Enhanced Characterness for Text Detection in the Wild

Text spotting is an interesting research problem as text may appear at any random place and may occur in various forms. Moreover, ability to detect text opens the horizons for improving many advanced computer vision problems. In this paper, we propose a novel language agnostic text detection method utilizing edge enhanced Maximally Stable Extremal Regions in natural scenes by defining strong characterness measures. We show that a simple combination of characterness cues help in rejecting the non text regions. These regions are further fine-tuned for rejecting the non-textual neighbor regions. Comprehensive evaluation of the proposed scheme shows that it provides comparative to better generalization performance to the traditional methods for this task

Role of hysterolaparoscopy in the diagnosis and management of infertility

Background: Infertility is defined by WHO and ICMART as a disease of the reproductive system by the failure to achieve a clinical pregnancy after 12 months or more regular unprotected sexual intercourse. Objective of this study were to assess the role of hysteroscopy and laparoscopy in the evaluation of female infertility. To assess the therapeutic role of these endoscopic modalities in cases of infertility.Methods: A prospective study of 112 women coming with the complain of infertility to a tertiary care centre hospital in Ahmedabad over a period of 30 months from January 2017 to June 2019.Results: Of the 112 cases, 69.7% had primary infertility and 30.3% had secondary infertility. Septum was the most common hysteroscopic finding (7.1%) followed by polyps (5.4%) and synechiae (3.6%). Adhesions was the most common laparoscopic finding (23.2%) followed by tubal blocks (19.7%) and fibroid (17.9%). Polycystic ovaries were seen in 12.5% patients followed by endometriosis in 10.7% women. Myomectomy was most common therapeutic procedure (17.9%) followed by adhesiolysis in 14.3% women and PCO drilling in 8.9% women.Conclusions: Hysterolaparoscopy is useful as a diagnostic and therapeutic measure for women having infertility

Plant Species Classification Using Transfer Learning by Pretrained Classifier VGG-19

Deep learning is currently the most important branch of machine learning, with applications in speech recognition, computer vision, image classification, and medical imaging analysis. Plant recognition is one of the areas where image classification can be used to identify plant species through their leaves. Botanists devote a significant amount of time to recognizing plant species by personally inspecting. This paper describes a method for dissecting color images of Swedish leaves and identifying plant species. To achieve higher accuracy, the task is completed using transfer learning with the help of pre-trained classifier VGG-19. The four primary processes of classification are image preprocessing, image augmentation, feature extraction, and recognition, which are performed as part of the overall model evaluation. The VGG-19 classifier grasps the characteristics of leaves by employing pre-defined hidden layers such as convolutional layers, max pooling layers, and fully connected layers, and finally uses the soft-max layer to generate a feature representation for all plant classes. The model obtains knowledge connected to aspects of the Swedish leaf dataset, which contains fifteen tree classes, and aids in predicting the proper class of an unknown plant with an accuracy of 99.70% which is higher than previous research works reported.Comment: Under review process in 'IETE Journal of Research

Correlation of clinical examination, ultrasound, magnetic resonance imaging and arthroscopy as diagnostic tools in shoulder pathology

Background: Shoulder pathologies can cause significant pain, discomfort, and affect the activity of daily living. The aim of this study was to compare the efficacy of clinical examination, ultrasound, magnetic resonance imaging (MRI) with shoulder arthroscopy in diagnosing various shoulder pathologies, considering shoulder arthroscopy as the gold standard tool.Methods: This was a prospective, comparative study conducted over 35 patients, between 18-75 years of age presenting with chronic shoulder pain or instability of more than 2 months duration. All patients were examined clinically, followed by high resolution ultrasound, MRI, arthroscopy of the affected shoulder.Results: The sensitivity and specificity of ultrasonography (USG) for diagnosing full thickness tear was 100% each and for MRI was 88% and 100% respectively. For subacromial impingement USG had sensitivity of 66.67%, specificity of 94.12%, positive predictive value of 50% and negative predictive value of 88.89%. For rotator cuff tear USG had sensitivity of 92.86%, specificity of 50%, positive predictive value of 81.25% and negative predictive value of 75% considering shoulder arthroscopy as gold standard.Conclusions: USG and MRI both are sensitive techniques for diagnosing of rotator cuff pathologies. USG has high accuracy in diagnosing partial thickness tears as compare to MRI. MRI proved to be superior in estimation of site and extent of tear. Considering shoulder arthroscopy as gold standard, it can be reserved for patients with suspicious of USG/MRI findings or those who may need surgical intervention simultaneously

Evaluation of prescription pattern and quality of life in postmenopausal osteoporosis: a cross sectional study

Background: Osteoporosis (OP) is a silently progressing metabolic bone disease that results in loss of mineralized bone and subsequent fractures with minor trauma. Fracture related pain and physical inability to perform activities of daily living can lead to psychological consequences that impair Quality of Life (QoF). However, much less is known about Indian scenario. Hence, our study becomes imperative. Aim of this study to the drug utilization pattern and to analyze Quality of life of postmenopausal women suffering from osteoporosis.Methods: An analytical cross-sectional study was done on 91 post-menopausal osteoporotic women. Drug utilization pattern was evaluated. Pre-validated QUALEFFO-31 questionnaire was administered to each patient to record patient’s perceived QoL. Scores were calculated according to the algorithm. Higher scores indicated poor QoL. The collected data was analyzed with SPSS software (version 23)and p value ≤0.05 was considered as statistically significant.Results: The mean age group of presenting patients was 56.2±6.6 years. All the patients received Calcium and vitamin D supplements and non-steroidal anti-inflammatory drugs for pain relief. But only 33% of the patients received any one of the bisphosphonates (BP). For analysis, patients were grouped into Group-1(n=60) who did not and Group-2 (n=31) who did receive a BP. QOL analysis showed that pain domain was affected the most. Also, patients in Group 2 reported worse score in all the domains in comparison to Group 1 (P<0.05). It is likely that BP might need more time to show considerable effect or because it was added only to those patients who already had more complaints and could afford the high cost.Conclusion: BP in spite of being the drug of choice for OP is used less commonly in India. OP causes pain and physical debilitation with detrimental effect on mental health. Longer duration prospective studies are needed to study the association of QoL and use of BP in OP patients

Effect of prenap coffee on daytime sleepiness in university students

Background: Daytime sleepiness impairs academic performance in college students. Napping is a counter to daytime sleepiness, but often causes sleep inertia on waking up. Caffeine absorption from beverages peaks 30 minutes after their ingestion presenting a window of opportunity to have a short nap such that the time of waking up is in synchrony with onset of action of caffeine; thereby abolishing post-nap inertia and achieving synergistic mitigation of fatigue.Objective of this study to assess effect of nap, coffee, ‘coffee and nap’ and ‘wakeful break without coffee’ on daytime sleepiness using Psychomotor Vigilance Tests (PVTs) and Karolinska Sleepiness Scale (KSS) score.Methods: After Institutional Review Board clearance, 10 subjects (aged 19-21 years) were selected using their Epworth Sleepiness Scale score (ESS >5) and called to the study site 8 times on different days to be exposed to these four conditions twice - only coffee (standardized), only nap (30min), coffee immediately followed by 30min nap, wakeful break (30min) without coffee or nap. Pre and post scores were recorded for electronic PVT (Reaction Time and Motor Responsiveness) and KSS for each attempt.Results: Test outcome was associated with intervention used (p=0.00001). ‘Nap only’ group was associated with deterioration in outcomes (p=0.00001), accounting for highest percentage (41%) of all deteriorated test outcomes. ‘Coffee only’ group was associated with improvement in test scores (p=0.00001), responsible for highest share (38.8%) of all improved test outcomes. ‘Nap only’ and ‘Coffee-nap’ group showed improvement in 11.67% and 21.67% of outcomes respectively. Conclusions: Pre-nap coffee is a proactive counter-measure to post nap sleep inertia

Adaptive Agent Architecture for Real-time Human-Agent Teaming

Teamwork is a set of interrelated reasoning, actions and behaviors of team members that facilitate common objectives. Teamwork theory and experiments have resulted in a set of states and processes for team effectiveness in both human-human and agent-agent teams. However, human-agent teaming is less well studied because it is so new and involves asymmetry in policy and intent not present in human teams. To optimize team performance in human-agent teaming, it is critical that agents infer human intent and adapt their polices for smooth coordination. Most literature in human-agent teaming builds agents referencing a learned human model. Though these agents are guaranteed to perform well with the learned model, they lay heavy assumptions on human policy such as optimality and consistency, which is unlikely in many real-world scenarios. In this paper, we propose a novel adaptive agent architecture in human-model-free setting on a two-player cooperative game, namely Team Space Fortress (TSF). Previous human-human team research have shown complementary policies in TSF game and diversity in human players' skill, which encourages us to relax the assumptions on human policy. Therefore, we discard learning human models from human data, and instead use an adaptation strategy on a pre-trained library of exemplar policies composed of RL algorithms or rule-based methods with minimal assumptions of human behavior. The adaptation strategy relies on a novel similarity metric to infer human policy and then selects the most complementary policy in our library to maximize the team performance. The adaptive agent architecture can be deployed in real-time and generalize to any off-the-shelf static agents. We conducted human-agent experiments to evaluate the proposed adaptive agent framework, and demonstrated the suboptimality, diversity, and adaptability of human policies in human-agent teams.Comment: The first three authors contributed equally. In AAAI 2021 Workshop on Plan, Activity, and Intent Recognitio

Giant high occipital encephalocele

Encephaloceles are rare embryological mesenchymal developmental anomalies resulting from inappropriate ossification in skull through with herniation of intracranial contents of the sac. Encephaloceles are classified based on location of the osseous defect and contents of sac. Convexity encephalocele with osseous defect in occipital bone is called occipital encephalocele. Giant occipital encephaloceles can be sometimes larger than the size of baby skull itself and they pose a great surgical challenge. Occipital encephaloceles (OE) are further classified as high OE when defect is only in occipital bone above the foramen magnum, low OE when involving occipital bone and foramen magnum and occipito-cervical when there involvement of occipital bone, foramen magnum and posterior upper neural arches. Chiari III malformation can be associated with high or low occipital encephaloceles. Pre-operatively, it is essential to know the size of the sac, contents of the sac, relation to the adjacent structures, presence or absence of venous sinuses/vascular structures and osseous defect size. Sometimes it becomes imperative to perform both CT and MRI for the necessary information. Volume rendered CT images can depict the relation of osseous defect to foramen magnum and provide information about upper neural arches which is necessary in classifying these lesions

Individualized Mutual Adaptation in Human-Agent Teams

The ability to collaborate with previously unseen human teammates is crucial for artificial agents to be effective in human-agent teams (HATs). Due to individual differences and complex team dynamics, it is hard to develop a single agent policy to match all potential teammates. In this paper, we study both human-human and humanagent teams in a dyadic cooperative task, Team Space Fortress (TSF). Results show that the team performance is influenced by both players’ individual skill level and their ability to collaborate with different teammates by adopting complementary policies. Based on human-human team results, we propose an adaptive agent that identifies different human policies and assigns a complementary partner policy to optimize team performance. The adaptation method relies on a novel similarity metric to infer human policy and then selects the most complementary policy from a pre-trained library of exemplar policies. We conducted human-agent experiments to evaluate the adaptive agent and examine mutual adaptation in humanagent teams. Results show that both human adaptation and agent adaptation contribute to team performanc