bluuebunny/crossref_metadata_2025 huggingface dataset:
Aortoduodenal syndrome in the emergency department
Authors: Nagoda Niklewicz Camila , Vijay Aditya , Witt Erik
Published: February 2025

An elderly man presented to the emergency department with symptoms suggestive of small bowel obstruction. His medical history included an asymptomatic abdominal aortic aneurysm. Investigations confirmed small bowel obstruction, caused by the aneurysm compressing his duodenum: a condition known as ‘aortoduodenal syndrome’. Due to the patient’s age, frailty and preference, he was managed non-operatively. The case highlights the importance of considering indolent complications of enlarging abdominal aneurysms in an ageing population.
Woven EndoBridge (WEB) device used for vertebral artery sacrifice
Authors: Sims Jason J , Khattar Nicolas , Mulpur Bhageeradh , Arthur Adam S
Published: February 2025

In this case study, we presented a challenging neurovascular intervention where a Woven EndoBridge (WEB) intrasaccular device was used to treat a 4.5 mm ruptured vertebral artery fusiform aneurysm by sacrificing the vessel with great precision. The patient had a dissection of the left vertebral artery at the posterior inferior cerebellar artery (PICA) origin. The WEB device was deployed immediately proximal to the origin of the PICA. After WEB deployment, further stasis and occlusion were achieved with coils. This combination of the WEB device and coils allowed for a precise and controlled sacrifice, ensuring continued filling of the PICA from the contralateral vertebral artery. The outcome of the procedure was successful, as the patient experienced complete vessel occlusion with no adverse effects.

This case demonstrates a non-standard use of the WEB device in tandem with coils to treat a ruptured fusiform vertebral artery aneurysm.
Transcoccygeal resection for a presacral GIST of the distal rectum
Authors: Savulionyte Goda , Bleday Ronald
Published: February 2025

Gastrointestinal stromal tumours (GISTs) of the rectum are rare and comprise about 5% of all GISTs. Workup includes biopsy with immunohistochemistry analysis. Surgical excision is the standard treatment, although tyrosine kinase inhibitors, particularly imatinib, are known to be useful in the neoadjuvant and adjuvant settings. We present a case of a woman in her 70s who was diagnosed with distal rectal GIST. MRI revealed the tumour to be located within the presacral space. She underwent core needle biopsy with immunohistochemistry confirming KIT-positive GIST. The patient received neoadjuvant imatinib therapy, followed by surgical excision via the transcoccygeal approach (Kraske procedure). She recovered appropriately and went on to receive adjuvant imatinib therapy. We describe the progression of this patient’s care and review various pertinent therapies for distal rectal GIST, highlighting the transcoccygeal resection as a safe and effective approach.
Public satisfaction with Healthy China Action Plan implementation: a web-based cross-sectional survey in southwest China
Authors: Chen Wuzhao , Bai Xinyu , Liu Dongyang , Pan Li , Chongsuvivatwong Virasakdi , Chen Yi
Published: February 2025

Objective

The Healthy China (Guangxi) Action Plan has been in effect for approximately 4 years, with its primary goals being to improve public health and prevent diseases. However, there is limited information available on the community-level public satisfaction with the outcomes of its implementation. Therefore, we intend to conduct an assessment based in southwest China.

Design

A population-based, cross-sectional survey was performed between 1 June 2023 and 31 July 2023.

Setting

Community members of all 14 prefecture-level cities within Guangxi, China, during June 2023 to July 2023.

Participants

A total of 62 4016 participants were included for analysis based on exclusion criteria, with 44.4% aged 18–35 and a male to female ratio of 3:2.

Primary outcome measure

‘Public satisfaction with the Healthy China (Guangxi) Action Plan implementation’ was measured by the combination of ‘Willingness to join health promotion’ and ‘Satisfaction to overall medical implementation measures and services’ using a structural equation model with a multiple-indicator, multiple-cause approach in R software.

Results

The study proposed a comprehensive five-factor model to evaluate the public satisfaction with Healthy China (Guangxi) Action Plan implementation. With the mediator of ‘awareness of Healthy China (Guangxi) Action Plan’, health education/promotion demonstrated important effect on overall public satisfaction with Healthy China (Guangxi) Action Plan implementation. Mental health had small but positive effect on the outcomes. High socio-characteristic variables showed negative effects on all latent variables except for the mental health status. All of the effects were statistically significant.

Conclusions

Understanding the impact of background variables on public satisfaction with Healthy China (Guangxi) Action Plan implementation was crucial, along with considering the sociodemographic traits of participants. This knowledge helped policymakers to develop tailored interventions and strategies.
Assessing the impact of antimicrobial resistance policies on antibiotic use and antimicrobial resistance-associated mortality in children and adults in low and middle-income countries: a global analysis
Authors: Zay Ya Kyaw , Patel Jay , Fink Günther
Published: February 2025

Introduction

Antimicrobial resistance (AMR) poses a major threat to global health security today. In recent years, many low and middle-income countries (LMICs) have implemented policies to optimise antibiotic use in both formal and informal healthcare settings. However, there is limited evidence on the effectiveness of these national efforts in LMICs.

Methods

We investigated the empirical relationship between national policies aimed at restricting antibiotic use and actual antibiotic consumption in 138 LMICs. Data on national policies were obtained from the Tripartite AMR Country Self-Assessment Survey (TrACSS) as well as from the Global Survey of Experts on AMR (GSEAR). Seven independent variables relating to AMR policies were evaluated. Outcomes included the proportion of children receiving antibiotics for lower respiratory tract infections and diarrhoea (specific to paediatric populations), along with total antibiotic consumption and AMR-associated mortality in general populations.

Results

Our analysis of 138 LMICs found wide variation in antibiotic use between countries and regions. We observed strong evidence of negative association (mean difference MD=−0.150, 95% CI (−0.2593 to –0.0407)) between the presence of regulatory or legislative policies that ban over-the-counter sales of antibiotics and the proportion of children receiving antibiotic drugs for lower respiratory tract infection. Furthermore, stronger AMR governance was associated with reduced total antibiotic consumption at the country level (MD=−1.259, 95% CI (−2.297 to –0.2216)). No associations were found between other policy variables and antibiotic use or mortality.

Conclusion

The results presented here suggest that there is some evidence of an empirical relationship between national policies aimed at limiting over-the-counter antibiotic sales and actual antibiotic usage practices. Further policy effectiveness research will be needed to better understand the true impact of government measures. In general, a multifaceted approach will likely be needed to fight AMR and preserve antibiotics’ effectiveness, including evidence-based policies, targeted education and research.
From the mat to mastery: a Judoka’s quest in the wake of injury – professional Judokas’ experiences of suffering an anterior cruciate ligament injury
Authors: Piussi Ramana , Engström Moa , Prill Robert , Hägglund Martin , Ivarsson Andreas , Samuelsson Kristian , Holmberg Hans-Christer , Hamrin Senorski Eric
Published: February 2025

This study explored professional Judokas’ experiences of the anterior cruciate ligament (ACL) injury process, the consequences of the injury, and their perceptions of underlying factors contributing to the injury. Eleven professional Judokas treated with ACL reconstruction were interviewed using semi-structured interviews. Qualitative content analysis of the transcripts revealed two main themes: ‘from the mat to mastery: a Judoka’s quest in the wake of injury’ and ‘beyond the physical: the silent struggle for psychological victory.’ Judokas described judo as a lifestyle, with their injuries impacting both physical performance and mental health. Fatigue, high training intensity, and bad luck were identified as contributing factors to their injuries. While the injury posed significant physical and mental challenges, Judokas emphasized the role of the discipline and structure learned through judo to navigate the recovery process. Judokas attributed their ACL injuries to a mix of preparation and unpredictable elements, and reported both positive and negative consequences of their experiences.
4-1BB agonist targeted to fibroblast activation protein α synergizes with radiotherapy to treat murine breast tumor models
Authors: Garate-Soraluze Eneko , Serrano-Mendioroz Irantzu , Fernández-Rubio Leticia , De Andrea Carlos E , Barrio-Alonso Celia , Herrero Claudia del Pilar , Teijeira Alvaro , Luri-Rey Carlos , Claus Christina , Tanos Tamara , Klein Christian , Umana Pablo , Rullan Antonio , Simón Jon Ander , Collantes María , Sánchez-Mateos Paloma , Melero Ignacio , Rodriguez-Ruiz Maria E
Published: February 2025

Background

Ionizing radiation (IR) is a double-edged sword for immunotherapy as it may have both immunosuppressive and immunostimulatory effects. The biological effects of IR on the tumor microenvironment (TME) are a key factor for this balance. Fibroblast activation protein (FAP) is expressed on the surface of cancer-associated fibroblasts (CAF) in many cancer types and its abundance is associated with the poor immune response to immune-checkpoint-blockade in patients. We hypothesized that IR increases FAP expression in CAFs, therefore the combination of IR with targeted immunomodulators such as an agonistic anti-FAP-4-1BBL fusion protein could enhance the immune-mediated antitumoral effects of these treatments.

Methods

The murine transplantable TS/A tumor-cell-line co-engrafted with CAFs was used to investigate increases in FAP expression in tumors following irradiation using immunohistochemistry, real-time polymerase chain reaction (RT-PCR) and multiplex tissue immunofluorescence. One lesion of bilateral tumor-bearing mice was only locally irradiated or combined with weekly injections of the bispecific muFAP-4-1BBL fusion protein (a mouse surrogate for RG7826). Tumor sizes were followed over time and TME was assessed by flow cytometry. Selective monoclonal antibody (mAb)-mediated depletions of immune cell populations, neutralizing interferon alpha/beta receptor 1 (IFNAR-I) IFNAR and interferon (IFN)-γ mAbs and gene-modified mice (4-1BB−/−) were used to delineate the immune cell subsets and mechanisms required for efficacy.67Ga labeled muFAP-4-1BBL tracked by SPECT-CT was used to study biodistribution. In human colorectal carcinoma samples, the inducibility of FAP expression following radiotherapy was explored by multiplex immunofluorescence.

Results

Irradiation of TS/A+CAF tumors in mice showed an increase in FAP levels after local irradiation. A suboptimal radiotherapy regimen in combination with muFAP-4-1BBL attained primary tumor control and measurable abscopal effects. Immune TME landscape analyses showed post-treatment increased infiltration of activated immune cells associated with the combined radioimmunotherapy treatment. Efficacy depended on CD8+T cells, type I IFN, IFN-γ and ability to express 4-1BB. Biodistribution studies of muFAP-4-1BBL indicated enriched tumor targeting to irradiated tumors. Human colorectal cancer samples pre and post irradiation showed enhanced FAP expression after radiotherapy.

Conclusion

Increased FAP expression in the TME as a result of radiotherapy can be exploited to target agonist 4-1BB immunotherapy to malignant tumor lesions using an FAP-4-1BBL antibody fusion protein.
Global, regional and national burdens of cardiovascular disease attributable to secondhand smoke from 1990–2019: an age-period-cohort analysis
Authors: Jiang Hong , Liu Zeye , Wei Peijian , Zhang Fengwen , Wang Shouzheng , Ou-yang Wen-Bin , Li Xiaofei , Pan Xiang-Bin
Published: February 2025

Background

Over the past three decades, significant disparities in the global burden of cardiovascular disease (CVD) have been observed, particularly CVD attributed to secondhand smoke. However, a comprehensive understanding of global trends and their interaction with secondhand smoke remains inadequate.

Methods

Using Global Burden of Disease data (1990–2019), an age-period-cohort analysis examined temporal trends in CVD mortality among secondhand smoke-exposed populations, considering age, period and cohort interactions.

Results

Over the 30-year period, the global number of CVD deaths attributed to secondhand smoke increased substantially, from 432.6 thousand in 1990 (95% UI: 357.4–508.3) to 598.5 thousand in 2019 (95% UI: 489.7–713.5), representing a 38.4% increase (95% UI: 26.8%–49.5%). In 2019, CVD accounted for 45.9% of all deaths attributable to secondhand smoke among both sexes globally. Among these CVD deaths, ischaemic heart disease predominated, accounting for 66.4% of cases, compared with stroke. The distribution by sex revealed a slightly lower percentage of males (46.5%) than females (53.5%). Age-period-cohort models show overall global decline in CVD mortality due to secondhand smoke over 30 years, with regional, sex and subtype variations. Notably, a higher Sociodemographic Index (SDI) correlated with a greater reduction in mortality, exhibiting a significant 39.1% decrease in high SDI areas (95% UI: 35.6%–42.3%), in stark contrast to the minimal change observed in low SDI areas (0.1%, 95% UI: −52.4%–62.2%).

Conclusions

This study highlights the importance of considering secondhand smoke as a modifiable CVD risk. Further research is needed to understand disparities in CVD burden across development levels, sexes and subtypes.
Frequency and treatment of venous thromboembolic events in patients with space-occupying brain infarction and decompressive craniectomy
Authors: Dabbagh Alhuda , Mielke Janine , Mühlberg Katja S , Hoffmann Karl Titus , Lindner Dirk , Huber Charlotte , Michalski Dominik , Pelz Johann Otto
Published: February 2025

Introduction

Venous thromboembolic events (VTEs) like deep vein thrombosis or pulmonary embolism are frequent complications in (neuro) critical ill patients. Anticoagulation for VTE after space-occupying brain infarction is a therapeutic dilemma. The aim of this retrospective study was to investigate the frequency of clinically apparent VTE in patients with acute ischaemic stroke (AIS) due to large vessel occlusion (LVO), its treatment, and the rate of complications.

Methods

Patients with first AIS due to LVO were assigned to one of the following groups: space-occupying brain infarction with (1) or without (2) decompressive craniectomy (DC), AIS comprising more than 2/3 (3) or less than 2/3 (4) of the middle cerebral artery territory. Clinically obtained parameters included risk factors for VTE, type of thromboprophylaxis, treatment of VTE and treatment-associated complications.

Results

15 of 173 (8.7%) patients had a VTE, which was diagnosed 10.9 ± 7.2 days after admission. Patients with a space-occupying brain infarction and DC had significantly more VTE (n=11/63; 17.5%) than patients with a space-occupying brain infarction without DC (0/26; p =0.023) or patients without DC (4/110; 3.6%; p = 0.004). Younger age, DC and cumulative duration of central venous catheter were identified as risk factors for VTE. Only three patients had major bleeding events while being anticoagulated (one asymptomatic cerebral and two extracranial bleedings).

Discussion

Patients with space-occupying brain infarction and DC hold a high risk for VTE. Despite extensive infarct size and DC, therapeutic anticoagulation required for VTE appeared to be safe regarding intracranial bleeding complications.
Satellitome analysis on Microtus thomasi (Arvicolinae) genome, a mammal species with high karyotype and sex chromosome variations
Authors: Aleix Mata Gael , Montiel Eugenia E. , Mora-Ruiz Pablo , Yurchenko Alona , Rico-Porras José M. , Anguita Francisco , Palomo Fátima , Marchal Juan Alberto , Rovatsos Michail , Sánchez Baca Antonio
Published: February 2025

The voles of the Microtus thomasi / M. atticus species complex (Arvicolinae) display extensive karyotypic variation, in the number of autosomes and the morphology of sex chromosomes. We analysed the satellitome of M. thomasi and identified 17 satDNA families, corresponding to 6.704% of the genome. Homogenization and divergence analyses showed that some satDNA families are more homogeneous, indicative of recent amplification, while others displayed higher variation, suggesting ancient amplification. Twelve of satDNA families are conserved across Arvicolinae with a substantial variation in the abundance and the composition. These results supports the "library" hypothesis, where a shared collection of satDNAs exists across related species, with differential amplification driving species-specific genomic profiles. Localization analysis demonstrated that an increased number of satDNA families are localized in the pericentromeric and the heterochromatic regions of autosomes and sex chromosomes. Our results suggest that the heterochromatin of the X and Y chromosomes co-evolved and that satDNA families might have contributed to the chromosomal rearrangements involved in the karyotypic variation and sex chromosome polymorphism of the chromosomal races. Our study contribute to a deeper understanding of the evolutionary mechanisms underlying karyotype diversification in Microtus species, which exhibit some of the highest rates of karyotypic variation among mammals.
The nuclear and mitochondrial genomes of the bellicose bumblebee (Bombus bellicosus, Hymenoptera: Apidae), a threatened pollinator in a changing South American landscape
Authors: Gonçalves Leonardo Tresoldi , Pezzi Pedro Henrique , Montagner Flávia Regina Girardi , Souza Wellington Vasconcelos de , Françoso Elaine , Deprá Maríndia
Published: February 2025

We present the first genome of a Brazilian bumblebee species, the bellicose bumblebee (Bombus bellicosus). This is an endemic species in southern South America facing local extinction due to habitat loss and climate change. During the COVID-19 social distancing in Brazil, we launched a citizen science initiative via social media to locate remaining bellicose bumblebee populations, leading to the collection of a specimen for genome sequencing. Analysis of the novel genome revealed lower genetic diversity in the bellicose bumblebee compared to a widespread related species (B. pascuorum). However, the absence of extensive runs of homozygosity indicated a lack of recent inbreeding, offering a promising perspective for the conservation of this species. Furthermore, demographic history analysis indicates population expansion during past glacial periods, in contrast to Palearctic bumblebees which suffered a stark decline during glaciations. Our findings provide invaluable information for the conservation of this species and for further studies about its biology and evolution, particularly under a scenario of rapid environmental change.
A High Data Rate Solution for Multi-Carrier DCSK
Authors: Yu Bin , Jiang Guo-Ping , Yang Hua , Jia Ya-Qiong , Liao Hao
Published: February 2025

A high data rate multi-carrier differential chaos shift keying system with hybrid index modulation, referred to as HDR-HIM-MC-DCSK, is proposed in this paper. In the proposed system, multiple data-bearing wavelets are parallelly transmitted with one reference signal. Different Walsh codes are utilized for separating these data-bearing wavelets. Some subcarriers are used for hybrid index modulation, in which the chaotic signal and its Hilbert transform are generated for representing the active and inactive subcarriers. In addition, the analytical Bit-Error-Rate (BER) expressions of the proposed HDR-HIM-MC-DCSK system are derived over the AWGN and the multipath Rayleigh fading channels. The hardware complexity, computational complexity, energy efficiency, spectral efficiency and data rate of the proposed system are analyzed. The analytical accuracy and the advantages of the proposed system are verified by relevant simulations.
Accelerating Core Decomposition in Billion-Scale Hypergraphs
Authors: Zhang Wenqian , Yang Zhengyi , Wen Dong , Li Wentao , Zhang Wenjie , Lin Xuemin
Published: February 2025

Hypergraphs provide a versatile framework for modeling complex relationships beyond pairwise interactions, finding applications in various domains.
k
-core decomposition is a fundamental task in hypergraph analysis that decomposes hypergraphs into cohesive substructures. Existing studies capture the cohesion in hypergraphs based on the vertex neighborhood size. However, such decomposition poses unique challenges, including the efficiency of core value updates, redundant computation, and high memory consumption. We observe that the state-of-the-art algorithms do not fully address the above challenges and are unable to scale to large hypergraphs. In this paper, we propose an efficient approach for hypergraph
k
-core decomposition. Novel concepts and strategies are developed to compute the core value of each vertex and reduce redundant computation of vertices. Experimental results on real-world and synthetic hypergraphs demonstrate that our approach significantly outperforms the state-of-the-art algorithm by 7 times on average while reducing the average memory usage by 36 times. Moreover, while existing algorithms fail on tens of millions hyperedges, our approach efficiently handles billion-scale hypergraphs in only a single thread.
An Elephant Under the Microscope: Analyzing the Interaction of Optimizer Components in PostgreSQL
Authors: Bergmann Rico , Hartmann Claudio , Habich Dirk , Lehner Wolfgang
Published: February 2025

Despite an ever-growing corpus of novel query optimization strategies, the interaction of the core components of query optimizers is still not well understood. This situation can be problematic for two main reasons: On the one hand, this may cause surprising results when two components influence each other in an unexpected way. On the other hand, this can lead to wasted effort in regard to both engineering and research, e.g., when an improvement for one component is dwarfed or entirely canceled out by problems of another component. Therefore, we argue that making improvements to a single optimization component requires a thorough understanding of how these changes might affect the other components. To achieve this understanding, we present results of a comprehensive experimental analysis of the interplay in the traditional optimizer architecture using the widely-used PostgreSQL system as prime representative. Our evaluation and analysis revisit the core building blocks of such an optimizer, i.e. per-column statistics, cardinality estimation, cost model, and plan generation. In particular, we analyze how these building blocks influence each other and how they react when faced with faulty input, such as imprecise cardinality estimates. Based on our results, we draw novel conclusions and make recommendations on how these should be taken into account.
An Experimental Comparison of Tree-data Structures for Connectivity Queries on Fully-dynamic Undirected Graphs
Authors: Chen Qing , Böhlen Michael H. , Helmer Sven
Published: February 2025

During the past decades significant efforts have been made to propose data structures for answering connectivity queries on fully dynamic graphs, i.e., graphs with frequent insertions and deletions of edges. However, a comprehensive understanding of how these data structures perform in practice is missing, since not all of them have been implemented, let alone evaluated experimentally. We provide reference implementations for the proposed data structures and experimentally evaluate them on a wide range of graphs. Our findings show that the current solutions are not ready to be deployed in systems as is, as every data structure has critical weaknesses when used in practice. Key limitations that must be overcome are the space and time overhead incurred by balanced data structures, the degeneration of the runtime of space-efficient data structures in worst case scenarios, and the maintenance costs for balanced data structures. We detail our findings in the experimental evaluation and provide recommendations for implementing robust solutions for answering connectivity queries on dynamic graphs.
AquaPipe: A Quality-Aware Pipeline for Knowledge Retrieval and Large Language Models
Authors: Yu Runjie , Huang Weizhou , Bai Shuhan , Zhou Jian , Wu Fei
Published: February 2025

The knowledge retrieval methods such as Approximate Nearest Neighbor Search (ANNS) significantly enhance the generation quality of Large Language Models (LLMs) by introducing external knowledge, and this method is called Retrieval-augmented generation (RAG). However, due to the rapid growth of data size, ANNS tends to store large-scale data on disk, which greatly increases the response time of RAG systems. This paper presents AquaPipe, which pipelines the execution of disk-based ANNS and the LLM prefill phase in an RAG system, effectively overlapping the latency of knowledge retrieval and model inference to enhance the overall performance, while guaranteeing data quality. First, ANNS's recall-aware prefetching strategy enables the early return of partial text with acceptable accuracy so the
prefill
phase can launch before getting the full results. Then, we adaptively choose the remove-after-prefill or re-prefill strategies based on the LLM cost model to effectively correct disturbed pipelines caused by wrong early returns. Finally, the pipelined prefill dynamically changes the granularity of chunk size to balance the overlap efficiency and GPU efficiency, adjusting to ANNS tasks that converge at different speeds. Our experiments have demonstrated the effectiveness of AquaPipe. It successfully masks the latency of disk-based ANNS by 56% to 99%, resulting in a 1.3× to 2.6× reduction of the response time of the RAG, while the extra recall loss caused by prefetching is limited to approximately 1%.
Dual-Hierarchy Labelling: Scaling Up Distance Queries on Dynamic Road Networks
Authors: Farhan Muhammad , Koehler Henning , Wang Qing
Published: February 2025

Computing the shortest-path distance between any two given vertices in road networks is an important problem. A tremendous amount of research has been conducted to address this problem, most of which are limited to static road networks. Since road networks undergo various real-time traffic conditions, there is a pressing need to address this problem for dynamic road networks. Existing state-of-the-art methods incrementally maintain an indexing structure to reflect dynamic changes on road networks. However, these methods suffer from either slow query response time or poor maintenance performance, particularly when road networks are large. In this work, we propose an efficient solution
Dual-Hierarchy Labelling (DHL)
for distance querying on dynamic road networks from a novel perspective, which incorporates two hierarchies with different but complementary data structures to support efficient query and update processing. Specifically, our proposed solution is comprised of three main components:
query hierarchy,
update hierarchy,
and
hierarchical labelling,
where
query hierarchy
enables efficient query answering by exploring only a small subset of vertices in the labels of two query vertices and
update hierarchy
supports efficient maintenance of distance labelling under edge weight increase or decrease. We further develop dynamic algorithms to reflect dynamic changes by efficiently maintaining the update hierarchy and hierarchical labelling. We also propose a parallel variant of our dynamic algorithms by exploiting labelling structure which aligns well with parallel processing. We evaluate our methods on 10 large road networks and it shows that our methods significantly outperform the state-of-the-art methods, i.e., achieving considerably faster construction and update time, while being consistently 2-4 times faster in terms of query processing and consuming only 10%-20% labelling space.
HyperMR: Efficient Hypergraph-enhanced Matrix Storage on Compute-in-Memory Architecture
Authors: Wu Yifan , Chen Ke , Chen Gang , Jiang Dawei , Li Huan , Shou Lidan
Published: February 2025

Matrix-vector multiplication (MVM) operations, essential for modern hardware architectures, suffer from heavy I/O overheads and costly serial multiply-add operations. The emerging Compute-in-Memory (CIM) architecture alleviates these issues by enabling in situ MVM operations with O(1) time complexity, eliminating the need to move matrices. However, current storage schemes are still inefficient on CIM due to limited optimization objectives and inflexible support for various access patterns and matrix structures. To address this, we propose HyperMR, a hypergraph-enhanced matrix storage scheme for CIM architectures. First, we identify two performance optimization objectives that are tailored to CIM and prove their NP-hardness. We then introduce a hypergraph modeling approach with a novel access-aware hypergraph generation algorithm to handle diverse matrix structures and access patterns. Moreover, we present a two-phase hypergraph partitioning method to efficiently tackle the NP-hard optimization objectives. Experimental results show that HyperMR outperforms multiple state-of-the-art storage schemes, offering valid optimization for all evaluated matrices, compared to the best-performing baseline which optimizes only 75%. HyperMR also achieves the best average optimization performance for matrix storage layouts, significantly improving efficiency in varied workload scenarios, with a 29.65% improvement on synthetic queries and up to 34.9% on scientific image filtering.
In-Database Time Series Clustering
Authors: Su Yunxiang , Liang Kenny Ye , Song Shaoxu
Published: February 2025

Time series data are often clustered repeatedly across various time ranges to mine frequent subsequence patterns from different periods, which could further support downstream applications. Existing state-of-the-art (SOTA) time series clustering method, such as K-Shape, can proficiently cluster time series data referring to their shapes. However, in-database time series clustering problem has been neglected, especially in IoT scenarios with large-volume data and high efficiency demands. Most time series databases employ LSM-Tree based storage to support intensive writings, yet causing underlying data points out-of-order in timestamps. Therefore, to apply existing out-of-database methods, all data points must be fully loaded into memory and chronologically sorted. Additionally, out-of-database methods must cluster from scratch each time, making them inefficient when handling queries across different time ranges. In this work, we propose an in-database adaptation of SOTA time series clustering method K-Shape. Moreover, to solve the problem that K-Shape cannot efficiently handle long time series, we propose Medoid-Shape, as well as its in-database adaptation for further acceleration. Extensive experiments are conducted to demonstrate the higher efficiency of our proposals, with comparable effectiveness. Remarkably, all proposals have already been implemented in an open-source commodity time series database, Apache IoTDB.
Minimum Spanning Tree Maintenance in Dynamic Graphs
Authors: Xu Lantian , Wen Dong , Qin Lu , Li Ronghua , Zhang Ying , Lu Yang , Lin Xuemin
Published: February 2025

Minimum Spanning Tree (MST) is a fundamental structure in graph analytics and can be applied in various applications. The problem of maintaining MSTs in dynamic graphs is significant, as many real-world graphs are frequently updated. Existing studies on MST maintenance primarily focus on theoretical analysis and lack practical efficiency. In this paper, we propose a novel algorithm to maintain MST in dynamic graphs, which achieves high practical efficiency. In addition to the tree structure, our main idea is to maintain a replacement edge for each tree edge. In this way, the tree structure can be immediately updated when a tree edge is deleted. We propose algorithms to maintain the replacement edge for each tree edge by sharing the computation cost in the updating process. Our performance studies on large datasets demonstrate considerable improvements over state-of-the-art solutions.
Optimizing Block Skipping for High-Dimensional Data with Learned Adaptive Curve
Authors: Chen Xu , Liu Shuncheng , Yuan Tong , Ye Tao , Zeng Kai , Su Han , Zheng Kai
Published: February 2025

In the realm of big data and cloud analytics, efficiently managing and retrieving high-dimensional data presents a critical challenge. Traditional indexes often struggle with the storage overhead inherent in large datasets. There is a growing interest in the adoption of Small Materialize Aggregation (SMA) among cloud database vendors due to its ability to maintain lightweight block-level metadata, facilitating efficient block skipping. However, SMA performance relies heavily on data layout. This is especially critical in scenarios with wide tables containing hundreds of dimensions, where the curse of dimensionality exacerbates the issue. In this paper, we propose
AdaCurve
, a novel approach aimed at enhancing block skipping in high-dimensional datasets through adaptive optimization of data layout. Unlike conventional static and non-adaptive space-filling curves (SFCs),
AdaCurve
leverages machine learning to develop an adaptive curve---a dynamically adjusting optimal projection function tailored to high-dimensional workloads and data characteristics. We introduce an attention-based network to handle high-dimensional data and a learnable objective for training adaptive curves in an end-to-end manner. Extensive experiments conducted on the Spark with real-world datasets demonstrate the effectiveness of
AdaCurve
. We have shown that
AdaCurve
effectively scales to datasets with dimensions of up to 1,000 columns, achieving a 2.8× improvement in block skipping compared to SFCs.
SHARQ: Explainability Framework for Association Rules on Relational Data
Authors: Ben-Efraim Hadar , Davidson Susan B. , Somech Amit
Published: February 2025

Association rules are an important technique for gaining insights over large relational datasets consisting of tuples of elements (i.e. attribute-value pairs). However, it is difficult to explain the relative importance of data elements with respect to the rules in which they appear. This paper develops a measure of an element's contribution to a set of association rules based on Shapley values, denoted SHARQ (ShApley Rules Quantification). As is the case with many Shapely-based computations, the cost of a naive calculation of the score is exponential in the number of elements. To that end, we present an efficient framework for computing the exact SHARQ value of a single element whose running time is practically linear in the number of rules. Going one step further, we develop an efficient multi-element SHARQ algorithm which amortizes the cost of the single element SHARQ calculation over a set of elements. Based on the definition of SHARQ for elements we describe two additional use-cases for association rules explainability: rule importance and attribute importance. Extensive experiments over a novel benchmark dataset containing 67 instances of mined rule sets show the effectiveness of our approach.
Federated Heavy Hitter Analytics with Local Differential Privacy
Authors: Zhang Yuemin , Ye Qingqing , Hu Haibo
Published: February 2025

Federated heavy hitter analytics enables service providers to better understand the preferences of cross-party users by analyzing the most frequent items. As with federated learning, it faces challenges of privacy concerns, statistical heterogeneity, and expensive communication. Local differential privacy (LDP), as the de facto standard for privacy-preserving data collection, solves the privacy challenge by letting each user perturb her data locally and report the sanitized version. However, in federated settings, applying LDP complicates the other two challenges, due to the deteriorated utility by the injected LDP noise or increasing communication/computation costs by perturbation mechanism. To tackle these problems, we propose a novel target-aligning prefix tree mechanism satisfying ε-LDP, for federated heavy hitter analytics. In particular, we propose an adaptive extension strategy to address the inconsistencies between covering necessary prefixes and estimating heavy hitters within a party to enhance the utility. We also present a consensus-based pruning strategy that utilizes noisy prior knowledge from other parties to further align the inconsistency between finding heavy hitters in each party and providing reasonable frequency information to identify the global ones. To the best of our knowledge, our study is the first solution to the federated heavy hitter analytics in a cross-party setting while satisfying the stringent ε-LDP. Comprehensive experiments on both real-world and synthetic datasets confirm the effectiveness of our proposed mechanism.
U-DPAP: Utility-aware Efficient Range Counting on Privacy-preserving Spatial Data Federation
Authors: Chen Yahong , Pang Xiaoyi , Li Xiaoguang , Wang Hanyi , Niu Ben , Hu Shengnan
Published: February 2025

Range counting is a fundamental operation in spatial data applications. There is a growing demand to facilitate this operation over a data federation, where spatial data are separately held by multiple data providers (a.k.a., data silos). Most existing data federation schemes employ Secure Multiparty Computation (SMC) to protect privacy, but this approach is computationally expensive and leads to high latency. Consequently, private data federations are often impractical for typical database workloads.This challenge highlights the need for a private data federation scheme capable of providing fast and accurate query responses while maintaining strong privacy.

To address this issue, we propose U-DPAP, a utility-aware efficient privacy-preserving method. It is the first scheme to exclusively use differential privacy for privacy protection in spatial data federation, without employing SMC. Moreover, it combines approximate query processing to further enhance efficiency. Our experimental results indicate that a straightforward combination of the two techniques results in unacceptable impacts on data utility. Thus, we design two novel algorithms: one to make differential privacy practical by optimizing the privacy-utility trade-off, and another to address the efficiency-utility trade-off in approximate query processing. The grouping-based perturbation algorithm reduces noise by grouping similar data and applying noise to the groups. The representative data silos selection algorithm minimizes approximate error by selecting representative silos using the similarity between data silos. We rigorously prove the privacy guarantees of U-DPAP. Moreover, experimental results demonstrate that U-DPAP enhances data utility by an order of magnitude while maintaining high communication efficiency.
CesASMe and Staticdeps: static detection of memory-carried dependencies for code analyzers
Authors: Bastian Théophile , Pompougnac Hugo , Dutilleul Alban , Rastello Fabrice
Published: February 2025

A variety of code analyzers, such as
IACA
,
uiCA
,
llvm-mca
or
Ithemal
, strive to statically predict the throughput of a computation kernel. Each analyzer is based on its own simplified CPU model reasoning at the scale of a basic block. Facing this diversity, evaluating their strengths and weaknesses is important to guide both their usage and their enhancement.

We present
CesASMe
, a fully-tooled solution to evaluate code analyzers on C-level benchmarks composed of a benchmark derivation procedure that feeds an evaluation harness. We conclude that memory-carried data dependencies are a major source of imprecision for these tools. We tackle this issue with
staticdeps
, a static analyzer extracting memory-carried data dependencies, including across loop iterations, from an assembly basic block. We integrate its output to
uiCA
, a state-of-the-art code analyzer, to evaluate
staticdeps
’ impact on a code analyzer’s precision through
CesASMe
.
Comprehensive Evaluation and Opportunity Discovery for Deterministic Concurrency Control
Authors: wang xinyuan , Li Xingchen , Peng Yun , Huang Hejiao
Published: February 2025

Deterministic concurrency control (DCC) guarantees that the same input transactions produce the same serializable result. It offers benefits in both distributed databases and blockchain systems. Dozens of DCC algorithms have emerged in the past decade. However, there is a lack of comprehensive evaluations for them.

To study the performance of existing DCC algorithms and discover further opportunities, we make the following contributions. First, we abstract five essential features from the existing DCC algorithms: generality, speculative mechanism, version strategy, batch strategy, and concurrency mode. Each distinct combination of these features corresponds to a specific algorithm. Second, we implement 13 DCC algorithms and conduct evaluations focused on their features by using 10 workloads, to conclude each feature’s strengths and weaknesses. Thirdly, based on our feature analysis, we discover opportunities for improvement in two existing DCC algorithms, resulting in performance boosts of up to 2.3x and 3.4x.
GANK: Dynamic Geometric and Appearance Features for Efficient and Robust Detection of Face Forgery
Authors: Sun Zekun , Ruan Na
Published: February 2025

Deepfakes refers to various deep-learning-based techniques that manipulate the face in videos. Maliciously manufactured face forgeries could result in serious problems such as portrait infringement, information confusion, or even public panic. Previous countermeasures focused mainly on promoting detection accuracy while relatively overlooking robustness and computational overhead. In this work, we propose an efficient and robust framework named
GANK
, which discriminates Deepfake videos through temporal modeling on decoupled geometric and appearance features. A temporal denoising technique featuring landmark tracking and Kalman filtering is introduced to optimize the feature sequences, and multi-stream Recurrent Neural Networks (RNN) are constructed for sufficient exploitation of dynamic features. Besides, we introduce two optimizations to alleviate overfitting and enhance the utilization of temporal information, including channel-wise dropout and temporal random cropping. Our framework achieves outstanding robustness using very lightweight network backbones, reaching the state-of-the-art performance on multiple benchmarks.
Improving the Summarization Effectiveness of Abstractive Datasets through Contrastive Learning
Authors: Shin Junho , Lee Younghoon
Published: February 2025

Most studies on abstractive summarization are conducted in a supervised learning framework, aiming to generate a golden summary from the original document. In this process, the model focuses on portions of the document that closely resemble the golden summary to produce a coherent output. Consequently, current methodologies tend to achieve higher performance on extractive datasets compared to abstractive datasets, indicating diminished effectiveness on more abstracted content. To address this, our study proposes a methodology that maintains high effectiveness on abstractive datasets. Specifically, we introduce a multi-task learning approach that incorporates both salient and non-salient information during training. This is implemented by adding a contrastive objective to the fine-tuning phase of an encoder–decoder language model. Salient and non-salient parts are selected based on ROUGE-L F1 scores, and their relationships are learned through a triplet loss function. The proposed method is evaluated on five benchmark summarization datasets, including two extractive and three abstractive datasets. Experimental results demonstrate significant performance improvements on abstractive datasets, particularly those with high levels of abstraction, compared to existing abstractive summarization methods.
Do Large Language Models have Spatial Cognitive Abilities?
Authors: Wu Ruoling , Guo Danhuai
Published: February 2025

Since the emergence of large language models, they have garnered significant attention from scholars and industry professionals alike. An important question that arises alongside this phenomenon is whether these large language models possess cognitive capabilities akin to humans. Spatial cognition, a crucial aspect of human cognitive capability, serves as a fundamental metric in this evaluation. This study endeavors to explore two central themes. Firstly, it seeks to ascertain whether large-scale models possess spatial cognitive capabilities. Secondly, it aims to discern optimal prompt methods for eliciting improved responses concerning spatial cognition, encompassing considerations of both result stability and accuracy. We design a series of experiments with 24 typical spatial scenes to assess whether the current array of eight popular large language models exhibits spatial cognition and examine the level of their spatial cognition proficiency. Subsequent discussions delve into strategies for enhancing the spatial cognition performance of large language models to bring them closer to human cognitive levels. Without additional prompts, the average accuracy of the eight large models in judging the three basic spatial relations (topological, direction, and distance relations) is 33.25%. After prompt optimization, the accuracy improves significantly, reaching 53.90%. Our methodological approach enabled us to systematically assess and compare these models, shedding light on their diverse capabilities in this domain. The benchmark is available at
https://github.com/LLING000/SCABenchmark
.
LLM-enhanced Multiple Instance Learning for Joint Rumor and Stance Detection with Social Context Information
Authors: Yang Ruichao , Ma Jing , Gao Wei , Lin Hongzhan
Published: February 2025

The proliferation of misinformation, such as rumors on social media, has drawn significant attention, prompting various expressions of stance among users. Although rumor detection and stance detection are distinct tasks, they can complement each other. Rumors can be identified by cross-referencing stances in related posts, and stances are influenced by the nature of the rumor. However, existing stance detection methods often require post-level stance annotations, which are costly to obtain. We propose a novel LLM-enhanced MIL approach to jointly predict post stance and claim class labels, supervised solely by claim labels, using an undirected microblog propagation model. Our weakly supervised approach relies only on bag-level labels of claim veracity, aligning with multi-instance learning (MIL) principles. To achieve this, we transform the multi-class problem into multiple MIL-based binary classification problems. We then employ a discriminative attention layer to aggregate the outputs from these classifiers into finer-grained classes. Experiments conducted on three rumor datasets and two stance datasets demonstrate the effectiveness of our approach, highlighting strong connections between rumor veracity and expressed stances in responding posts. Our method shows promising performance in joint rumor and stance detection compared to the state-of-the-art methods.