Analysis of Nepotism in Bollywood using Personalized PageRank and Effective Influence

Bollywood is one of the largest film-producing industries with a large worldwide audience. In this paper, we will try to find the most important stars in the era of 1990 to 2014, as well as try to use social network analysis methods and metrics to analyze the role of blood connections in getting opportunities in the industry. We created the actor relationship data of around 1000 debutants using OpenAI API and used a novel approach "Effective Influence" to study the effect of having a blood-related established actor inside the industry. We found that on an average every actor/director or actor/actor pair is reachable by a path of length at most 4 and a correlation of 0.6 indicating the advantage of having a blood connection inside the network in getting a good co-cast in the debut film.Comment: 7 pages, 6 Figure

Meme-ingful Analysis: Enhanced Understanding of Cyberbullying in Memes Through Multimodal Explanations

Internet memes have gained significant influence in communicating political, psychological, and sociocultural ideas. While memes are often humorous, there has been a rise in the use of memes for trolling and cyberbullying. Although a wide variety of effective deep learning-based models have been developed for detecting offensive multimodal memes, only a few works have been done on explainability aspect. Recent laws like "right to explanations" of General Data Protection Regulation, have spurred research in developing interpretable models rather than only focusing on performance. Motivated by this, we introduce {\em MultiBully-Ex}, the first benchmark dataset for multimodal explanation from code-mixed cyberbullying memes. Here, both visual and textual modalities are highlighted to explain why a given meme is cyberbullying. A Contrastive Language-Image Pretraining (CLIP) projection-based multimodal shared-private multitask approach has been proposed for visual and textual explanation of a meme. Experimental results demonstrate that training with multimodal explanations improves performance in generating textual justifications and more accurately identifying the visual evidence supporting a decision with reliable performance improvements.Comment: EACL202

3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning

Generative models have been successfully used to synthesize completely novel images, text, music and speech. As such, they present an exciting opportunity for the design of new materials for functional applications. So far, generative deep-learning methods applied to molecular and drug discovery have yet to produce stable and novel 3-D crystal structures across multiple material classes. To that end, we herein present an autoencoder-based generative deep-representation learning pipeline for geometrically optimized 3-D crystal structures that simultaneously predicts the values of eight target properties. The system is highly general, as demonstrated through creation of novel materials from three separate material classes: binary alloys, ternary perovskites and Heusler compounds. Comparison of these generated structures to those optimized via electronic-structure calculations shows that our generated materials are valid and geometrically optimized

Modeling Dark- and Light-Induced Crystal Structures and Single-Crystal Optical Absorption Spectra of Ruthenium-Based Complexes that Undergo SO2-Linkage Photoisomerization

A family of coordination complexes of the type [Ru(SO2)(NH3)4X]Y exhibit optical-switching capabilities in their single-crystal state. This striking effect is caused by photoisomerization of metastable photoinduced states, which are metastable if kept at suitably low temperatures. This illustrates the possibility of these materials to operate as nanotechnological devices themselves, with the potential for optical actuation, optical-signal processing and nano-optomechanical function. This thesis presents a Plane-Wave (PW) – Density Functional Theory (DFT) based periodic methodology that can effectively model the dark- and light-induced structures, and single-crystal optical absorption spectra of these complexes. Initially, these crystalline materials are modeled via PW-based periodic and Molecular Orbital (MO)-DFT based molecular fragment models, and time-dependent DFT (TDDFT), to calculate their structural and optical properties, which are compared with experimental data. Both the periodic and molecular fragment models simulate these complexes effectively, with small deviations in key bond lengths, successfully replicating experimentally-determined structures. Both models also simulate trends in experimentally-determined optical absorption spectra effectively, with optical absorbance and coverage of the visible region increasing with the formation of the photoinduced geometries. This represents the first study of the optical properties of materials from this family of complexes via DFT-based methods. The PW-DFT study is then expanded to consider more complexes, including both photoswitches and transducers. Periodic models are shown to appreciate the competing chemical and crystallographic forces present in these complexes, namely the possible effects of the trans-influence and intermolecular interactions on the simulated optical absorption spectra. Density of states calculations are also showed to appreciate these forces, whilst illustrating the potential for optical tuning capabilities. The periodic models are also used to conduct a study of the photoisomerization process from the dark to the light-induced structures via the Nudged Elastic Band (NEB) method; thereby a ‘cause-and-effect’ relationship between photoisomerization and transduction is suggested to be dependent heavily on the intermolecular forces present. Synthetic work has also been carried out in parallel, resulting in the development of a more sustainable precursor-synthesis route and the synthesis of two new complexes. This thesis demonstrates that PW-TDDFT should be considered as a more than viable method for simulating the optical and electronic properties of this family of single-crystal optical switches whose functionality is based on linkage photoisomerism. It also illustrates the potential for optically tuning these complexes so that they can be developed with desired properties for tailored applications

Giant cell tumour of extensor tendon sheath: Preventing recurrence

Giant Cell Tumour of tendon sheath is relatively rare tumour with an overall incidence of around 1 in 50,000 individuals. Marginal excision of giant cell tumour of the tendon sheath is the treatment of choice. It is also the commonest hand lesion to recur after excision. The incidence of local recurrence is high, ranging from 9-44%. Here we present a case report of a giant cell tumour of extensor tendon sheath in hand which was successfully treated with special emphasis on ways of prevention of recurrence

Scalable Optimization of Multivariate Performance Measures in Multi-instance Multi-label Learning

The problem of multi-instance multi-label learning (MIML) requires a bag of instances to be assigned a set of labels most relevant to the bag as a whole. The problem finds numerous applications in machine learning, computer vision, and natural language processing settings where only partial or distant supervision is available. We present a novel method for optimizing multivariate performance measures in the MIML setting. Our approach MIML-perf uses a novel plug-in technique and offers a seamless way to optimize a vast variety of performance measures such as macro and micro-F measure, average precision, which are performance measures of choice in multi-label learning domains. MIML-perf offers two key benefits over the state of the art. Firstly, across a diverse range of benchmark tasks, ranging from relation extraction to text categorization and scene classification, MIML-perf offers superior performance as compared to state of the art methods designed specifically for these tasks. Secondly, MIML-perf operates with significantly reduced running times as compared to other methods, often by an order of magnitude or more

Rare case of neonate with meconium peritonitis

It's unusual to find a meconium cyst in a premature newborn. Meconium pseudo cyst is a consequence of meconium peritonitis, a sterile chemical peritonitis caused by a perforation of the uterine tract. When a puncture in the intestine does not heal and communication with the cyst continues after birth, the cyst can expand, the cyst can get infected, and the pseudo cyst can rupture. This is a case report of a newborn who developed perforated peritonitis due to the rupture of a meconium pseudo cyst [1]. Our patient was born prematurely at 34 weeks and had a caesarean procedure. She had a large abdominal distention that was later confirmed as a meconium cyst

Multimodality Management of Two Pairs of Pyopagus Twins

Background and Aim: Conjoined twins, due to their rarity and complex anatomy, pose not only a technical, but also a physiological challenge for their separation, with each case being uniquely distinct. The aim of the present article is to describe the surgical approach and management strategy for two cases of pyopagus conjoined twins operated at our center. Case Report: Case 1: Antenatally detected conjoined twin girls presented postnatally to our centre. They were found to have a common vestibule with single anal opening facing partially away from each other. On evaluation they were found to have a single sacrum and fused conus and filum terminale. They were taken up for separation at 2years of age & the 24hour long surgery, culminated in successful separation. The children had good post-operative outcome at 10months post separation. Case 2: Conjoined pyopagus twin girls presented postnatally, and were found to have fused cords, having a terminal syrinx and partially separate sacrum. They were separated at 2.5 years of age, with a multi-departmental effort and coordination. They are doing well 2 months post-operatively. Conclusion: A multidisciplinary team support with thorough preoperative planning significantly aids in improving the outcome of surgical separation. This has been possible by using modern technology. Each reported case contributes significantly to literature