16 research outputs found
Study on Modular Lattice and Boolean Algebra
No full textThis thesis is submitted to the Department of Mathematics, Khulna University of Engineering & Technology in partial fulfillment of the requirements for the degree of Master of Philosophy in Mathematics, April 2008.Cataloged from PDF Version of Thesis.Includes bibliographical references (pages 85-86).This thesis studies extensively the nature of modular lattices and Boolean algebras. The modular lattices have been study by several authors including Abbott [2] , Birkhoff [ 3] and Rutherford [19 ]. A poset is said to form a lattice if for every a, b ϵ L, a ˅ b and a ˄ b exists in L, where ˅ , , ˄ are two binary operation . A lattice L is called modular lattice ifforall a,b,c ϵ L with a ≥b, a, ˄ (b ˅ c) = [b ˅ (a ˄ c)]. In this thesis we give several results on modular lattices which certainly extend and generalized many result in lattice theory. In chapter one we discuss ideals , complete lattices , relatively complemented lattices and other results on lattices which are basic to this thesis . If every interval in a lattice is complemented the lattice is said to be relatively complemented. Chapter two discusses Embeddings, Kernels and dual homomorphisms. If L, M be two lattices, a one-one homomorphism θ : L→ M is called an embedding mapping . Also in that case we say L is embedded in M. We prove that the definition of dual meet homomorphism and dual join homomorphism are equivalent.
In chapter three we discuss on modular lattices and distributive lattices Distributive lattices have been studied by sever author including Cignoli [ 4 ] , Cornish [ 5 ] , Cornish and Hicman [ 6 ] and Evans [ 7 ] Nieminen [15 ], [16] . Hence we prove a lattice L is distributive if and only if
(a ˅ b) ˄ (b ˅ c) ˄ (c ˅ a)=(a ˄ b) ˅ (b ˄ c) ˅ (c ˄ a) ˅ a, b, c ϵ L
In chapter four we discuss Boolean algebras and Boolean functions Previously Boolean algebras, Disjunctive Normal forms and Conjunctive Normal forms have studied by Abbott [ 1 ] . Here we extend several result on Boolean Algebras and also find the DN form of the function whose CN form is
f =(x ˅ y ˅ z) ˄ (x ˅ y ˅ z') ˄ (x ˅ y' ˅ z) ˄ (x ˅ y' ˅ z') ˄ (x' ˅ y ˅ z).Md. Rashidul IslamMaster of Philosophy in Mathematic
Earnings quality and financial flexibility: A moderating role of corporate governance
No full textThe aim of this study is primarily to demonstrate how earnings quality is an influential determinant of financial flexibility. Secondly, how earnings quality affects financial flexibility. And finally, to provide evidence of the role of corporate governance between earnings quality and financial flexibility composing overall corporate governance index (CG-INDEX). This study considered unbalanced panel data from the year 2007 to 2020 from the database CSMAR yielding 14,088 firm-year observations. This study used liquidity as the proxy of financial flexibility, and also used a comprehensive index of corporate governance constructed by adopting the principal component analysis and STATA has been used for analyzing data. The study used System GMM regression for analysis and controls endogeneity by applying lag financial flexibility as an instrumental variable. The empirical results reveal that poor earnings quality significantly negatively influences the level of corporate financial flexibility. The results also demonstrate that corporate governance can significantly positively moderate the relationship between earnings quality and financial flexibility. This suggests that when the earnings quality is poor, firms are less likely to be financially flexible in holding liquidity. More specifically, firms with poor earnings quality will reduce their financial flexibility of firms; hence, firms need to provide high-quality earnings in order to be more financially flexible. Earnings quality is an important factor, which led the author to examine how earnings quality influences financial flexibility. Under the views of agency theory and positive accounting theory, poor earnings quality is a source of amplified shareholder’s concern of increased informational asymmetry, which may adversely affect the firm’s financial flexibility. Conversely, higher earnings quality reduces the information asymmetry which leads to higher financial flexibility. This study provides a way how to achieve financial flexibility with the assistance of corporate governance which is essential to combat financial crises and smooth business operations successfully
Can We Obtain Fairness For Free?
No full textAIES '21: AAAI/ACM Conference on AI, Ethics, and Society, Virtual Event USA, May 19 - 21, 2021There is growing awareness that AI and machine learning systems can in some cases learn to behave in unfair and discriminatory ways with harmful consequences. However, despite an enormous amount of research, techniques for ensuring AI fairness have yet to see widespread deployment in real systems. One of the main barriers is the conventional wisdom that fairness brings a cost in predictive performance metrics such as accuracy which could affect an organization's bottom-line. In this paper we take a closer look at this concern. Clearly fairness/performance trade-offs exist, but are they inevitable? In contrast to the conventional wisdom, we find that it is frequently possible, indeed straightforward, to improve on a trained model's fairness without sacrificing predictive performance. We systematically study the behavior of fair learning algorithms on a range of benchmark datasets, showing that it is possible to improve fairness to some degree with no loss (or even an improvement) in predictive performance via a sensible hyper-parameter selection strategy. Our results reveal a pathway toward increasing the deployment of fair AI methods, with potentially substantial positive real-world impacts.This work was performed under the following financial assistance award: 60NANB18D227 from U.S. Department of Commerce, National Institute of Standards and Technology. This material is based upon work supported by the National Science Foundation under Grant No.’s IIS2046381; IIS1850023; IIS1927486. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.https://dl.acm.org/doi/10.1145/3461702.346261
Bayesian Modeling of Intersectional Fairness: The Variance of Bias
No full textProceedings of the 2020 SIAM International Conference on Data Mining (SDM), Hilton Cincinnati Netherland Plaza, Cincinnati, USA, on May 7–9, 2020.Intersectionality is a framework that analyzes how interlocking systems of power and oppression affect individuals along overlapping dimensions including race, gender, sexual orientation, class, and disability. Intersectionality theory therefore implies it is important that fairness in artificial intelligence systems be protected with regard to multi-dimensional protected attributes. However, the measurement of fairness becomes statistically challenging in the multi-dimensional setting due to data sparsity, which increases rapidly in the number of dimensions, and in the values per dimension. We present a Bayesian probabilistic modeling approach for the reliable, data-efficient estimation of fairness with multidimensional protected attributes, which we apply to two existing intersectional fairness metrics. Experimental results on census data and the COMPAS criminal justice recidivism dataset demonstrate the utility of our methodology, and show that Bayesian methods are valuable for the modeling and measurement of fairness in intersectional contexts.This work was performed under the following ?nancial as-sistance award: 60NANB18D227 from U.S. Department of Com-merce, National Institute of Standards and Technology. This ma-terial is based upon work supported by the National Science Foun-dation under Grant No. IIS 1850023. Any opinions, ?ndings, andconclusions or recommendations expressed in this material arethose of the author(s) and do not necessarily re?ect the views ofthe National Science Foundationhttps://epubs.siam.org/doi/abs/10.1137/1.9781611976236.4
Purity and properties of gelatins extracted from the head tissue of the hybrid kalamtra sturgeon
Get PDFThe head is a major by-product (17.1% of body weight) of sturgeon aquaculture farming and remains unutilized. In this study, the sturgeon head was separated into the skull cartilage and mixed tissue: skin, scales, fins, muscles, bones, gills, and small cartilage pieces. Type A and B gelatins were extracted from the mixed tissue. The type B yield was higher, at 5.8% gelatin dry weight per tissue wet weight. The relative gelatin content in the sample weight, estimated from the sample's hydroxyproline content relative to that of the purified collagen, was higher in type A (60.3%) than in type B (39.7%). Type A gelatin showed higher intensities of the alpha- and beta-bands of gelatin in SDS-PAGE, indicating that gelatin purity was higher in type A. The breaking force-strain curve showed a larger breaking force and lower breaking strain in type A (2.0 N, 12.1%), indicating that this gelatin is stronger than type B (0.7 N, 15.8%). Type B gelatin featured higher emulsion activity and stability and higher foam expansion and stability. In conclusion, type A and B gelatins with distinct proximate composition and functional properties were successfully extracted from sturgeon heads, which could act as a promising source of gelatins for industrial applications
An open label hepatoprotective activity of Jawārish bisbāsā in central obesity patients
No full text57-62Liver plays a major role in metabolism and excretion of xenobiotics. Liver dysfunction is a major health problem that
challenges health care professionals, pharmaceutical industry and drug regulatory agencies. The most common symptoms
are fatigue and discomfort in abdomen while in patients who are obese with BMI > 25, about one third have metabolic
syndromes. The present clinical research was conducted in Regional Research Institute of Unani Medicine, Aligarh. The
patients under trial were selected from GOPD of institute for obesity and it was observed that out of total registered patients’
approx 28% have elevated liver enzymes from its normal range without any sign and symptom of hepatitis etc. Total 23
patients were selected from the study whose liver enzymes (SGOT, SGPT and ALP) were high at the baseline. Unani
pharmacopoeial drug Jawārish bisbāsā 7 g daily was prescribed with lukewarm water in the morning and evening empty
stomach for 8 weeks. At the end of study the result were compared with base line. It was observed that Jawārish bisbāsā
significantly reduced the liver enzymes, e.g. SGOT, SGPT, Alkaline Phosphatase in comparison to baseline values. It may
be concluded that Jawārish bisbāsā showed hepatoprotective effect in this clinical study
Integrated Approach of Majoon-e-Falasfa and Halwa Gheekwar in Geriatric Care
No full textAims and Objectives: Old age is the stage of life which is full of health related issues and some other conditions including physiological, pathological, psychological, economic and sociological problems. The classical literature has abundance of text related to natural medicine that could prove to be beneficial in old age group individuals.
The various Unani physician mentioned many compound formulations which are very useful in old age like Majoon-e-Falasfa and Halwa Gheekwar. These medicines provide an extensive range of potentially beneficial drugs having various chemotherapeutic actions which have significantly enhanced human health and well-being.
In Unani classical literature comprehensive explanation of geriatric care is also mentioned under the heading of Tadabeer-e-Mashaikh. Unani literature record numerous single and compound plant based medicines, herbo-mineral, formulations for general wellbeing and in disease-specific conditions relating to geriatrics. In this paper, the authors have tried to highlight the use of unani compound formulation use for geriatric care.
Keywords: Unani medicine, Tadabeer-e-Mashaikh, Health, Majoon-e-Falasfa and Halwa Gheekwa
Equitable Allocation of Healthcare Resources with Fair Cox Models
No full textHealthcare programs such as Medicaid provide crucial services to vulnerable populations, but due to limited resources, many of the individuals who need these services the most languish on waiting lists. Survival models, e.g. the Cox proportional hazards model, can potentially improve this situation by predicting individuals' levels of need, which can then be used to prioritize the waiting lists. Providing care to those in need can prevent institutionalization for those individuals, which both improves quality of life and reduces overall costs. While the benefits of such an approach are clear, care must be taken to ensure that the prioritization process is fair or independent of demographic information-based harmful stereotypes. In this work, we develop multiple fairness definitions for survival models and corresponding fair Cox proportional hazards models to ensure equitable allocation of healthcare resources. We demonstrate the utility of our methods in terms of fairness and predictive accuracy on two publicly available survival datasets.This material is based upon work supported by the National Science Foundation under Grant No.’s IIS1927486; IIS1850023. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. This work was performed under the following financial assistance award: 60NANB18D227 from U.S. Department of Commerce, National Institute of Standards and Technologyhttps://epubs.siam.org/doi/10.1137/1.9781611976700.2
Debiasing Career Recommendations with Neural Fair Collaborative Filtering
No full textWWW ’21, April 19–23, 2021, Ljubljana, SloveniaA growing proportion of human interactions are digitized on social media platforms and subjected to algorithmic decision-making,
and it has become increasingly important to ensure fair treatment from these algorithms. In this work, we investigate gender bias
in collaborative-filtering recommender systems trained on social media data. We develop neural fair collaborative filtering (NFCF), a practical framework for mitigating gender bias in recommending career-related sensitive items (e.g. jobs, academic concentrations, or courses of study) using a pre-training and fine-tuning approach to neural collaborative filtering, augmented with bias correction techniques. We show the utility of our methods for gender de-biased career and college major recommendations on the MovieLens dataset and a Facebook dataset, respectively, and achieve better performance and fairer behavior than several state-of-the-art models.This work was performed under the following financial assistance award: 60NANB18D227 from U.S. Department of Commerce, National Institute of Standards and Technology. This material is based upon work supported by the National Science Foundation under Grant No.’s IIS1850023; IIS1927486. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.https://dl.acm.org/doi/abs/10.1145/3442381.344990
When Biased Humans Meet Debiased AI: A Case Study in College Major Recommendation
No full textCurrently, there is a surge of interest in fair Artificial Intelligence (AI) and Machine Learning (ML) research which aims to mitigate discriminatory bias in AI algorithms, e.g. along lines of gender, age, and race. While most research in this domain focuses on developing fair AI algorithms, in this work, we examine the challenges which arise when humans and fair AI interact. Our results show that due to an apparent conflict between human preferences and fairness, a fair AI algorithm on its own may be insufficient to achieve its intended results in the real world. Using college major recommendation as a case study, we build a fair AI recommender by employing gender debiasing machine learning techniques. Our offline evaluation showed that the debiased recommender makes fairer career recommendations without sacrificing its accuracy in prediction. Nevertheless, an online user study of more than 200 college students revealed that participants on average prefer the original biased system over the debiased system. Specifically, we found that perceived gender disparity is a determining factor for the acceptance of a recommendation. In other words, we cannot fully address the gender bias issue in AI recommendations without addressing the gender bias in humans. We conducted a follow-up survey to gain additional insights into the effectiveness of various design options that can help participants to overcome their own biases. Our results suggest that making fair AI explainable is crucial for increasing its adoption in the real world.This work was performed under the following inancial assistance award: 60NANB18D227 from U.S. Department
of Commerce, National Institute of Standards and Technology. This material is based upon work supported by
the National Science Foundation under Grant No.’s IIS2046381; IIS1850023; IIS1927486. Any opinions, indings,
and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily
relect the views of the National Science Foundation.https://dl.acm.org/doi/10.1145/361131
