Solution of linear ill-posed problems using random dictionaries

In the present paper we consider application of overcomplete dictionaries to solution of general ill-posed linear inverse problems. In the context of regression problems, there has been enormous amount of effort to recover an unknown function using such dictionaries. One of the most popular methods, lasso and its versions, is based on minimizing empirical likelihood and unfortunately, requires stringent assumptions on the dictionary, the, so called, compatibility conditions. Though compatibility conditions are hard to satisfy, it is well known that this can be accomplished by using random dictionaries. In the present paper, we show how one can apply random dictionaries to solution of ill-posed linear inverse problems. We put a theoretical foundation under the suggested methodology and study its performance via simulations

Solution of Linear Ill-posed Problems Using Overcomplete Dictionaries

In this dissertation, we consider an application of overcomplete dictionaries to the solution of general ill-posed linear inverse problems. In the context of regression problems, there has been an enormous amount of effort to recover an unknown function using such dictionaries. While some research on the subject has been already carried out, there are still many gaps to address. In particular, one of the most popular methods, lasso, and its variants, is based on minimizing the empirical likelihood and unfortunately, requires stringent assumptions on the dictionary, the so-called, compatibility conditions. Though compatibility conditions are hard to satisfy, it is well known that this can be accomplished by using random dictionaries. In the first part of the dissertation, we show how one can apply random dictionaries to the solution of ill-posed linear inverse problems with Gaussian noise. We put a theoretical foundation under the suggested methodology and study its performance via simulations and real-data example. In the second part of the dissertation, we investigate the application of lasso to the linear ill-posed problems with non-Gaussian noise. We have developed a theoretical background for the application of lasso to such problems and studied its performance via simulations

The Role of Mesenchymal Stromal Cells in the Management of Osteoarthritis of the Knee

Osteoarthritis (OA) is one of the most common chronic, inflammatory, and degenerative diseases affecting the synovial joints, the hip, and the knee. OA is commonly managed clinically by treating pain with anti-inflammatory medicines using nonsteroidal anti-inflammatory drugs (NSAIDS) or analgesics. In severe OA patients, invasive knee replacement surgery is the last option. Treatment of OA using mesenchymal stromal cells (MSCs) has been widely explored due to their anti-inflammatory properties and chondrogenic differentiation potential. In this chapter, we comprehensively discuss in detail the in vitro OA potency development, OA preclinical studies, and clinical trials conducted using MSCs

Analogy-based Instruction for Effective Teaching of Abstract Concepts in Computer Science

[EN] In the analogy-based learning method we map a concept that is being learned to a well-understood concept. An analogy is mainly useful when learners lack prior knowledge of the topic being learned. Computer Science (CS) is a subject whose concepts tend to be highly abstract and therefore difficult for undergraduate students to understand. Analogy-based instruction can greatly reduce a student’s burden of learning these abstract CS concepts. Role of analogy in teaching CS topics has not been adequately explored. In this paper we discuss analogy-based instruction in computer science and its advantages. Over the last decade we have developed analogies for a large number of difficult CS concepts and extensively used them in the classroom at our institution. We list these analogies and as an illustration discuss one of them (from the subfield of operating systems) in detail. We also present the evaluation of our analogy-based instruction method. Our results indicate that our techniques are quite effective in improving student learning outcomes.Saxena, P.; Singh, S.; Gupta, G. (2021). Analogy-based Instruction for Effective Teaching of Abstract Concepts in Computer Science. En 7th International Conference on Higher Education Advances (HEAd'21). Editorial Universitat Politècnica de València. 377-385. https://doi.org/10.4995/HEAd21.2021.13115OCS37738

Study of plasma fibrinogen level and its relation to glycemic control in type-2 diabetes mellitus patients attending diabetes clinic at a tertiary care teaching hospital in Madhya Pradesh, India

Background: Diabetes mellitus comprises a group of common metabolic disorders where increased fibrinogen levels can act as a thrombogenic factor. Diabetic patients have higher cardiovascular morbidity than non-diabetic subjects. Several studies have shown that haemostatic factor especially hyperfibrinogenemia is implicated as a source of atherosclerosis and its complications.Methods: A comparative observational study was conducted to compare fibrinogen levels between type 2 diabetes patients and healthy controls. Their fibrinogen levels were compared and co-related with glycemic status and other risk factors and parameters like glycosylated haemoglobin, age, sex, smoking, body mass index (kg/m2), hypertension and ischemic heart disease.Results: It was seen that in the diabetic subset, the plasma fibrinogen levels are significantly higher than the non-diabetic subset (386.04±132.87 vs. 314.38±97.42; p<0.001). Our study re-established correlation between HbA1c and fibrinogen levels of the diabetic patient is positive i.e., poorer the glycemic status, higher the fibrinogen levels (r=0.24).Conclusions: It can be concluded from the study that fibrinogen levels are in­creased in type 2 diabetic subjects with and without CHD. Plasma fibrinogen levels usually increased in type 2 diabetes, thus suggesting that hyperfibrinogenemia could contribute to the excess cardiovascular morbidity and mortality in this disease

Citation sentence reuse behavior of scientists: A case study on massive bibliographic text dataset of computer science

Our current knowledge of scholarly plagiarism is largely based on the similarity between full text research articles. In this paper, we propose an innovative and novel conceptualization of scholarly plagiarism in the form of reuse of explicit citation sentences in scientific research articles. Note that while full-text plagiarism is an indicator of a gross-level behavior, copying of citation sentences is a more nuanced micro-scale phenomenon observed even for well-known researchers. The current work poses several interesting questions and attempts to answer them by empirically investigating a large bibliographic text dataset from computer science containing millions of lines of citation sentences. In particular, we report evidences of massive copying behavior. We also present several striking real examples throughout the paper to showcase widespread adoption of this undesirable practice. In contrast to the popular perception, we find that copying tendency increases as an author matures. The copying behavior is reported to exist in all fields of computer science; however, the theoretical fields indicate more copying than the applied fields

Development and Validation of the SPoRT AOD Composite Product for Data Assimilation

No abstract availabl

An assessment of antihypertensive drug prescription patterns and adherence to joint national committee-8 hypertension treatment guidelines among hypertensive patients attending a tertiary care teaching hospital

Background: The new guidelines issued by the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure (JNC-8) emphasize that aggressive blood pressure (BP) control is essential to reducing morbidity and mortality. Patient non-adherence is a serious obstacle to the effective treatment of many acute and chronic disorders. Successful treatment and outcome of a chronic disease such as hypertension depend on many factors, including resources (e.g., funds, space, and people), avoidance of serious adverse events, patient adherence with treatment plans, and the availability of effective therapies. The aim of this study is to assess the antihypertensive drug prescription patterns and adherence to joint national committee (JNC-8) hypertension (HT) treatment recommendations among hypertensive patients attending a tertiary care teaching hospital.Methods: An observational and cross-sectional prospective prescription audit study was carried over a period of 1 year in ambulatory patients attending medicine OPD. A total of 500 prescriptions prescribed to diagnose HT were analyzed. Drug prescription patterns, and their adherence to JNC-8 report was assessed.Results: Out of 500 patients, 299 (59.8%) were male and 201 (40.2%) were female. Mean age of male and female patients were found to be 57.68±15.32 and 61.29±12.65 years respectively. As per present study, most of the physicians prescribed single drug (monotherapy, 34.6%) to control BP followed by two-drug combination (18.4%), three-drug combination (11.8%) and four-drug combination (3%). Two drugs regimen was prescribed in 18.4% of the hypertensive patients. Angiotensin receptor blocker + diuretic combination (4.4%) was mostly used in two drug combination therapy followed by Angiotensin receptor blockers + Diuretics (3.6%) and Calcium channel blocker + ACEIs combination (2.6%). No combination of ACEIs + ARBs was prescribed in any prescription. The overall rate of adherence was 16.5 % (Pre-hypertension); 87.90% (Stage 1 hypertension); and 68.20% (Stage 2 hypertension).Conclusions: In conclusion, present study demonstrated that physicians are not completely adhering to standard guidelines while treating hypertension with comorbid conditions.