Assessing Time-Varying Stock Market Integration in EMU for Normal and Crisis Periods

In this paper, we examine the financial integration process amongst 17 EMU countries from January 2002 to June 2013 over a normal period as well as for the Global Financial Crisis (GFC) and Eurozone Debt Crisis (EDC) periods. We classify the economies in three groups (A, B and C) based on their GDP to examine whether the economic size influences financial integration. Seven indicators are used for the purpose, namely, Beta Convergence, Sigma Convergence, Variance Ratio, Asymmetric DCC, Dynamic Cointegration, Market Synchronisation Measure and Common Components Approach. The results suggest that large sized EMU economies (termed as Group A) exhibit strong financial integration. Moderate financial integration is observed for middle-sized EMU economies with old membership (termed as Group B). Small sized economies (termed as Group C) economies seemed to be least integrated within the EMU stock market system. The findings further suggest presence of contagion effects as one moves from normal to crisis periods, which are specifically stronger for more integrated economies of Group A. We recommend institutional, regulatory and other policy reforms for Group B and especially Group C to achieve higher level of integration

Assessing Time-Varying Stock Market Integration in EMU for Normal and Crisis Periods

In this paper, we examine the financial integration process amongst 17 EMU countries from January 2002 to June 2013 over a normal period as well as for the Global Financial Crisis (GFC) and Eurozone Debt Crisis (EDC) periods. We classify the economies in three groups (A, B and C) based on their GDP to examine whether the economic size influences financial integration. Seven indicators are used for the purpose, namely, Beta Convergence, Sigma Convergence, Variance Ratio, Asymmetric DCC, Dynamic Cointegration, Market Synchronisation Measure and Common Components Approach. The results suggest that large sized EMU economies (termed as Group A) exhibit strong financial integration. Moderate financial integration is observed for middle-sized EMU economies with old membership (termed as Group B). Small sized economies (termed as Group C) economies seemed to be least integrated within the EMU stock market system. The findings further suggest presence of contagion effects as one moves from normal to crisis periods, which are specifically stronger for more integrated economies of Group A. We recommend institutional, regulatory and other policy reforms for Group B and especially Group C to achieve higher level of integration

Allergic Fungal Sinusitis with Bilateral Optic Neuropathy

Introduction Allergic Fungal Rhinosinusitis (AFRS) is characterized by an inflammatory response to a non-invasive fungus which leads to sinus obstruction. Ophthalmic manifestations of AFRS are rare but can be of grave consequence. Case Report A 22-year-old female patient presented with a 5-day history of headache and decreased vision in both eyes (right- perception of light; left-6/18), along with a past history of nasal blockage and recurrent rhinorrhoea. She had thick viscid mucous secretion with polyposis in bilateral nasal cavities. Computed tomography showed pansinusitis with heterogenous opacification and polypoidal mucosal hypertrophy. Bony erosion was seen in bilateral orbital apices with oedematous optic nerves. MRI was suggestive of bilateral optic neuritis. Patient underwent emergency surgical debridement via endoscopic sinus surgery. Histopathological examination of the specimen showed cellular debris, eosinophilic prominence and numerous Charcot-Leyden crystals, with presence of branched septate fungal hyphae. Fungal culture grew Aspergillus flavus. Patient was treated with steroids (injectable followed by oral in gradually tapering doses). Over a period of 3 months vision returned to normal. Conclusion Vision loss is a rare complication of AFRS and constitutes an emergency. Prompt treatment with surgical debridement and corticosteroids is essential for reversal of visual complications

Code Switched and Code Mixed Speech Recognition for Indic languages

Training multilingual automatic speech recognition (ASR) systems is challenging because acoustic and lexical information is typically language specific. Training multilingual system for Indic languages is even more tougher due to lack of open source datasets and results on different approaches. We compare the performance of end to end multilingual speech recognition system to the performance of monolingual models conditioned on language identification (LID). The decoding information from a multilingual model is used for language identification and then combined with monolingual models to get an improvement of 50% WER across languages. We also propose a similar technique to solve the Code Switched problem and achieve a WER of 21.77 and 28.27 over Hindi-English and Bengali-English respectively. Our work talks on how transformer based ASR especially wav2vec 2.0 can be applied in developing multilingual ASR and code switched ASR for Indic languages.Comment: This paper for submitted to Interspeech 202

Vakyansh: ASR Toolkit for Low Resource Indic languages

We present Vakyansh, an end to end toolkit for Speech Recognition in Indic languages. India is home to almost 121 languages and around 125 crore speakers. Yet most of the languages are low resource in terms of data and pretrained models. Through Vakyansh, we introduce automatic data pipelines for data creation, model training, model evaluation and deployment. We create 14,000 hours of speech data in 23 Indic languages and train wav2vec 2.0 based pretrained models. These pretrained models are then finetuned to create state of the art speech recognition models for 18 Indic languages which are followed by language models and punctuation restoration models. We open source all these resources with a mission that this will inspire the speech community to develop speech first applications using our ASR models in Indic languages

A novel approach to low-cost, rapid and simultaneous colorimetric detection of multiple analytes using 3D printed microfluidic channels

This research paper presents an inventive technique to swiftly create microfluidic channels on distinct membrane papers, enabling colorimetric drug detection. Using a modified DIY RepRap 3D printer with a syringe pump, microfluidic channels (µPADs) are crafted on a flexible nylon-based substrate. This allows simultaneous detection of four common drugs with a single reagent. An optimized blend of polydimethylsiloxane (PDMS) dissolved in hexane is used to create hydrophobic channels on various filter papers. The PDMS-hexane mixture infiltrates the paper's pores, forming hydrophobic barriers that confine liquids within the channels. These barriers are cured on the printer's hot plate, controlling channel width and preventing spreading. Capillary action drives fluid along these paths without spreading. This novel approach provides a versatile solution for rapid microfluidic channel creation on membrane papers. The DIY RepRap 3D printer integration offers precise control and faster curing. The PDMS-hexane solution accurately forms hydrophobic barriers, containing liquids within desired channels. The resulting microfluidic system holds potential for portable, cost-effective drug detection and various sensing applications

Improving Speech Recognition for Indic Languages using Language Model

We study the effect of applying a language model (LM) on the output of Automatic Speech Recognition (ASR) systems for Indic languages. We fine-tune wav2vec $2.0$ models for $18$ Indic languages and adjust the results with language models trained on text derived from a variety of sources. Our findings demonstrate that the average Character Error Rate (CER) decreases by over $28$ \% and the average Word Error Rate (WER) decreases by about $36$ \% after decoding with LM. We show that a large LM may not provide a substantial improvement as compared to a diverse one. We also demonstrate that high quality transcriptions can be obtained on domain-specific data without retraining the ASR model and show results on biomedical domain.Comment: Need to upgrade the content completel