bluuebunny/crossref_metadata_2025 huggingface dataset:
Sport Fans and Flourishing: Examining the Mediating Role of Sport Fan Well-Being in Predicting Flourishing
Authors: Gordon Brian S. , Yoshida Masayuki , Inoue Yuhei , Biscaia Rui
Published: February 2025

Sport spectatorship has been viewed as a relatively passive leisure activity, but it has the potential to provide a multitude of well-being benefits to individuals. To bridge the gap between sport spectatorship and human flourishing, defined as a state of positive functioning in both individual and social life, the authors focused on the role of context-specific fan well-being and conducted a 2-year predictive study using three-wave panel data (n = 417) in professional baseball. The results showed the effects of team identification and fan engagement behavior at Time 1 on future flourishing at Time 3 was fully mediated by context-specific fan well-being at Time 2. Moreover, the effect of team identification (t1) on flourishing (t3) was sequentially mediated first through fan engagement behavior (t1) and then through fan well-being (t2). The findings reinforce the importance of sport-specific fan well-being for leading fans to flourish in daily life.
Evolution of ubiquitin, cytoskeleton, and vesicular trafficking machinery in giant viruses
Authors: Karki Sangita , Aylward Frank O.
Published: February 2025

Members of the phylum
Nucleocytoviricota
, which include “giant viruses” known for their large physical dimensions and genome lengths, are a diverse group of dsDNA viruses that infect a wide range of eukaryotic hosts. The genomes of nucleocytoviruses frequently encode eukaryotic signature proteins (ESPs) such as RNA- and DNA-processing proteins, vesicular trafficking factors, cytoskeletal components, and proteins involved in ubiquitin signaling. Despite the prevalence of these genes in many nucleocytoviruses, the timing and number of gene acquisitions remains unclear. While the presence of DNA- and RNA-processing proteins in nucleocytoviruses likely reflects ancient gene transfers, the origins and evolutionary history of other proteins are largely unknown. In this study, we examined the distribution and evolutionary history of a subset of viral-encoded ESPs (vESPs) that are widespread in nucleocytoviruses. Our results demonstrate that most vESPs involved in vesicular trafficking were acquired multiple times independently by nucleocytoviruses at different time points after the emergence of the eukaryotic supergroups, while viral proteins associated with cytoskeletal and ubiquitin system proteins exhibited a more complex evolutionary pattern exhibited by both shallow and deep branching viral clades. This pattern reveals a dynamic interplay between the co-evoluton of eukaryotes and their viruses, suggesting that the viral acquisition of many genes involved in cellular processes has occurred both through ancient and more recent horizontal gene transfers. The timing and frequency of these gene acquisitions may provide insight into their role and functional significance during viral infection.

IMPORTANCE

This research is pertinent for understanding the evolution of nucleocytoviruses and their interactions with eukaryotic hosts. By investigating the distribution and evolutionary history of viral-encoded eukaryotic signature proteins, the study reveals gene transfer patterns, highlighting how viruses acquire genes that allow them to manipulate host cellular processes. Identifying the timing and frequency of gene acquisitions related to essential cellular functions provides insights into their roles during viral infections. This work expands our understanding of viral diversity and adaptability, contributing valuable knowledge to virology and evolutionary biology, while offering new perspectives on the relationship between viruses and their hosts.
mGem: Transmission and exposure risks of dairy cow H5N1 influenza virus
Authors: Campbell A. J. , Brizuela Kayla , Lakdawala Seema S.
Published: February 2025

In March 2024, highly pathogenic H5N1 was detected in dairy cows; as of 12 December 2024, it had spread to over 800 herds in 16 states. The ongoing outbreak is a public health crisis affecting both humans and animals, as interspecies transmission has emerged as a common characteristic of this virus. As of 12 December 2024, >30 humans have been infected in the United States related to dairy cow exposure. In this mGem, we discuss transmission modalities between cows within herds, the spread of the virus between dairy farms, and exposure risks for humans. We also highlight major gaps in knowledge constituting barriers to our ability to effectively control the spread of H5N1 in dairy cows and reduce the risks to humans.
Draft genome sequences of three
Serratia liquefaciens
strains isolated from vegetables in South Africa
Authors: Kleyn Mariska S. , Akinyemi Muiz O. , Bezuidenhout Carlos , Adeleke Rasheed A.
Published: February 2025

Strains of the Gram-negative, facultatively anaerobic, rod-shaped
Serratia
genus are plant endophytes. Here, we described three draft genome sequences of
Serratia liquefaciens
isolated in South Africa from the leaves of tomatoes and lettuce.
Clinical performance of Abbott ID NOW™ COVID-19 2.0 rapid molecular point-of-care test compared to three real-time RT-PCR assays
Authors: Iglesias-Ussel Maria D. , Bowie Aleah , Anderson Jack G. , Li Yin , Park Lawrence P. , Cardona Jose F. , Dennis Patrick , Ebuh Valentine , Geller Steven A. , Jain Manish , McKenzie Mark M. , Merchant-Borna Kian , Patel Anand , Siegel Amy , Strauss Guy S. , Thoppil Joby J. , Weinberg Aaron S. , Woods Christopher W.
Published: February 2025

Timely diagnosis of SARS-CoV-2 is important for infection control and treatment. Real-time reverse transcriptase PCR (rRT-PCR) tests are the reference standard for diagnosis but often require a centralized laboratory, making them time-intensive and unsuitable for resource-limited settings. The Abbott ID NOW™ COVID-19 2.0 assay is a rapid point-of-care (POC), isothermal molecular test for qualitative detection of SARS-CoV-2. We prospectively evaluated its clinical performance against three reference rRT-PCR tests: Hologic Panther Fusion, Roche Cobas, and CDC 2019-nCoV RT-PCR Diagnostic Panel. Investigators enrolled 3,530 subjects, with 3,146 evaluable. In symptomatic subjects (
n
= 914), the test showed a positive percent agreement (PPA) of 91.7% (95% confidence interval [CI]: 87.8, 94.4) and a negative percent agreement (NPA) of 98.4% (95% CI: 97.1, 99.1). The PPA improved with lower cycle threshold (Ct) values: 94.7% (95% CI: 91.2, 97.2) for Ct ≤36, 97.6% (95% CI: 94.5, 99.2) for Ct ≤33, and 99.4% (95% CI: 96.8, 100.0) for Ct ≤30. Discordant results were observed among the three reference rRT-PCR tests across evaluable subjects with suspected COVID-19 infection. For 1,630 cases of symptomatic and asymptomatic subjects suspected of COVID-19, where all three rRT-PCR methods were evaluable, CDC test results differed the most, with 144 discordant results with Roche and 119 with Panther rRT-PCR tests. Roche and Panther test results differed in 67 cases. In summary, the Abbott ID NOW™ COVID-19 2.0 assay can serve as a valuable diagnostic tool in acute symptomatic subjects in point-of-care settings.

IMPORTANCE

The Abbott ID NOW
TM
COVID-19 2.0 assay is a suitable rapid test for diagnosing COVID-19 in acute symptomatic subjects and can be used in point-of-care settings and low-resource settings. With results reported in 12 minutes or less, Abbott ID NOW
TM
COVID-19 2.0 facilitates timely diagnosis, enabling linkage to appropriate antiviral medication.
Continuous renal replacement therapy in combination with oXiris haemofilter in a paediatric patient with sodium valproate-induced acute pancreatitis
Authors: Wang Ying , Gao Yang , Zhao Lin , Kang Kai
Published: February 2025

Some clinical trials have validated a new perspective on continuous renal replacement therapy (CRRT) in combination with oXiris haemofilter to more effectively enhance endotoxins clearance and circulating cytokines adsorption, stabilise vital signs, correct haemodynamic disorders and accelerate the recovery of organ function, thus providing survival benefits. However, no such combination mode has been reported in paediatric patients with sodium valproate (SV)-induced acute pancreatitis (AP).

An early childhood boy was admitted to the department of critical care medicine, due to sudden upper abdominal pain lasting for 2 days. The child had a 2-year history of epilepsy and took 120 mg of SV two times per day for more than 2 months, after which he was diagnosed with the first episode of AP, bronchopneumonia and paroxysmal supraventricular tachycardia. The culprit of AP was identified as SV. After admission to the intensive care unit, the child immediately stopped taking SV and received comprehensive treatment for AP, followed by CRRT in combination with oXiris haemofilter. On the 3rd day after admission, a nasojejunal tube was implanted, followed by enteral nutrition, probiotics and levetiracetam. On the 22nd day after admission, the child was discharged from the hospital. At a 3-month follow-up after discharge, the child has returned to normal life without any sequelae.

To the best of our knowledge, this is the first clinical case of SV-induced AP in a paediatric patient who achieved complete recovery after undergoing CRRT in combination with oXiris haemofilter, suggesting that this combination mode could be safely and effectively applied in patients with similar pathogenesis mechanisms as adjuvant therapy.
Effect of high flow nasal cannula oxygenation on incidence of hypoxia during sedated gastrointestinal endoscopy in patients with obesity: multicentre randomised controlled trial
Authors: Wang Leilei , Zhang Yuanyuan , Han Dan , Wei Mengyun , Zhang Jie , Cheng Xiangyang , Zhang Yizhe , Shi Muyan , Song Zijian , Wang Xiangrui , Zhang Xiaoqing , Su Diansan
Published: February 2025

Objective

To determine whether high flow nasal cannula (HFNC) oxygenation can reduce the incidence of hypoxia during sedated gastrointestinal endoscopy in patients with obesity.

Design

Multicentre, randomised, parallel group trial.

Setting

Three tertiary hospitals in Shanghai, China.

Participants

1000 adult patients with obesity (body mass index ≥28) who were scheduled for gastrointestinal endoscopy.

Interventions

Participants were randomly allocated to receive regular nasal cannula oxygenation or HFNC oxygenation during a sedated procedure with propofol and low dose sufentanil.

Main outcome measures

The primary outcome was the incidence of hypoxia (75%≤SpO
2
<90% for <60 s) during the procedure. Secondary outcomes included the incidences of subclinical respiratory depression (90%≤SpO
2
<95% for any duration) and severe hypoxia (SpO
2
<75% for any duration or 75%≤SPO
2
<90% for >60 s) during the procedure.

Results

From 6 May 2021 to 26 May 2023, 984 patients (mean age 49.2 years; 36.9% (n=363) female) completed the study and were analysed. Compared with regular nasal cannula oxygenation, HFNC oxygenation reduced the incidence of hypoxia from 21.2% (103/487) to 2.0% (10/497) (difference −19.14, 95% confidence interval −23.09 to −15.36; P<0.001), subclinical respiratory depression from 36.3% (177/487) to 5.6% (28/497) (difference −30.71, −35.40 to −25.92; P<0.001), and severe hypoxia from 4.1% (20/487) to 0% (0/497) (difference −4.11%, −6.26 to −2.48; P<0.001). Other sedation related adverse events did not differ between the two groups.

Conclusions

In patients with obesity, oxygenation via HFNC during sedated gastrointestinal endoscopy significantly reduced the incidences of hypoxia, subclinical respiratory depression, and severe hypoxia without increasing other adverse events.

Trial registration

ClinicalTrials.gov
NCT04500392
.
Changes in faecal haemoglobin values over sequential rounds of faecal immunochemical tests (FIT) in a surveillance population
Authors: Mortell Gemma , Wooldrage Kate , Murphy Gwen A , Cross Amanda J
Published: February 2025

Objective

Colorectal cancer (CRC) screening enables resection of polyp precursor lesions, preventing cancer or detecting it earlier. Post-polypectomy, people can remain at increased CRC risk, prompting surveillance colonoscopy. Less invasive faecal immunochemical tests (FIT) could reduce the burden of surveillance colonoscopy. We investigated whether changes in FIT values over multiple rounds were associated with advanced colorectal neoplasia (ACN) detection.

Methods

A cohort of men and women aged 60–72 years deemed intermediate risk by the 2002 UK adenoma surveillance guidelines and scheduled for three yearly colonoscopies were recruited (January 2012–December 2013) within the English Bowel Cancer Screening Programme and offered a quantitative FIT at 1, 2 and 3 years post-baseline colonoscopy for a prospective analysis within a diagnostic accuracy study. Participants positive (≥40 µg haemoglobin/g faeces) at 1 year or 2 years were offered early colonoscopy and excluded, otherwise, colonoscopy was offered at 3 years. Only those who completed three FIT rounds and attended the 3-year colonoscopy were included. Participants were grouped based on changes between FIT rounds, with changes defined as absolute differences ≥4 µg/g, and positivity at round 3.

Results

Among 4412 participants, the largest group (n=2773) was the no change category, which had the lowest ACN detection rate (4.7%, 95% CI: 3.9 to 5.5). The serial increase group with a positive round 3 value (n=46) had the highest ACN detection rate (32.6%, 95% CI: 19.5 to 48.0).

Conclusion

No change in FIT result across multiple rounds was associated with a low ACN detection rate, while a serial increase was associated with higher ACN detection rates. Further research should consider if sequential rounds of FIT could be used for stratifying individual risk.
How to prove her wrong: hierarchies of watching in the case of the fasting girl Sarah Jacob
Authors: Jacob Marie-Andrée
Published: February 2025

The article revisits the story of the watch of Sarah Jacob and her miraculous fasting, which was showcased as a lucrative spectacle in her family home in Wales, followed by her death in 1869. The Sarah Jacob case rehearses the familiar urge among Victorian English medical men to watch and detect, including a calculated drive to make breakthroughs in popular research areas of hysteria and simulation within the domain of female maladies. It also embodies a particular historical moment of London metropolitan expertise’s curiosity towards ‘Welsh culture’. Yet the article explains how the case reveals the need for medical men to turn away from the fasting girl’s bedside and, in turn, to outsource the act of watching to nurses. Shifting the emphasis from the girl to the watch itself, I argue that the function of nurses in the case has been unjustifiably ignored. Their role as mediators between the different worlds that the case brings into conflict sheds further light on the ‘clinical gaze’ and more specifically on the hierarchies of professional observations of bodies that defy rational explanations.
Development of MMC-based lithium molybdate cryogenic calorimeters for AMoRE-II
Authors: Agrawal A. , Alenkov V. V. , Aryal P. , Beyer J. , Bhandari B. , Boiko R. S. , Boonin K. , Buzanov O. , Byeon C. R. , Chanthima N. , Cheoun M. K. , Choe J. S. , Choi Seonho , Choudhury S. , Chung J. S. , Danevich F. A. , Djamal M. , Drung D. , Enss C. , Fleischmann A. , Gangapshev A. M. , Gastaldo L. , Gavrilyuk Y. M. , Gezhaev A. M. , Gileva O. , Grigorieva V. D. , Gurentsov V. I. , Ha C. , Ha D. H. , Ha E. J. , Hwnag D. H. , Jeon E. J. , Jeon J. A. , Jo H. S. , Kaewkhao J. , Kang C. S. , Kang W. G. , Kazalov V. V. , Kempf S. , Khan A. , Khan S. , Kim D. Y. , Kim G. W. , Kim H. B. , Kim Ho-Jong , Kim H. J. , Kim H. L. , Kim H. S. , Kim M. B. , Kim S. C. , Kim S. K. , Kim S. R. , Kim W. T. , Kim Y. D. , Kim Y. H. , Kirdsiri K. , Ko Y. J. , Kobychev V. V. , Kornoukhov V. , Kuzminov V. V. , Kwon D. H. , Lee C. H. , Lee DongYeup , Lee E. K. , Lee H. J. , Lee H. S. , Lee J. , Lee J. Y. , Lee K. B. , Lee M. H. , Lee M. K. , Lee S. W. , Lee Y. C. , Leonard D. S. , Lim H. S. , Mailyan B. , Makarov E. P. , Nyanda P. , Oh Y. , Olsen S. L. , Panasenko S. I. , Park H. K. , Park H. S. , Park K. S. , Park S. Y. , Polischuk O. G. , Prihtiadi H. , Ra S. , Ratkevich S. S. , Rooh G. , Sari M. B. , Seo J. , Seo K. M. , Sharma B. , Shin K. A. , Shlegel V. N. , Siyeon K. , So J. , Sokur N. V. , Son J. K. , Song J. W. , Srisittipokakun N. , Tretyak V. I. , Wirawan R. , Woo K. R. , Yeon H. J. , Yoon Y. S. , Yue Q. ,   The AMoRE Collaboration
Published: February 2025

The AMoRE collaboration searches for neutrinoless double beta decay of

$$^{100}$$

100

Mo using molybdate scintillating crystals via low temperature thermal calorimetric detection. The early phases of the experiment, AMoRE-pilot and AMoRE-I, have demonstrated competitive discovery potential. Presently, the AMoRE-II experiment, featuring a large detector array with about 90 kg of

$$^{100}$$

100

Mo isotope, is under construction. This paper discusses the baseline design and characterization of the lithium molybdate cryogenic calorimeters to be used in the AMoRE-II detector modules. The results from prototype setups that incorporate new housing structures and two different crystal masses (316 g and 517–521 g), operated at 10 mK temperature, show energy resolutions (FWHM) of 7.55–8.82 keV at the 2.615 MeV

$$^{208}$$

208

Tl

$$\gamma $$

γ

line and effective light detection of 0.79–0.96 keV/MeV. The simultaneous heat and light detection enables clear separation of alpha particles with a discrimination power of 12.37–19.50 at the energy region around

$$^{6}$$

6

Li

$$(n,\alpha )^3$$

(
n
,
α
)

3

H with Q-value = 4.785 MeV. Promising detector performances were demonstrated at temperatures as high as 30 mK, which relaxes the temperature constraints for operating the large AMoRE-II array.
Double-beta decay of $$^{150}\hbox {Nd}$$ to excited levels of $$^{150}\hbox {Sm}$$
Authors: Barabash A. S. , Belli P. , Bernabei R. , Boiko R. S. , Cappella F. , Caracciolo V. , Cerulli R. , Danevich F. A. , Fang D. L. , Ferella F. , Incicchitti A. , Kobychev V. V. , Konovalov S. I. , Laubenstein M. , Leoncini A. , Merlo V. , Nisi S. , Niţescu O. , Poda D. V. , Polischuk O. G. , Shcherbakov I. B.-K. , Šimkovic F. , Timonina A. , Tinkova V. S. , Tretyak V. I. , Umatov V. I.
Published: February 2025

The

$$2\nu 2\beta $$

2
ν
2
β

decay of

$$^{150}\hbox {Nd}$$

150

Nd

to the first excited 740.5 keV

$$0^{+}_{1}$$

0
1
+

level of

$$^{150}\hbox {Sm}$$

150

Sm

was measured over 5.845 years with the help of a four-crystal low-background HPGe

$$\gamma $$

γ

spectrometry system in the underground low-background laboratory STELLA of LNGS-INFN. A 2.381 kg highly purified Nd-containing sample was employed as the decay source. The expected de-excitation gamma-quanta of the

$$0^{+}_{1}$$

0
1
+

level with energies 334.0 keV and 406.5 keV were observed both in one-dimensional spectrum and in coincidence data resulting in the half-life

$$T_{1/2}=[0.83^{+0.18}_{-0.13}\mathrm {(stat)}^{+0.16}_{-0.19}\mathrm {(syst)}]\times 10^{20}$$

T

1
/
2

=

[
0
.

83

-
0.13

+
0.18

(
stat
)

-
0.19

+
0.16

(
syst
)

]

×

10
20

year. Interpreting an excess of the 334.0-keV peak area as an indication of the

$$2\beta $$

2
β

decay of

$$^{150}\hbox {Nd}$$

150

Nd

to the 334.0 keV

$$2^+_1$$

2
1
+

excited level of

$$^{150}\hbox {Sm}$$

150

Sm

with a half-life of

$$T_{1/2}=[1.5^{+2.3}_{-0.6}\mathrm {(stat)}\pm 0.4\mathrm {(syst)}]\times 10^{20}$$

T

1
/
2

=

[
1
.

5

-
0.6

+
2.3

(
stat
)

±
0.4

(
syst
)

]

×

10
20

year, the

$$2\nu 2\beta $$

2
ν
2
β

half-life of

$$^{150}\hbox {Nd}$$

150

Nd

for the transition to the 0

$$^{+}_{1}$$

1
+

level is

$$T_{1/2}=[1.03^{+0.35}_{-0.22}\mathrm {(stat)}^{+0.16}_{-0.19}\mathrm {(syst)}]\times 10^{20}$$

T

1
/
2

=

[
1
.

03

-
0.22

+
0.35

(
stat
)

-
0.19

+
0.16

(
syst
)

]

×

10
20

year, in agreement with the previous experiments. Both half-life values reasonably agree with the theoretical calculations in the framework of proton-neutron QRPA with isospin restoration combined with like nucleon QRPA for description of excited states in the final nuclei. For

$$2\nu 2\beta $$

2
ν
2
β

and

$$0\nu 2\beta $$

0
ν
2
β

transitions of

$$^{150}\hbox {Nd}$$

150

Nd

and

$$^{148}\hbox {Nd}$$

148

Nd

to several excited levels of

$$^{150}\hbox {Sm}$$

150

Sm

and

$$^{148}\hbox {Sm}$$

148

Sm

, limits were set at level of

$$T_{1/2}>10^{20}-10^{21}$$

T

1
/
2

>

10
20

-

10
21

year.
State Complexity of Partial Word Finite Automata
Authors: Kutrib Martin , Wendlandt Matthias
Published: February 2025

Partial word finite automata are deterministic finite automata that may have state transitions on a special symbol ◇ which represents an unknown symbol or a hole in the word. Together with a subset of the input alphabet that gives the symbols which may be substituted for the ◇, a partial word finite automaton represents a regular language. However, this substitution implies a certain form of limited nondeterminism in the computations when the ◇-transitions are replaced by ordinary transitions. In this paper we first reconsider the problem to prove the minimality of partial word finite automata and present a method to utilize minimal [Formula: see text]s with certain properties for this purpose. Then we study the operational state complexity of partial word finite automata with respect to Boolean operations. It turns out that the upper and lower bounds for all these operations are exponential. Moreover, we establish state complexity hierarchies on the number of productive ◇-transitions that may appear in partial word finite automata for general and unary regular languages. In the general case, the levels of the hierarchy are separated by exponential state costs, whereas in the unary case the levels are separated by quadratic state costs.
Online Marketplace: A Benchmark for Data Management in Microservices
Authors: Laigner Rodrigo , Zhang Zhexiang , Liu Yijian , Gomes Leonardo Freitas , Zhou Yongluan
Published: February 2025

Microservice architectures have become a popular approach for designing scalable distributed applications. Despite their extensive use in industrial settings for over a decade, there is limited understanding of the data management challenges that arise in these applications. Consequently, it has been difficult to advance data system technologies that effectively support microservice applications. To fill this gap, we present
Online Marketplace,
a microservice benchmark that highlights core data management challenges that existing benchmarks fail to address. These challenges include transaction processing, query processing, event processing, constraint enforcement, and data replication. We have defined criteria for various data management issues to enable proper comparison across data systems and platforms. Through case studies with state-of-the-art data platforms, we discuss the issues encountered while implementing and meeting
Online Marketplace's
criteria. By capturing the overhead of meeting the key data management requirements that are overlooked by existing benchmarks, we gain actionable insights into the experimental platforms. This highlights the significance of
Online Marketplace
in advancing future data systems to meet the needs of microservice practitioners.
A Rank-Based Approach to Recommender System's Top-K Queries with Uncertain Scores
Authors: Scharf Coral , Domshlak Carmel , Gal Avigdor , Roitman Haggai
Published: February 2025

Top-
K
queries provide a ranked answer using a score that can either be given explicitly or computed from tuple values. Recommender systems use scores, based on user feedback on items with which they interact, to answer top-
K
queries. Such scores pose the challenge of correctly ranking elements using scores that are more often than not, uncertain. In this work, we address top-
K
queries based on uncertain scores. We propose to explicitly model the inherent uncertainty in the provided data and to consider a distribution of scores instead of a single score. Rooted in works of database probabilistic ranking, we offer the use of probabilistic ranking as a tool of choice for generating recommendation in the presence of uncertainty. We argue that the ranking approach should be chosen in a manner that maximizes user satisfaction, extending state-of-the-art on quality aspect of top-
K
answers over uncertain data, their relationship to top-
K
semantics, and improve ranking with uncertain scores in recommender systems. Towards this end, we introduce RankDist, an algorithm for efficiently computing probability of item position in a ranked recommendation. We show that rank-based (rather than score-based) methods that are computed using RankDist, which were not applied in recommender systems before, offer a guaranteed optimality by expectation and empirical superiority when tested on common benchmarks.
Agree to Disagree: Robust Anomaly Detection with Noisy Labels
Authors: Hofmann Dennis M. , VanNostrand Peter M. , Ma Lei , Zhang Huayi , DeOliveira Joshua C. , Cao Lei , Rundensteiner Elke A.
Published: February 2025

Due to the scarcity of reliable anomaly labels, recent anomaly detection methods leveraging noisy auto-generated labels either select clean samples or refurbish noisy labels. However, both approaches struggle due to the unique properties of anomalies.
Sample selection
often fails to separate sufficiently many clean anomaly samples from noisy ones, while
label refurbishment
erroneously refurbishes
marginal
clean samples. To overcome these limitations, we design Unity, the
first
learning from noisy labels (LNL) approach for anomaly detection that elegantly leverages the merits of both sample selection and label refurbishment to iteratively prepare a diverse clean sample set for network training. Unity uses a pair of deep anomaly networks to collaboratively select samples with clean labels based on prediction agreement, followed by a disagreement resolution mechanism to capture marginal samples with clean labels. Thereafter, Unity utilizes unique properties of anomalies to design an anomaly-centric contrastive learning strategy that accurately refurbishes the remaining noisy labels. The resulting set, composed of
selected and refurbished
clean samples, will be used to train the anomaly networks in the next training round. Our experimental study on 10 real-world benchmark datasets demonstrates that Unity consistently outperforms state-of-the-art LNL techniques by up to 0.31 in F-1 Score (0.52 \rightarrow 0.83).
An Adaptive Benchmark for Modeling User Exploration of Large Datasets
Authors: Purich Joanna , Wise Anthony , Battle Leilani
Published: February 2025

In this paper, we present a new DBMS performance benchmark that can
simulate
user exploration with any specified dashboard design made of standard visualization and interaction components. The distinguishing feature of our SImulation-BAsed (or SIMBA) benchmark is its ability to
model user analysis goals
as a set of SQL queries to be generated through a valid sequence of user interactions, as well as
measure the completion of analysis goals
by testing for equivalence between the user's previous queries and their goal queries. In this way, the SIMBA benchmark can simulate how an analyst opportunistically searches for interesting insights at the beginning of an exploration session and eventually hones in on specific goals towards the end. To demonstrate the versatility of the SIMBA benchmark, we use it to test the performance of four DBMSs with six different dashboard specifications and compare our results with IDEBench. Our results show how goal-driven simulation can reveal gaps in DBMS performance missed by existing benchmarking methods and across a range of data exploration scenarios.
Automatic Database Configuration Debugging using Retrieval-Augmented Language Models
Authors: Chen Sibei , Fan Ju , Wu Bin , Tang Nan , Deng Chao , Wang Pengyi , Li Ye , Tan Jian , Li Feifei , Zhou Jingren , Du Xiaoyong
Published: February 2025

Database management system (DBMS) configuration debugging, e.g., diagnosing poorly configured DBMS knobs and generating troubleshooting recommendations, is crucial in optimizing DBMS performance. However, the configuration debugging process is tedious and, sometimes challenging, even for seasoned database administrators (DBAs) with sufficient experience in DBMS configurations and good understandings of the DBMS internals (e.g., MySQL or Oracle). To address this difficulty, we propose Andromeda, a framework that utilizes large language models (LLMs) to enable automatic DBMS configuration debugging. Andromeda serves as a natural surrogate of DBAs to answer a wide range of natural language (NL) questions on DBMS configuration issues, and to generate diagnostic suggestions to fix these issues. Nevertheless, directly prompting LLMs with these professional questions may result in overly generic and often unsatisfying answers. To this end, we propose a retrieval-augmented generation (RAG) strategy that effectively provides matched domain-specific contexts for the question from multiple sources. They come from related historical questions, troubleshooting manuals and DBMS telemetries, which significantly improve the performance of configuration debugging. To support the RAG strategy, we develop a document retrieval mechanism addressing heterogeneous documents and design an effective method for telemetry analysis. Extensive experiments on real-world DBMS configuration debugging datasets show that Andromeda significantly outperforms existing solutions.
BⓈ
X
: Subgraph Matching with Batch Backtracking Search
Authors: Lu Yujie , Zhang Zhijie , Zheng Weiguo
Published: February 2025

Subgraph matching is a fundamental problem in graph analysis. Recently, many algorithms have been developed, often using classic backtracking search. This traditional backtracking search matches one vertex at a time, denoted as BⓈ1, which can lead to redundant computations due to overlapping search spaces. To address this problem, we propose a novel
batch-backtracking search
framework that enables matching a set of data vertices
X
, denoted as BⓈ
X
, in each backtracking step. BⓈ
X
models the search space as a "search box", allowing for flexible search space exploration and significantly minimizing the overlap between search spaces. It effectively selects batches to cluster data vertices with similar search spaces. For each search box, we introduce a refinement method to filter out unpromising candidate mappings. Furthermore, we propose a homomorphism termination to break the backtracking process as early as possible and an efficient embedding enumeration method to list all embeddings within the search box simultaneously. Extensive experiments on real-world graphs demonstrate that BⓈ
X
significantly outperforms existing state-of-the-art algorithms, achieving a speedup of one to two orders of magnitude on most graphs under the EPS metric.
BCviz: A Linear-Space Index for Mining and Visualizing Cohesive Bipartite Subgraphs
Authors: Ye Jianxiong , Zou Zhaonian , Liu Dandan , Yang Bin , Liu Xudong
Published: February 2025

Finding the maximum biclique in a bipartite graph is a fundamental graph analysis problem. Existing methods for maximum biclique search are not very efficient because they cannot effectively reduce the size of a bipartite graph composed of large bicliques that are loosely linked together because the graph reduction strategies adopted by these methods only consider local densities of vertices.

This paper proposes a novel approach to maximum biclique search. The unique feature of this approach is building a linear-space data-driven index called BCviz that helps accurately identify subgraphs containing all bicliques with sizes no less than a certain threshold. The core technique of BCviz is determining a total order of vertices that can reveal both the local density and the connectivity of the vertices. Notably, our work is the first one to take connectivity into account in graph reduction. Interestingly, the total order of vertices entails BCviz an illustrative visualization of the distribution of cohesive subgraphs in the input graph.

To deeply understand BCviz, we carry out a theoretical study on its properties and reveal how it enables more effective graph reduction. Based on BCviz, we propose an exact maximum biclique search algorithm that searches for results on much smaller subgraphs than any existing method does.

In addition, we improve the efficiency of index construction by two techniques. One is approximating an edge's local density with an upper bound that can be derived in linear time. The other is a lightweight vertex ordering method called one-spot ordering which reduces unnecessary cohesion computations.

Extensive experiments indicate that the proposed maximum biclique search methods based on BCviz and its variants outperform the state-of-the-art search-based methods by 2--3 orders of magnitude. Compared with the state-of-the-art index for maximum biclique search, the improved BCviz index can reduce the index size by 1--2 orders of magnitude and the index construction time by up to 2 orders of magnitude.
CRDV: Conflict-free Replicated Data Views
Authors: Faria Nuno , Pereira José
Published: February 2025

There are now multiple proposals for Conflict-free Replicated Data Types (CRDTs) in SQL databases aimed at distributed systems. Some, such as ElectricSQL, provide only relational tables as convergent replicated maps, but this omits semantics that would be useful for merging updates. Others, such as Pg\_crdt, provide access to a rich library of encapsulated column types. However, this puts merge and query processing outside the scope of the query optimizer and restricts the ability of an administrator to influence access paths with materialization and indexes.

Our proposal, CRDV, overcomes this challenge by using two layers implemented as SQL views: The first provides a replicated relational table from an update history, while the second implements varied and rich types on top of the replicated table. This allows the definition of merge semantics, or even entire new data types, in SQL itself, and enables global optimization of user queries together with merge operations. Therefore, it naturally extends the scope of query optimization and local transactions to operations on replicated data, can be used to reproduce the functionality of common CRDTs with simple SQL idioms, and results in better performance than alternatives.
DataVinci:
Learning Syntactic and Semantic String Repairs
Authors: Singh Mukul , Cambronero José , Gulwani Sumit , Le Vu , Negreanu Carina , Radhakrishna Arjun , Verbruggen Gust
Published: February 2025

String data is common in real-world datasets: 67.6% of values in a sample of 1.8 million real Excel spreadsheets from the web were represented as text. Automatically cleaning such string data can have a significant impact on users. Previous approaches are limited to error detection, require that the user provides annotations, examples, or constraints to fix the errors, and focus independently on syntactic errors or semantic errors in strings, but ignore that strings often contain both syntactic and semantic substrings. We introduce
DataVinci,
a fully unsupervised string data error detection and repair system.
DataVinci
learns regular-expression-based patterns that cover a majority of values in a column and reports values that do not satisfy such majority patterns as data errors.
DataVinci
can automatically derive edits to the data error based on the majority patterns and using row tuples associated with majority values as examples. To handle strings with both syntactic and semantic substrings,
DataVinci
uses an LLM to abstract (and re-concretize) portions of strings that are semantic. Because not all data columns can result in majority patterns, when available,
DataVinci
can leverage execution information from an existing data program (which uses the target data as input) to identify and correct data repairs that would not otherwise be identified.
DataVinci
outperforms eleven baseline systems on both data error detection and repair as demonstrated on four existing and new benchmarks.
Deep Overlapping Community Search via Subspace Embedding
Authors: Sima Qing , Yu Jianke , Wang Xiaoyang , Zhang Wenjie , Zhang Ying , Lin Xuemin
Published: February 2025

Overlapping Community Search (OCS) identifies nodes that interact with multiple communities based on a specified query. Existing community search approaches fall into two categories: algorithm-based models and Machine Learning-based (ML) models. Despite the long-standing focus on this topic within the database domain, current solutions face two major limitations: 1) Both approaches fail to address personalized user requirements in OCS, consistently returning the same set of nodes for a given query regardless of user differences. 2) Existing ML-based CS models suffer from severe training efficiency issues. In this paper, we formally redefine the problem of OCS. By analyzing the gaps in both types of approaches, we then propose a general solution for OCS named
S
parse
S
ubspace
F
ilter (SSF), which can extend any ML-based CS model to enable personalized search in overlapping structures. To overcome the efficiency issue in the current models, we introduce
S
implified
M
ulti-hop Attention
N
etworks (SMN), a lightweight yet effective community search model with larger receptive fields. To the best of our knowledge, this is the first ML-based study of overlapping community search. Extensive experiments validate the superior performance of SMN within the SSF pipeline, achieving a 13.73% improvement in F1-Score and up to 3 orders of magnitude acceleration in model efficiency compared to state-of-the-art approaches.
DEG: Efficient Hybrid Vector Search Using the Dynamic Edge Navigation Graph
Authors: Yin Ziqi , Gao Jianyang , Balsebre Pasquale , Cong Gao , Long Cheng
Published: February 2025

Bimodal data, such as image-text pairs, has become increasingly prevalent in the digital era. The Hybrid Vector Query (HVQ) is an effective approach for querying such data and has recently garnered considerable attention from researchers. It calculates similarity scores for objects represented by two vectors using a weighted sum of each individual vector's similarity, with a query-specific parameter α to determine the weight. Existing methods for HVQ typically construct Approximate Nearest Neighbors Search (ANNS) indexes with a fixed α value. This leads to significant performance degradation when the query's α dynamically changes based on the different scenarios and needs.

In this study, we introduce the Dynamic Edge Navigation Graph ( DEG ), a graph-based ANNS index that maintains efficiency and accuracy with changing α values. It includes three novel components: (1) a greedy Pareto frontier search algorithm to compute a candidate neighbor set for each node, which comprises the node's approximate nearest neighbors for all possible α values; (2) a dynamic edge pruning strategy to determine the final edges from the candidate set and assign each edge an active range. This active range enables the dynamic use of the Relative Neighborhood Graph's pruning strategy based on the query's α values, skipping redundant edges at query time and achieving a better accuracy-efficiency trade-off; and (3) an edge seed method that accelerates the querying process. Extensive experiments on real-world datasets show that DEG demonstrates superior performance compared to existing methods under varying α values.
Density Decomposition of Bipartite Graphs
Authors: Zhang Yalong , Li Rong-Hua , Zhang Qi , Qin Hongchao , Qin Lu , Wang Guoren
Published: February 2025

Mining dense subgraphs in a bipartite graph is a fundamental task in bipartite graph analysis, with numerous applications in community detection, fraud detection, and e-commerce recommendation. Existing dense subgraph models, such as biclique,
k
-biplex,
k
-bitruss, and (α,β)-core, often face challenges due to their high computational complexity or limitations in effectively capturing the density of the graph. To overcome these issues, in this paper, we propose a new dense subgraph model for bipartite graphs, namely (α,β)-dense subgraph, designed to capture the density structure inherent in bipartite graphs. We show that all (α,β)-dense subgraphs are nested within each other, forming a hierarchical density decomposition of the bipartite graph. To efficiently compute the (α,β)-dense subgraph, we develop a novel network flow algorithm with a carefully-designed core pruning technique. The time complexity of our algorithm is O(|E|+|E(R)|
1.5
), where |E| denotes the number of edges and |E(R)| is the number of edges of the pruned graph, often significantly smaller than |E|. Armed with this algorithm, we also propose a novel and efficient divide-and-conquer algorithm to compute the entire density decomposition of the bipartite graph within O(p ⋅ log d
max
⋅ |E|
1.5
) time, where
p
is typically a small constant in real-world bipartite graphs and d
max
is the maximum degree. Extensive experiments and case studies on 11 real-world datasets demonstrate the effectiveness of our (α,β)-dense subgraph model and the high efficiency and scalability of our proposed algorithms.
ISSD: Indicator Selection for Time Series State Detection
Authors: Wang Chengyu , Zhou Tongqing , Chen Lin , Zhao Shan , Cai Zhiping
Published: February 2025

Time series data from monitoring applications captures the behaviours of objects, which can often be split into distinguishable segments that reflect the underlying state changes. Despite the recent advances in time series state detection, the indicator selection for state detection is rarely studied, most of state detection work assumes the input indicators have been properly or manually selected. However, this assumption is disconnected from practice, on one hand, manual selection is not scalable, there can be up to thousands of indicators for the runtime monitoring of certain objects, e.g., supercomputer systems. On the other hand, performing state detection on a large amount of raw indicators is both inefficient and redundant. We argue that indicator selection should be made an upstream task for selecting a subset of indicators to facilitate state analysis. To this end, we propose ISSD (
I
ndicator
S
election for
S
tate
D
etection), an indicator selection method for time series state detection. At its core, ISSD attempts to find an indicator subset that has as much high-quality states, which is measured by the channel set completeness and quality we invent based on segment-level sampling statistics. Such an indicator selection process is transformed into a multi-objective optimization problem and an approximation algorithm is designed to solve the NP-hard searching for specific end point in the Pareto front. Experiments on 5 datasets and 4 downstream methods show that ISSD has significant selection superiority compared with 6 baselines. We also elaborate on two observations of selection resilience and channel sensitivity of existing state detection methods and appeal to further research on them.
Nezha: An Efficient Distributed Graph Processing System on Heterogeneous Hardware
Authors: Cui Pengjie , Liu Haotian , Jiang Dong , Tang Bo , Yuan Ye
Published: February 2025

The growing scale of graph data across various applications demands efficient distributed graph processing systems. Despite the widespread use of the
Scatter-Gather
model for large-scale graph processing across distributed machines, the performance still can be significantly improved as the computation ability of each machine is not fully utilized and the communication costs during graph processing are expensive in the distributed environment. In this work, we propose a novel and efficient distributed graph processing system Nezha on heterogeneous hardware, where each machine is equipped with both CPU and GPU processors and all these machines in the distributed cluster are interconnected via Remote Direct Memory Access (RDMA).To reduce the communication costs, we devise an effective communication mode with a graph-friendly communication protocol in the graph-based RDMA communication adapter of Nezha. To improve the computation efficiency, we propose a multi-device cooperative execution mechanism in Nezha, which fully utilizes the CPU and GPU processors of each machine in the distributed cluster. We also alleviate the workload imbalance issue at inter-machine and intra-machine levels via the proposed workload balancer in Nezha. We conduct extensive experiments by running 4 widely-used graph algorithms on 5 graph datasets to demonstrate the superiority of Nezha over existing systems.
OBIR-tree: An Efficient Oblivious Index for Spatial Keyword Queries on Secure Enclaves
Authors: Ye Zikai , Wang Xiangyu , Liu Zesen , Zhu Dan , Ma Jianfeng
Published: February 2025

In recent years, the widely collected spatial-textual data has given rise to numerous applications centered on spatial keyword queries. However, securely providing spatial keyword query services in an outsourcing environment has been challenging. Existing schemes struggle to enable top-
k
spatial keyword queries on encrypted data while hiding search, access, and volume patterns, which raises concerns about availability and security. To address the above issue, this paper proposes OBIR-tree, a novel index structure for oblivious (provably hides search, access, and volume patterns) top-
k
spatial keyword queries on encrypted data. As a tight spatial-textual index tailored from the IR-tree and PathORAM, OBIR-tree can support sublinear search without revealing any useful information. Furthermore, we present extension designs to optimize the query latency of the OBIR-tree: (1) combine the OBIR-tree with hardware secure enclaves (
e.g.,
Intel SGX) to minimize client-server interactions; (2) build a Real/Dummy block Tree (RDT) to reduce the computational cost of oblivious operations within enclaves. Extensive experimental evaluations on real-world datasets demonstrate that the search efficiency of OBIR-tree outperforms state-of-the-art baselines by 25x ~ 723× and is practical for real-world applications.
Pandora: An Efficient and Rapid Solution for Persistence-Based Tasks in High-Speed Data Streams
Authors: Li Weihe
Published: February 2025

In data streams, persistence characterizes items that appear repeatedly across multiple non-overlapping time windows. Addressing persistence-based tasks, such as detecting highly persistent items and estimating persistence, is crucial for applications like recommendation systems and anomaly detection in high-velocity data streams. However, these tasks are challenging due to stringent requirements for rapid processing and limited memory resources. Existing methods often struggle with accuracy, especially given highly skewed data distributions and tight fastest memory budgets, where hash collisions are severe. In this paper, we introduce Pandora, a novel approximate data structure designed to tackle these challenges efficiently. Our approach incorporates the insight that items absent for extended periods are likely non-persistent, increasing their probability of eviction to accommodate potential persistent items more effectively. We validate this insight empirically and integrate it into our update strategy, providing better protection for persistent items. We formally analyze Pandora's error bounds to validate its theoretical soundness. Through extensive trace-driven tests, we demonstrate that Pandora achieves superior accuracy and processing speed compared to state-of-the-art methods across various persistence-based tasks. Additionally, we further accelerate Pandora's update speed using Single Instruction Multiple Data (SIMD) instructions, enhancing its efficiency in high-speed data stream environments. The code for our method is open-sourced.