Elongation of Sets in Soft Lattice Topological Spaces

The aim of this paper, we investigate some Lattice sets such as soft lattice exterior, soft lattice interior, soft lattice boundary and soft lattice border sets in soft lattice topological spaces which are defined over a soft lattice L with a fixed set of parameter A and it is also a generalization of soft topological spaces. Further, we develop and continue the initial views of some soft lattice sets, which are deep-seated for further research on soft lattice topology and will consolidate the origin of the theory of soft topological spaces

Further Diversification of Nano Binary Open Sets

The purpose of this paper is to introduce and study the nano binary exterior, nano binary border and nano binary derivedin nano binary topological spaces. Also studied their characterization

The Extension of Generalized Intuitionistic Topological Spaces

In this paper, irresolute functions in generalized intuitionistic topological spaces were introduced. Regarding this function, some minimal and maximal irresolute functions were introduced. In addition, the generalized intuitionistic topological spaces were extended by using their open sets which is finer than of it and their basic characterizations were investigated. Also some continuous functions in extension of generalized intuitionistic topological spaces were discussed

The Effect Of Growing Cells And In-Situ Biotransformation Of 2,6,6-Trimethylcyclohex-2-Ene-1,4-Dione Towards Products Formation

Actinol (ACT) is the substrate needed for the synthesis of optically active xanthophylls, xanthoxin and zeaxanthins as well as aroma constituent of tobacco and saffron can be produced by the reduction of ketoisophorone (KIP) with high yield and enantioselectivity. The ACT can be produced from the biotransformation of KIP. The biotransformation of KIP forms levodione (DOIP), which is intermediate before further reduction into ACT. The biotransformation of KIP was studied during the growth phase of S. cerevisiae with the presence and absence of glucose. During the growth phase of S. cerevisiae, the biotransformation of KIP was only able to produce intermediate and the product was yet to be formed. The rate of biotransformation of KIP was faster with the presence of glucose. This was because presence of glucose increases the concentration of NADH and NADPH which was the essential cofactor needed for biotransformation of KIP. Thus, the rate of reduction of KIP was higher resulting in higher concentration of DOIP. When the rate of biotransformation was high, the production of ACT will also be faster. Therefore, the presence of glucose was important for the biotransformation of KIP

An Analysis Of Business Process Outsourcing Strategies Of Public And Private Sector Banks In India

This paper is a study of the recent trends of Business Process Outsourcing (BPO) strategies and practices among banking institutions in India.  The study attempts to analyze BPOs used by private and public banks using four dimensional descriptive conceptual dimensions of outsourcing:  1) shoring model (vendor location/service creation), 2) sourcing model (vendor type), 3) engagement model (number of vendors engaged), and 4) duration of the engagement (contract period).  The comparison of results reveals similar trends of outsourcing for public and private banks. However, public banks are more regulated, and thus are restricted from outsourcing of certain processes to avoid excessive risks of privacy of data and information related to customers.  From a financial strategic point of view, in the long run, the underlying profit margins of a public bank might have adverse effects. &nbsp

Safety-Critical Scenario Generation Via Reinforcement Learning Based Editing

Generating safety-critical scenarios is essential for testing and verifying the safety of autonomous vehicles. Traditional optimization techniques suffer from the curse of dimensionality and limit the search space to fixed parameter spaces. To address these challenges, we propose a deep reinforcement learning approach that generates scenarios by sequential editing, such as adding new agents or modifying the trajectories of the existing agents. Our framework employs a reward function consisting of both risk and plausibility objectives. The plausibility objective leverages generative models, such as a variational autoencoder, to learn the likelihood of the generated parameters from the training datasets; It penalizes the generation of unlikely scenarios. Our approach overcomes the dimensionality challenge and explores a wide range of safety-critical scenarios. Our evaluation demonstrates that the proposed method generates safety-critical scenarios of higher quality compared with previous approaches

ALBERTA: ALgorithm-Based Error Resilience in Transformer Architectures

Vision Transformers are being increasingly deployed in safety-critical applications that demand high reliability. It is crucial to ensure the correctness of their execution in spite of potential errors such as transient hardware errors. We propose a novel algorithm-based resilience framework called ALBERTA that allows us to perform end-to-end resilience analysis and protection of transformer-based architectures. First, our work develops an efficient process of computing and ranking the resilience of transformers layers. We find that due to the large size of transformer models, applying traditional network redundancy to a subset of the most vulnerable layers provides high error coverage albeit with impractically high overhead. We address this shortcoming by providing a software-directed, checksum-based error detection technique aimed at protecting the most vulnerable general matrix multiply (GEMM) layers in the transformer models that use either floating-point or integer arithmetic. Results show that our approach achieves over 99% coverage for errors that result in a mismatch at less than 0.2% computation overhead. Lastly, we present the applicability of our framework in various modern GPU architectures under different numerical precisions. We introduce an efficient self-correction mechanism for resolving erroneous detection with an average overhead of less than 0.002% (with a 2% overhead to resolve each erroneous detection)