Comparing Information-Theoretic Measures of Complexity in Boltzmann Machines

In the past three decades, many theoretical measures of complexity have been proposed to help understand complex systems. In this work, for the first time, we place these measures on a level playing field, to explore the qualitative similarities and differences between them, and their shortcomings. Specifically, using the Boltzmann machine architecture (a fully connected recurrent neural network) with uniformly distributed weights as our model of study, we numerically measure how complexity changes as a function of network dynamics and network parameters. We apply an extension of one such information-theoretic measure of complexity to understand incremental Hebbian learning in Hopfield networks, a fully recurrent architecture model of autoassociative memory. In the course of Hebbian learning, the total information flow reflects a natural upward trend in complexity as the network attempts to learn more and more patterns.Comment: 16 pages, 7 figures; Appears in Entropy, Special Issue "Information Geometry II

Pre-asymptotic expansions

We introduce the concepts of pre-asymptotic schemes and pre-asymptotic expansions to study the divergent series that formally are solutions of various types of equations. © 1996 Academic Press, Inc

Balancing Privacy and Progress in Artificial Intelligence: Anonymization in Histopathology for Biomedical Research and Education

The advancement of biomedical research heavily relies on access to large amounts of medical data. In the case of histopathology, Whole Slide Images (WSI) and clinicopathological information are valuable for developing Artificial Intelligence (AI) algorithms for Digital Pathology (DP). Transferring medical data "as open as possible" enhances the usability of the data for secondary purposes but poses a risk to patient privacy. At the same time, existing regulations push towards keeping medical data "as closed as necessary" to avoid re-identification risks. Generally, these legal regulations require the removal of sensitive data but do not consider the possibility of data linkage attacks due to modern image-matching algorithms. In addition, the lack of standardization in DP makes it harder to establish a single solution for all formats of WSIs. These challenges raise problems for bio-informatics researchers in balancing privacy and progress while developing AI algorithms. This paper explores the legal regulations and terminologies for medical data-sharing. We review existing approaches and highlight challenges from the histopathological perspective. We also present a data-sharing guideline for histological data to foster multidisciplinary research and education.Comment: Accepted to FAIEMA 202

Impact of Leaders’ Emotional and Cultural Intelligence on Leadership Effectiveness: Mediating Role of Transformational Leadership

For leaders to serve as effective change agents in the organization, significance of leaders’ intelligence cannot be overlooked. Current empirical study was carried out with an intention to investigate the impact of leaders’ emotional and cultural intelligence on leadership effectiveness through the mediating role of transformational leadership as perceived by followers. With the help of questionnaire, data was collected from the employees of private banks of twin cities (Islamabad and Rawalpindi) of Pakistan. A total of 262 responses were entered in SPSS for analyzing data and interpreting results. The mediating, dependent and independent variables were modeled in a path diagram and tested through structural equation modeling (SEM). The findings of the study indicate that transformational leadership fully mediates the relationships of leaders’ emotional and cultural intelligence with leadership effectiveness. The results of the study provide useful insight into the fact that emotionally and culturally intelligent leaders are more effective because they exhibit more transformational leadership style. Theoretical and practical implications of the findings are discussed along with limitations and recommendations for future researc

Fuzz-classification (p, l)-Angel: An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches

The inability of traditional privacy-preserving models to protect multiple datasets based on sensitive attributes has prompted researchers to propose models such as SLOMS, SLAMSA, (p, k)-Angelization, and (p, l)-Angelization, but these were found to be insufficient in terms of robust privacy and performance. (p, l)-Angelization was successful against different privacy disclosures, but it was not efficient. To the best of our knowledge, no robust privacy model based on fuzzy logic has been proposed to protect the privacy of sensitive attributes with multiple records. In this paper, we suggest an improved version of (p, l)-Angelization based on a hybrid AI approach and privacy-preserving approach like Generalization. Fuzz-classification (p, l)-Angel uses artificial intelligence based fuzzy logic for classification, a high-dimensional segmentation technique for segmenting quasi-identifiers and multiple sensitive attributes. We demonstrate the feasibility of the proposed solution by modelling and analyzing privacy violations using High-Level Petri Nets. The results of the experiment demonstrate that the proposed approach produces better results in terms of efficiency and utility

Antiviral activity of organic molecules having sulfonamide moiety: An insight of recent research

Sulfonamide derivatives are well known for their antibacterial activity as manifested by ‘Sulfa Drugs’, for example, sulfamethoxazole etc. In addition, they are associated with a large number of pharmacological activities such as anti-microbial, anti-inflammatory, anti-cancer, anti-oxidant, anti-viral etc. This work has emphasized their application as antiviral agents such as HIV (human immunodeficiency virus), HCV (hepatitis C virus) etc. We have presented here a number of sulfonamide derivatives exhibiting remarkable antiviral potential

Hyperhomocysteinemia - An unidentified risk factor for stroke in our population

Introduction: Various studies show that moderate elevation of plasma homocysteine level has been associated with increased risk for cardiovascular and cerebrovascular disease. Objective: To observe the frequency of increased homocysteine level in ischemic stroke patients; and its association with other risk factors. Methodology: Observational pilot study was conducted on a sample of 75 ischemic stroke patients, enrolled regardless of their age, gender and comorbidities, at Ziauddin university hospital, Karachi. Fasting serum homocysteine, folate and vitamin B12 levels were measured. Results were interpreted using spss 20.0. Results and Discussion: Mean homocysteine level in our population was 19.51 (SD: 11.47)micromol/l. It was higher in groups with vitamin B12 and folic acid deficiency, difference being statistically significant (p=0.013 and 0.017, respectively). Males had greater propensity to hyperhomocysteinemia; the mean homocysteine value being higher, and the difference, statistically significant (p=0.010). Other factors that affect homocysteine levels were also evaluated, that is hypertension, increased cholesterol levels and smoking. There was no significant statistical difference in the homocysteine value between the groups of patients who had these risk factors and the groups that did not (p=0.747, 0.252 and 0.565, respectively). Conclusion: It was speculated that hyperhomocysteinemia is an imperative risk factor for stroke

CatSper ion channels: Bioinformatics analysis in Homo sapiens

Due to the availability of huge amount of molecular biology data, our main focus was to determine the protein structures, functions and their role in different molecular pathways. The 3-D structure prediction of protein is important in medicine and biotechnology. Molecular docking not only finds the interaction between proteins but also the accurate models of energy of these interacting proteins and helps in further designing of the better drug for that particular protein. The drug targeting is either to inhibit, restore or for the modification of the protein structure. CatSper protein family is calcium ion permeable channels, located in the plasma membrane of sperm tail. It contains a conserved domain of six transmembrane helices in their protein sequence. These four CatSper proteins (1 to 4) assemble and form tetramer, calcium selective channel. It has been found that all members of CatSper protein family (1-4) have a role in hyperactivation in sperm and fertilization processes. As a result of deletion of certain regions (bps) containing these genes along with some other genes, male infertility occurs. We have predicted and analyzed the 3D structures of all members of CatSper protein family in this article. Docking of predicted 3D structures of CatSper protein family, with calcium ion was also performed to verify their interactions.Key words: CatSper, bioinformatics analysis, infertility, cation channel

SATL Based Lesson for Teaching Grignard Reagents in Synthetic Organic Chemistry

Synthesizing new products from raw materials has been very popular aspects of research in organic chemistry. Traditionally, Grignard reagent has been very vital component of such synthetic procedures. Hence learning of various issues concerning with applications of Grignard reactions in synthetic organic chemistry is vital for enhancing the students creative capability. In this paper we will illustrate the uses of SATL methodology, which is recently getting popular [1- 3], in an SATL-based model lesson concerning teaching and learning of synthetic organic reactions related to Grignard reagents