological recycling, and microbial engineering in the area of textile waste managment. This review highlight the important parameters that affect the textile recycling performance and are significant for the success of the transformation process. To reach the zero-waste goal, textile recycling and converting this waste into value-added bioproducts are the necessary steps. The present review addresses the current status of textile recycling strategies, and the valorisation processes for converting textile waste materials into value-added products like biofuels, bioplastics, and others sustainable materials.</p
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
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