Attr2Style: A Transfer Learning Approach for Inferring Fashion Styles via Apparel Attributes

Popular fashion e-commerce platforms mostly provide details about low-level attributes of an apparel (eg, neck type, dress length, collar type) on their product detail pages. However, customers usually prefer to buy apparel based on their style information, or simply put, occasion (eg, party/ sports/ casual wear). Application of a supervised image-captioning model to generate style-based image captions is limited because obtaining ground-truth annotations in the form of style-based captions is difficult. This is because annotating style-based captions requires a certain amount of fashion domain expertise, and also adds to the costs and manual effort. On the contrary, low-level attribute based annotations are much more easily available. To address this issue, we propose a transfer-learning based image captioning model that is trained on a source dataset with sufficient attribute-based ground-truth captions, and used to predict style-based captions on a target dataset. The target dataset has only a limited amount of images with style-based ground-truth captions. The main motivation of our approach comes from the fact that most often there are correlations among the low-level attributes and the higher-level styles for an apparel. We leverage this fact and train our model in an encoder-decoder based framework using attention mechanism. In particular, the encoder of the model is first trained on the source dataset to obtain latent representations capturing the low-level attributes. The trained model is fine-tuned to generate style-based captions for the target dataset. To highlight the effectiveness of our method, we qualitatively and quantitatively demonstrate that the captions generated by our approach are close to the actual style information for the evaluated apparel. A Proof Of Concept for our model is under pilot at Myntra where it is exposed to some internal users for feedback.Comment: In Annual Conference on Innovative Applications of Artificial Intelligence (IAAI), colocated with AAAI Conference on Artificial Intelligence (AAAI) 202

Application of nano-curcumin as a natural antimicrobial agent against Gram-positive pathogens

Gram-positive bacteria cause various diseases from the superficial skin to deep tissue infections. The capability of causing numerous diseases is due to the production of virulence factors which are tightly regulated by the virulence genes. Various Gram-positive pathogenic bacteria e.g. Staphylococcus, Mycobacterium, and Listeria are capable of causing lethal infections in humans and animals. Conventional antibiotics, targeted antibiotics, and combinatorial drugs are used as therapeutic agents against Gram-positive pathogens. Due to intricate virulence pathway bacteria readily adopt resistance to available drugs. Therefore, there is need to develop some alternative approaches to combat these infections. Various natural extracts are effective against pathogenic bacteria with or without the available drugs. Curcumin is a natural extract of Curcuma longas rhizome, known as turmeric. Curcumin shows various biological activities such as antimicrobial, antioxidant and anti-inflammatory. It also shows strong antibacterial activity against Gram-positive and few Gram-negative bacteria. Besides all these beneficial applications, major drawbacks of curcumin are poor aqueous solubility and less bioavailability. However, drug delivery approaches including nanoformulation are developed to increase its stability in vitro and in vivo settings. The present review article focused on the translation of potential applications of curcumin in various diseases specifically caused by Gram-positive pathogens. Various methods used for the formulations of curcumin nanoparticles, combinatorial strategies with curcumin nanoparticles and their application in the prevention of infections have been discussed. The present article also discusses the future aspects of curcumin-nanoparticles and its use as an alternative therapeutic approach against pathogens

Non-stationary stochastic inventory lot-sizing with emission and service level constraints in a carbon cap-and-trade system

Firms worldwide are taking major initiatives to reduce the carbon footprint of their supply chains in response to the growing governmental and consumer pressures. In real life, these supply chains face stochastic and non-stationary demand but most of the studies on inventory lot-sizing problem with emission concerns consider deterministic demand. In this paper, we study the inventory lot-sizing problem under non-stationary stochastic demand condition with emission and cycle service level constraints considering carbon cap-and-trade regulatory mechanism. Using a mixed integer linear programming model, this paper aims to investigate the effects of emission parameters, product- and system-related features on the supply chain performance through extensive computational experiments to cover general type business settings and not a specific scenario. Results show that cycle service level and demand coefficient of variation have significant impacts on total cost and emission irrespective of level of demand variability while the impact of product’s demand pattern is significant only at lower level of demand variability. Finally, results also show that increasing value of carbon price reduces total cost, total emission and total inventory and the scope of emission reduction by increasing carbon price is greater at higher levels of cycle service level and demand coefficient of variation.The analysis of results helps supply chain managers to take right decision in different demand and service level situations

Ocimum sanctum: a review on the pharmacological properties

Herbal medicine, the backbone of traditional medicine in many countries have played an important role in curing the diseases of humans since ancient time. Medicinal plants are great source of bioactive compounds and chemical structures that have potential beneficial effects. The present review compiles information on ethnopharmacologically useful information and pharmacological properties of Ocimum sanctum. Ocimum sanctum (OS) has many medicinal properties like antioxidant, antidiabetic, antiulcer, anticancer, antibacterial, antifungal and other. The phytochemicals compounds of Ocimum, alkaloids, flavonoids, phenolics, essential oils, tannins and saponins play an important role in herbal medicine. Bioactive compounds of Ocimum responsible for its various medicinal properties and their effects at the molecular level need to be investigated in more detail. Furthermore, pharmacological properties of bioactive compounds in Ocimum sanctum are required to confirm the ethnomedicinal claims of Ocimum sanctum for pharmaceutical therapeutic applications

A new thermodynamic parameter GCE for identification of glass forming compositions

This work underlines the easy use of thermodynamics-based approach in identifying the glass forming systems. In this work, a simple and effective thermodynamic model named GCE is devised to numerically predict easy glass forming compositions. Further, to validate its reliability, co-relation of GCE with the Miedema model, PHSS model, critical diameter, Turnbull’s reduced glass transition temperature and Inoue supercooled liquid region in V–Ti–Cr, Zr–Cu–Ag, Mg–Zn–Ca, Ca–Mg–Cu and Cu–Zr systems respectively is demonstrated. The model co-related well, and therefore the calculations based on this model are wellcapable of anticipating the glass forming compositions. With a proven accuracy for the prediction, this proposed model can be used as an efficient tool for alloy-designing

Non-stationary stochastic inventory lot-sizing with emission and service level constraints in a carbon cap-and-trade system

Firms worldwide are taking major initiatives to reduce the carbon footprint of their supply chains in response to the growing governmental and consumer pressures. In real life, these supply chains face stochastic and non-stationary demand but most of the studies on inventory lot-sizing problem with emission concerns consider deterministic demand. In this paper, we study the inventory lot-sizing problem under non-stationary stochastic demand condition with emission and cycle service level constraints considering carbon cap-and-trade regulatory mechanism. Using a mixed integer linear programming model, this paper aims to investigate the effects of emission parameters, product- and system-related features on the supply chain performance through extensive computational experiments to cover general type business settings and not a specific scenario. Results show that cycle service level and demand coefficient of variation have significant impacts on total cost and emission irrespective of level of demand variability while the impact of product's demand pattern is significant only at lower level of demand variability. Finally, results also show that increasing value of carbon price reduces total cost, total emission and total inventory and the scope of emission reduction by increasing carbon price is greater at higher levels of cycle service level and demand coefficient of variation. The analysis of results helps supply chain managers to take right decision in different demand and service level situations

The Drosophila melanogaster Seminal Fluid Protease “Seminase” Regulates Proteolytic and Post-Mating Reproductive Processes

Proteases and protease inhibitors have been identified in the ejaculates of animal taxa ranging from invertebrates to mammals and form a major protein class among Drosophila melanogaster seminal fluid proteins (SFPs). Other than a single protease cascade in mammals that regulates seminal clot liquefaction, no proteolytic cascades (i.e. pathways with at least two proteases acting in sequence) have been identified in seminal fluids. In Drosophila, SFPs are transferred to females during mating and, together with sperm, are necessary for the many post-mating responses elicited in females. Though several SFPs are proteolytically cleaved either during or after mating, virtually nothing is known about the proteases involved in these cleavage events or the physiological consequences of proteolytic activity in the seminal fluid on the female. Here, we present evidence that a protease cascade acts in the seminal fluid of Drosophila during and after mating. Using RNAi to knock down expression of the SFP CG10586, a predicted serine protease, we show that it acts upstream of the SFP CG11864, a predicted astacin protease, to process SFPs involved in ovulation and sperm entry into storage. We also show that knockdown of CG10586 leads to lower levels of egg laying, higher rates of sexual receptivity to subsequent males, and abnormal sperm usage patterns, processes that are independent of CG11864. The long-term phenotypes of females mated to CG10586 knockdown males are similar to those of females that fail to store sex peptide, an important elicitor of long-term post-mating responses, and indicate a role for CG10586 in regulating sex peptide. These results point to an important role for proteolysis among insect SFPs and suggest that protease cascades may be a mechanism for precise temporal regulation of multiple post-mating responses in females