SemSup-XC: Semantic Supervision for Zero and Few-shot Extreme Classification

Extreme classification (XC) involves predicting over large numbers of classes (thousands to millions), with real-world applications like news article classification and e-commerce product tagging. The zero-shot version of this task requires generalization to novel classes without additional supervision. In this paper, we develop SemSup-XC, a model that achieves state-of-the-art zero-shot and few-shot performance on three XC datasets derived from legal, e-commerce, and Wikipedia data. To develop SemSup-XC, we use automatically collected semantic class descriptions to represent classes and facilitate generalization through a novel hybrid matching module that matches input instances to class descriptions using a combination of semantic and lexical similarity. Trained with contrastive learning, SemSup-XC significantly outperforms baselines and establishes state-of-the-art performance on all three datasets considered, gaining up to 12 precision points on zero-shot and more than 10 precision points on one-shot tests, with similar gains for recall@10. Our ablation studies highlight the relative importance of our hybrid matching module and automatically collected class descriptions.Comment: Published at ICML 2023. V2: camera ready version at ICML 202

Learning Representations on Logs for AIOps

AI for IT Operations (AIOps) is a powerful platform that Site Reliability Engineers (SREs) use to automate and streamline operational workflows with minimal human intervention. Automated log analysis is a critical task in AIOps as it provides key insights for SREs to identify and address ongoing faults. Tasks such as log format detection, log classification, and log parsing are key components of automated log analysis. Most of these tasks require supervised learning; however, there are multiple challenges due to limited labelled log data and the diverse nature of log data. Large Language Models (LLMs) such as BERT and GPT3 are trained using self-supervision on a vast amount of unlabeled data. These models provide generalized representations that can be effectively used for various downstream tasks with limited labelled data. Motivated by the success of LLMs in specific domains like science and biology, this paper introduces a LLM for log data which is trained on public and proprietary log data. The results of our experiments demonstrate that the proposed LLM outperforms existing models on multiple downstream tasks. In summary, AIOps powered by LLMs offers an efficient and effective solution for automating log analysis tasks and enabling SREs to focus on higher-level tasks. Our proposed LLM, trained on public and proprietary log data, offers superior performance on multiple downstream tasks, making it a valuable addition to the AIOps platform.Comment: 11 pages, 2023 IEEE 16th International Conference on Cloud Computing (CLOUD

AutoMix: Automatically Mixing Language Models

Large language models (LLMs) are now available in various sizes and configurations from cloud API providers. While this diversity offers a broad spectrum of choices, effectively leveraging the options to optimize computational cost and performance remains challenging. In this work, we present AutoMix, an approach that strategically routes queries to larger LMs, based on the approximate correctness of outputs from a smaller LM. Central to AutoMix is a few-shot self-verification mechanism, which estimates the reliability of its own outputs without requiring training. Given that verifications can be noisy, we employ a meta verifier in AutoMix to refine the accuracy of these assessments. Our experiments using LLAMA2-13/70B, on five context-grounded reasoning datasets demonstrate that AutoMix surpasses established baselines, improving the incremental benefit per cost by up to 89%. Our code and data are available at https://github.com/automix-llm/automix.Comment: The first two authors contributed equally. Work started and partly done during Aman's internship at Google. This version adds results on mixing 3 models, and will be presented at the workshop on robustness of zero/few-shot learning in foundation models, Neurips 202

Production of He-4 and (4) in Pb-Pb collisions at root(NN)-N-S=2.76 TeV at the LHC

Results on the production of He-4 and (4) nuclei in Pb-Pb collisions at root(NN)-N-S = 2.76 TeV in the rapidity range vertical bar y vertical bar <1, using the ALICE detector, are presented in this paper. The rapidity densities corresponding to 0-10% central events are found to be dN/dy4(He) = (0.8 +/- 0.4 (stat) +/- 0.3 (syst)) x 10(-6) and dN/dy4 = (1.1 +/- 0.4 (stat) +/- 0.2 (syst)) x 10(-6), respectively. This is in agreement with the statistical thermal model expectation assuming the same chemical freeze-out temperature (T-chem = 156 MeV) as for light hadrons. The measured ratio of (4)/He-4 is 1.4 +/- 0.8 (stat) +/- 0.5 (syst). (C) 2018 Published by Elsevier B.V.Peer reviewe

Not Available

In: abstract book of National Seminar on “Maize for Crop Diversification under Changing Climatic Scenario” jointly organized by MTAI, IIMR and PAU held at Ludhiana, February 9-10, 2020.The post green revolution agriculture is based on generous application of nitrogen (N)-based fertilizers and high-yielding genotypes. Generally, plants cannot utilize more than 40% of the applied nitrogenous fertilizer; hence more than half of the applied fertilizer is lost to the environment and results in environmental pollution via acidification, eutrophication, and depletion of ozone layer by emission of greenhouse gas. Therefore, genetic improvement in nitrogen use efficiency (NUE) in crops is desirable for a sustainable and profitable agriculture. There is a need to identify key regulatory factors playing pivotal role in acquisition, transportation and utilization of N in plants. Among other factors, microRNA (miRNA) mediated gene regulation plays a crucial role in controlling low N stress adaptation and tolerance in plants. In this endeavor, the present study was undertaken to identify N stress responsive miRNAs in maize in tropical maize using high-throughput sequencing. The HKI-163 maize inbred line was grown hydroponically with sufficient nitrogen (2mM) and without nitrogen for 21 days. Observations were recorded on all important shoot and root physiological parameters. The root and shoot samples were deep sequenced for miRNA study. The expression analysis revealed 23 known miRNAs (11 up & 12 down-regulated) in leaf and 3 known miRNAs (1 up & 2 down-regulated) in root, which expressed differentially under N stress. We also identified 53 (20 up & 33 down-regulated) and 26 (9 up & 17 down-regulated) novel miRNAs in leaf and roots respectively. The knowledge gained will help understand the important roles that miRNAs play in maize, while responding to a nitrogen limiting environment.Not Availabl

Not Available

In: abstract book of National Seminar on ‘Maize for Crop Diversification under Changing Climatic Scenario’ jointly organized by MTAI, IIMR and PAU at Ludhiana from 09-10 Feb, 2020.The post green revolution agriculture is based on generous application of nitrogen (N)-based fertilizers and high-yielding genotypes. Generally, plants cannot utilize more than 40% of the applied nitrogenous fertilizer; hence more than half of the applied fertilizer is lost to the environment and results in environmental pollution via acidification, eutrophication, and depletion of ozone layer by emission of greenhouse gas. Therefore, genetic improvement in nitrogen use efficiency (NUE) in crops is desirable for a sustainable and profitable agriculture. There is a need to identify key regulatory factors playing pivotal role in acquisition, transportation and utilization of N in plants. Among other factors, microRNA (miRNA) mediated gene regulation plays a crucial role in controlling low N stress adaptation and tolerance in plants. In this endeavor, the present study was undertaken to identify N stress responsive miRNAs in maize in tropical maize using high-throughput sequencing. The HKI-163 maize inbred line was grown hydroponically with sufficient nitrogen (2mM) and without nitrogen for 21 days. Observations were recorded on all important shoot and root physiological parameters. The root and shoot samples were deep sequenced for miRNA study. The expression analysis revealed 23 known miRNAs (11 up & 12 down-regulated) in leaf and 3 known miRNAs (1 up & 2 down-regulated) in root, which expressed differentially under N stress. We also identified 53 (20 up & 33 down-regulated) and 26 (9 up & 17 down-regulated) novel miRNAs in leaf and roots respectively. The knowledge gained will help understand the important roles that miRNAs play in maize, while responding to a nitrogen limiting environment.Not Availabl

Not Available

Not AvailableStudying plant response and adaptation under low nitrogen stress condition is pre-requisite to enhance nitrogen use efficiency in crops. The present study investigated the physiological and molecular responses of maize (Zea mays L.) to nitrogen stress during early vegetative stage. Maize seedlings were grown hydroponically under controlled environmental conditions in phytotron. One set of plants were nutritionally stressed by eliminating nitrogen source in hydroponic culture while the other set was provided with nitrogen (2 mM KNO3). Under nitrogen-starvation condition, plant growth and physiological parameters changed dramatically. Significant reduction in chlorophyll content, total soluble proteins and nitrate reductase activity was observed. Further, nitrogen-starvation resulted into differential expression of genes related to nitrogen-assimilation and metabolism. The present study might be useful to improve our understanding towards plants adaptive response under nitrogen-starvation conditions.Not Availabl

Not Available

Not AvailableHigh temperature stress is one of the most detrimental abiotic stresses which adversely affect productivity of maize (Zea mays L.) in tropics and subtropics. Plants respond to high temperature stress by regulating expression of an array of genes, heat shock proteins (HSPs) being one of them. Owing to highly differential expression of HSPs in various crop species under high temperature stress, these could be considered as key stress responsive genes. Since HSPs gene family contain various members, identification of specific gene(s) playing crucial role in heat stress tolerance could be beneficial for developing stress resilient genotypes. Here we report in-silico characterization of five HSP genes and their expression analysis in two contrasting maize inbred lines i.e. LM17 (heat tolerant) and HKI1015WG8 (heat susceptible) subjected to high temperature stress at seedling stage. The five maize specific HSP genes, viz., ZmHsp26, ZmHsp60, ZmHsp70, ZmHsp82 and ZmHsp101 exhibited distinctive expression pattern in response to heat stress. Higher upregulation of ZmHsp70 was found throughout the stress exposure in the heat tolerant line as compared to the susceptible line. Sharp up-regulation and rapid decline in expression of ZmHsp82 in LM17 than HKI1015WG8 after 12 hours heat stress exposure suggested its possible role in plant acclimatization to heat-stress conditions. Further, higher upregulation of ZmHsp101 even after removal of stress (recovery for 24 hrs) indicated its possible role in recovering plant from adverse effects of heat stress. The study opens up scope for investigation through transgenic (RNAi and/or over expression) approach to further characterize and elucidate precise role of ZmHsp101, ZmHsp82 and ZmHsp70 in heat stress tolerance in maize.Not Availabl

A review of modern and Vedic practices on use of umbilical cord

Stromal cells possess unique properties to regenerate themselves and cure various chronic illnesses. An easily available and ethical source for procurement of stromal cells is umbilical cord blood which is now being stored for future use. Vedic texts also describe the cord blood as a source of life. However, Indian traditions seem to preserve one more alternative for storage and procurement of stromal cells. Traditionally, in many parts of India, the umbilical cord stump is dried and stored for future use. It is used as a medicine for some illness and to treat infertility. Since Indian traditions are an excerpt of Vedic science, it points towards the possible emergence of dried stump as an easy and cost-effective means for stromal cell procurement and storage. The present review compiles the literature available on these traditional practices and stresses upon the need of rigorous experimental and theoretical research in the area