CIRCULAR MICROSTRIP TEXTILE ANTENNA

The speed of technology and its evolution with the help of human efforts and his thinking is growing like wildfire. Also the man machine relation has further taken forward big technical leaps in the world of Antenna and Microwave Technology. In near future we will see clothing and textile material to be lined up for antenna technology and together will be known as “Smart Clothes”. In this paper, circular microstrip for wearable application is been designed. This wearable is used to meet Bluetooth specifications and has been developed by using copper conducting parts and electrotextile (smart clothes). In this case jeans or cotton materials are used

Integrating Column-Oriented Storage and Query Processing Techniques into Graph Database Management Systems

Column-oriented RDBMSs, which support traditional read-heavy analytics workloads, employ a specific set of storage and query processing techniques for scalability and performance, such as positional tuple IDs, column-specific compression, and block-oriented processing. We revisit these techniques in the context of contemporary graph database management systems (GDBMSs). GDBMSs support a new set of analytics workloads, such as fraud detection in financial transaction networks or recommendations in social networks, that are also read-heavy but have fundamentally different access patterns than traditional analytics workloads. We first review the data characteristics and query access patterns in GDBMS to identify components of GDBMSs where existing columnar techniques can and cannot directly be used. We then present the physical data layout of columnar data structures, new columnar compression, and query-processing techniques that are optimized for GDBMSs. Our techniques include a new compact vertex and edge ID scheme, a new null and empty list compression scheme based on prefix-sums, and list-based query processing. We have integrated our techniques into GraphflowDB, an in-memory GDBMS. Compared to uncompressed storage, our compression techniques has scaled the system by 3.55x with minimal performance overheads. Our null compression scheme outperforms existing columnar schemes in query performance, with minor loss in compression rate and achieves both higher compression rate and better query performance as compared to row-oriented storage techniques adopted by existing GDBMSs. Finally, our list-based query processor techniques improve query performance by 2.7x on a variety of path queries and significantly outperform their corresponding conventional versions

Printing On Paper: Costly Nuisance Or Pedagogical Imperative?

What are the typical printing behaviors of students? What is the extent of wastage? What are student attitudes towards different pay-per-print schemes? What might be strategies for educational institutions to achieve less printing while not impeding pedagogical quality? “If all printers were determined not to print anything till they were sure it would offend nobody, there would be very little printed” - Benjamin Frankli

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

A+ Indexes: Tunable and Space-Efficient Adjacency Lists in Graph Database Management Systems

Graph database management systems (GDBMSs) are highly optimized to perform fast traversals, i.e., joins of vertices with their neighbours, by indexing the neighbourhoods of vertices in adjacency lists. However, existing GDBMSs have system-specific and fixed adjacency list structures, which makes each system efficient on only a fixed set of workloads. We describe a new tunable indexing subsystem for GDBMSs, we call A+ indexes, with materialized view support. The subsystem consists of two types of indexes: (i) vertex-partitioned indexes that partition 1-hop materialized views into adjacency lists on either the source or destination vertex IDs; and (ii) edge-partitioned indexes that partition 2-hop views into adjacency lists on one of the edge IDs. As in existing GDBMSs, a system by default requires one forward and one backward vertex-partitioned index, which we call the primary A+ index. Users can tune the primary index or secondary indexes by adding nested partitioning and sorting criteria. Our secondary indexes are space-efficient and use a technique we call offset lists. Our indexing subsystem allows a wider range of applications to benefit from GDBMSs' fast join capabilities. We demonstrate the tunability and space efficiency of A+ indexes through extensive experiments on three workloads

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

Identification of SKOR2 IgG as a novel biomarker of paraneoplastic neurologic syndrome

IntroductionThe development of new autoantigen discovery techniques, like programmable phage immunoprecipitation sequencing (PhIP-Seq), has accelerated the discovery of neural-specific autoantibodies. Herein, we report the identification of a novel biomarker for paraneoplastic neurologic syndrome (PNS), Sloan-Kettering-Virus-Family-Transcriptional-Corepressor-2 (SKOR2)-IgG, utilizing PhIP-Seq. We have also performed a thorough clinical validation using normal, healthy, and disease/cancer control samples.MethodsStored samples with unclassified staining at the junction of the Purkinje cell and the granule cell layers were analyzed by PhIP-Seq for putative autoantigen identification. The autoantigen was confirmed by recombinant antigen-expressing cell-based assay (CBA), Western blotting, and tissue immunofluorescence assay colocalization.ResultsPhIP-Seq data revealed SKOR2 as the candidate autoantigen. The target antigen was confirmed by a recombinant SKOR-2-expressing, and cell lysate Western blot. Furthermore, IgG from both patient samples colocalized with a commercial SKOR2–specific IgG on cryosections of the mouse brain. Both SKOR2 IgG-positive patients had central nervous system involvement, one presenting with encephalitis and seizures (Patient 1) and the other with cognitive dysfunction, spastic ataxia, dysarthria, dysphagia, and pseudobulbar affect (Patient 2). They had a refractory progressive course and were diagnosed with adenocarcinoma (Patient 1: lung, Patient 2: gallbladder). Sera from adenocarcinoma patients without PNS (n=30) tested for SKOR2-IgG were negative.DiscussionSKOR2 IgG represents a novel biomarker for PNS associated with adenocarcinoma. Identification of additional SKOR2 IgG-positive cases will help categorize the associated neurological phenotype and the risk of underlying malignancy

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

Motivations for Luxury Consumption: Insights from Tunisia’s Emerging Market

Luxury consumption and the desire for luxury are well-accepted phenomena. Myriad studies have documented the pervasiveness of the luxury market in the West and the high growth and strong potential of Asian luxury markets. It is also evident that as resources have grown in a region, luxury consumption and the desire for luxury products have followed. Nevertheless, the need for more far-reaching studies that explore emerging markets are important to understand the differences in how luxury may penetrate these markets, given variations in resources and culture. This paper investigates a number of factors that may contribute to the emergence of new luxury markets. Specifically, the authors focus on the psychology of luxury consumption in the post-revolution Tunisian market. In particular, this research attempts to understand the psychology behind the consumption of luxury items in Tunisia and provide managerial insights into strategies for entry into such emerging markets. The empirical data was collected using online surveys of participants from French-speaking Tunisia. Overall, this analysis of the Tunisian market for branded products and services informs international luxury managers in developing their strategies to penetrate emerging markets. Besides the managerial insights that the results provide, the hypotheses tested in this paper relating luxury consumption to the variables of age, income, and education within the framework of national culture are important contributions from the theoretical standpoint