Visual and Lingual Emotion Recognition using Deep Learning Techniques

Emotion recognition has been an integral part of many applications like video games, cognitive computing, and human computer interaction. Emotion can be recognized by many sources including speech, facial expressions, hand gestures and textual attributes. We have developed a prototype emotion recognition system using computer vision and natural language processing techniques. Our goal hybrid system uses mobile camera frames and features abstracted from speech named Mel Frequency Cepstral Coefficient (MFCC) to recognize the emotion of a person. To acknowledge the emotions based on facial expressions, we have developed a Convolutional Neural Network (CNN) model, which has an accuracy of 68%. To recognize emotions based on Speech MFCCs, we have developed a sequential model with an accuracy of 69%. Out Android application can access the front and back camera simultaneously. This allows our application to predict the emotion of the overall conversation happening between the people facing both cameras. The application is also able to record the audio conversation between those people. The two emotions predicted (Face and Speech) are merged into one single emotion using the Fusion Algorithm. Our models are converted to TensorFlow-lite models to reduce the model size and support the limited processing power of mobile. Our system classifies emotions into seven classes: neutral, surprise, happy, fear, sad, disgust, and angr

Study of vdW Magnetic Materials for Spintronic Applications

Energy consumption of artificial intelligence (AI) systems are projected to grow at an alarming rate over the next two decades and stand to stress the global energy sector. A way forward is to replace the traditional von-Neumann computing hardware with technologies like neuromorphic and stochastic computing which are better suited for AI applications. Here, I study van der Waals magnetic materials for their application in developing spintronic devices to form the building blocks of neuromorphic and stochastic computing architectures. Use of correlated systems like ferromagnets provides a way towards low energy device switching, while 2D nature of the materials provides an avenue for building spintronic devices with maximum dimensional scalability and have strong prospects of enabling highly energy efficient mechanism of switching magnetism. A reliable protocol for fabricating devices with air-sensitive vdW magnetic materials and characterising them has been developed, including the electrochemical exfoliation of bulk vdW crystals, the design and building of a 2D material transfer setup and nanofabrication of devices using lithography, and magneto-transport measurements. This work will serve as a strong foundation for future work which would involve developing spin-valve devices with vdW materials and exploring energy-efficient modes of switching magnetism in them.S.M

Deterministic and non-volatile switching of all-van der Waals spin-orbit torque system above room temperature without external magnetic fields

Two-dimensional van der Waals (vdW) magnetic materials hold promise for the development of high-density, energy-efficient spintronic devices for memory and computation. Recent breakthroughs in material discoveries and spin-orbit torque (SOT) control of vdW ferromagnets have opened a path for integration of vdW magnets in commercial spintronic devices. However, a solution for field-free electric control of perpendicular magnetic anisotropy (PMA) vdW magnets at room temperatures, essential for building compact and thermally stable spintronic devices, is still missing. Here, we report the first demonstration of field-free deterministic and non-volatile switching of a PMA vdW ferromagnet, Fe$_3$GaTe$_2$ above room temperature (up to 320 K). We use the unconventional out-of-plane anti-damping torque from an adjacent WTe$_2$ layer to enable such switching with a low current density of $2.23 \times 10^6$ A/cm$^2$. This study exemplifies the efficacy of low-symmetry vdW materials for spin-orbit torque control of vdW ferromagnets and provides an all-vdW solution for the next generation of scalable and energy-efficient spintronic devices

L3Cube-MahaSBERT and HindSBERT: Sentence BERT Models and Benchmarking BERT Sentence Representations for Hindi and Marathi

Sentence representation from vanilla BERT models does not work well on sentence similarity tasks. Sentence-BERT models specifically trained on STS or NLI datasets are shown to provide state-of-the-art performance. However, building these models for low-resource languages is not straightforward due to the lack of these specialized datasets. This work focuses on two low-resource Indian languages, Hindi and Marathi. We train sentence-BERT models for these languages using synthetic NLI and STS datasets prepared using machine translation. We show that the strategy of NLI pre-training followed by STSb fine-tuning is effective in generating high-performance sentence-similarity models for Hindi and Marathi. The vanilla BERT models trained using this simple strategy outperform the multilingual LaBSE trained using a complex training strategy. These models are evaluated on downstream text classification and similarity tasks. We evaluate these models on real text classification datasets to show embeddings obtained from synthetic data training are generalizable to real datasets as well and thus represent an effective training strategy for low-resource languages. We also provide a comparative analysis of sentence embeddings from fast text models, multilingual BERT models (mBERT, IndicBERT, xlm-RoBERTa, MuRIL), multilingual sentence embedding models (LASER, LaBSE), and monolingual BERT models based on L3Cube-MahaBERT and HindBERT. We release L3Cube-MahaSBERT and HindSBERT, the state-of-the-art sentence-BERT models for Marathi and Hindi respectively. Our work also serves as a guide to building low-resource sentence embedding models.Comment: Accepted at Computing Conference 202

Dynamic modeling of bulk milk cooler

Paper presented at the 5th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, South Africa, 1-4 July, 2007.It is well known fact that operation of many refrigeration systems is dynamic in nature and bulk milk coolers are no exception to this. It is necessary to study bulk milk cooling systems and improve them to reduce chilling time and energy consumption within desired limits. Bulk milk coolers are analyzed and diagnosed with a simple thermodynamic model. A global approach is required whereby detailed component models are linked together to develop an accurate and complete simulation model package of vapour compression bulk milk cooler. These component models include mass, momentum and energy balance equation along with thermo physical property data and the appropriate heat or work transfer relationships. Solving these equations by method of substitution yields a prediction of refrigeration system behaviour. This helps in large reduction in time and cost spent in designing a system for given application, leading to the opportunity of achieving a more optimal design through evaluation of different design configurations. This dynamic model helps in adopting better control strategies to improve energy efficiency. The authors have attempted to develop such a global model for bulk milk coolers using vapour compression system with R22 as refrigerant.cs201

FORMULATION AND EVALUATION OF ORAL FLOATING BEADS OF TRAMODOL HYDROCHLORIDE

Aim of present work was to formulate and evaluate oral floating beads of Tramadol hydrochloride. Floating beads were fabricated using modified ionotropic gelation technique using various natural and synthetic polymers in different proportion. In the formulated batches BT1 to BT11 not have ability to extend drug release for 12 hours. In formulated batch BT 12 (Sodium alginate 6%: Carbapol 940 1.2 %) show in vitro release for 12 hours (100.26 ± 0.66% ). It also has floating lag time and floating time immediate, and floating duration more than 12 hours. In kinetic model fitting it follows Korsemeyers Peppas equation. Also it was found stable after 6 months in accelerated stability study. From all the formulation of oral floating beads of Tramadol Hydrochloride; it was concluded that, among all different polymer ratio Sodium Alginate 6% and CaCl2 3% (2:1 ratio) gives best results. Among different polymers Carbapol 940 gives retarded drug release for 12 hours. The optimized batch was found to be stable after 6 Months in accelerated stability studies. Key Words: Ionotropic gelation, Tramadol Hydrochloride, floating beads, Carbapol 94

Biosimilars: An Emerging Therapeutic Approach

A biosimilar is an extremely similar version of an existing medication. Biologics' cost, manufacture, administration, and clinical efficacy differ from those of chemically produced medications in certain aspects. Chemical and clinical equivalency to branded, original, low molecular weight chemical pharmaceuticals whose patents have expired defines generic medications. These are offered under a generic name and are practically the same thing as the original product. By 2020, many important biologics are expected to lose their patent protection, giving other biopharmaceutical companies the chance to create comparable biologics. After the first biosimilar was approved in early 2000, the use of biosimilars and similar biologics has expanded in recent years. One of the top producers of comparable biologics is India. In 2012, India created a new regulation for the pre- and post-marketing approval of comparable biologics. The biosimilar’s mode of action mirrors that of its reference drugs and results in very comparable outcomes. Some disorders, including some forms of cancer, can be treated with biologics and their biosimilars. Additionally, numerous medical disorders are treated with biosimilars. Crohn’s disease, arthritis, and ulcerative colitis

Archaeogenetic study of prehistoric rice remains from Thailand and India: Evidence of early japonica in South and Southeast Asia

We report successful extraction and sequencing of ancient DNA from carbonised rice grains (Oryza sativa) from six archaeological sites, including two from India and four from Thailand, ranging in age from ca. 2500 to 1500 BP. In total, 221 archaeological grains were processed by PCR amplification and primary-targeted fragments were sequenced for comparison with modern sequences generated from 112 modern rice populations, including crop and wild varieties. Our results include the genetic sequences from both the chloroplast and the nuclear genomes, based on 4 markers from the chloroplast and 6 from the nuclear genome. These markers allow differentiation of indica rice from japonica rice, the two major subspecies of Asian rice (Oryza sativa) considered to have separate geographical origins. One nuclear marker differentiates tropical and temperate forms of subspecies japonica. Other markers relate to phenotypic variation selected for under domestication, such as non-shattering, grain stickiness (waxy starch), and pericarp colour. Recovery and identification of sequences from nuclear markers was generally poor, whereas recovery of chloroplast sequences was successful, with at least one of four markers recovered in 61% of archaeological grains. This allowed for successful differentiation of indica or japonica subspecies variety, with japonica identified in all the Thai material and a mixture of indica and japonica chloroplasts in the two Indian assemblages. Rice subspecies was also assessed through conventional archaeobotanical methods relying on grain metrics, based on measurements from 13 modern populations and 2 499 archaeological grains. Grain metrics also suggest a predominance of japonica type grains in the Southeast Asian sites and a mixture of japonica and indica in the Indian sites with indica in the minority. The similar results of grain metrics and aDNA affirms grain measurements have some degree of reliability in rice subspecies identification. The study also highlights the great potential of ancient DNA recovery from archaeological rice. The data generated in the present study adds support to the model of rice evolution that includes hybridization between japonica and proto-indica