AutoCkt: Deep Reinforcement Learning of Analog Circuit Designs

Domain specialization under energy constraints in deeply-scaled CMOS has been driving the need for agile development of Systems on a Chip (SoCs). While digital subsystems have design flows that are conducive to rapid iterations from specification to layout, analog and mixed-signal modules face the challenge of a long human-in-the-middle iteration loop that requires expert intuition to verify that post-layout circuit parameters meet the original design specification. Existing automated solutions that optimize circuit parameters for a given target design specification have limitations of being schematic-only, inaccurate, sample-inefficient or not generalizable. This work presents AutoCkt, a machine learning optimization framework trained using deep reinforcement learning that not only finds post-layout circuit parameters for a given target specification, but also gains knowledge about the entire design space through a sparse subsampling technique. Our results show that for multiple circuit topologies, AutoCkt is able to converge and meet all target specifications on at least 96.3% of tested design goals in schematic simulation, on average 40× faster than a traditional genetic algorithm. Using the Berkeley Analog Generator, AutoCkt is able to design 40 LVS passed operational amplifiers in 68 hours, 9.6× faster than the state-of-the-art when considering layout parasitics.

STUDY OF DISTINGUISHING FEATURES OF PAKISTANI STANDARD ENGLISH

The current research has adopted a qualitative approach to investigate the linguistic differences of Pakistani Standard English in contrast to British Standard English. We studied morphological, lexical, and hybrid characteristics of Pakistani Standard English. Besides, we investigated the linguistic features to prove the fact that cultural context determines the use of a language. Moreover, the findings of this research also support the fact that a language keeps evolving in different contexts leading to the development of different varieties of the language. However, the researchers have studied comparatively many varieties of Englishes, but this research investigates the distinguishing features of Pakistani Standard English employing secondary data from Dawn e-newspaper. Additionally, the researchers have also qualitatively codified the data into broader themes. The findings of this research will help readers in understanding the role of a cultural context in developing a new variety of a language. Consequently, they will be able to carry out further research in the field of World Englishes. Hence, this research is a systematic investigation of Pakistani Standard English and its differentiating features.

A CRITICAL DISCOURSE ANALYSIS OF AN ESSAY: EMPLOYING RUTH WODAK’S MODEL

Critical Discourse Analysis demystifies power mechanisms operating in different kinds of discourse. It sets forth hidden discourses and meanings for common people. Besides, the current research assignment has studied Bernard Lewis’ essay: The Roots of Muslim Rage using Ruth Wodak’s Discourse Historical Model (2004). Moreover, the researchers have employed purposive sampling as a research design to collect the data. The research is qualitative as it answers the research questions using Wodak’s model. The purpose of this research was to expose hermeneutic interpretations of orientalist discourse that reflect implausible thinking about Muslims. However, much work has been done on discourse of orientalism, yet from Wodak’s point of view much work is to be done; hence, the current research paper has also contributed to the field of critical discourse analysis. The subjectification of the Muslims through oriental norms is the main theme in Lewis’ essay as revealed by analysing lexical and syntactic units. Thus, the current research has concluded the findings in accordance with the research questions and research objectives.

BRCA1 Gene Expression is Down Regulated in Both Familial and Sporadic Breast Cancer Cases in Baghdad- Iraq

Breast cancer is the commonest cancer and the leading cause of malignancies-related mortality in women worldwide. Understanding the underlying biology of the disease could improve patients’ stratification and may offer novel therapeutic targets and strategies. This study was set to investigate the association between BRCA1 gene expression and some of the clinical features of breast cancer patients in Baghdad-Iraq. Eighty peripheral blood samples were collected from sixty patients diagnosed with breast cancer and twenty healthy age-matched controls for BRCA1 qPCR gene expression analysis.      The results showed a significant reduction in BRCA1 gene expression in all of the breast cancer patients with the vast majority of them (75%) having BRCA1expression below 25%. The down regulation of BRCA1 expression also showed consistency in breast cancer patients of both sporadic (n=45) and family history (n=15) cases,with expression averages of 18% and 20.19%, respectively. Moreover, the redcuation in BRCA1 expression was negatively associated with the disease’s grades,asbreast cancer patients with the advanced stage III (n=19) showed the lowest expression average of BRCA1 (13.8%) as compared to those in stages II (n=29) and I (n=12) of the disease (17.7% and 19.8%, respectively). Overall, the study highlights the key role of BRCA1gene expression in the development of breast cancer and suggests its potential utility in the diagnosis strategies and preventing the progression of the disease, especially the sporadic type.

Human Action Recognition Based on Bag-of-Words

Human action recognition has gained popularity because of its wide applicability, such as in patient monitoring systems, surveillance systems, and a wide diversity of systems that contain interactions between people and electrical devices, including human computer interfaces. The proposed method includes sequential stages of object segmentation, feature extraction, action detection and then action recognition. Effective results of human actions using different features of unconstrained videos was a challenging task due to camera motion, cluttered background, occlusions, complexity of human movements, and variety of same actions performed by distinct subjects. Thus, the proposed method overcomes such problems by using the fusion of features concept for the development of a powerful human action descriptor. This descriptor is modified to create a visual word vocabulary (or codebook) which yields a Bag-of-Words representation. The True Positive Rate (TPR) and False Positive Rate (FPR) measures gave a true indication about the proposed HAR system. The computed Accuracy (Ar) and the Error (misclassification) Rate (Er) reveal the effectiveness of the system with the used dataset.

ENCRYPTION THREE-DIMENSION IMAGE USING TINY ALGORITHM

English Text Classification Using Improved Recursive Feature Elimination (IRFE) Algorithm

Documents classification is from most important fields for Natural language processing and text mining. There are many algorithms can be used for this task. In this paper, focuses on improving Text Classification by feature selection. This means determine some of the original features without affecting the accuracy of the work, where our work is a new feature selection method was suggested which can be a general formulation and mathematical model of Recursive Feature Elimination (RFE). The used method was compared with other two well-known feature selection methods: Chi-square and threshold. The results proved that the new method is comparable with the other methods, The best results were 83% when 60% of features used, 82% when 40% of features used, and 82% when 20% of features used. The tests were done with the Naïve Bayes (NB) and decision tree (DT) classification algorithms , where the used dataset is a well-known English data set “20 newsgroups text” consists of approximately 18846 files. The results showed that our suggested feature selection method is comparable with standard Like Chi-square.

Impact of Rheumatoid Arthritis on Memory: An Observational Case-control study

Rheumatoid arthritis (RA) is the most common chronic in lammatory polyarthritis with a signi icant impact on the quality of life. It is usually not associatedwith the central nervous system and brain changes.so Neuropsychological impairment is not commonly associated with RA. However, recent studies have indicated that a linkmay exist betweenRA and cognitive impairment, but the prevalence rate was uncertain. This study aimed to evaluate the impact of rheumatoid arthritis (RA) on memory. This case-control study included 130 consecutive RA patients compared with 130 healthy controls matched in age and sex. Demographic and clinical characteristics were recorded. Sixitem Cognitive Impairment Test (6CIT) Kingshill Version 2000 was used to assess the memory. RA patients had more memory impairment than controls [(47(69.1%) versus 21(30.9%) [OR (95% CI) =2.939(1.632-5.294, p<0.001]. Age and prednisolone use were signi icantly and positively correlated with cognitive impairment (partial correlation ( r = 0.346, p<0.001; r=0.224, p= 0.012 respectively) while educational level was signi icantly and negatively correlated with CIT ( r=-0.489, p<0.001). In conclusion, RA patients had a high risk of cognitive impairment. Increased age, being employed, use of oral glucocorticoids, low education. And low family income was a signi icant predictor of cognitive impairment. This suggest early diagnosis and treatment of RA may prevent cognitive impairment.

The potential source for composite flours as food ingredient from local grown crops

The market of composite flour is growing as consumer nowadays choosing a healthy diet as personal preference. The suitability of the composite flour for use as intermediate or finish food ingredients highly depends on its physicochemical properties and its nutritional value. In this study, four types of local fruit crops (particularly their seeds) namely rambutan, cempedak, durian and nangka were dried and ground into powder form. The physicochemical properties such as bulk density, pH, water absorption capacity (WAC), oil absorption capacity (OAC), foam stability (FS), foam capacity (FC) as well as gelatinization properties of these composite flour were studied. Mineral content and heavy metal analytes were also determined. Results for bulk density from the least to the higher amount was 0.54±0.00 g/mL, 0.57±0.00 g/mL, 0.58±0.01 g/mL, 0.66±0.00 g/mL , 0.70±0.00 g/mL and 0.72±0.00 g/mL for rambutan flour, cempedak flour, tapioca flour, nangka flour, wheat flour and durian flour, respectively. Both cempedak flour and nangka flour showed the lowest pH value (5.72±0.01, 5.73±0.00), followed by rambutan flour and durian flour (6.67±0.00, 6.90±0.00) which similar to that tapioca flour and wheat flour (6.65±0.1, 6.08±0.0), respectively. Rambutan flour, cempedak flour and wheat flours showed the highest value in% of foam stability meanwhile these composite flours showed the lowest value in% of foam capacity. Results for water absorption capacity (WAC) and oil absorption capacity (OAC) in a range of 6% to 42% and 8% to 12% respectively, however, durian flour obtained the highest value for WAC while the value for OAC was the lowest. All of the composite flour possesses gelling properties at 13% concentration except for cempedak flour which completely gels at 20% of concentration. Rambutan flour showed the highest mineral analyte particularly in Zinc (107.19±0.17) and Copper (14.22±0.27) followed by nangka flour [Zinc (64.20±0.32) and Copper (10.40±0.12)] and durian flour [Zinc (52.38±0.42) and Copper (7.97±0.05)]. Level of heavy metal toxicity was under risk for all types of composite flour.

Testicular Torsion - Our 14 Year Experience and Outcomes