Feature Selection Method for Iris Recognition Authentication System

Iris-based biometric authentication is gaining importance in recent times. Iris biometric processing however, is a complex process and computationally very expensive. In the overall processing of iris biometric in an iris-based biometric authentication system, feature selection is an important task. In feature selection, we ex-tract iris features, which are ultimately used in matching. Since there is a large number of iris features and computational time increases as the number of features increases, it is therefore a challenge to develop an iris processing system with as few as possible number of features and at the same time without compromising the correctness. In this paper, we address this issue and present an approach to feature Selection Method

Reimplantation: clinical Implications and outcome of dry storage of avulsed teeth

Reimplantation refers to the insertion and temporary fixation of completely or partially avulsed teeth that have resulted from traumatic injury. Reimplantation of an avulsed tooth depends on certain clinical conditions like phy- siological status of periodontal ligament (PDL), the stage of root development and the length of extra oral time. Depending on the patient’s age, retention of the permanent incisor can maintain the aesthetic appearance, occlusal function and alveolar ridge height. Though the risk of progressive replacement resorption and subsequent tooth loss is high after a long dry storage, reimplantation makes a provision for an aesthetically acceptable permanent prosthesis at a later age. This article presents management of two cases with avulsed permanent incisors that were stored in dry conditions for seven hours and three days respectivel

Lower Bound on Expected Communication Cost of Quantum Huffman Coding

Data compression is a fundamental problem in quantum and classical information theory. A typical version of the problem is that the sender Alice receives a (classical or quantum) state from some known ensemble and needs to transmit them to the receiver Bob with average error below some specified bound. We consider the case in which the message can have a variable length and the goal is to minimize its expected length. For classical messages this problem has a well-known solution given by Huffman coding. In this scheme, the expected length of the message is equal to the Shannon entropy of the source (with a constant additive factor) and the scheme succeeds with zero error. This is a single-shot result which implies the asymptotic result, viz. Shannon\u27s source coding theorem, by encoding each state sequentially. For the quantum case, the asymptotic compression rate is given by the von-Neumann entropy. However, we show that there is no one-shot scheme which is able to match this rate, even if interactive communication is allowed. This is a relatively rare case in quantum information theory when the cost of a quantum task is significantly different than the classical analogue. Our result has implications for direct sum theorems in quantum communication complexity and one-shot formulations of Quantum Reverse Shannon theorem

Learning to Relate from Captions and Bounding Boxes

In this work, we propose a novel approach that predicts the relationships between various entities in an image in a weakly supervised manner by relying on image captions and object bounding box annotations as the sole source of supervision. Our proposed approach uses a top-down attention mechanism to align entities in captions to objects in the image, and then leverage the syntactic structure of the captions to align the relations. We use these alignments to train a relation classification network, thereby obtaining both grounded captions and dense relationships. We demonstrate the effectiveness of our model on the Visual Genome dataset by achieving a recall@50 of 15% and recall@100 of 25% on the relationships present in the image. We also show that the model successfully predicts relations that are not present in the corresponding captions.Comment: ACL 201

Separating Quantum Communication and Approximate Rank

One of the best lower bound methods for the quantum communication complexity of a function H (with or without shared entanglement) is the logarithm of the approximate rank of the communication matrix of H. This measure is essentially equivalent to the approximate gamma-2 norm and generalized discrepancy, and subsumes several other lower bounds. All known lower bounds on quantum communication complexity in the general unbounded-round model can be shown via the logarithm of approximate rank, and it was an open problem to give any separation at all between quantum communication complexity and the logarithm of the approximate rank. In this work we provide the first such separation: We exhibit a total function H with quantum communication complexity almost quadratically larger than the logarithm of its approximate rank. We construct H using the communication lookup function framework of Anshu et al. (FOCS 2016) based on the cheat sheet framework of Aaronson et al. (STOC 2016). From a starting function F, this framework defines a new function H=F_G. Our main technical result is a lower bound on the quantum communication complexity of F_G in terms of the discrepancy of F, which we do via quantum information theoretic arguments. We show the upper bound on the approximate rank of F_G by relating it to the Boolean circuit size of the starting function F

Machine learning based framework for network intrusion detection system using stacking ensemble technique

Cybersecurity issues are increasing day by day, and it is becoming essential to address them aggressively. An efficient IDS system should be placed to identify abnormal behaviour by dynamically tracing the network traffic pattern. In this work, we proposed a framework for Network Intrusion Detection System using stacking ensemble technique of machine learning, which is testified on Random Forest Regressor and Extra Tree Classifier approaches for feature selections from the subjected dataset. The extensive experimentation has been done by applying 11 states of the art and hybrid machine learning algorithms to select the best performing algorithms. During the investigation, Random Forest, ID3 and XGBoost algorithms are found as best performers among different machine learning algorithms based on accuracy, precision, recall, F1-score and time to increase real-time attack detection performance. Three case studies have been carried out. Our results indicate that the proposed stacking ensemble-based framework of NIDS outperformed compared to the different state of art machine learning algorithms with average 0.99 prediction accuracy

Unraveling Prostaglandin and NLRP3 Inflammasomemediated Pathways of Primary Dysmenorrhea and the Role of Mefenamic Acid and Its Combinations

Painful menstrual cramps during or around the time of the monthly cycle are known as dysmenorrhea. The estimated global prevalence in women of reproductive age ranges from 45% to 95%. It has a significant negative impact on regular activities and productivity at work. However, despite the severe consequences on quality of life, primary dysmenorrhea (PD) is underdiagnosed. Dysmenorrhea has complex pathogenesis. It involves the release of prostaglandins and activation of the nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) inflammasome and also includes the involvement of other mediators such as bradykinin, histamine and acetylcholine. Even though nonsteroidal anti-inflammatory drugs (NSAIDs) remain the most common type of pain medication, the question of which one should be the most preferred is still open to debate. The current review examines the existing evidence for the pathogenesis of PD and makes evidence based and clinical experience based recommendations for the use of mefenamic acid and its combination in the treatment of dysmenorrhea. Mefenamic acid alleviates PD by inhibiting endometrial prostaglandin formation, restoring normal uterine activity, and reducing the inflammatory response by inhibiting the NLRP3 inflammasome and reducing the release of cytokines such as interleukin (IL)-1β. It is also known to have bradykinin antagonist activity. Dicyclomine has a dual action of blocking the muscarinic action of acetylcholine in postganglionic parasympathetic effect or regions and acting directly on uterine smooth muscle by blocking bradykinin and histamine receptors to relieve spasms. According to the experts, mefenamic acid and dicyclomine act synergistically by acting on the different pathways of dysmenorrhea by blocking multifactorial agents attributed to the cause of dysmenorrhea. Hence, the combination of mefenamic acid and dicyclomine should be the preferred treatment option for dysmenorrhea

Plant-Based Antioxidant Extracts and Compounds in the Management of Oral Cancer

Oral cancer continues to be a leading cause of death worldwide, and its prevalence is particularly high in developing countries, where people chew tobacco and betel nut on a regular basis. Radiation-, chemo-, targeted-, immuno-, and hormone-based therapies along with surgery are commonly used as part of a treatment plan. However, these treatments frequently result in various unwanted short- to long-term side effects. As a result, there is an urgent need to develop treatment options for oral cancer that have little or no adverse effects. Numerous bioactive compounds derived from various plants have recently attracted attention as therapeutic options for cancer treatment. Antioxidants found in medicinal plants, such as vitamins E, C, and A, reduce damage to the mucosa by neutralizing free radicals found in various oral mucosal lesions. Phytochemicals found in medicinal plants have the potential to modulate cellular signalling pathways that alter the cellular defence mechanisms to protect normal cells from reactive oxygen species (ROS) and induce apoptosis in cancer cells. This review aims to provide a comprehensive overview of various medicinal plants and phytoconstituents that have shown the potential to be used as oral cancer therapeutics

Phase IIb, Randomized, Double-Blind Trial of GC4419 Versus Placebo to Reduce Severe Oral Mucositis Due to Concurrent Radiotherapy and Cisplatin For Head and Neck Cancer

PURPOSE: Oral mucositis (OM) remains a common, debilitating toxicity of radiation therapy (RT) for head and neck cancer. The goal of this phase IIb, multi-institutional, randomized, double-blind trial was to compare the efficacy and safety of GC4419, a superoxide dismutase mimetic, with placebo to reduce the duration, incidence, and severity of severe OM (SOM). PATIENTS AND METHODS: A total of 223 patients (from 44 institutions) with locally advanced oral cavity or oropharynx cancer planned to be treated with definitive or postoperative intensity-modulated RT (IMRT; 60 to 72 Gy [≥ 50 Gy to two or more oral sites]) plus cisplatin (weekly or every 3 weeks) were randomly assigned to receive 30 mg (n = 73) or 90 mg (n = 76) of GC4419 or to receive placebo (n = 74) by 60-minute intravenous administration before each IMRT fraction. WHO grade of OM was assessed biweekly during IMRT and then weekly for up to 8 weeks after IMRT. The primary endpoint was duration of SOM tested for each active dose level versus placebo (intent-to-treat population, two-sided α of .05). The National Cancer Institute Common Terminology Criteria for Adverse Events, version 4.03, was used for adverse event grading. RESULTS: Baseline patient and tumor characteristics as well as treatment delivery were balanced. With 90 mg GC4419 versus placebo, SOM duration was significantly reduced (P = .024; median, 1.5 v 19 days). SOM incidence (43% v 65%; P = .009) and severity (grade 4 incidence, 16% v 30%; P = .045) also were improved. Intermediate improvements were seen with the 30-mg dose. Safety was comparable across arms, with no significant GC4419-specific toxicity nor increase of known toxicities of IMRT plus cisplatin. The 2-year follow-up for tumor outcomes is ongoing. CONCLUSION: GC4419 at a dose of 90 mg produced a significant, clinically meaningful reduction of SOM duration, incidence, and severity with acceptable safety