Dynamic behavior of Sandwich Beam with Piezoelectric layers

Sandwich beams with composite faces sheets and foam core are widely used as lightweight components in many of the industries such as automotive, marine and aerospace applications due to its high bending stiffness and strength combined with low weight. Thus, it is important to gain knowledge of their flexural behavior under static as well as dynamic loads. Although extensive research has been devoted to the flexural behavior of composite laminates in general, the flexural behavior of sandwich structures is quite and obviously different. Several works treating the dynamic flexural behavior of sandwich beams have also confirmed the marked susceptibility of sandwich structures to damage caused by the low velocity impact of foreign objects. Impacts can damage the face sheets, the core material, and the core face interface. The type of damage usually found in the faces is similar to that observed after impacts on monolithic composites. However, the damage initiation thresholds and damage area depend on the properties of the core material and the relationship between the properties of the core and those of the face sheets.The modelling is done for sandwich beam with create volume option with dimensions known in the software

Barrett’s Esophagus: An update

Barrett’s esophagus is premalignant condition in which the stratified squamous epithelium is replaced by metaplastic intestinal epithelium. The cause is usually long-standing gastro-esophageal reflux. Infection with Helicobacter pylori is also believed to play a role in this. The most significant complication is development of dysplasia with an increase in relative risk for development of adenocarcinoma 40–120 times

Reimagining education

Gramin Shiksha Kendra works in villages on the periphery of the Ranthambhore National Park in Sawai Madhopur and the Khandar blocks of the Sawai Madhopur district. The total population of the district is around 14.5 lakhs, having a sex ratio of 897* females per 1000 males. Around 80 percent of the district’s population lives in rural areas. The female and male literacy rates (7+ years) in rural Sawai Madhopur are 42.40 percent and 79.40 percent, respectively. In 2006, the district was declared backward by the Ministry of Panchayati Raj. Sawai Madhopur is largely an agriculture-based economy. The Gurjars (traditionally pastoralists) and the Meenas (a Scheduled Tribe but now mainly involved in agriculture) are the two majority communities here. There is a small but significant population of other caste groups - Malis, Bairwas, Harijans, Bhopas, Jaggas, and some de-notified tribal groups - Gadiya Lauhars, Moghiyas, Bawariyas, Kanjars, to name a few. Tourism is another sector in which the rural population is engaged in, as cleaners, cooks, or tourist guides. Some of them are also running their own dhabas (roadside food-stalls)

Code-Switched Text Synthesis in Unseen Language Pairs

Existing efforts on text synthesis for code-switching mostly require training on code-switched texts in the target language pairs, limiting the deployment of the models to cases lacking code-switched data. In this work, we study the problem of synthesizing code-switched texts for language pairs absent from the training data. We introduce GLOSS, a model built on top of a pre-trained multilingual machine translation model (PMMTM) with an additional code-switching module. This module, either an adapter or extra prefixes, learns code-switching patterns from code-switched data during training, while the primary component of GLOSS, i.e., the PMMTM, is frozen. The design of only adjusting the code-switching module prevents our model from overfitting to the constrained training data for code-switching. Hence, GLOSS exhibits the ability to generalize and synthesize code-switched texts across a broader spectrum of language pairs. Additionally, we develop a self-training algorithm on target language pairs further to enhance the reliability of GLOSS. Automatic evaluations on four language pairs show that GLOSS achieves at least 55% relative BLEU and METEOR scores improvements compared to strong baselines. Human evaluations on two language pairs further validate the success of GLOSS.Comment: Paper accepted by ACL2023 as a Finding pape

Outage-Watch: Early Prediction of Outages using Extreme Event Regularizer

Cloud services are omnipresent and critical cloud service failure is a fact of life. In order to retain customers and prevent revenue loss, it is important to provide high reliability guarantees for these services. One way to do this is by predicting outages in advance, which can help in reducing the severity as well as time to recovery. It is difficult to forecast critical failures due to the rarity of these events. Moreover, critical failures are ill-defined in terms of observable data. Our proposed method, Outage-Watch, defines critical service outages as deteriorations in the Quality of Service (QoS) captured by a set of metrics. Outage-Watch detects such outages in advance by using current system state to predict whether the QoS metrics will cross a threshold and initiate an extreme event. A mixture of Gaussian is used to model the distribution of the QoS metrics for flexibility and an extreme event regularizer helps in improving learning in tail of the distribution. An outage is predicted if the probability of any one of the QoS metrics crossing threshold changes significantly. Our evaluation on a real-world SaaS company dataset shows that Outage-Watch significantly outperforms traditional methods with an average AUC of 0.98. Additionally, Outage-Watch detects all the outages exhibiting a change in service metrics and reduces the Mean Time To Detection (MTTD) of outages by up to 88% when deployed in an enterprise cloud-service system, demonstrating efficacy of our proposed method.Comment: Accepted to ESEC/FSE 202

ESRO: Experience Assisted Service Reliability against Outages

Modern cloud services are prone to failures due to their complex architecture, making diagnosis a critical process. Site Reliability Engineers (SREs) spend hours leveraging multiple sources of data, including the alerts, error logs, and domain expertise through past experiences to locate the root cause(s). These experiences are documented as natural language text in outage reports for previous outages. However, utilizing the raw yet rich semi-structured information in the reports systematically is time-consuming. Structured information, on the other hand, such as alerts that are often used during fault diagnosis, is voluminous and requires expert knowledge to discern. Several strategies have been proposed to use each source of data separately for root cause analysis. In this work, we build a diagnostic service called ESRO that recommends root causes and remediation for failures by utilizing structured as well as semi-structured sources of data systematically. ESRO constructs a causal graph using alerts and a knowledge graph using outage reports, and merges them in a novel way to form a unified graph during training. A retrieval-based mechanism is then used to search the unified graph and rank the likely root causes and remediation techniques based on the alerts fired during an outage at inference time. Not only the individual alerts, but their respective importance in predicting an outage group is taken into account during recommendation. We evaluated our model on several cloud service outages of a large SaaS enterprise over the course of ~2 years, and obtained an average improvement of 27% in rouge scores after comparing the likely root causes against the ground truth over state-of-the-art baselines. We further establish the effectiveness of ESRO through qualitative analysis on multiple real outage examples.Comment: Accepted to 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023

A Dataset of Relighted 3D Interacting Hands

The two-hand interaction is one of the most challenging signals to analyze due to the self-similarity, complicated articulations, and occlusions of hands. Although several datasets have been proposed for the two-hand interaction analysis, all of them do not achieve 1) diverse and realistic image appearances and 2) diverse and large-scale groundtruth (GT) 3D poses at the same time. In this work, we propose Re:InterHand, a dataset of relighted 3D interacting hands that achieve the two goals. To this end, we employ a state-of-the-art hand relighting network with our accurately tracked two-hand 3D poses. We compare our Re:InterHand with existing 3D interacting hands datasets and show the benefit of it. Our Re:InterHand is available in https://mks0601.github.io/ReInterHand/.Comment: Accepted by NeurIPS 2023 (Datasets and Benchmarks Track

PLAS-5k: Dataset of Protein-Ligand Affinities from Molecular Dynamics for Machine Learning Applications

Computational methods and recently modern machine learning methods have played a key role in structure-based drug design. Though several benchmarking datasets are available for machine learning applications in virtual screening, accurate prediction of binding affinity for a protein-ligand complex remains a major challenge. New datasets that allow for the development of models for predicting binding affinities better than the state-of-the-art scoring functions are important. For the first time, we have developed a dataset, PLAS-5k comprised of 5000 protein-ligand complexes chosen from PDB database. The dataset consists of binding affinities along with energy components like electrostatic, van der Waals, polar and non-polar solvation energy calculated from molecular dynamics simulations using MMPBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) method. The calculated binding affinities outperformed docking scores and showed a good correlation with the available experimental values. The availability of energy components may enable optimization of desired components during machine learning-based drug design. Further, OnionNet model has been retrained on PLAS-5k dataset and is provided as a baseline for the prediction of binding affinities