CrossNorm: Normalization for Off-Policy TD Reinforcement Learning

Off-policy temporal difference (TD) methods are a powerful class of reinforcement learning (RL) algorithms. Intriguingly, deep off-policy TD algorithms are not commonly used in combination with feature normalization techniques, despite positive effects of normalization in other domains. We show that naive application of existing normalization techniques is indeed not effective, but that well-designed normalization improves optimization stability and removes the necessity of target networks. In particular, we introduce a normalization based on a mixture of on- and off-policy transitions, which we call cross-normalization. It can be regarded as an extension of batch normalization that re-centers data for two different distributions, as present in off-policy learning. Applied to DDPG and TD3, cross-normalization improves over the state of the art across a range of MuJoCo benchmark tasks

Role of Ubiquitin-Mediated Degradation System in Plant Biology.

Ubiquitin-mediated proteasomal degradation is an important mechanism to control protein load in the cells. Ubiquitin binds to a protein on lysine residue and usually promotes its degradation through 26S proteasome system. Abnormal proteins and regulators of many processes, are targeted for degradation by the ubiquitin-proteasome system. It allows cells to maintain the response to cellular level signals and altered environmental conditions. The ubiquitin-mediated proteasomal degradation system plays a key role in the plant biology, including abiotic stress, immunity, and hormonal signaling by interfering with key components of these pathways. The involvement of the ubiquitin system in many vital processes led scientists to explore more about the ubiquitin machinery and most importantly its targets. In this review, we have summarized recent discoveries of the plant ubiquitin system and its involvement in critical processes of plant biology

Voltage Source Converter based Hybrid STATCOM for Reactive Power Compensation in Utility Grid

The availability of high voltage, high current and high-speed power electronic devices has led to increase in popularity of several power electronic applications such as FACTS. A STATCOM is one such power electronic converter, from the FACTS family, which can be used to improve the power factor of a transmission line, maintain the connected bus at the required voltage level, etc. In distribution power level, D-STATCOMs are used to achieve the same objectives. Several power converter topologies have been proposed for STATCOMs and D-STATCOMs, ranging from a standard two-level VSC based topology to a cascaded full-bridge based topology. The cascaded full-bridge based topology might be suitable for high power STATCOM applications but might not be the best option at the lower power level of a D-STATCOM. D-STATCOMs therefore often use a standard two-level converter-based topology owing to cost constraints. The research work presented in this thesis proposes a new power electronic topology which can be used for D-STATCOM applications. This topology is essentially composed of multiple cascaded h-bridge cells in each phase of a standard two-level converter. The two-level converter provides bulk of the power output and operates at a low switching frequency, whereas the h-bridge cell operates at a higher switching frequency and achieve power quality objectives. This research work initially presents simulations to validate the proposed topology. Outer control is proposed to operate the proposed topology as a D-STATCOM. Inner control loops are proposed to maintain the DC-link voltage of the h-bridge cells. An experimental prototype of the proposed topology is also developed. The results obtained from the proposed topology are compared with that obtained from a standard two-level converter-based topology. It is shown that due to the h-bridge cell action in the proposed topology, the obtained current THD is low in comparison to a standard two-level VSC based topology being used as a D-STATCOM

Developing Measures of Automation Implementation in Indian Industries

In the international business market, Automation has increased the competence of Indian Industry by making them fast, error free and providing them with greater customization option. This paper performs the review of automation and attempts to develop a framework for the implementation of automation by validating “IMPLAUT” (IMPLementing AUTomation) for Indian Industries. An exhaustive literature survey proceeded by simple meta-analysis have been carried out to find out various research gaps and further to address these gaps few objectives of this research study have been explored. For developing model for automation, the different variables are explored using ‘Churchill’s approach’ as may be applicable to Indian industrial scenario. It is evident from the model of “IMPLAUT” that automation will lead to the rise of competence in Indian industry provided the various input and output model suggested by the generic model are to be kept in view. It has been observed that the application of “IMPLAUT” reduces the manufacturing and downtime therefore increasing the overall efficiency of the industry. So “IMPLAUT” can be further researched and must be considered as an emerging field for research in engineering discipline. Keywords: Automation, IMPLAUT, classification schemes, Meta analysis, dimension

Influence of integrated nutrient management on physiological parameters of lentil (Lens culinaris Medik.)

During the rabi season of 2021, a field experiment was conducted in the North Western plains of Uttarakhand at Crop Research Centre, School of Agriculture, Uttaranchal University, Dehradun to examine the impact of integrated nutrient management (INM) on lentil growth, yield, and economics (Lens culinaris Medik.). The experiment was laid in Randomized Block Design with seven treatments i.e. T1 (Control, 100% RDF (Recommended Dose of Fertilizers), T2 (75 % NPK (Nitrogen, Phosphorus, Potassium) + 25 % FYM (Farm Yard Manure), T3 (50 % NPK + 50 % FYM), T4 (75 % NPK + 25 % Azotobacter), T5 (50 % NPK + 50 % Azotobacter), T6 (75 % NPK + 25 % (Vermicompost + Azotobacter)) & T7 (50 % NPK+ 50 % (Vermicompost + Azotobacter)). The treatments T7 with the combination of 50 per cent NPK and 50 per cent vermicompost plus Azotobacter showed maximum LAI (Leaf Area Index) (0.25), NAR (Net Assimilation Rate) (0.0020), chlorophyll content (3.05), dry matter (4.44 g), and protein content (26.99 %) in contrast to other six treatments

Multilingual CheckList: Generation and Evaluation

The recently proposed CheckList (Riberio et al,. 2020) approach to evaluation of NLP systems has revealed high failure rates for basic capabilities for multiple state-of-the-art and commercial models. However, the CheckList creation process is manual which creates a bottleneck towards creation of multilingual CheckLists catering 100s of languages. In this work, we explore multiple approaches to generate and evaluate the quality of Multilingual CheckList. We device an algorithm -- Automated Multilingual Checklist Generation (AMCG) for automatically transferring a CheckList from a source to a target language that relies on a reasonable machine translation system. We then compare the CheckList generated by AMCG with CheckLists generated with different levels of human intervention. Through in-depth crosslingual experiments between English and Hindi, and broad multilingual experiments spanning 11 languages, we show that the automatic approach can provide accurate estimates of failure rates of a model across capabilities, as would a human-verified CheckList, and better than CheckLists generated by humans from scratch

Matryoshka Representation Learning

Learned representations are a central component in modern ML systems, serving a multitude of downstream tasks. When training such representations, it is often the case that computational and statistical constraints for each downstream task are unknown. In this context rigid, fixed capacity representations can be either over or under-accommodating to the task at hand. This leads us to ask: can we design a flexible representation that can adapt to multiple downstream tasks with varying computational resources? Our main contribution is Matryoshka Representation Learning (MRL) which encodes information at different granularities and allows a single embedding to adapt to the computational constraints of downstream tasks. MRL minimally modifies existing representation learning pipelines and imposes no additional cost during inference and deployment. MRL learns coarse-to-fine representations that are at least as accurate and rich as independently trained low-dimensional representations. The flexibility within the learned Matryoshka Representations offer: (a) up to 14x smaller embedding size for ImageNet-1K classification at the same level of accuracy; (b) up to 14x real-world speed-ups for large-scale retrieval on ImageNet-1K and 4K; and (c) up to 2% accuracy improvements for long-tail few-shot classification, all while being as robust as the original representations. Finally, we show that MRL extends seamlessly to web-scale datasets (ImageNet, JFT) across various modalities -- vision (ViT, ResNet), vision + language (ALIGN) and language (BERT). MRL code and pretrained models are open-sourced at https://github.com/RAIVNLab/MRL.Comment: 35 pages, 12 figures. NeurIPS 2022 camera ready publicatio

Study of sodium potassium tantalate mixed system

184-187Ceramic pellets of Na1-xKxTaO3 (x= 0 & 0.5) system have been prepared by solid state reaction method and sintering process. The prepared samples are characterized by XRD and SEM techniques. Lattice parameters have been calculated by XRD pattern and grain size has been calculated by SEM. It has been observed that the prepared samples show orthorhombic structure at room temperature

Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space

We focus on the challenge of finding a diverse collection of quality solutions on complex continuous domains. While quality diver-sity (QD) algorithms like Novelty Search with Local Competition (NSLC) and MAP-Elites are designed to generate a diverse range of solutions, these algorithms require a large number of evaluations for exploration of continuous spaces. Meanwhile, variants of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) are among the best-performing derivative-free optimizers in single-objective continuous domains. This paper proposes a new QD algorithm called Covariance Matrix Adaptation MAP-Elites (CMA-ME). Our new algorithm combines the self-adaptation techniques of CMA-ES with archiving and mapping techniques for maintaining diversity in QD. Results from experiments based on standard continuous optimization benchmarks show that CMA-ME finds better-quality solutions than MAP-Elites; similarly, results on the strategic game Hearthstone show that CMA-ME finds both a higher overall quality and broader diversity of strategies than both CMA-ES and MAP-Elites. Overall, CMA-ME more than doubles the performance of MAP-Elites using standard QD performance metrics. These results suggest that QD algorithms augmented by operators from state-of-the-art optimization algorithms can yield high-performing methods for simultaneously exploring and optimizing continuous search spaces, with significant applications to design, testing, and reinforcement learning among other domains.Comment: Accepted to GECCO 202