bluuebunny/crossref_metadata_2025_split huggingface dataset:
Uncertainty and Sensitivity Evaluation of the QUENCH-02 Experiment Simulation Using the ASYST Code
Authors: Šadek Siniša , Pavlinac Renato , Ivanjko Karlo , Grgić Davor 
Published: June 2023

Abstract
Uncertainty and sensitivity methods are increasingly used in safety analyzes of nuclear power plants to address the unreliability of input data, numerical models, and, in general, the lack of knowledge regarding certain physical phenomena, in determining safety margins and acceptance criteria. The ASYST code, developed as part of an international nuclear technology ASYST Development and Training Program (ADTP) managed by Innovative Systems Software (ISS), is used to perform an uncertainty analysis of the QUENCH-02 experiment conducted at the Karlsruhe Institute of Technology. The code uses a probabilistic methodology based on the propagation of input uncertainties. The QUENCH facility contains electrically heated PWR fuel rod simulators and the aim of the experiment is to examine hydrogen generation and the behavior of the fuel rod cladding during core reflood. For selected input parameters, such as steam/water flow, electrical power, and other relevant boundary conditions, it is necessary to define their probability density functions. Input databases are then prepared for individual calculations based on the selected confidence level and confidence interval. The number of performed calculations is 60, large enough to ensure at least 95% coverage of expected output results and uncertainty limits. The results of the calculations are compared with the experimental measurements. The Pearson correlation coefficient is used to obtain correlation between the input uncertain parameters and the output data. Sensitivity analyses cover the influence of variations in the heater's electrical power and the steam flow rate on the hydrogen production and the maximum cladding temperature.
Stenting versus medical therapy alone for symptomatic intracranial arterial stenosis: protocol for a systematic review and individual patient data meta-analysis
Authors: Wang Tao , Xing Zixuan , Gong Haozhi , Derdeyn Colin P , Zaidat Osama O , Almallouhi Eyad , Luo Jichang , Gao Peng , Wang Haibo , Jiao Liqun 
Published: June 2023

IntroductionIntracranial atherosclerotic stenosis (ICAS) is a common cause of stroke worldwide. However, whether the treatment options for symptomatic ICAS is stent placement or medical therapy alone is still controversial. At present, three multicentre randomised controlled trials (RCTs) have been published, but their research designs are also slightly different and the conclusions are not completely consistent. Therefore, we plan to conduct a systematic review and individual patient data (IPD) meta-analysis of randomised clinical trials to ascertain safety and efficacy of stenting versus medical therapy alone for symptomatic patients with intracranial arterial stenosis.Methods and analysesWe will identify RCTs comparing stenting vs medical therapy alone in patients with symptomatic ICAS stenosis (70%–99%) through a systematic search, mainly including PubMed, MEDLINE, EMBASE, the Cochrane Library and ClinicalTrials.gov. Individual-level patient data for a prespecified list of variables will be sought from authors of all eligible studies. The primary outcome was a composite of stroke or death within 30 days, or stroke in territory of qualifying artery beyond 30 days after randomisation. IPD meta-analysis will be conducted with a one-stage approach.Ethics and disseminationEthical approval and individual patient consent will not be required in most cases since this IPD meta-analysis will use pseudoanonymised data from RCTs. Results will be disseminated through peer-reviewed journals and international conferences.PROSPERO registration numberCRD42022369922.
Quality improvement intervention to increase colorectal cancer screening at the primary care setting: a cluster-randomised controlled trial
Authors: Irazola Vilma , Santero Marilina , Sanchez Mario , Tristao Ignez , Ruiz Juan Ignacio , Spira Cintia , Ismael Julia , Cavallo Ana Soledad , Gutierrez Laura , Mazzaresi Yanina , Nadal Ana Maria , García Elorrio Ezequiel 
Published: June 2023

BackgroundApproximately 81% of deaths in Argentina are from chronic non-communicable diseases and 21% caused by cancer. Colorectal cancer (CRC) is the second most frequent cancer in Argentina. Even though CRC screening has been recommended for adults from 50 to 75 years old by using a faecal immunochemical test (FIT) annually, screening rates remain below 20% in the country.MethodsWe conducted an 18-month, two-arm, pragmatic cluster-randomised controlled trial evaluating the effect of a quality improvement intervention, based on the Plan-Do-Study-Act cycles, considering barriers and catalysts to articulate theory and practice, to increase CRC screening rates using FITs at primary care level. The study involved ten public primary health centres in Mendoza province, Argentina. The primary outcome measure was the rate of effective CRC screening. Secondary outcomes were the rate of participants with a positive FIT, tests with invalid results and the rate of participants referred for colonoscopy.ResultsScreening was effective in 75% of the participants in the intervention arm vs 54.2% in the control arm, OR 2.5 (95% CI 1.4 to 4.4, p=0.001). These results remained unchanged after adjusting for individual demographic and socioeconomic characteristics. Regarding secondary outcomes, the overall prevalence of positive tests was 17.7% (21.1% in the control arm and 14.7% in the intervention arm, p=0.3648). The overall proportion of participants with inadequate test results was 5.2% (4.9% in the control arm vs 5.5% in the intervention arm, p=0.8516). All the participants with positive tests were referred for colonoscopy in both groups.ConclusionsAn intervention based on quality improvement strategies proved to be highly successful in increasing effective CRC screening in Argentina’s primary care setting within the public healthcare system.Trial registration numberNCT04293315.
HSPA4 regulated glioma progression via activation of AKT signaling pathway
Authors: Yuan Xi , Sun Xiangdong , Zhou Bin , Zhao Shuang , Li Yikun , Ming Haolang 
Published: June 2023

Glioma is still an incurable disease with high invasiveness. Heat shock 70 kDa protein 4 (HSPA4) is a member of the HSP110 family, and is associated with the development and progression of various cancers. In the current study, we assessed the expression of HSPA4 in clinical samples, and found that HSPA4 was up-regulated in glioma tissues and correlated with tumor recurrence and grade. Survival analyses demonstrated that glioma patients with high HSPA4 expression had lower overall survival and disease-free survival times. In vitro knockdown of HSPA4 inhibited glioma cell proliferation, mediated cell cycle arrest at G2 phase and apoptosis, and reduced the migration ability. In vivo, the growth of HSPA4-knockdown xenografts was markedly suppressed compared to the tumors formed by HSPA4-positive control cells. Additionally, Gene set enrichment analyses disclosed that HSPA4 was associated with the PI3K/Akt signaling pathway. The regulatory effect of the AKT activator SC79 on cell proliferation and apoptosis was suppressed by HSPA4 knockdown, indicating that HSPA4 is capable of promoting glioma development. In summary, these data showed that HSPA4 is likely to play a pivotal role in the progression of glioma, and consequently may be a promising therapeutic target for glioma therapy.
Security and application of semi-quantum key distribution protocol for users with different quantum capabilities
Authors: Ye Chong-Qiang , Li Jian , Chen Xiu-Bo , Hou Yanyan , Wang Zhuo 
Published: June 2023

AbstractSemi-quantum protocols serve as a bridge between quantum users and “classical” users with limited quantum capabilities, providing support for application scenarios that cannot afford the excessively high cost of quantum resources. In this paper, we present a semi-quantum key distribution (SQKD) protocol based on Bell states and single particles, which is designed for key distribution between different types of users. The protocol enables simultaneous key distribution between quantum and classical users, as well as key establishment between two classical users. The security analysis demonstrates that the protocol can reach the same level of security as the full quantum protocol. Furthermore, we extrapolate the proposed protocol to other semi-quantum protocols, such as semi-quantum key agreement and semi-quantum private comparison protocols. Compared with previous similar ones, our SQKD protocol and its extended versions can fulfill the requirements of their respective counterparts individually. Therefore, our SQKD protocol has the potential for broader applications in practical scenarios.
Experimental alteration of allanite at 200°C: the role of pH and aqueous ligands
Authors: Denys Axel , Auzende Anne-Line , Janots Emilie , Montes-Hernandez German , Findling Nathaniel , Lanari Pierre , Magnin Valérie 
Published: June 2023

Abstract

Allanite is a major host of rare earth elements (REEs) in the continental crust. In this study, reaction mechanisms behind allanite alteration are investigated through batch experiment runs on natural allanite grains in carbonate-bearing hydrothermal fluids at 200°C, with initial acidic (pH = 4) or alkaline (pH = 8) conditions and with different aqueous ligands (120 mmol kg
−1
of F, Cl, P or S). Time-series experiment runs in F-doped systems at different durations between 15 and 180 days reached a steady state at 120 days. The pH efficiently controls the allanite alteration process, with initial high pH, alkaline conditions being more reactive (75% alteration compared with 25% under acidic conditions). The ligand also significantly influences the alteration process under initial acidic conditions with the P-doped system (70%) almost non-reactive for the Cl- and S-doped systems (<5%). In the alteration rim, REEs are mainly redistributed in REE-bearing phases either as carbonates (F-doped) or phosphates (P-doped). The relatively flat REE-normalized patterns of the recovered experimental fluids suggest a fractionation of light rare earth elements (LREEs) over heavy rare earth elements (HREEs) during the course of the alteration reactions. It is proposed that secondary REE mineral precipitation at the reaction front creates a local disequilibrium in the solution and a steep chemical gradient promoting allanite dissolution and thus its alterability.
Design and Analysis of a Processing-in-DIMM Join Algorithm: A Case Study with UPMEM DIMMs
Authors: Lim Chaemin , Lee Suhyun , Choi Jinwoo , Lee Jounghoo , Park Seongyeon , Kim Hanjun , Lee Jinho , Kim Youngsok 
Published: June 2023

Modern dual in-line memory modules (DIMMs) support processing-in-memory (PIM) by implementing in-DIMM processors (IDPs) located near memory banks. PIM can greatly accelerate in-memory join, whose performance is frequently bounded by main-memory accesses, by offloading the operations of join from host central processing units (CPUs) to the IDPs. As real PIM hardware has not been available until very recently, the prior PIM-assisted join algorithms have relied on PIM hardware simulators which assume fast shared memory between the IDPs and fast inter-IDP communication; however, on commodity PIM-enabled DIMMs, the IDPs do not share memory and demand the CPUs to mediate inter-IDP communication. Such discrepancies in the architectural characteristics make the prior studies incompatible with the DIMMs. Thus, to exploit the high potential of PIM on commodity PIM-enabled DIMMs, we need a new join algorithm designed and optimized for the DIMMs and their architectural characteristics.
In this paper, we design and analyze Processing-In-DIMM Join (PID-Join), a fast in-memory join algorithm which exploits UPMEM DIMMs, currently the only publicly-available PIM-enabled DIMMs. The DIMMs impose several key challenges on efficient acceleration of join including the shared-nothing nature and limited compute capabilities of the IDPs, the lack of hardware support for fast inter-IDP communication, and the slow IDP-wise data transfers between the IDPs and the main memory. PID-Join overcomes the challenges by prototyping and evaluating hash, sort-merge, and nested-loop algorithms optimized for the IDPs, enabling fast inter-IDP communication using host CPU cache streaming and vector instructions, and facilitating fast rank-wise data transfers between the IDPs and the main memory. Our evaluation using a real system equipped with eight UPMEM DIMMs and 1,024 IDPs shows that PID-Join greatly improves the performance of in-memory join over various CPU-based in-memory join algorithms.
rkHit: Representative Query with Uncertain Preference
Authors: Xiao Xingxing , Li Jianzhong 
Published: June 2023

A top-k query retrieves the k tuples with highest scores according to a user preference, defined as a scoring function. It is difficult for a user to precisely specify the scoring function. Instead, obtaining the distribution on scoring functions, i.e., the preference distribution, has been extensively explored in many fields.
Motivated by this, we introduce the uniform (r,k)-hit (UrkHit) problem. Given a preference distribution, UrkHit aims to select a representative set of r tuples to maximize the probability of containing a tuple attractive to the user. We say a tuple attracts a user, if it is a top-k tuple for the scoring function adopted by the user. Further, we generalize UrkHit and propose the (r,k)-hit (rkHit) problem with an additional penalty function to model the user satisfaction with the tuple ranked i-th. rkHit aims to maximize the expected user satisfaction with the representative set. In 2D space, we design an exact algorithm 2DH for rkHit, indicating rkHit is in P for d=2. We show that rkHit is NP-hard when d\ge3. In 3D space, assuming a uniform preference distribution, we propose a (1-1/e)-approximation algorithm 3DH based on space partitioning. In addition, we propose an approximate algorithm MDH suitable for any dimension and distribution, which creatively combines the ideas of sampling and clustering. It relaxes the approximation guarantee slightly. Comprehensive experiments demonstrate the efficiency and effectiveness of our algorithms.
Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines
Authors: Grafberger Stefan , Groth Paul , Schelter Sebastian 
Published: June 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 pipeline variant to see how the change impacts the pipeline's output score. The application of existing analysis techniques to ML pipelines is technically challenging as they are hard to integrate into existing pipeline code and their execution introduces large overheads due to repeated work.
We propose mlwhatif to address these integration and efficiency challenges for data-centric what-if analyses on ML pipelines. mlwhatif enables data scientists to declaratively specify what-if analyses for an ML pipeline, and to automatically generate, optimize and execute the required pipeline variants. Our approach employs pipeline patches to specify changes to the data, operators and models of a pipeline. Based on these patches, we define a multi-query optimizer for efficiently executing the resulting pipeline variants jointly, with four subsumption-based optimization rules. Subsequently, we detail how to implement the pipeline variant generation and optimizer of mlwhatif. For that, we instrument native ML pipelines written in Python to extract dataflow plans with re-executable operators.
We experimentally evaluate mlwhatif, and find that its speedup scales linearly with the number of pipeline variants in applicable cases, and is invariant to the input data size. In end-to-end experiments with four analyses on more than 60 pipelines, we show speedups of up to 13x compared to sequential execution, and find that the speedup is invariant to the model and featurization in the pipeline. Furthermore, we confirm the low instrumentation overhead of mlwhatif.
InfiniFilter: Expanding Filters to Infinity and Beyond
Authors: Dayan Niv , Bercea Ioana , Reviriego Pedro , Pagh Rasmus 
Published: June 2023

Filter data structures have been used ubiquitously since the 1970s to answer approximate set-membership queries in various areas of computer science including architecture, networks, operating systems, and databases. Such filters need to be allocated with a given capacity in advance to provide a guarantee over the false positive rate. In many applications, however, the data size is not known in advance, requiring filters to dynamically expand. This paper shows that existing methods for expanding filters exhibit at least one of the following flaws: (1) they entail an expensive scan over the whole data set, (2) they require a lavish memory footprint, (3) their query, delete and/or insertion performance plummets, (4) their false positive rate skyrockets, and/or (5)~they cannot expand indefinitely.
We introduce InfiniFilter, a new method for expanding filters that addresses these shortcomings. InfiniFilter is a hash table that stores a fingerprint for each entry. It doubles in size when it reaches capacity, and it sacrifices one bit from each fingerprint to map it to the expanded hash table. The core novelty is a new and flexible hash slot format that sets longer fingerprints to newer entries. This keeps the average fingerprint length long and thus the false positive rate stable. At the same time, InfiniFilter provides stable insertion/query/delete performance as it is comprised of a unified hash table. We implement InfiniFilter on top of Quotient Filter, and we demonstrate theoretically and empirically that it offers superior cost properties compared to existing methods: it better scales performance, the false positive rate, and the memory footprint, all at the same time.
Query-Guided Resolution in Uncertain Databases
Authors: Drien Osnat , Freiman Matanya , Amarilli Antoine , Amsterdamer Yael 
Published: June 2023

We present a novel framework for uncertain data management. We start with a database whose tuple correctness is uncertain and an oracle that can resolve the uncertainty, i.e., decide if a tuple is correct or not. Such an oracle may correspond, e.g., to a data expert or to a crowdsourcing platform. We wish to use the oracle to clean the database with the goal of ensuring the correct answer for specific mission-critical queries. To avoid the prohibitive cost of cleaning the entire database and to minimize the expected number of calls to the oracle, we must carefully select tuples whose resolution would suffice to resolve the uncertainty in query results. In other words, we need a query-guided process for the resolution of uncertain data.
We develop an end-to-end solution to this problem, based on the derivation of query answers and on correctness probabilities for the uncertain data. At a high level, we first track Boolean provenance to identify which input tuples contribute to the derivation of each output tuple, and in what ways. We then design an active learning solution for iteratively choosing tuples to resolve, based on the provenance structure and on an evolving estimation of tuple correctness probabilities. We conduct an extensive experimental study to validate our framework in different use cases.
GraphINC: Graph Pattern Mining at Network Speed
Authors: Hussein Rana , Lerner Alberto , Ryser Andre , Bürgi Lucas David , Blarer Albert , Cudre-Mauroux Philippe 
Published: June 2023

Graph Pattern Mining (GPM) is a class of algorithms that identifies given shapes within a graph, e.g., cliques of a certain size. Any area of a graph can contain a shape of interest, but in real-world graphs, these shapes tend to be concentrated in areas deemed skewed. Because mining skewed areas can dominate GPM computations, the overwhelming majority of state-of-the-art GPM techniques break such areas into many small parts and load balance them across servers. This paper takes a diametrically opposite approach: we suggest a framework that concentrates rather than divides the skewed areas.
Our framework, called GraphINC, relies on two key innovations. First, it introduces a new graph partitioning scheme capable of separating the skewed area from the rest of the graph. Second, it offloads the skewed part onto a new class of hardware accelerator, a programmable network switch. We implemented our framework to leverage a commercial 100 Gbps switch and obtained results 6.5 to 52.4× faster thanks to our novel offloading technique.
Keep Your Distributed Data Warehouse Consistent at a Minimal Cost
Authors: Xu Zhichen , Gao Ying , Davidson Andrew 
Published: June 2023

Large data warehouses store interdependent tables that are updated independently in response to business logic changes or late arrival of critical data. To keep the warehouse consistent, changes to upstream tables need to be propagated to downstream tables in a timely fashion. However, a naive change propagation algorithm can cause many unnecessary updates or recalculations of downstream tables, which drives up the cost of data warehouse management.
In this paper, we describe our solution that can ensure the eventual consistency of the data warehouse while avoiding unnecessary table updates. We also show that the optimal trade-off between computational cost reduction and meeting data freshness constraints can be found by solving a dynamic programming problem.
The proposed solution is currently in production to manage the YouTube Data Warehouse and has reduced update requests by 25% by eliminating non-trivial duplicates. These requests would have been carried out by large batch jobs over big data. Eliminating them has led to a proportionate reduction in computing resources.
One key advantage of our approach is that it can be used in a heterogeneous, distributed data warehouse environment where the operator software may not have complete control over the query processors. This is because our approach only relies on having dependency information for tables and can operate on the post-state of data sources.