Determining Smallest Path Size of Multiplication Transducers Without a Restricted Digit Set

Directed multiplication transducers are a tool for performing non-decimal base multiplication without an additional conversion to base 10. This allows for faster computation and provides easier visualization depending on the problem at hand. By building these multiplication transducers computationally, new patterns can be identified as these transducers can be built with much larger bases and multipliers. Through a recursive approach, we created artificial multiplication transducers, allowing for the formation of several unique conjectures specifically focused on the smallest closed loop around a multiplication transducer starting and ending at zero. We show a general recursive pattern for this loop; through this recurrence relation, the length of the smallest closed loop for a particular transducer base b along with the range of multipliers having this particular length for multiplier m was also identified. This research is expected to be explored further by testing reductions of the digit set and determining whether similar properties will hold.Comment: 15 pages, 4 figures, submitted at SoCal-Nevada MAA Session 2022 and Cal State East Bay Student Research Symposium 202

What pre-merger conditions are necessary for mergers to be successful?

In this paper I examine the various pre-merger conditions necessary for a merger and acquisition activity to be successful. I have done this by analyzing information from a sample of random companies and then looking at factors such as the merger value, the long term debt to income ratio, the average pre merger earnings per share and the long term asset-income ratio of the companies The results of my analysis shows that a higher value of merger leads to failure of the merger, the higher value of asset-income ratio leads to a successful merger. Higher debt Income ratio increases chances of failure. I take into account strictly these pre-merger conditions for the acquiring company

On Multi-Agent Deep Deterministic Policy Gradients and their Explainability for SMARTS Environment

Multi-Agent RL or MARL is one of the complex problems in Autonomous Driving literature that hampers the release of fully-autonomous vehicles today. Several simulators have been in iteration after their inception to mitigate the problem of complex scenarios with multiple agents in Autonomous Driving. One such simulator--SMARTS, discusses the importance of cooperative multi-agent learning. For this problem, we discuss two approaches--MAPPO and MADDPG, which are based on-policy and off-policy RL approaches. We compare our results with the state-of-the-art results for this challenge and discuss the potential areas of improvement while discussing the explainability of these approaches in conjunction with waypoints in the SMARTS environment.Comment: 6 pages, 5 figure

Screenwriting Ethnographies: Seeing the Urban as a Becoming-Space in the Classroom

Written by a teacher and two students in an undergraduate course titled Housing: Planning and Policy, this commentary explores screenwriting as a pedagogical device used in service of experiential learning about the city in the classroom. It reflects on the employment of this device over two semesters wherein ethnographic vignettes were drawn upon to iteratively craft scripts, with fictional interventions guided by critical frames derived from the learning objectives of the course. We highlight the usefulness of screenwriting as a tool to embrace the urban as a becoming-space in the classroom, wherein students: 1) freely express their encounters with the built environment and feed them into the process of learning by doing; 2) immerse themselves in the ongoing city politics outside the classroom; and 3) appreciate the entangled realms of policy, governance, markets, bureaucracy, and media. Our experiments with screenwriting have been inspired by anthropological research that has brought out the multiplicity and perpetual becoming of urban political spaces. We articulate here our arrival at the screenwriting exercise and point to its potential for teaching and learning the city in anti-positivist ways.&nbsp

The Interaction of Display Advertisement and E-Wom on Omnichannel Purchase Intention Using Sem: an Age Moderation Effect

Purpose: This study looks at how display advertisements affect customers' Omni-buy intentions and the impact of e-WOM in determining if this influence is reflected in their purchase intentions.   Theoretical framework: Consumers' daily lives have grown more reliant on display advertising. Consumers depend on the Internet as a source of readily accessible information regarding advertising and businesses. Consequently, a customer becomes linked and an omnichannel shopper, intending to purchase products both online and offline. Electronic word of mouth (e-WOM) has also emerged as a powerful force that must be understood in the context of the omnichannel buyer.   Design/methodology/approach: A survey was performed to confirm the study's assumptions. Consumers who purchase fashion products omnichannel were polled for information. A questionnaire of 28 questions was developed for the study. The questionnaire includes questions about the respondent's age, gender, and educational level.   Findings: The results of the study show that there is a link between display advertising, e-WOM, and Omni-Online Purchase Intention. Several suggestions are produced to assist managers in navigating their brand's online presence in a manner that fits their customers' Omni-purchase intention.   Research, Practical & Social Implication: Through this investigation, the prevalence of many display ads on consumer purchase intention on the omnichannel market is determined, which contributes to the literature on advertising efficiency.   Originality/value: This study would most likely propose a method for market communication to determine how different types of display advertisements influence consumer purchase intentions, allowing firms to better manage the customer experience

Giant adrenal myelolipoma - clinical spectrum and management: a single centre experience

Background: Adrenal myelolipomas are rare benign tumor composed of mature adipocytes and normal haematopoetic cells. Giant adrenal myelolipomas are rare clinical entities. Most of them are symptomatic. We present 15 such cases, their clinical spectrum and management.Methods: Retrospective analysis of giant adrenal myelolipomas from a tertiary level institute. Initial diagnosis was made by computed tomography.Results: Mean age of patient was 45.6±11 years with slightly higher female preponderance. All patients were overweight with predominantly left sided adrenal myelolipoma. Majority of them (93%) were symptomatic and presented with abdominal pain, anemia or fever. Mean size of the mass on imaging was 14 cm±6 cm, with largest lesion measuring 26 cm.Conclusions: The article highlights the varying clinical presentations including rare emergency presentations of giant adrenal myelolipomas. A brief literature review is also presented

Class II fusion protein of alphaviruses drives membrane fusion through the same pathway as class I proteins

Viral fusion proteins of classes I and II differ radically in their initial structures but refold toward similar conformations upon activation. Do fusion pathways mediated by alphavirus E1 and influenza virus hemagglutinin (HA) that exemplify classes II and I differ to reflect the difference in their initial conformations, or concur to reflect the similarity in the final conformations? Here, we dissected the pathway of low pH–triggered E1-mediated cell–cell fusion by reducing the numbers of activated E1 proteins and by blocking different fusion stages with specific inhibitors. The discovered progression from transient hemifusion to small, and then expanding, fusion pores upon an increase in the number of activated fusion proteins parallels that established for HA-mediated fusion. We conclude that proteins as different as E1 and HA drive fusion through strikingly similar membrane intermediates, with the most energy-intensive stages following rather than preceding hemifusion. We propose that fusion reactions catalyzed by all proteins of both classes follow a similar pathway

EHI: End-to-end Learning of Hierarchical Index for Efficient Dense Retrieval

Dense embedding-based retrieval is now the industry standard for semantic search and ranking problems, like obtaining relevant web documents for a given query. Such techniques use a two-stage process: (a) contrastive learning to train a dual encoder to embed both the query and documents and (b) approximate nearest neighbor search (ANNS) for finding similar documents for a given query. These two stages are disjoint; the learned embeddings might be ill-suited for the ANNS method and vice-versa, leading to suboptimal performance. In this work, we propose End-to-end Hierarchical Indexing -- EHI -- that jointly learns both the embeddings and the ANNS structure to optimize retrieval performance. EHI uses a standard dual encoder model for embedding queries and documents while learning an inverted file index (IVF) style tree structure for efficient ANNS. To ensure stable and efficient learning of discrete tree-based ANNS structure, EHI introduces the notion of dense path embedding that captures the position of a query/document in the tree. We demonstrate the effectiveness of EHI on several benchmarks, including de-facto industry standard MS MARCO (Dev set and TREC DL19) datasets. For example, with the same compute budget, EHI outperforms state-of-the-art (SOTA) in by 0.6% (MRR@10) on MS MARCO dev set and by 4.2% (nDCG@10) on TREC DL19 benchmarks

Remaining Useful Life Prediction of Lithium-ion Batteries using Spatio-temporal Multimodal Attention Networks

Lithium-ion batteries are widely used in various applications, including electric vehicles and renewable energy storage. The prediction of the remaining useful life (RUL) of batteries is crucial for ensuring reliable and efficient operation, as well as reducing maintenance costs. However, determining the life cycle of batteries in real-world scenarios is challenging, and existing methods have limitations in predicting the number of cycles iteratively. In addition, existing works often oversimplify the datasets, neglecting important features of the batteries such as temperature, internal resistance, and material type. To address these limitations, this paper proposes a two-stage remaining useful life prediction scheme for Lithium-ion batteries using a spatio-temporal multimodal attention network (ST-MAN). The proposed model is designed to iteratively predict the number of cycles required for the battery to reach the end of its useful life, based on available data. The proposed ST-MAN is to capture the complex spatio-temporal dependencies in the battery data, including the features that are often neglected in existing works. Experimental results demonstrate that the proposed ST-MAN model outperforms existing CNN and LSTM-based methods, achieving state-of-the-art performance in predicting the remaining useful life of Li-ion batteries. The proposed method has the potential to improve the reliability and efficiency of battery operations and is applicable in various industries, including automotive and renewable energy