bluuebunny/crossref_metadata_2025 huggingface dataset:
Ocular findings in patients with acquired ATTRv amyloidosis following domino liver transplantation
Authors: Kitahara Junya , Yoshinaga Tsuneaki , Kakihara Shinji , Hirano Takao , Imai Akira , Miyahara Teruyoshi , Yazaki Masahide , Sekijima Yoshiki , Murata Toshinori
Published: September 2023

Purpose

To investigate the presence of amyloidosis-related ocular findings in patients who received domino liver transplantation from ATTRv amyloidosis donors.

Methods

We reviewed the ocular findings in patients who had previously undergone domino liver transplantation and received ophthalmologic examinations between January 2009 and March 2023. The presence of amyloidosis-related ocular findings was retrospectively assessed by two ophthalmologists.

Results

During the study period, a total of 7 patients with 14 eyes were examined. All patients were considered as acquired ATTRv amyloidosis. The mean age at the final visit was 64.6±8.4 years (52–75 years), and the mean time since domino liver transplantation was 167.6±76.2 months (69–257 months). The two evaluators’ assessments for amyloidosis-related ocular findings were completely identical. No amyloid fibril deposition was observed in the pupil, lens, or vitreous. Five patients (10 eyes) had a Schirmer test result of 5mm or less than 5 mm, and four patients with a total of 8 eyes underwent fluorescein angiography and indocyanine green angiography, and no evidence of retinal amyloid angiopathy was found on fluorescein angiography. However, three patients with 6 eyes showed choroidal amyloid angiopathy on indocyanine green angiography.

Conclusion

While cases of choroidal amyloid angiopathy were observed, serious amyloidosis-related ocular complications such as vitreous opacity or secondary glaucoma did not occur even in the long term after domino liver transplantation.
Curriculum for Justice and Harmony: Deliberation, Knowledge, and Action in Social and Civic Education, Keith C. Barton and Li-Ching Ho (2022)
Authors: Dang Jing , Chia Yeow-Tong
Published: September 2023

Review of: Curriculum for Justice and Harmony: Deliberation, Knowledge, and Action in Social and Civic Education, Keith C. Barton and Li-Ching Ho (2022)

Abingdon and New York: Routledge, 228 pp.,

ISBN 978-1-00301010-4, e-book, £35.09

ISBN 978-0-36744-503-4, p/bk, £38.99
Upsetting the gender imbalance in African popular music: The example of Diepreye Osi of the Ịjọ (Ijaw) of Nigeria
Authors: Armstrong Imomotimi
Published: September 2023

For sometime now, there has been a conversation among scholars about male hegemony in African popular music and how some women performers resist the established status quo. In joining the discourse in this article, I focus on the owigiri music of Diepreye Osi, a female bandleader among the Ịjọ of Nigeria’s Niger Delta region. With particular attention to the departments in the bands of that premier neo-traditional music in the Ịjọ community, including instrumentation, dancing and singing, I argue that Diepreye contests patriarchy in her musical ensemble. Data for the study was gathered from unstructured interviews of artists and non-artists, and observation of live performances.
Older Adult-Onset of Inflammatory Bowel Diseases Is Associated With Higher Utilization of Analgesics: A Nationwide Cohort Study
Authors: Dalal Rahul S. , Nørgård Bente Mertz , Zegers Floor D. , Kjeldsen Jens , Friedman Sonia , Allegretti Jessica R. , Lund Ken
Published: September 2023

INTRODUCTION:

Patients with inflammatory bowel diseases (IBD) commonly require analgesic medications to treat pain, which may be associated with complications. We examined trends of analgesic use according to age at IBD onset.

METHODS:

This nationwide cohort study included adults diagnosed with IBD between 1996 and 2021 in Denmark. Patients were stratified according to their age at IBD onset: 18–39 years (young adult), 40–59 years (adult), and older than 60 years (older adult). We examined the proportion of patients who received prescriptions for analgesic medications within 1 year after IBD diagnosis: strong opioids, tramadol, codeine, nonsteroidal anti-inflammatory drugs, and paracetamol. Multivariable logistic regression analysis was performed to examine the association between age at IBD onset and strong opioid prescriptions and the composite of strong opioid/tramadol/codeine prescriptions.

RESULTS:

We identified 54,216 adults with IBD. Among them, 25,184 (46.5%) were young adults, 16,106 (29.7%) were adults, and 12,926 (23.8%) were older adults at IBD onset. Older adults most commonly received analgesic prescriptions of every class. Between 1996 and 2021, strong opioid, tramadol, and codeine prescriptions were stable, while paracetamol prescriptions increased and nonsteroidal anti-inflammatory drug prescriptions decreased. After multivariable logistic regression analysis, older adults had higher adjusted odds of receiving strong opioid prescriptions (adjusted odds ratio 1.95, 95% confidence interval 1.77–2.15) and the composite of strong opioid/tramadol/codeine prescriptions (adjusted odds ratio 1.93, 95% confidence interval 1.81–2.06) within 1 year after IBD diagnosis compared with adults.

DISCUSSION:

In this nationwide cohort, older adults most commonly received analgesic prescriptions within 1 year after IBD diagnosis. Additional research is needed to examine the etiology and sequelae of increased analgesic prescribing to this demographic.
La emergencia de los Estudios Visuales: resonancias en América Latina
Authors: Sienra Chaves Sofía
Published: September 2023

El artículo aborda la emergencia de los Estudios Visuales desde finales del siglo pasado, para observar sus resonancias con las propuestas antihegemónicas de los Estudios Culturales en América Latina. A través de una revisión teórica y un análisis crítico se argumenta que la búsqueda activa de desmarque por parte de los Estudios Visuales respecto de los métodos consolidados en las disciplinas de la Estética y la Historia del Arte, junto con la desjerarquización de la producción artística en favor de una amplia gama de objetos culturales, dan lugar a un debate que continúa siendo relevante para concebir los fundamentos e implicaciones del carácter indisciplinado de la investigación. Se concluirá enfatizando las coincidencias de dichos Estudios Críticos de la Visualidad con las perspectivas decoloniales latinoamericanas.
INTERAKSI EKONOMI ANTAR WILAYAH MENUJU KEPULAUAN BANGKA BELITUNG SEJAHTERA
Authors: Pratiwi Kandi Dwi
Published: September 2023

Perekonomian suatu wilayah dapat saling berhubungan dengan perekonomian wilayah lain melalui mekanisme perdagangan barang dan jasa. Pada kenyataannya, perubahan perekonomian tidak hanya berdampak untuk wilayah itu sendiri, tetapi juga berdampak pada wilayah lainnya. Suatu wilayah dapat mengimpor barang dan jasa dari wilayah lain untuk digunakan dalam proses produksi dan permintaan akhir, begitu pula sebaliknya. Hubungan saling ketergantungan tersebut menciptakan keterkaitan antar sektor dan antar wilayah. Penelitian ini bertujuan menganalisis perekonomian Kepulauan Bangka Belitung berdasarkan keterkaitan antar lapangan usaha dan keterkaitan antar provinsi menggunakan tabel Inter-Regional Input Output (IRIO). Hasil analisis menunjukkan bahwa lapangan usaha kunci di Kepulauan Bangka Belitung yaitu Lapangan Usaha Pengadaan Listrik dan Gas, Industri Pengolahan, Transportasi dan Perdagangan, dan Konstruksi. Transaksi dalam wilayah Kepulauan Bangka Belitung lebih besar daripada transaksi antar wilayahnya. Lebih jauh, provinsi yang memperoleh dampak limpahan tinggi adalah Provinsi Sumatera Selatan, DKI Jakarta, Riau, Jawa Barat, dan Jawa Timur.
A Framework for Problem Formulation in Climate Change Issues: Formulating Policy Recommendations Using Energy Indicators for Sustainable Development
Authors: Lestari Rizky Dwi , Nirmala Renata Jati
Published: September 2023

This paper aims to explore the application of William Dunn's framework for problem formulation in the context of addressing climate change issues. The study focuses on formulating policy recommendations by utilizing energy indicators within a sustainable development framework. The objective is to assess two potential policy options: Local Technology Innovation with Public-Private Partnership (PPP) Scheme and Model Energy as a Service. By employing Dunn's framework, the paper provides an in-depth explanation of its utilization during the problem formulation phase. This framework offers a systematic approach to understanding and defining complex problems, such as climate change, by considering various perspectives, stakeholders, and causal factors involved. The paper then proceeds to apply the framework to the climate change issue, specifically by incorporating energy indicators within a sustainable development context. The findings of this study contribute to the ongoing discourse on climate change policy formulation and provide insights into the practical application of Dunn's framework. The policy recommendations derived from this research can assist policymakers, stakeholders, and practitioners in making informed decisions and shaping effective strategies for addressing climate change while advancing sustainable development goals.
Faktor Gejala dan Komorbid yang Berhubungan dengan Kematian Pasien COVID-19 di Tangerang Selatan, November 2020-Mei 2021
Authors: Harahap Ade Ayu Lanniari , Helda Helda
Published: September 2023

Background : Coronavirus disease 2019 (COVID-19) is an infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The number of confirmed cases and deaths of COVID-19 continues to increase globally. Epidemiology and clinical characteristics of COVID-19 cases from Indonesia are scanty. High-quality evidence to understand the disease and can be used as a basis for policymaking. Therefore, this study was conducted to determine the symptom factors and comorbidities associated with mortality in COVID-19 patients in South Tangerang from November 2020 to May 2021.Methode : A Case-control study of confirmed cases of COVID-19 in South Tangerang. The cases included 337 COVID-19 patients who died and 347 controls who were still alive. Data on age, gender, symptoms, and comorbidities were collected through Epidemiological Surveillance (ES) of COVID-19 patients at the South Tangerang Health Office from November 2020 to May 2021 in association with death and analyzed using chi-square test and simple logistic regression.Result : The results of multivariate analysis showed that risk factors such as age (OR=5.059; 95% CI: 3.209-7.974), gender (OR=2.650; 95% CI: 1.758-3.996), fever symptoms (OR=0.499; 0.331- 0.753), co-morbidities with diabetes mellitus (OR=15.94; 95% CI: 7.397-34.350), hypertension (OR=4.005; 95% CI: 2.440-6.572), heart disease (OR=7.249; 95% CI: 2.893-18.162 ) and chronic kidney failure (OR=12,523, 95% CI: 1,571-99,808) had an influence on the incidence of death of COVID-19 positive patients in South Tangerang.Conclusion : These findings indicate that older age, male gender, symptoms of fever, and comorbidities include a history of diabetes mellitus, hypertension, heart disease, and kidney failure due to the death of COVID-19 patients in South Tangerang. The need to enhance specific public health measures to reduce the risk of death in patients infected with COVID-19.
Uji Penghambatan Reaksi Pencokelatan pada Sari Buah Pisang Ambon (Musa paradisiaca var sapientum) oleh Asam Hipoiodous (HIO) Berdasarkan Analisis Spektral
Authors: Ardianti Dea Kumala , Legowo Anang Mohamad , Al-Baarri Ahmad Ni’matullah
Published: September 2023

Reaksi pencokelatan enzimatis merupakan salah satu tanda penurunan kualitas buah selama proses penyimpanan dan dapat dicegah dengan cara menghambat aktivitas enzim oksidase. Tujuan dari penelitian ini adalah untuk mengetahui kemampuan asam hipoiodous (HIO) dalam menghambat reaksi pencokelatan. Penghambatan reaksi pencokelatan dianalisis dengan mengukur browning intensity pada panjang gelombang 420 nm. Hasil penelitian adalah HIO dapat menurunkan nilai absorbansi pencokelatan sari buah pisang. Kesimpulannya, HIO dapat menghambat reaksi pencokelatan enzimatis pada buah pisang.
Pemanfaatan Limbah Serbuk Kayu dan Fly-Ash Sebagai Bahan Tambah Pembuatan Genteng Beton Eco-Friendly
Authors: Faqrudin Ahcmad Faruk , Setiawan Syaiful Kharis , Susanti Riza , Widodo Puji
Published: September 2023

Atap di indonesia sebagian masih memakai genteng beton dikarenakan mahal dan berat. Indonesia juga merupakan negara penghasil kayu dan batu bara terbanyak di dunia, hal tersebut yang akan menyebabkan adanya limbah serbuk kayu dan fly ash. Oleh karena itu,dilakukan penelitian yang bertujuan memanfaatkan limbah serbuk kayu dan limbah fly-ash sebagai bahan campuran dan mencoba membuat genteng beton yang lebih ringan. Sehingga dalam penggunaanya bisa meringankan konstruksi atap dan juga memanfaatkan limbah sehingga genteng beton menjadi lebih ekonomis namun mempunyai kualitas yang memenuhi SNI. Metode yang digunakan adalah metode eksperimental, dengan komposisi campuran semen dan pasir yaitu 1 : 3 dan menggunakan 4 variasi benda uji yaitu untuk serbuk kayu variasi A, 0%; B, 25%; C, 0%; D, 25%  dari berat pasir sedangkan untuk fly ash variasi A, 0%; B, 0%; C, 25%; D, 25% dari berat semen. Pengujian ini meliputi sifat tampak, ukuran, kerataan, beban lentur, penyerapan serta rembesan air. Hasil beban lentur rata-rata pada variasi A = 170,632 kg, B = 168,933 kg, C = 195,446 kg, D = 179,812 kg. Untuk hasil pengujian penyerapan air rata-rata pada variasi A = 9,44%, B = 10,07%, C = 6,12%, D = 8,13%. Penggunaan serbuk kayu dapat menurunkan kuat lentur dan meningkatkan penyerapan air dan penambahan fly ash akan meningkatkan kuat lentur dan menurunkan penyerapan air dibandingkan genteng beton normal. Maka dari itu bisa disimpulkan bahwa genteng beton dengan penambahan serbuk kayu dan fly ash lebih baik, ekonomis dan ringan daripada genteng beton tanpa bahan tambah serta masih memenuhi SNI.
Krypton: Real-Time Serving and Analytical SQL Engine at ByteDance
Authors: Chen Jianjun , Shi Rui , Chen Heng , Zhang Li , Li Ruidong , Ding Wei , Fan Liya , Wang Hao , Xiong Mu , Chen Yuxiang , Dong Benchao , Guo Kuankuan , Lin Yuanjin , Liu Xiao , Shi Haiyang , Wang Peipei , Wang Zikang , Yang Yemeng , Zhao Junda , Zhou Dongyan , Zuo Zhikai , Liang Yuming
Published: September 2023

In recent years, at ByteDance, we have started seeing more and more business scenarios that require performing real-time data serving besides complex Ad Hoc analysis over large amounts of freshly imported data. The serving workload requires performing complex queries over massive newly added data items with minimal delay. These systems are often used in mission-critical scenarios, whereas traditional OLAP systems cannot handle such use cases. To work around the problem, ByteDance products often have to use multiple systems together in production, forcing the same data to be ETLed into multiple systems, causing data consistency problems, wasting resources, and increasing learning and maintenance costs.

To solve the above problem, we built a single Hybrid Serving and Analytical Processing (HSAP) system to handle both workload types. HSAP is still in its early stage, and very few systems are yet on the market. This paper demonstrates how to build Krypton, a competitive cloud-native HSAP system that provides both excellent elasticity and query performance by utilizing many previously known query processing techniques, a hierarchical cache with persistent memory, and a native columnar storage format. Krypton can support high data freshness, high data ingestion rates, and strong data consistency. We also discuss lessons and best practices we learned in developing and operating Krypton in production.
Towards General and Efficient Online Tuning for Spark
Authors: Li Yang , Jiang Huaijun , Shen Yu , Fang Yide , Yang Xiaofeng , Huang Danqing , Zhang Xinyi , Zhang Wentao , Zhang Ce , Chen Peng , Cui Bin
Published: September 2023

The distributed data analytic system - Spark is a common choice for processing massive volumes of heterogeneous data, while it is challenging to tune its parameters to achieve high performance. Recent studies try to employ auto-tuning techniques to solve this problem but suffer from three issues: limited functionality, high overhead, and inefficient search.

In this paper, we present a general and efficient Spark tuning framework that can deal with the three issues simultaneously. First, we introduce a generalized tuning formulation, which can support multiple tuning goals and constraints conveniently, and a Bayesian optimization (BO) based solution to solve this generalized optimization problem. Second, to avoid high overhead from additional offline evaluations in existing methods, we propose to tune parameters along with the actual periodic executions of each job (i.e., online evaluations). To ensure safety during online job executions, we design a safe configuration acquisition method that models the safe region. Finally, three innovative techniques are leveraged to further accelerate the search process: adaptive sub-space generation, approximate gradient descent, and meta-learning method.

We have implemented this framework as an independent cloud service, and applied it to the data platform in Tencent. The empirical results on both public benchmarks and large-scale production tasks demonstrate its superiority in terms of practicality, generality, and efficiency. Notably, this service saves an average of 57.00% memory cost and 34.93% CPU cost on 25K in-production tasks within 20 iterations, respectively.
MINT: Detecting Fraudulent Behaviors from Time-Series Relational Data
Authors: Xiao Fei , Wu Yuncheng , Zhang Meihui , Chen Gang , Ooi Beng Chin
Published: September 2023

The e-commerce platforms, such as Shopee, have accumulated a huge volume of time-series relational data, which contains useful information on differentiating fraud users from benign users. Existing fraud behavior detection approaches typically model the time-series data with a vanilla Recurrent Neural Network (RNN) or combine the whole sequence as a single intention without considering the temporal behavioral patterns, row-level interactions, and different view intentions. In this paper, we present MINT, a
M
ultiview row-
IN
teractive
T
ime-aware framework to detect fraudulent behaviors from time-series structured data. The key idea of MINT is to build a
time-aware behavior graph
for each user's time-series relational data with each row represented as an
action node.
We utilize the user's temporal information to construct three different graph convolutional matrices for hierarchically learning the user's intentions from different views, that is, short-term, medium-term, and long-term intentions. To capture more meaningful row-level interactions and alleviate the over-smoothing issue in a vanilla time-aware behavior graph, we propose a novel
gated neighbor interaction
mechanism to calibrate the aggregated information by each action node. Since the receptive fields of the three graph convolutional layers are designed to grow nearly exponentially, our MINT requires many fewer layers than traditional deep graph neural networks (GNNs) to capture multi-hop neighboring information, and avoids recurrent feedforward propagation, thus leading to higher training efficiency and scalability. Our extensive experiments on the large-scale e-commerce datasets from Shopee with up to 4.6 billion records and a public dataset from Amazon show that MINT achieves superior performance over 10 state-of-the-art models and provides better interpretability and scalability.
TPCx-AI - An Industry Standard Benchmark for Artificial Intelligence and Machine Learning Systems
Authors: Brücke Christoph , Härtling Philipp , Palacios Rodrigo D Escobar , Patel Hamesh , Rabl Tilmann
Published: September 2023

Artificial intelligence (AI) and machine learning (ML) techniques have existed for years, but new hardware trends and advances in model training and inference have radically improved their performance. With an ever increasing amount of algorithms, systems, and hardware solutions, it is challenging to identify good deployments even for experts. Researchers and industry experts have observed this challenge and have created several benchmark suites for AI and ML applications and systems. While they are helpful in comparing several aspects of AI applications, none of the existing benchmarks measures end-to-end performance of ML deployments. Many have been rigorously developed in collaboration between academia and industry, but no existing benchmark is standardized.

In this paper, we introduce the TPC Express Benchmark for Artificial Intelligence (TPCx-AI), the first industry standard benchmark for end-to-end machine learning deployments. TPCx-AI is the first AI benchmark that represents the pipelines typically found in common ML and AI workloads. TPCx-AI provides a full software kit, which includes data generator, driver, and two full workload implementations, one based on Python libraries and one based on Apache Spark. We describe the complete benchmark and show benchmark results for various scale factors. TPCx-AI's core contributions are a novel unified data set covering structured and unstructured data; a fully scalable data generator that can generate realistic data from GB up to PB scale; and a diverse and representative workload using different data types and algorithms, covering a wide range of aspects of real ML workloads such as data integration, data processing, training, and inference.
Angel-PTM: A Scalable and Economical Large-Scale Pre-Training System in Tencent
Authors: Nie Xiaonan , Liu Yi , Fu Fangcheng , Xue Jinbao , Jiao Dian , Miao Xupeng , Tao Yangyu , Cui Bin
Published: September 2023

Recent years have witnessed the unprecedented achievements of large-scale pre-trained models, especially Transformer models. Many products and services in Tencent Inc., such as WeChat, QQ, and Tencent Advertisement, have been opted in to gain the power of pre-trained models. In this work, we present Angel-PTM, a productive deep learning system designed for pre-training and fine-tuning Transformer models. Angel-PTM can train extremely large-scale models with hierarchical memory efficiently. The key designs of Angel-PTM are a fine-grained memory management via the
Page
abstraction and a unified scheduling method that coordinates computations, data movements, and communications. Furthermore, Angel-PTM supports extreme model scaling with SSD storage and implements a lock-free updating mechanism to address the SSD I/O bottlenecks. Experimental results demonstrate that Angel-PTM outperforms existing systems by up to 114.8% in terms of maximum model scale as well as up to 88.9% in terms of training throughput. Additionally, experiments on GPT3-175B and T5-MoE-1.2T models utilizing hundreds of GPUs verify our strong scalability.
Kora: A Cloud-Native Event Streaming Platform for Kafka
Authors: Povzner Anna , Mahajan Prince , Gustafson Jason , Rao Jun , Juma Ismael , Min Feng , Sridharan Shriram , Bhatia Nikhil , Attaluri Gopi , Chandra Adithya , Kozlovski Stanislav , Sivaram Rajini , Bradstreet Lucas , Barrett Bob , Shah Dhruvil , Jacot David , Arthur David , Dagostino Ron , McCabe Colin , Obili Manikumar Reddy , Prakasam Kowshik , Sancio Jose Garcia , Singh Vikas , Nikhil Alok , Gupta Kamal
Published: September 2023

Event streaming is an increasingly critical infrastructure service used in many industries and there is growing demand for cloud-native solutions. Confluent Cloud provides a massive scale event streaming platform built on top of Apache Kafka with tens of thousands of clusters running in 70+ regions across AWS, Google Cloud, and Azure. This paper introduces
Kora
, the cloud-native platform for Apache Kafka at the core of Confluent Cloud. We describe Kora's design that enables it to meet its cloud-native goals, such as reliability, elasticity, and cost efficiency. We discuss Kora's abstractions which allow users to think in terms of their workload requirements and not the underlying infrastructure, and we discuss how Kora is designed to provide consistent, predictable performance across cloud environments with diverse capabilities.
Automatic SQL Error Mitigation in Oracle
Authors: Pasupuleti Krishna Kantikiran , Li Jiakun , Su Hong , Ziauddin Mohamed
Published: September 2023

Despite best coding practices, software bugs are inevitable in a large codebase. In traditional databases, when errors occur during query processing, they disrupt user workflow until workarounds are found and applied. Manual identification of workarounds often relies on a trial-and-error method. The process is not only time-consuming but also requires domain expertise that users are often lacking. In this paper, we propose a framework to automatically mitigate errors that occur during query compilation (including optimization and code generation) without any user intervention. An error is intercepted by the database internally, a workaround is identified for it, and the query is recompiled using the workaround. The entire process remains transparent to the user with the query being executed seamlessly. The proposed technique handles SQL errors during query compilation and provides three types of mitigation strategies - i) quickly failover to one of the readily-available historical plans for the statement ii) apply targeted error-correcting directives (hints) identified from the optimizer context at the time of the error iii) modify the global configuration of the optimizer using hints.

This feature has been implemented and will be released in an upcoming version of Oracle Autonomous Database.
DoveDB: A Declarative and Low-Latency Video Database
Authors: Xiao Ziyang , Zhang Dongxiang , Li Zepeng , Wu Sai , Tan Kian-Lee , Chen Gang
Published: September 2023

Concerning the usability and efficiency to manage video data generated from large-scale cameras, we demonstrate DoveDB, a declarative and low-latency video database. We devise a more comprehensive video query language called VMQL to improve the expressiveness of previous SQL-like languages, which are augmented with functionalities for model-oriented management and deployment. We also propose a light-weight ingestion scheme to extract tracklets of all the moving objects and build semantic indexes to facilitate efficient query processing. For user interaction, we construct a simulation environment with 120 cameras deployed in a road network and demonstrate three interesting scenarios. Using VMQL, users are allowed to 1) train a visual model using SQL-like statement and deploy it on dozens of target cameras simultaneously for online inference; 2) submit multi-object tracking (MOT) requests on target cameras, store the ingested results and build semantic indexes; and 3) issue an aggregation or top-
k
query on the ingested cameras and obtain the response within milliseconds. A preliminary video introduction of DoveDB is available at https://www.youtube.com/watch?v=N139dEyvAJk
Lynx: A Graph Query Framework for Multiple Heterogeneous Data Sources
Authors: Shen Zhihong , Hu Chuan , Zhao Zihao
Published: September 2023

Graph model are increasingly popular among modern applications for its ability to model complex relationships between entities. Users tend to query the data as a graph with graph operations (e.g., graph navigation and exploration). However, a large fraction of the data resides in relational databases or other storage systems. Challenges arise in uniformly querying multiple heterogeneous data sources as a graph. Traditional solutions are limited by time-consuming data integration, expensive development effort, and incomplete query requirements. Thus, we developed Lynx, a general graph query framework, to simplify querying graph data by converting complex statements into basic graph operations. Instead of connecting directly to the data sources, Lynx retrieves data through user-implemented interfaces for those graph operations. We demonstrate Lynx's capabilities through real-world scenarios, showcasing Lynx's ability to process graph queries on multiple heterogeneous data sources and also to be used as a generic graph query engine development framework.
CM-Explorer: Dissecting Data Ingestion Problems
Authors: Bylois Niels , Neven Frank , Vansummeren Stijn
Published: September 2023

Data ingestion validation, the task of certifying the quality of continuously collected data, is crucial to ensure trustworthiness of analytics insights. A widely used approach for validating data quality is to specify, either manually or automatically, so-called data unit tests that check whether data quality metrics lie within expected bounds. We employ conditional unit tests based on conditional metrics (CMs) that compute data quality signals over specific parts of the ingestion data and therefore allow for a fine-grained detection of errors. A violated conditional unit test specifies a set of erroneous tuples in a natural way: the subrelation that its CM refers to. Unfortunately, the downside of their fine-grained nature is that violating unit tests are often correlated: a single error in an ingestion batch may cause multiple tests (each referring to different parts of the batch) to fail. The key challenge is therefore to untangle this correlation and filter out the most relevant violated conditional unit tests, i.e., tests that identify a core set of erroneous tuples
and
act as an explanation for the errors. We present CM-Explorer, a system that supports data stewards in quickly finding the most relevant violated conditional unit tests. The system consists of three components: (1) a graph explorer for visualizing the correlation structure of the violated unit tests; (2) a relation explorer for browsing the tuples selected by conditional unit tests; and, (3) a history explorer to get insight why conditional unit tests are violated. In this paper, we discuss these components and present the different scenarios that we make available for the demonstration.
Explaining Differentially Private Query Results with DPXPlain
Authors: Wang Tingyu , Tao Yuchao , Gilad Amir , Machanavajjhala Ashwin , Roy Sudeepa
Published: September 2023

Employing Differential Privacy (DP), the state-of-the-art privacy standard, to answer aggregate database queries poses new challenges for users to understand the trends and anomalies observed in the query results: Is the unexpected answer due to the data itself, or is it due to the extra noise that must be added to preserve DP? We propose to demonstrate DPXPlain, the first system for explaining group-by aggregate query answers with DP. DPXPlain allows users to compare values of two groups and receive a validity check, and further provides an explanation table with an interactive visualization, containing the approximately 'top-k' explanation predicates along with their relative influences and ranks in the form of confidence intervals, while guaranteeing DP in all steps.
mlwhatif: What If You Could Stop Re-Implementing Your Machine Learning Pipeline Analyses over and over?
Authors: Grafberger Stefan , Guha Shubha , Groth Paul , Schelter Sebastian
Published: September 2023

Software systems that learn from data with machine learning (ML) are used in critical decision-making processes. Unfortunately, real-world experience shows that the pipelines for data preparation, feature encoding and model training in ML systems are often brittle with respect to their input data. As a consequence, data scientists have to run different kinds of
data centric what-if analyses
to evaluate the robustness and reliability of such pipelines, e.g., with respect to data errors or preprocessing techniques. These what-if analyses follow a common pattern: they take an existing ML pipeline, create a pipeline variant by introducing a small change, and execute this variant to see how the change impacts the pipeline's output score.

We recently proposed mlwhatif, a library that enables data scientists to declaratively specify what-if analyses for an ML pipeline, and to automatically generate, optimize and execute the required pipeline variants. We demonstrate how data scientists can leverage mlwhatif for a variety of pipelines and three different what-if analyses focusing on the robustness of a pipeline against data errors, the impact of data cleaning operations, and the impact of data preprocessing operations on fairness. In particular, we demonstrate step-by-step how mlwhatif generates and optimizes the required execution plans for the pipeline analyses. Our library is publicly available at https://github.com/stefan-grafberger/mlwhatif.
AQUA: Automatic Collaborative Query Processing in Analytical Database
Authors: Peng Yuchen , Chen Ke , Shou Lidan , Jiang Dawei , Chen Gang
Published: September 2023

Data analysts nowadays are keen to have analytical capabilities involving deep learning (DL). Collaborative queries, which employ relational operations to process structured data and DL models to process unstructured data, provide a powerful facility for DL-based in-database analysis. The classical approach to support collaborative queries in relational databases is to integrate DL models with user-defined functions (UDFs) in a general-purpose language (e.g., C++) to process unstructured data. This approach suffers from suboptimal performance as the opaque UDFs preclude the generation of an optimal query plan. A recent work, DL2SQL, addresses the problem of collaborative query optimization by first converting DL computations into SQL subqueries and then using a classical relational query optimizer to optimize the entire collaborative query. However, the DL2SQL approach compromises usability by requiring data analysts to manually manage DL-related data and tune query performance. To this end, this paper introduces AQUA, an analytical database designed for efficient collaborative query processing. Built on DL2SQL, AQUA automates translations from collaborative queries into SQL queries. To enhance usability, AQUA introduces two techniques: 1) a declarative scheme for DL-related data management, and 2) DL-specific optimizations for collaborative query processing, eliminating the burden of manual data management and performance tuning from the data analysts. We demonstrate the key contributions of AQUA via a web APP that allows the audience to perform collaborative queries on the CIFAR-10 dataset.
VisualNeo: Bridging the Gap between Visual Query Interfaces and Graph Query Engines
Authors: Huang Kai , Liang Houdong , Yao Chongchong , Zhao Xi , Cui Yue , Tian Yao , Zhang Ruiyuan , Zhou Xiaofang
Published: September 2023

Visual Graph Query Interfaces (VQIs) empower non-programmers to query graph data by constructing visual queries intuitively. Devising efficient technologies in Graph Query Engines (GQEs) for interactive search and exploration has also been studied for years. However, these two vibrant scientific fields are traditionally independent of each other, causing a vast barrier for users who wish to explore the full-stack operations of graph querying. In this demonstration, we propose a novel VQI system built upon Neo4j called VisualNeo that facilities an efficient subgraph query in large graph databases. VisualNeo inherits several advanced features from recent advanced VQIs, which include the data-driven gui design and canned pattern generation. Additionally, it embodies a database manager module in order that users can connect to generic Neo4j databases. It performs query processing through the Neo4j driver and provides an aesthetic query result exploration.
Web Connector: A Unified API Wrapper to Simplify Web Data Collection
Authors: Wu Weiyuan , Wang Pei , Xie Yi , Liu Yejia , Chow George , Wang Jiannan
Published: September 2023

Collecting structured data from Web APIs, such as the Twitter API, Yelp Fusion API, Spotify API, and DBLP API, is a common task in the data science lifecycle, but it requires advanced programming skills for data scientists. To simplify web data collection and lower the barrier to entry, API wrappers have been developed to wrap API calls into easy-to-use functions. However, existing API wrappers are not standardized, which means that users must download and maintain multiple API wrappers and learn how to use each of them, while developers must spend considerable time creating an API wrapper for any new website. In this demo, we present the Web Connector, which unifies API wrappers to overcome these limitations. First, the Web Connector has an easy-to-use program-ming interface, designed to provide a user experience similar to that of reading data from relational databases. Second, the Web Connector's novel system architecture requires minimal effort to fetch data for end-users with an existing API description file. Third, the Web Connector includes a semi-automatic API description file generator that leverages the concept of
generation by example
to create new API wrappers without writing code.
FS-Real: A Real-World Cross-Device Federated Learning Platform
Authors: Gao Dawei , Chen Daoyuan , Li Zitao , Xie Yuexiang , Pan Xuchen , Li Yaliang , Ding Bolin , Zhou Jingren
Published: September 2023

Federated learning (FL) is a general distributed machine learning paradigm that provides solutions for tasks where data cannot be shared directly. Due to the difficulties in communication management and heterogeneity of distributed data and devices, initiating and using an FL algorithm for real-world cross-device scenarios requires significant repetitive effort but may not be transferable to similar projects. To reduce the effort required for developing and deploying FL algorithms, we present FS-Real, an open-source FL platform designed to address the need of a general and efficient infrastructure for real-world cross-device FL. In this paper, we introduce the key components of FS-Real and demonstrate that FS-Real has the following capabilities: 1) reducing the programming burden of FL algorithm development with plug-and-play and adaptable runtimes on Android and other Internet of Things (IoT) devices; 2) handling a large number of heterogeneous devices efficiently and robustly with our communication management components; 3) supporting a wide range of advanced FL algorithms with flexible configuration and extension; 4) alleviating the costs and efforts for deployment, evaluation, simulation, and performance optimization of FL algorithms with automatized tool kits.
PikePlace: Generating Intelligence for Marketplace Datasets
Authors: Qiao Shi , Jindal Alekh
Published: September 2023

There is a renewed interest in data marketplaces with cloud data warehouses that make sharing and accessing data on-demand and extremely easy. However, analyzing marketplace datasets is challenge since current tools for creating the data models are manual and slow. In this paper, we propose to demonstrate a learning-based approach to discover, deploy, and optimize data models. We present the resulting system, PikePlace, show an evaluation over Snowflake marketplace and TPC-H datasets, and describe several demonstration scenarios that the audience can play with.
Research and optimization of tunnel construction scheme for super-large span high-speed railway tunnel in poor tuff strata
Authors: Ma Jianfei , He Shaohui , Liu Xiabing , He Jiaxin
Published: September 2023

The rapid traffic developments create demands for shallow four-line high-speed railway tunnels in weak soils, while construction technologies of such tunnels have yet to be reported. In this article, the construction schemes for a shallow four-line high-speed railway tunnel with a span of 26.3 m in weak tuff strata are investigated by numerical analysis and in-situ tests. First, 20 construction schemes of the shallow super-large span high-speed railway tunnel are proposed and simulated by the Finite Difference Method, including eight schemes for grade V rocks and 12 schemes for grades IV and III rocks. The mechanical response of grade V rock mass is simulated by the Bolt–Kelvin Mohr–Coulomb rheological model. The influence of construction method, rock grade, area, quantity, and excavation sequence of pilot tunnels on the rock deformation and the internal force of the primary support are comparatively explored. For further analysis, an 8 month field test was conducted to study the optimized effect of the construction scheme. Finally, a suggested construction method selection chart for super-large span tunnels in weak rocks (spans between 17 and 34 m) has been proposed. The research conclusion could provide a reference for building the super-large span tunnel in complex conditions.
Existence and multiplicity of solutions for a class of p-Kirchhoff-type equation R

N
Authors: Chen Lijuan , Xue Changfeng , Wang Fangfang
Published: September 2023

This article shows the existence and multiplicity of solutions for the following

p

p

-Kirchhoff-type equation:

a
+
b

∫

R

N

(

∣

∇

u
∣

p

+
V

(

x

)

∣
u
∣

p

)

d
x

(

−

△

p

u
+
V

(

x

)

∣
u
∣

p
−
2

u

)

=
λ
g

(

x

)

∣
u
∣

r
−
2

u
−
h

(

x

)

∣
u
∣

q
−
2

u
,

in

R

N

.

\left(a+b\mathop{\int }\limits_{{{\mathbb{R}}}^{N}}\left({| \nabla u| }^{p}+V\left(x){| u| }^{p}){\rm{d}}x\right)\left(-{\bigtriangleup }_{p}u+V\left(x){| u| }^{p-2}u)=\lambda g\left(x){| u| }^{r-2}u-h\left(x){| u| }^{q-2}u,\hspace{1.0em}\hspace{0.1em}\text{in}\hspace{0.1em}\hspace{0.33em}{{\mathbb{R}}}^{N}.

where

λ

\lambda

is a real parameter and

1
<
p
<
q
<
∞

1\lt p\lt q\lt \infty

,

a

a

and

b

b

are positive constants. Depending on the relationship of

p
,
q

p,q

, and

r

r

, we obtain the existence, multiplicity, and nonexistence of solutions to the abovementioned equation using variational methods.
Systemic observation and measurement of microgap formation in dental implant–abutment connection interface under fatigue
Authors: Mercan Cigdem , Gunay Bulutsuz Asli , Katiboglu Ahmet Bulent
Published: September 2023

Today, a variety of implant designs with different attachment types are widely used in dental implant systems to eliminate connection problems. However, there are still design issues that can significantly affect the long-term success of dental implants. One of these problems is the formation of a microgap in the abutment–implant interface, which can lead to abutment screw loosening, screw fracture, and abutment fracture under masticatory loading. In this in vitro study, the effects of gingival height, dental implant diameter, and abutment type (solid, digital, and ball) on microgap formation were observed after repetitive fatigue tests using an experimental design. After the tests, the optimal design factors were determined to minimize microgap formation. According to the results of the experimental design, the abutment type was found to be the most influential parameter on microgap formation. An implant diameter of 4.1 mm, the smallest gingival height of 0.5 mm, and a digital attachment combination were identified as the optimal design parameters based on the statistical approach (R
2 = 85.3 %).
Effects of shear deformation on grain size and mechanical properties of the forged B4Cp/Al composite
Authors: Gong Dongmei , Xu Feng , Shi Wenchao , Chen An
Published: September 2023

To flake aluminum powder and strengthen the B4Cp/Al composite, pure aluminum powder is natural oxidized and ball milled. The effect of shear deformation on mechanical properties of the forged B4Cp/Al composite with flaked Al are studied. The results show that ball milling can flake the pure aluminum powder, reduce grain size of aluminum, and increase the volume fraction of Al2O3 on the surface of the flaked aluminum. The forging helps to refine the grain size of aluminum. The flow of aluminum matrix is helpful to the uniform distribution of the B4C particles. Shear deformation can refine grain size and also can break the Al2O3 layer, with the increment of strength, hardness, and relative density. After shear deformation, the ultimate tensile strength of the B4Cp/Al composite is 157.0 MPa.
The crystal structure of a cobalt-vanadium-oxido hydrate
Authors: Wu-Hua Chen , Ran Chen , Jin-Xiao Mi , Hua-Feng Wang , Jia-Yu Song
Published: September 2023

H4O14CoV4.4, monolinic, P2/m (no. 10), a = 8.1091(8) Å, b = 3.5128(2) Å, c = 9.7590(9) Å, β = 111.553(11)°, V = 258.55(4) Å3, Z = 1, R

gt
(F) = 0.0401, wR

ref
(F
2) = 0.0922, T = 293(2) K.
Exploration of generalized two-phase free convection magnetohydrodynamic flow of dusty tetra-hybrid Casson nanofluid between parallel microplates
Authors: Khan Dolat , Hussien Mostafa A. , Elsiddieg Awatif M. A. , Lone Showkat Ahmad , Hassan Ahmed M.
Published: September 2023

Dusty Casson fluids and tetra-hybrid nanofluids are complex phenomena that find their extensive uses in engineering and industrial applications. For instance, dusty fluids are used in gas-freezing systems and nuclear power reactors. The main objective of this article is to focus on the characterization of generalized two-phase free convection magnetohydrodynamic flow of dusty tetra-hybrid Casson nanofluid among parallel microplates: dusty Casson fluid and tetra-hybrid nanofluid exhibit free movement and electrical conductivity. The Caputo–Fabrizio fractional derivative recently discovered generalizes the partial differential equations governing the flow. Highly accurate temperature and velocity distributions can be obtained using finite sine Fourier and Laplace transform together. This study examines the relationships between temperature, dust particle velocity, and Casson fluid velocity, along with the effects of magnetic parameter, Grashof number, dusty fluid parameter, Peclet number, Reynold number, and particle mass parameter. The Mathcad-15 software provides Casson, dusty, and temperature profiles graphically. The Nusselt number and skin friction are also examined for the tetra-hybrid nanofluid. The fractional Casson fluid model is more accurate than the classical model in terms of velocity, temperature, heat transfer, and skin friction. Graphical results conclude that the fractional Casson fluid model describes a more realistic aspect of both (fluid and dust particle) velocities and temperature profiles, heat transfer rate, and skin friction than the classical Casson fluid model. Furthermore, the heat transfer rate enhanced from 0 to 39.3111% of the tetra-hybrid nanofluid.
Freiwilliges Engagement in sozialen Organisationen im Hinblick auf die Motive des Engagements, Erleben emotionaler Distanzierung (Cool Down) und Lebenszufriedenheit bei Menschen mit unterschiedlichen Indikatoren der Spiritualität
Authors: Büssing Arndt , Baumann Klaus , Bär-Mennigen Carina
Published: September 2023

Viele Menschen engagieren sich freiwillig für andere und kommen in sozialen Bereichen zum Einsatz, die oft physisch und emotional anstrengend und belastend sind. Im vorliegenden Beitrag geht es um das Engagement im sozialen Bereich. Er geht der Frage nach, ob es bei spirituellen, religiösen oder nicht religiösen Menschen Unterschiede in der Motivkonstellation sowie bezüglich der erlebten emotionalen Distanzierung von den begleiteten Personen in Sinne des Cool Down gibt. Hierfür wurde eine anonyme Querschnittsbefragung unter 343 Freiwilligen mittels standardisierter Fragebögen durchgeführt. Das Werte-Motiv steht bei ihnen im Vordergrund, kaum das Wachsen-Motiv. Religiöse und nicht-religiöse Engagierte unterscheiden sich hinsichtlich ihrer Motivationen nicht signifikant, jedoch zeigen sich für das Werte-Motiv deutliche Zusammenhänge mit einem bewussten Umgang mit anderen sowie Compassion und Altruismus, aber auch mit einer Suche nach Einsicht und Weisheit. Die Beweggründe des Engagements sind also bedeutsam, weniger die Religionszugehörigkeit. Das Cool Down-Empfinden korreliert nicht mit Indikatoren der Spiritualität. Für Freiwilligen-Organisationen ist es wichtig, das aktuelle Risikoprofil der Engagierten im Sinne einer emotionalen Überforderung und Distanzierung von den begleiteten Personen zu kennen, um notwendige Unterstützung anbieten zu können. Basierend auf dieser Arbeit können Empfehlungen ausgesprochen werden, die prinzipielle auch für diejenigen gelten, die sich im Kontext des Spiritual Care ehrenamtlich engagieren.