Ovarian sex cord stromal tumors: an institutional experience

Background: Sex cord stromal tumors are a heterogeneous group of rare neoplasms of ovary. These tumors arise from different cells of the ovary and have a fascinating variety of clinical presentations. They are mostly diagnosed on histopathology after surgical removal.Methods: Our study aims at discussing the clinical and histomorphological spectrum of these rare tumors at a tertiary care centre.Results: In our study 158 ovarian sex cord stromal tumors were received over a period of eight years at our institute. Out of these, the most common age group was 30 to 40 years and the chief complaint was abdominal pain and lump in majority of cases. Most common tumor histologically was Adult Granulosa cell tumor (42.4%). There were 8 (5.1%) Juvenile granulosa cell tumors, 31 (19.6%) fibromas, 6 (3.8%) thecomas, 14 (8.9%) fibrothecomas, 24 (15.2%) sertoli leydig cell tumors and 7 (4.4%) sclerosing stromal tumors. We encountered one case of sex cord tumor with annular tubules.Conclusions: Sex cord stromal tumors are uncommon ovarian tumors in Indians but have a wide range of distribution of age, clinical features and histopathological types. Since most of these have a relatively good prognosis, a high index of suspicion and thorough knowledge of clinicopathological findings is important for correct diagnosis and appropriate treatment

A Survey on Challenges to the Media Cloud

Content of a media over Internet consumes significant amount of energy. Numerous application media applications, services and devices have introduced and clients are consuming more and more media. Media processing requires great capacity and capability.[1] Cloud computing has proven a best technology for providing various services, great computing power, massive storage and bandwidth with modest cost. Integration of Media and Cloud can become very beneficial for both and hence becomes media cloud. In this paper we have discussed several challenges of media cloud. Those include Integration, Storage, Processing and Delivery

Robustness Analysis of Video-Language Models Against Visual and Language Perturbations

Joint visual and language modeling on large-scale datasets has recently shown good progress in multi-modal tasks when compared to single modal learning. However, robustness of these approaches against real-world perturbations has not been studied. In this work, we perform the first extensive robustness study of video-language models against various real-world perturbations. We focus on text-to-video retrieval and propose two large-scale benchmark datasets, MSRVTT-P and YouCook2-P, which utilize 90 different visual and 35 different text perturbations. The study reveals some interesting initial findings from the studied models: 1) models are generally more susceptible when only video is perturbed as opposed to when only text is perturbed, 2) models that are pre-trained are more robust than those trained from scratch, 3) models attend more to scene and objects rather than motion and action. We hope this study will serve as a benchmark and guide future research in robust video-language learning. The benchmark introduced in this study along with the code and datasets is available at https://bit.ly/3CNOly4.Comment: NeurIPS 2022 Datasets and Benchmarks Track. This projects webpage is located at https://bit.ly/3CNOly

Semi-supervised Active Learning for Video Action Detection

In this work, we focus on label efficient learning for video action detection. We develop a novel semi-supervised active learning approach which utilizes both labeled as well as unlabeled data along with informative sample selection for action detection. Video action detection requires spatio-temporal localization along with classification, which poses several challenges for both active learning informative sample selection as well as semi-supervised learning pseudo label generation. First, we propose NoiseAug, a simple augmentation strategy which effectively selects informative samples for video action detection. Next, we propose fft-attention, a novel technique based on high-pass filtering which enables effective utilization of pseudo label for SSL in video action detection by emphasizing on relevant activity region within a video. We evaluate the proposed approach on three different benchmark datasets, UCF-101-24, JHMDB-21, and Youtube-VOS. First, we demonstrate its effectiveness on video action detection where the proposed approach outperforms prior works in semi-supervised and weakly-supervised learning along with several baseline approaches in both UCF101-24 and JHMDB-21. Next, we also show its effectiveness on Youtube-VOS for video object segmentation demonstrating its generalization capability for other dense prediction tasks in videos. The code and models is publicly available at: \url{https://github.com/AKASH2907/semi-sup-active-learning}.Comment: AAAI Conference on Artificial Intelligence, Main Technical Track (AAAI), 2024, Code: https://github.com/AKASH2907/semi-sup-active-learnin

Enhanced Search And Efficient Storage Using Data Compression In Nand Flash Memories

NAND flash memories are popular due to their density and lower cost. However, due to serial access, NAND flash memories have low read and write speeds. As the flash sizes increase to 64GB and beyond, searches through flash memories become painfully slow. In this work we present a hardware design enhancement technique to speed-up search through flash memories. The basic idea is to generate a small signature for every memory block and store them in a signature block(s). When a search is initiated, signature block is searched which produces reference of possible blocks where data might be contained, reducing the total number of read operations. The additional hardware has no impact on read access times or sequential write times but increases the random write times by an average of 8-9%. Simulation experiments were performed for flash memory of size up to 16Gb. Simulation results show that the performance of searches improve by 2000X by using the proposed technique. The signature-based technique is used to find exact matching data. A discrete cosine transform based technique is used when partial matching of data is required. The same setup is also used to increase storage efficiency of data by performing data deduplication on the flash memory. The hardware implementation of the search technique results in 0.02% increase in area, 3.53% increase in power and can operate at a maximum frequency of 0.47GHz

BIOLEACHING OF SPENT CATALYST AND APPLICATION OF ULTRASOUND

Ph.DDOCTOR OF PHILOSOPH

Infants’ ability to recognize and respond to negative emotional expressions

Perceiving others’ emotional facial and vocal expressions is nearly effortless for adults, and most discrete emotions are universally recognizable. Although the ability to accurately detect and distinguish emotions is present in adulthood, it is still unclear how this ability develops early in life. Both behavioral and neurocognitive studies suggest that in the first year of life, infants can differentiate discrete emotions; however, this is only evidenced by differential processing of emotions that belong to contrasting valence categories (positive vs. negative); it remains unclear whether infants demonstrate differential processing of emotions that belong to the same valence category (i.e., negative emotions). I considered the limitations of classic paradigms used to investigate emotion processing in infancy and the gaps left in the literature as a consequence, and conducted two experiments that explore new avenues to measure infants’ ability to differentiate negative emotions. These studies investigated how infants integrate sensory information and differentially respond to facial expressions to understand how infants distinguish between negative emotions. </p