bluuebunny/crossref_metadata_2025 huggingface dataset:
Design of a 2RRU-RRS Parallel Kinematic Mechanism for an Inner-Cavity Machining Hybrid Robot
Authors: Xu Lingmin , Chai Xinxue , Ding Ye
Published: May 2023

This article presents a hybrid robot for machining the inner cavity of large-scale workpiece, and it is composed of three parts: a mobile device with 1T degree-of-freedom (DoF), a serial module with 1R1T DoFs, and a 2RRU-RRS parallel kinematic mechanism (PKM) with 2R1T DoFs. The 2RRU-RRS PKM has some advantages with a folding structure, only one S joint, two certain rotational axes, and all the fixed actuators. In this article, the conceptual design, theoretical kinematic and dynamic modeling, performance evaluation, and optimization of the parallel system are investigated. A 3D printing model is built to demonstrate the application potential. This article plays an exemplary role in the design of inner-cavity machining hybrid robots.
AVbook, a high-frame-rate corpus of narrative audiovisual speech for investigating multimodal speech perception
Authors: Varano Enrico , Guilleminot Pierre , Reichenbach Tobias
Published: May 2023

Seeing a speaker's face can help substantially with understanding their speech, particularly in challenging listening conditions. Research into the neurobiological mechanisms behind audiovisual integration has recently begun to employ continuous natural speech. However, these efforts are impeded by a lack of high-quality audiovisual recordings of a speaker narrating a longer text. Here, we seek to close this gap by developing AVbook, an audiovisual speech corpus designed for cognitive neuroscience studies and audiovisual speech recognition. The corpus consists of 3.6 h of audiovisual recordings of two speakers, one male and one female, each reading 59 passages from a narrative English text. The recordings were acquired at a high frame rate of 119.88 frames/s. The corpus includes phone-level alignment files and a set of multiple-choice questions to test attention to the different passages. We verified the efficacy of these questions in a pilot study. A short written summary is also provided for each recording. To enable audiovisual synchronization when presenting the stimuli, four videos of an electronic clapperboard were recorded with the corpus. The corpus is publicly available to support research into the neurobiology of audiovisual speech processing as well as the development of computer algorithms for audiovisual speech recognition.
Exploring acoustic characteristics of different aircraft types by fusing with aircraft tracking data
Authors: Zhang Xinxiang , Hayward Chris , McComas Sarah , Arrowsmith Stephen
Published: May 2023

In this study, we develop a method that assigns acoustic signals with Automatic Dependent Surveillance-Broadcast (ADS-B) data to build a labeled dataset of acoustic signals from aircraft without expensive ground-truth experiments. An exploration of the resultant labeled dataset enables an assessment of the acoustic characteristics from three types of aircraft. The fusion framework is evaluated using data from an acoustic sensor and collocated ADS-B receiver in the middle of a large urban area at Southern Methodist University in Dallas, Texas. Our results demonstrate the benefit of combining multiple types of data to generate a labeled dataset leveraging open-source aircraft surveillance data. By studying three classes of aircraft, we find that the smaller fixed wing single engine (FWSE) class is mostly detected within approximately 5000 m, while the larger fixed wing multi-engine (FWME) class is commonly detected out to greater distances above 7500 m. The FWSE class has a median source frequency at 100 Hz, compared to FWME class with median source frequency at 80 Hz, while rotorcraft has a source frequency falling into a lower range of 30–100 Hz.
Mutations in AcrR and RNA Polymerase Confer High-Level Resistance to Psoralen-UVA Irradiation
Authors: Worley Travis K. , Weber Emma A. , Acott Jedidiah D. , Shimpi Rahul S. , Cole Jessica M. , Courcelle Charmain T. , Courcelle Justin
Published: May 2023

Psoralen DNA interstrand cross-links are highly toxic lesions with antimicrobial and anticancer properties. Despite the lack of effective mechanisms for repair, cells can become resistant to cross-linking agents through mechanisms that remain poorly defined.
Whole-Genome Sequences of Three Psychrotolerant
Mycolicibacterium
sp. Strains Isolated from Antarctic Soil
Authors: Shimada Sho , Aoki Kotaro , Nakai Ryosuke , Miura Shota , Komori Kohji , Kudoh Sakae , Imura Satoshi , Ishii Yoshikazu , Tateda Kazuhiro
Published: May 2023

We report the whole-genome sequences of three psychrotolerant
Mycolicibacterium
strains, TUM20983, TUM20984, and TUM20985, isolated from Antarctic soils. Taxonomic analyses indicate that these strains are putative new species. These genome sequences may provide insight into the cold adaptation mechanisms of
Mycolicibacterium
spp. through future comparative genomic studies.
Draft Genome Sequences of Three Lactic Acid Bacterial Strains Investigated for B Vitamin Biosynthesis
Authors: Gao Lu , Cho Gyu-Sung , Brinks Erik , Pichner Rohtraud , Franz Charles M. A. P.
Published: May 2023

The draft genome sequences of three lactic acid bacteria, namely,
Limosilactobacillus reuteri
92071,
Lactiplantibacillus plantarum
92117-i3, and
Limosilactobacillus fermentum
92072, and the presence of genes involved in the biosynthesis of B vitamins were determined.
Limosilactobacillus reuteri
92071 showed complete gene clusters for vitamin B
12
biosynthesis, with a GC content of 38.52 mol%.
Complete Genome Sequence of Pseudomonas aeruginosa Phage UF_RH6, Isolated from Human Lung
Authors: Hashemi Shahraki Abdolrazagh , Vahed Majid , Masood Shahla , Mirsaeidi Mehdi
Published: May 2023

We report the genome sequence of a lytic phage named UF_RH6, which infects
Pseudomonas aeruginosa
. This phage was isolated from a respiratory secretion sample from a patient with pulmonary
P. aeruginosa
. UF_RH6 belongs to the family
Caudoviricetes
and the genus
Samunavirus
. Its genome is 94,715 bp in length and encodes 130 proteins.
Effectiveness of a multicomponent intervention to face the COVID-19 pandemic in Rio de Janeiro’s favelas: difference-in-differences analysis
Authors: Batista-da-Silva Amanda de Araujo , Moraes Camila Barros , Bozza Helena Rodrigues , Bastos Leonardo dos Santos Lourenço , Ranzani Otavio T , Hamacher Silvio , Bozza Fernando Augusto
Published: May 2023

Introduction

Few community-based interventions addressing the transmission control and clinical management of COVID-19 cases have been reported, especially in poor urban communities from low-income and middle-income countries. Here, we analyse the impact of a multicomponent intervention that combines community engagement, mobile surveillance, massive testing and telehealth on COVID-19 cases detection and mortality rates in a large vulnerable community (Complexo da Maré) in Rio de Janeiro, Brazil.

Methods

We performed a difference-in-differences (DID) analysis to estimate the impact of the multicomponent intervention inMaré,before (March–August 2020) and after the intervention (September 2020 to April 2021), compared with equivalent local vulnerable communities. We applied a negative binomial regression model to estimate the intervention effect in weekly cases and mortality rates inMaré.

Results

Before the intervention,Marépresented lower rates of reported COVID-19 cases compared with the control group (1373 vs 1579 cases/100 000 population), comparable mortality rates (309 vs 287 deaths/100 000 population) and higher case fatality rates (13.7% vs 12.2%). After the intervention,Marédisplayed a 154% (95% CI 138.6% to 170.4%) relative increase in reported case rates. Relative changes in reported death rates were −60% (95% CI −69.0% to −47.9%) in Maré and −28% (95% CI −42.0% to −9.8%) in the control group. The case fatality rate was reduced by 77% (95% CI −93.1% to −21.1%) inMaréand 52% (95% CI −81.8% to −29.4%) in the control group. The DID showed a reduction of 46% (95% CI 17% to 65%) of weekly reported deaths and an increased 23% (95% CI 5% to 44%) of reported cases inMaréafter intervention onset.

Conclusion

An integrated intervention combining communication, surveillance and telehealth, with a strong community engagement component, could reduce COVID-19 mortality and increase case detection in a large vulnerable community in Rio de Janeiro. These findings show that investment in community-based interventions may reduce mortality and improve pandemic control in poor communities from low-income and middle-income countries.
Novel machine learning model for predicting multiple unplanned hospitalisations
Authors: Conilione Paul , Jessup Rebecca , Gust Anthony
Published: May 2023

Background

In the Australian public healthcare system, hospitals are funded based on the number of inpatient discharges and types of conditions treated (casemix). Demand for services is increasing faster than public funding and there is a need to identify and support patients that have high service usage. In 2016, the Victorian Department of Health and Human Services developed an algorithm to predict multiple unplanned admissions as part of a programme, Health Links Chronic Care (HLCC), that provided capitation funding instead of activity based funding to support patients with high admissions.

Objectives

The aim of this study was to determine whether an algorithm with higher performance than previously used algorithms could be developed to identify patients at high risk of three or more unplanned hospital admissions 12 months from discharge.

Methods

The HLCC and Hospital Unplanned Readmission Tool (HURT) models were evaluated using 34 801 unplanned inpatient episodes (27 216 patients) from 2017 to 2018 with an 8.3% prevalence of 3 or more unplanned admissions in the following year of discharge.

Results

HURT had a higher AUROC (84%, 95% CI 83.4% to 84.9% vs 71%, 95% CI 69.4% to 71.8%) than HLCC, that was statistically significant using Delong test at p<0.05.

Discussion

We found features that appear to be strong predictors of admission risk that have not been previously used in models, including socioeconomic status and social support.

Conclusion

The high AUROC, moderate sensitivity and high specificity for the HURT algorithm suggests it is a very good predictor of future multi-admission risk and that it can be used to provide targeted support for at-risk individual.
Health professionals’ routine practice documentation and its associated factors in a resource-limited setting: a cross-sectional study
Authors: Demsash Addisalem Workie , Kassie Sisay Yitayih , Dubale Abiy Tasew , Chereka Alex Ayenew , Ngusie Habtamu Setegn , Hunde Mekonnen Kenate , Emanu Milkias Dugassa , Shibabaw Adamu Ambachew , Walle Agmasie Damtew
Published: May 2023

Objectives

Documenting routine practice is significant for better diagnosis, treatment, continuity of care and medicolegal issues. However, health professionals’ routine practice documentation is poorly practised. Therefore, this study aimed to assess health professionals’ routine practice documentation and associated factors in a resource-limited setting.

Methods

An institution-based cross-sectional study design was used from 24 March up to 19 April 2022. Stratified random sampling and a pretested self-administered questionnaire were used among 423 samples. Epi Info V.7.1 and STATA V.15 software were used for data entry and analysis, respectively. Descriptive statistics and a logistic regression model were employed to describe the study subjects and to measure the strength of association between dependent and independent variables, respectively. A variable with a p value of <0.2 in bivariate logistic regression was considered for multivariable logistic regression. In multivariable logistic regression, ORs with 95% CIs and a p value of <0.05 were considered to determine the strength of association between dependent and independent variables.

Results

Health professionals’ documentation practice was 51.1% (95% CI: 48.64 to 53.1). Lack of motivation (adjusted OR (AOR): 0.41, 95% CI: 0.22 to 0.76), good knowledge (AOR: 1.35, 95% CI: 0.72 to 2.97), taking training (AOR: 4.18, 95% CI: 2.99 to 8.28), using electronic systems (AOR: 2.19, 95% CI: 1.36 to 3.28), availability of standard documentation tools (AOR: 2.45, 95% CI: 1.35 to 4.43) were statistically associated factors.

Conclusions

Health professionals’ documentation practice is good. Lack of motivation, good knowledge, taking training, using electronic systems and the availability of documentation tools were significant factors. Stakeholders should provide additional training, and encourage professionals to use an electronic system for documentation practices.
Moving from non-emergency bleeps and long-range pagers to a hospital-wide, EHR-integrated secure messaging system: an implementer report
Authors: Ercole Ari , Tolliday Claire , Gelson William , Rudd James H F , Cameron Ewen , Chaudhry Afzal , Hamer Fiona , Davies Justin
Published: May 2023

Introduction

Obsolete bleep/long-range pager equipment remains firmly embedded in the National Health Service (NHS).

Objective

To introduce a secure, chart-integrated messaging system (Epic Secure Chat) in a large NHS tertiary referral centre to replace non-emergency bleeps/long-range pagers.

Methods

The system was socialised in the months before go-live. Operational readiness was overseen by an implementation group with stakeholder engagement. Cutover was accompanied by a week of Secure Chat and bleeps running in parallel.

Results

Engagement due to socialisation was high with usage stabilising approximately 3 months after go-live. Contact centre internal call activity fell significantly after go-live. No significant patient safety concerns were reported.

Discussion

Uptake was excellent with substantial utilisation well before cutover indirectly supporting high levels of engagement. The majority of those who previously carried bleeps were content to use personal devices for messaging because of user convenience after reassurance about privacy.

Conclusion

An integrated secure messaging system can replace non-emergency bleeps with beneficial impact on service.
Identification of palliative care needs and prognostic factors of survival in tailoring appropriate interventions in advanced oncological, renal and pulmonary diseases: a prospective observational protocol
Authors: Valenti Vanessa , Rossi Romina , Scarpi Emanuela , Dall'Agata Monia , Bassi Ilaria , Cravero Paola , La Manna Gaetano , Magnoni Giacomo , Marchello Martina , Mosconi Giovanni , Nanni Oriana , Nava Stefano , Pallotti Maria Caterina , Rapposelli Ilario Giovanni , Ricci Marianna , Scrivo Anna , Spazzoli Alessandra , Valenti Danila , Zambianchi Loretta , Caraceni Augusto , Maltoni Marco
Published: May 2023

Introduction

It is estimated that of those who die in high-income countries, 69%–82% would benefit from palliative care with a high prevalence of advanced chronic conditions and limited life prognosis. A positive response to these challenges would consist of integrating the palliative approach into all healthcare settings, for patients with all types of advanced medical conditions, although poor clinician awareness and the difficulty of applying criteria to identify patients in need still pose significant barriers. The aim of this project is to investigate whether the combined use of the NECPAL CCOMS-ICO and Palliative Prognostic (PaP) Score tools offers valuable screening methods to identify patients suffering from advanced chronic disease with limited life prognosis and likely to need palliative care, such as cancer, chronic renal or chronic respiratory failure.

Methods and analysis

This multicentre prospective observational study includes three patient populations: 100 patients with cancer, 50 patients with chronic renal failure and 50 patients with chronic pulmonary failure. All patients will be treated and monitored according to local clinical practice, with no additional procedures/patient visits compared with routine clinical practice. The following data will be collected for each patient: demographic variables, NECPAL CCOMS-ICO questionnaire, PaP Score evaluation, Palliative Performance Scale, Edmonton Symptom Assessment System, Eastern Cooperative Oncology Group Performance Status and data concerning the underlying disease, in order to verify the correlation of the two tools (PaP and NECPAL CCOMS-ICO) with patient status and statistical analysis.

Ethics and dissemination

The study was approved by local ethics committees and written informed consent was obtained from the patient. Findings will be disseminated through typical academic routes including poster/paper presentations at national and international conferences and academic institutes, and through publication in peer-reviewed journals.
Adverse drug reactions of non-statin antihyperlipidaemic drugs in China from 1989 to 2019: a national database analysis
Authors: Tsui Leo , Chen Liwei , Ye Peiying , Xu Shiling , Wu Si-Jia , Chen Sonia Chien-I , Xie Wei
Published: May 2023

Objective

This study aims to understand the adverse drug reactions (ADRs) for non-statin antihyperlipidaemic drugs included in the China Anti-hyperlipidemic Drug Database.

Design

An approach of Chinese national database analysis was employed to screen clinical trials involving non-statin antihyperlipidaemic drugs from 1989 to 2019.

Setting

The database was provided by the China National Medical Products Administration Information Centre.

Participants

In total, 117 clinical studies with 8800 patients were selected from 2650 clinical trials of the Anti-hyperlipidemic Drug Database.

Interventions

The non-statin antihyperlipidaemic drugs were divided into three groups: (1) fibrates (fenofibrate, gemfibrozil, bezafibrate, etofylline clofibrate); (2) nicotinic acid and derivatives (niacin, acipimox) and (3) others (probucol, cholestyramine).

Results

The results of this study show that first, gastrointestinal symptoms were the most common reactions (6.975%), which account for approximately 50% of the reported cases with ADRs. Second, cholestyramine (16.418%) and gemfibrozil (13.158%) were the most common gastrointestinal side effect-causing non-statin antihyperlipidaemic drugs, which account for one-third of the population. Third, niacin (7.879%) and gemfibrozil (5.000%) were the most likely cause of liver disease symptoms. Finally, niacin (10.909%) and acipimox (18.847%) were the major non-statin antihyperlipidaemic drugs with skin symptoms.

Conclusion

This study revealed that gastrointestinal symptoms were the most common ADRs of fibrates, probucol and cholestyramine in the Chinese population. For nicotinic acid and derivatives, the ADRs of skin symptoms were the most common in China.
Usefulness of the lupus low disease activity state as a treatment target in childhood-onset SLE
Authors: Cody Ellen M , Wilson Bridget E , Ogbu Ekemini A , Huggins Jennifer L , Chen Chen , Qiu Tingting , Ting Tracy V , Flores Francisco , Huang Bin , Brunner Hermine I
Published: May 2023

Objective

Treat-to-target (T2T) strategies are advocated to improve prognosis in childhood-onset SLE (cSLE). Proposed T2T states include SLEDAI score of<4 (SLEDAI-LD), limited corticosteroid use (low-CS), and lupus low disease activity state (LLDAS). We sought to compare T2T states for their association with cSLE prognosis under consideration of relevant disease characteristics such as pre-existing damage, race and lupus nephritis (LN).

Methods

Longitudinal data from 165 patients enrolled in the Cincinnati Lupus Registry were included. LN presence was based on renal biopsy, and patients were followed up until 18 years of age.

Results

The 165 patients (LN: 45, white: 95) entered the registry within a median of 0 (IQR: 0–1) year post diagnosis and were followed up for a median of 4 (IQR: 2–5) years during which 80%, 92% and 94% achieved LLDAS, low-CS and SLEDAI-LD. Patients with LN were significantly less likely to achieve any T2T state (all p<0.03) and required a significantly longer time to reach them (all p<0.0001). Over the study period, patients maintained low-CS, SLEDAI-LD or LLDAS for a median of 76% (IQR: 48%–100%), 86% (IQR: 55%–100%) or 39% (IQR: 13%–64%) of their follow-up. Significant predictors of failure to maintain LLDAS included LN (p≤0.0062), pre-existing damage (p≤0.0271) and non-white race (p≤0.0013). There were 22%, 20% and 13% of patients who reached SLEDAI-LD, CS-low and LLDAS and nonetheless acquired new damage. Patients with LN had a higher risk of new damage than patients without LN even if achieving low-CS (p=0.009) or LLDAS (p=0.04).

Conclusions

Patients with LN and pre-existing damage are at higher risk of increased future damage acquisition, even if achieving a T2T state such as LLDAS. Among proposed common T2T states, the LLDAS is the hardest to achieve and maintain. The LLDAS may be considered the preferred T2T measure as it conveys the highest protection from acquiring additional disease damage.
Modelling sapling density for sugar maple-dominated mixed stands of eastern Canada
Authors: Rijal Baburam , Power Hugues , Auger Isabelle , Duchateau Emmanuel , Schneider Robert , Bédard Steve , Guillemette François
Published: May 2023

Saplings (tree species whose diameter at breast height ranges from 1.1 to 9.0 cm) are important in forest growth and development. Their abundance and density can lead to specific forest successional trajectories and ecosystem characteristics. Yet, the consideration of saplings in forest management planning is a relatively new topic and is still rarely included in forest growth models. We developed sapling density models for 10 species groups with the objective of providing forest managers with additional tools to support the development of more precise prediction systems. We used dendrometric and environmental variables to model sapling density and species-wise density ratios. We evaluated Poisson and gamma regressions for the modelling of sapling density and zero-inflated logistic regressions to model species ratios. We used repeated measurements (from 1982 to 2019) from permanent sample plots located in hardwood forests in northeastern North America. Our results show that the gamma regression was superior to the Poisson regression. The cross-validated mean bias using gamma regression was 55 stems·ha−1with a relative percentage error of 2.5% and an R2of 0.43. The species-wise sapling density ratio model had an overall R2of 0.93, and the species-wise mean  R2ranged between 0.90 and 0.96. Among the examined model covariates, stem density of merchantable-sized trees and latitude were significant in both models. We believe that the models we developed can be useful for forest management planning and sustainable merchantable production.
Accretion onto a static spherically symmetric regular MOG dark compact object
Authors: Nozari Kourosh , Saghafi Sara , Aliyan Fateme
Published: May 2023

In astrophysics, the process of a massive body acquiring matter is referred to as accretion. The extraction of gravitational energy occurs as a result of the infall. Since it converts gravitational energy into radiation, accretion onto dark compact objects, e.g. black holes, neutron stars, and white dwarfs is an extremely significant process in the astrophysical context. Accretion process is a fruitful way to explore the features of modified gravity (MOG) theories by testing the behavior of their solutions associated with dark compact objects. In this paper, we study the motion of electrically neutral and charged particles moving in around a regular spherically symmetric MOG dark compact object to explore their related innermost stable circular orbit (ISCO) and energy flux. Then, we turn to investigate the accretion of perfect fluid onto the regular spherically symmetric MOG dark compact object. We obtain analytical expressions for four-velocity and proper energy density of the accreting fluid. We see that the MOG parameter increases the ISCO radius of either electrically neutral or charged test particles while it decreases the corresponding energy flux. Moreover, the energy density and the radial component of the four-velocity of the infalling fluid decrease by increasing the MOG parameter near the central source.
Chemical reaction on MHD convective flow of second-grade fluid through a vertical porous channel with non-uniform wall temperature
Authors: Veera Krishna M. , Swarnalathamma B. V. , Praveen Babu D. M.
Published: May 2023

We have examined the impacts of the suction and/ or injection on the magnetohydrodynamic convection fluctuating flow of secondary order fluids through the absorbent medium into a perpendicular channel by non-uniformed walls’ temperature. The fluid is subjected to a transversal magnetic domain as well as the velocity slips near the left side plate. The systematic resolutions of the non-dimensional equations were found and the impacts of the governed variables on velocity, temperatures, concentration profiles, skin frictions, rates of temperature and mass transports were explored. It is motivating to note that skin friction enhances channel plates as permeability enlarges. The magnitude of the velocity retards with an enhancement in Hartmann number and enlarges with an increase in the permeability parameter.
AWARE: Workload-aware, Redundancy-exploiting Linear Algebra
Authors: Baunsgaard Sebastian , Boehm Matthias
Published: May 2023

Compression is an effective technique for fitting data in available memory, reducing I/O, and increasing instruction parallelism. While data systems primarily rely on lossless compression, modern machine learning (ML) systems exploit the approximate nature of ML and mostly use lossy compression via low-precision floating- or fixed-point representations. The resulting unknown impact on learning progress, and model accuracy, however, create trust concerns, that require trial and error, and are problematic for declarative ML pipelines. Given the trend towards increasingly complex, composite ML pipelines---with outer loops for hyper-parameter tuning, feature selection, and data cleaning/augmentation---it is hard for a user to infer the impact of lossy compression. Sparsity exploitation is a common lossless scheme used to improve performance without this uncertainty. Evolving this concept to general redundancy-exploiting compression is a natural next step. Existing work on lossless compression and compressed linear algebra (CLA) enable such exploitation to a degree, but face challenges for general applicability. In this paper, we address these limitations with a workload-aware compression framework, comprising a broad spectrum of new compression schemes and kernels. Instead of a data-centric approach that optimizes compression ratios, our workload-aware compression summarizes the workload of an ML pipeline, and optimizes the compression and execution plan to minimize execution time. On various micro benchmarks and end-to-end ML pipelines, we observe improvements for individual operations up to 10,000x and ML algorithms up to νmprint6.6 x compared to uncompressed operations.
Unsupervised Hashing with Semantic Concept Mining
Authors: Tu Rong-Cheng , Mao Xian-Ling , Lin Kevin Qinghong , Cai Chengfei , Qin Weize , Wei Wei , Wang Hongfa , Huang Heyan
Published: May 2023

Recently, to improve the unsupervised image retrieval performance, plenty of unsupervised hashing methods have been proposed by designing a semantic similarity matrix, which is based on the similarities between image features extracted by a pre-trained CNN model. However, most of these methods tend to ignore high-level abstract semantic concepts contained in images. Intuitively, concepts play an important role in calculating the similarity among images. In real-world scenarios, each image is associated with some concepts, and the similarity between two images will be larger if they share more identical concepts. Inspired by the above intuition, in this work, we propose a novel Unsupervised Hashing with Semantic Concept Mining, called UHSCM, which leverages a VLP model to construct a high-quality similarity matrix. Specifically, a set of randomly chosen concepts is first collected. Then, by employing a vision-language pretraining (VLP) model with the prompt engineering which has shown strong power in visual representation learning, the set of concepts is denoised according to the training images. Next, the proposed method UHSCM applies the VLP model with prompting again to mine the concept distribution of each image and construct a high-quality semantic similarity matrix based on the mined concept distributions. Finally, with the semantic similarity matrix as guiding information, a novel hashing loss with a modified contrastive loss based regularization item is proposed to optimize the hashing network. Extensive experiments on three benchmark datasets show that the proposed method outperforms the state-of-the-art baselines in the image retrieval task.
CompressGraph: Efficient Parallel Graph Analytics with Rule-Based Compression
Authors: Chen Zheng , Zhang Feng , Guan JiaWei , Zhai Jidong , Shen Xipeng , Zhang Huanchen , Shu Wentong , Du Xiaoyong
Published: May 2023

Modern graphs exert colossal time and space pressure on graph analytics applications. In 2022, Facebook social graph reaches 2.91 billion users with trillions of edges. Many compression algorithms have been developed to support direct processing on compressed graphs to address this challenge. However, previous graph compression algorithms do not focus on leveraging redundancy in repeated neighbor sequences, so they do not save the amount of computation for graph analytics. We develop CompressGraph, an efficient rule-based graph analytics engine that leverages data redundancy in graphs to achieve both performance boost and space reduction for common graph applications. CompressGraph has three advantages over previous works. First, the rule-based abstraction of CompressGraph supports the reuse of intermediate results during graph traversal, thus saving time. Second, CompressGraph has intense expressiveness to support a wide range of graph applications. Third, CompressGraph scales well under high parallelism because the context-free rules have few dependencies. Experiments show that CompressGraph provides significant performance and space benefits on both CPUs and GPUs. On evaluating six typical graph applications, CompressGraph can achieve 1.97× speedup on the CPU, while 3.95× speedup on the GPU, compared to the state-of-the-art CPU and GPU methods, respectively. Moreover, CompressGraph can save an average of 71.27% memory savings on CPU and 70.36 on GPU.
Virtual-Memory Assisted Buffer Management
Authors: Leis Viktor , Alhomssi Adnan , Ziegler Tobias , Loeck Yannick , Dietrich Christian
Published: May 2023

Most database management systems cache pages from storage in a main memory buffer pool. To do this, they either rely on a hash table that translates page identifiers into pointers, or on pointer swizzling which avoids this translation. In this work, we propose vmcache, a buffer manager design that instead uses hardware-supported virtual memory to translate page identifiers to virtual memory addresses. In contrast to existing mmap-based approaches, the DBMS retains control over page faulting and eviction. Our design is portable across modern operating systems, supports arbitrary graph data, enables variable-sized pages, and is easy to implement. One downside of relying on virtual memory is that with fast storage devices the existing operating system primitives for manipulating the page table can become a performance bottleneck. As a second contribution, we therefore propose exmap, which implements scalable page table manipulation on Linux. Together, vmcache and exmap provide flexible, efficient, and scalable buffer management on multi-core CPUs and fast storage devices.
Fast Continuous Subgraph Matching over Streaming Graphs via Backtracking Reduction
Authors: Yang Rongjian , Zhang Zhijie , Zheng Weiguo , Yu Jeffrey Xu
Published: May 2023

Streaming graphs are drawing increasing attention in both academic and industrial communities as many graphs in real applications evolve over time. Continuous subgraph matching (shorted as CSM) aims to report the incremental matches of a query graph in such streaming graphs. It involves two major steps, i.e., candidate maintenance and incremental match generation, to answer CSM. Throughout the course of continuous subgraph matching, incremental match generation backtracking over the search space dominates the total cost. However, most previous approaches focus on developing techniques for efficient candidate maintenance, while incremental match generation receives less attention despite its importance in CSM. Aiming to minimize the overall cost, we propose two techniques to reduce backtrackings in this paper. We present a cost-effective index CaLiG that yields tighter candidate maintenance, shrinking the search space of backtracking. In addition, we develop a novel incremental matching paradigm KSS that decomposes the query vertices into conditional kernel vertices and shell vertices. With the matches of kernel vertices, the incremental matches can be produced immediately by joining the candidates of shell vertices without any backtrackings. Benefiting from reduced backtrackings, the elapsed time of CSM decreases significantly. Extensive experiments over real graphs show that our method runs faster than the state-of-the-art algorithm orders of magnitude.
Time2State: An Unsupervised Framework for Inferring the Latent States in Time Series Data
Authors: Wang Chengyu , Wu Kui , Zhou Tongqing , Cai Zhiping
Published: May 2023

Time series data from monitoring applications reflect the physical or logical states of the objects, which may produce time series of distinguishable characteristics in different states. Thus, time series data can usually be split into different segments, each reflecting a state of the objects. These states carry rich high-level semantic information, e.g., run, walk, or jump, which helps people better understand the behaviour of the monitored objects. Nevertheless, these states are latent and hard to discover, because the characteristic of time series is complicated and the computational cost is high. This paper develops an efficient and effective unsupervised approach for inferring the latent states of massive multivariate time data. To reduce the computational cost, we present Time2State, a scalable framework that utilizes a sliding window and an encoder to greatly reduce the length of raw time series. To train the encoder, we propose a novel unsupervised loss function, LSE-Loss. Extensive experiments show that compared to the state-of-the-art time series representation learning methods of the same kind, LSE-Loss brings a performance improvement of up to 15% in accuracy.
One-shot Garbage Collection for In-memory OLTP through Temporality-aware Version Storage
Authors: Raza Aunn , Chrysogelos Periklis , Anadiotis Angelos Christos , Ailamaki Anastasia
Published: May 2023

Most modern in-memory online transaction processing (OLTP) engines rely on multi-version concurrency control (MVCC) to provide data consistency guarantees in the presence of conflicting data accesses. MVCC improves concurrency by generating a new version of a record on every write, thus increasing the storage requirements. Existing approaches rely on garbage collection and chain consolidation to reduce the length of version chains and reclaim space by freeing unreachable versions. However, finding unreachable versions requires the traversal of long version chains, which incurs random accesses right into the critical path of transaction execution, hence limiting scalability.

This paper introduces OneShotGC, a new multi-version storage design that eliminates version traversal during garbage collection, with minimal discovery and memory management overheads. OneShotGC leverages the temporal correlations across versions to opportunistically cluster them into contiguous memory blocks that can be released in one shot. We implement OneShotGC in Proteus and use YCSB and TPC-C to experimentally evaluate its performance with respect to the state-of-the-art, where we observe an improvement of up to 2x in transactional throughput.
AutoOD: Automatic Outlier Detection
Authors: Cao Lei , Yan Yizhou , Wang Yu , Madden Samuel , Rundensteiner Elke A.
Published: May 2023

Outlier detection is critical in real world. Due to the existence of many outlier detection techniques which often return different results for the same data set, the users have to address the problem of determining which among these techniques is the best suited for their task and tune its parameters. This is particularly challenging in the unsupervised setting, where no labels are available for cross-validation needed for such method and parameter optimization. In this work, we propose AutoOD which uses the existing unsupervised detection techniques to automatically produce high quality outliers without any human tuning. AutoOD's fundamentally new strategy unifies the merits of unsupervised outlier detection and supervised classification within one integrated solution. It automatically tests a diverse set of unsupervised outlier detectors on a target data set, extracts useful signals from their combined detection results to reliably capture key differences between outliers and inliers. It then uses these signals to produce a "custom outlier classifier" to classify outliers, with its accuracy comparable to supervised outlier classification models trained with ground truth labels - without having access to the much needed labels. On a diverse set of benchmark outlier detection datasets, AutoOD consistently outperforms the best unsupervised outlier detector selected from hundreds of detectors. It also outperforms other tuning-free approaches from 12 to 97 points (out of 100) in the F-1 score.
How To Optimize My Blockchain? A Multi-Level Recommendation Approach
Authors: Chacko Jeeta Ann , Mayer Ruben , Jacobsen Hans-Arno
Published: May 2023

Aside from the conception of new blockchain architectures, existing blockchain optimizations in the literature primarily focus on system or data-oriented optimizations within prevailing blockchains. However, since blockchains handle multiple aspects ranging from organizational governance to smart contract design, a holistic approach that encompasses all the different layers of a given blockchain system is required to ensure that all optimization opportunities are taken into consideration. In this vein, we define a multi-level optimization recommendation approach that identifies optimization opportunities within a blockchain at the system, data, and user level. Multiple metrics and attributes are derived from a blockchain log and nine optimization recommendations are formalized. We implement an automated optimization recommendation tool, BlockOptR, based on these concepts. The system is extensively evaluated with a wide range of workloads covering multiple real-world scenarios. After implementing the recommended optimizations, we observe an average of 20% improvement in the success rate of transactions and an average of 40% improvement in latency.
Personalized PageRank on Evolving Graphs with an Incremental Index-Update Scheme
Authors: Hou Guanhao , Guo Qintian , Zhang Fangyuan , Wang Sibo , Wei Zhewei
Published: May 2023

\em Personalized PageRank (PPR) stands as a fundamental proximity measure in graph mining. Given an input graph G with the probability of decay α, a source node s and a target node t, the PPR score π(s,t) of target t with respect to source s is the probability that an α-decay random walk starting from s stops at t. A \em single-source PPR (SSPPR) query takes an input graph G with decay probability α and a source s, and then returns the PPR π(s,v) for each node v ∈ V. Since computing an exact SSPPR query answer is prohibitive, most existing solutions turn to approximate queries with guarantees. The state-of-the-art solutions for approximate SSPPR queries are index-based and mainly focus on static graphs, while real-world graphs are usually dynamically changing. However, existing index-update schemes can not achieve a sub-linear update time. Motivated by this, we present an efficient indexing scheme for single-source PPR queries on evolving graphs. Our proposed solution is based on a classic framework that combines the forward-push technique with a random walk index for approximate PPR queries. Thus, our indexing scheme is similar to existing solutions in the sense that we store pre-sampled random walks for efficient query processing. One of our main contributions is an incremental updating scheme to maintain indexed random walks in expected O(1) time after each graph update. To achieve O(1) update cost, we need to maintain auxiliary data structures for both vertices and edges. To reduce the space consumption, we further revisit the sampling methods and propose a new sampling scheme to remove the auxiliary data structure for vertices while still supporting O(1) index update cost on evolving graphs. Extensive experiments show that our update scheme achieves orders of magnitude speed-up on update performance over existing index-based dynamic schemes without sacrificing the query efficiency.
I/O-Efficient Butterfly Counting at Scale
Authors: Wang Zhibin , Lai Longbin , Liu Yixue , Shui Bing , Tian Chen , Zhong Sheng
Published: May 2023

Butterfly (a cyclic graph motif) counting is a fundamental task with many applications in graph analysis, which aims at computing the number of butterflies in a large graph. With the rapid growth of graph data, it is more and more challenging to do butterfly counting due to the super-linear time complexity and large memory consumption. In this paper, we study I/O-efficient algorithms for doing butterfly counting on hierarchical memory. Existing algorithms of the kind cannot guarantee I/O optimality. Observing that in order to count butterflies, it suffices to "witness" a subgraph instead of the whole structure, a new class of algorithms called semi-witnessing algorithm is proposed. We prove that a semi-witnessing algorithm is not restricted by the lower bound Ømega(|E|2/MB) of a witnessing algorithm, and give a new bound of Ømega(min(|E|2/MB, |E|/|V| √M B)). We further develop the IOBufs algorithm that manages to approach the I/O lower bound, and thus claim its optimality. Finally, we make efforts to parallelize IOBufs to further improve the performance and scalability. We show in the experiment that IOBufs significantly outperforms the state-of-the-art algorithms EMRC and BFC-EM. In addition, IOBufs can scale to conducting butterfly counting on the Clueweb graph with 37 billion edges and quintillions (10^18 ) of butterflies.
Probabilistic Reasoning at Scale: Trigger Graphs to the Rescue
Authors: Tsamoura Efthymia , Lee Jaehun , Urbani Jacopo
Published: May 2023

The role of uncertainty in data management has become more prominent than ever before, especially because of the growing importance of machine learning-driven applications that produce large uncertain databases. A well-known approach to querying such databases is to blend rule-based reasoning with uncertainty. However, techniques proposed so far struggle with large databases. In this paper, we address this problem by presenting a new technique for probabilistic reasoning that exploits Trigger Graphs (TGs) -- a notion recently introduced for the non-probabilistic setting. The intuition is that TGs can effectively store a probabilistic model by avoiding an explicit materialization of the lineage and by grouping together similar derivations of the same fact. Firstly, we show how TGs can be adapted to support the possible world semantics. Then, we describe techniques for efficiently computing a probabilistic model and formally establish the correctness of our approach. We also present an extensive empirical evaluation using a prototype called LTGs. Our comparison against other leading engines shows that LTGs is not only faster, even against approximate reasoning techniques, but can also reason over probabilistic databases that existing engines cannot scale to.
Polaris: Enabling Transaction Priority in Optimistic Concurrency Control
Authors: Ye Chenhao , Hwang Wuh-Chwen , Chen Keren , Yu Xiangyao
Published: May 2023

Transaction priority is a critical feature for real-world database systems. Under high contention, certain classes of transactions should be given a higher chance to commit than others. Such a prioritization mechanism is commonly implemented in locking-based concurrency control protocols as some lock scheduling mechanisms, but it is rarely supported in the world of optimistic concurrency control.

We present Polaris, an optimistic concurrency control protocol that supports multiple priority levels. To enforce priority, Polaris introduces a minimal amount of pessimism through a lightweight reservation mechanism. The protocol is fully optimistic among transactions within the same priority level and preserves the high throughput advantage of optimistic protocols. Our evaluation with YCSB workload shows that Polaris can make the p999 tail latency of high-priority transactions 13x lower than that of low-priority ones. With an abort-aware priority assignment policy, Polaris can deliver 1.9x higher throughput and 17x lower tail latency compared to Silo for high-contention workloads.
An Efficient Algorithm for Distance-based Structural Graph Clustering
Authors: Liu Kaixin , Wang Sibo , Zhang Yong , Xing Chunxiao
Published: May 2023

Structural graph clustering (SCAN) is a classic graph clustering algorithm. In SCAN, a key step is to compute the structural similarity between vertices according to the overlap ratio of one-hop neighborhoods. Given two vertices u and v, existing studies only consider the case when u and v are neighbors. However, the structural similarity between non-neighboring vertices in SCAN is always zero, and using only one-hop neighbors on weighted graphs discards the weights on each edge. Both may not reflect the true closeness of two vertices and may fail to return high-quality clustering results.

To tackle this issue, we define and study the distance-based structural graph clustering problem. Given a distance threshold d and two vertices u and v, the structural similarity between u and v is defined as the ratio of their respective neighbors within a distance of no more than d. We show that the newly defined distance-based SCAN achieves better clustering results compared to the vanilla version of SCAN. However, the new definition brings challenges in the computation of final clustering results. To tackle this efficiency issue, we propose DistanceSCAN, an efficient approximate algorithm for solving the distance-based SCAN problem. The main idea of DistanceSCAN is to use all-distances bottom-k sketches (ADS) to speed up the computation of similarities. Given the ADS, we can derive the similarity between two vertices with a bounded cost of O(k).

However, to ensure that the estimated similarity has an approximation guarantee, the value of k still needs to be set to as large as thousands. This brings high computational costs when computing the similarities between neighboring vertices. To tackle this issue, we further construct histograms to prune the structural similarity computations of vertices pairs. Extensive experiments on real datasets validate the effectiveness and efficiency of DistanceSCAN.