Dermatofibrosarcoma Protuberans of the Suprapubic Region: A Rare Clinical Entity

Introduction: Dermatofibrosarcoma Protuberans (DFSP), an incompletely understood disease entity, has been reported in literature at different sites. However, the suprapubic region as its location further adds to its unusual persona.Case: A 51 year old gentleman presented with a slow growing suprapubic lesion, which on clinical and laboratory evaluation was confirmed to be a Dermatofibrosarcoma Protuberans, subsequently managed adequately with wide excision and groin flap coverage.Conclusion: Dermatofibrosarcoma Protuberans, in itself, is a rare entity and the suprapubic region as its location further adds to the unknown persona of the condition. Thus, the importance of maintaining a vigil for DFSP even at the most unusual sites, for optimal and timely diagnosis and management. As is rightly said, “What the mind knows, is what the eyes see”

Joint equidistribution of approximates

We consider the joint asymptotic distribution of \emph{best} and $\varepsilon$ Diophantine approximates of matrices in several aspects. Our main results describe the resulting limiting measures for almost every matrix. Multiplicative Diophantine approximation is treated for the first time in this context and a number of Diophantine corollaries are derived including a matrix L\'{e}vy-Khintchine type theorem. While we treat the general case of approximation of matrices, our results are already new for the case of simultaneous Diophantine approximation of vectors. Our approach is dynamical and is inspired by work of several authors, notably Shapira and Weiss. It is based on the construction of an appropriate Poincar\'{e} section for certain diagonal group actions on the space of unimodular lattices. The main new idea in our paper is a method which allows us to treat actions of higher rank groups.Comment: 47 Pages, Comments Welcom

Two Central limit theorems in Diophantine approximation

We prove central limit theorems for Diophantine approximations with congruence conditions and for inhomogeneous Diophantine approximations following the approach of Bj\"{o}rklund and Gorodnik. The main tools are the cumulant method and dynamics on homogeneous spaces.Comment: 40 Pages, Comments Welcom

Management of Chronic Kidney Disease - An Ayurveda Case Study

CKD has become a common disease with a high Prevalence rate of nowadays [1] in people including young individuals, with dialysis and renal transplant as its mainstream healing treatment, but due to low financial backgrounds among large percentage of individuals in India, everyone cannot afford to live a good quality of life. Hence, every individual is not a candidate for renal transplant or dialysis. So, to overcome this issue, an alternate has to be taken keeping in mind a healthy life which is the need of hour. CKD, i.e., Chronic Kidney Disease itself indicates chronicity with irreversible damage to the kidneys due to main leading factor in many cases nowadays as a major cause which is Hypertension and Diabetes mellitus type 2, which clinically presents as symptomless sometimes in initial stages and further presents with pedal edema, decreased appetite, nausea, difficulty in micturition/decreased urine output, frothy/foamy urine, fatigue.[2] Usually it is manifested through various lab investigations such as kidney function test in which increase in serum urea levels, serum creatinine and other are seen i.e., a waste product made by our muscles, also Kidney’s one of the vital function is production of erythropoietin[3] gets hampered due to underlying cause results in decline in Hb levels. So, to overcome this Punarnavadi Mandoor along with other were advised to the patient. Here the focus is on improvement in various Hematological levels before and after treatment of CKD, by giving Renogrit tablet, Punarnavadi Mandoor, Corighan Vati etc., having special effect on kidneys/as renoprotective

Prediction of Machining Conditions Using Machine Learning

The new blast of Machine Learning (ML) and Artificial Intelligence (AI) shows extraordinary expectations in the forward leap of additive manufacturing (AM) process displaying, which is an important step toward determining the cycle structure-property relationship. The advancement of standard AI apparatuses in information science was primarily attributed to the extraordinarily huge amount of named informational collections, that may be obtained throughout the trials or first-rate reenactments. To completely take advantage of the force of AI in AM metal while lightening the reliance on "enormous information", everybody set an Improved Neural Network (INN) structure if the wires the two information and first actual standards include the preservation laws of energy, mass, and energies, towards the NN to illuminate the growing experiences. We suggest compressed-type strategies in the Dirichlet limit regulation in light of a Heaviside capability, that may precisely uphold the BCs and speed up the growing experience. The hotel structure was applied to two agent metal assembling issues, that includes the NIST AM-Benchmark series test. The examinations show that the Motel, owing to the extra actual information, may precisely foresee the temperature and also liquefy pool elements throughout the AM processes in metal along a moderate measure of named informational collections

Recognizing Human Faces: Physical Modeling and Pattern Classification

Although significant work has been done in the field of face recognition, the performance of the state-of-the-art face recognition algorithms is not good enough to be effective in operational systems. Most algorithms work well for controlled images but are quite susceptible to changes in illumination, pose, etc. In this dissertation, we propose methods which address these issues, to recognize faces in more realistic scenarios. The developed approaches show the importance of physical modeling, contextual constraints and pattern classification for this task. For still image-based face recognition, we develop an algorithm to recognize faces illuminated by arbitrarily placed, multiple light sources, given just a single image. Though the problem is ill-posed in its generality, linear approximations to the subspace of Lambertian images in combination with rank constraints on unknown facial shape and albedo are used to make it tractable. In addition, we develop a purely geometric illumination-invariant matching algorithm that makes use of the bilateral symmetry of human faces. In particular, we prove that the set of images of bilaterally symmetric objects can be partitioned into equivalence classes such that it is always possible to distinguish between two objects belonging to different equivalence classes using just one image per object. For recognizing faces in videos, the challenge lies in suitable characterization of faces using the information available in the video. We propose a method that models a face as a linear dynamical system whose appearance changes with pose. Though the proposed method performs very well on the available datasets, it does not explicitly take the 3D structure or illumination conditions into account. To address these issues, we propose an algorithm to perform 3D facial pose tracking in videos. The approach combines the structural advantages of geometric modeling with the statistical advantages of a particle filter based inference to recover the 3D configuration of facial features in each frame of the video. The recovered 3D configuration parameters are further used to recognize faces in videos. From a pattern classification point of view, automatic face recognition presents a very unique challenge due to the presence of just one (or a few) sample(s) per identity. To address this, we develop a cohort-based framework that makes use of the large number of non-match samples present in the database to improve verification and identification performance

Movie Recommendations using Hybrid Recommendation Systems

A recommendation system for movies is important in our social life due to its strength in providing enhanced entertainment. Such a system can suggest a set of movies, to users based on their interest, and personal information. Although, there are many recommendation systems present, new recommender system technologies are needed that can quickly produce high quality recommendations, even for very large-scale information resources. The proposed system has the ability to recommend movies, to a new user as well as the others by using their Facebook data. It collects all the important information, such as, popularity, liking and disliking, required for recommendation. It also takes minimal information from new user without social network login. It generates recommendations for the user based on his/her behavior on social media. This system could also be used by movie producers to get an idea about the response of their upcoming movies since all the data is extracted from the user’s behaviors