bluuebunny/crossref_metadata_2025_split huggingface dataset:
SplinterDB and Maplets: Improving the Tradeoffs in Key-Value Store Compaction Policy
Authors: Conway Alex , Farach-Colton Martín , Johnson Rob 
Published: May 2023

A critical aspect of modern key-value stores is the interaction between compaction policy and filters. Aggressive compaction reduces the on-disk footprint of a key-value store and can improve query performance, but can reduce insertion throughput because it is I/O and CPU expensive. Filters can mitigate the query costs of lazy compaction, but only if they fit in RAM, limiting the scalability of queries with lazy compaction. And, with fast storage devices, the CPU costs of querying filters in a lazy compacting system can be significant.
In this work, we present Mapped SplinterDB, a key-value store that achieves excellent insertion performance, query performance, space efficiency, and scalability by replacing filters with maplets, space-efficient data structures that act as lossy maps with false positives. Critically, we use quotient maplets, which can be merged and resized without access to the underlying data, enabling us to decouple compaction of the data from compaction of the quotient maplets. Thus Mapped SplinterDB can compact data lazily and quotient maplets aggressively, so that each level has multiple sorted runs of data but only one quotient maplet. Quotient maplets are so small that compacting them aggressively is still cheaper than compacting the (much larger) data lazily, so overall we get the insertion performance of a lazily compacted system. And, since there is only one quotient maplet to query on each level, we get the query performance of an aggressively compacted system. Furthermore, quotient maplets can accelerate queries even when they don't fit in RAM, improving scalability to huge datasets. We also show how to use quotient maplets to estimate when a compaction could resolve a high density of updates, enabling Mapped SplinterDB to perform targeted compactions for space recovery.
In our benchmarks, Mapped SplinterDB matches the insertion performance of SplinterDB, a state-of-the-art lazily compacted system, and beats RocksDB, an aggressive compacting system, by up to 9×. On queries, Mapped SplinterDB outperforms SplinterDB and RocksDB by up to 89% and 83%, respectively, and scales gracefully to huge datasets. Mapped SplinterDB is able to dynamically trade update performance for space efficiency, resulting in space overheads on update-heavy workloads as low as 15-61%, whereas RocksDB had 80-117% and SplinterDB had up to 137% space overhead.
Runtime Variation in Big Data Analytics
Authors: Zhu Yiwen , Sen Rathijit , Horton Robert , Agosta John Mark 
Published: May 2023

The dynamic nature of resource allocation and runtime conditions on Cloud can result in high variability in a job's runtime across multiple iterations, leading to a poor experience. Identifying the sources of such variation and being able to predict and adjust for them is crucial to cloud service providers to design reliable data processing pipelines, provision and allocate resources, adjust pricing services, meet SLOs and debug performance hazards.
In this paper, we analyze the runtime variation of millions of production Scope jobs on Cosmos, an exabyte-scale internal analytics platform at Microsoft. We propose an innovative 2-step approach to predict job runtime distribution by characterizing typical distribution shapes combined with a classification model with an average accuracy of >96%, using an innovative interpretable machine-learning algorithm out-performing traditional regression models and better capturing long tails. We examine factors such as job plan characteristics and inputs, resource allocation, physical cluster heterogeneity and utilization, and scheduling policies.
To the best of our knowledge, this is the first study on predicting categories of runtime distributions for enterprise analytics workloads at scale. Furthermore, we examine how our methods can be used to analyze what-if scenarios, focusing on the impact of resource allocation, scheduling, and physical cluster provisioning decisions on a job's runtime consistency and predictability.
Regularized Pairwise Relationship based Analytics for Structured Data
Authors: Luo Zhaojing , Cai Shaofeng , Wang Yatong , Ooi Beng Chin 
Published: May 2023

In line with the increasing machine learning model inference accuracy, deep learning (DL) models have been increasingly applied to structured data for a wide spectrum of real-world applications, including product recommendations, online advertisement, healthcare analytics and risk analysis. However, unlike unstructured data, structured data is high-dimensional and sparse and therefore engenders a large number of parameters in DL, making DL models more prone to overfitting. To alleviate the overfitting problem, various regularization methods have been designed to constrain the model parameters as a means to control the model complexity. Unfortunately, these methods are often restricted to regularizing the parameter values directly without considering the intrinsic correlations and dependencies between attribute fields of structured data which is however key to effective structured data modeling.
In this paper, we re-examine DL for structured data from a new perspective of attribute interactions. In particular, we seek to explicitly model and regularize the pairwise relationships between attribute fields of structured data, in a field-adaptive manner, via a proposed attentive and interpretable framework called ATT-Reg. Specifically, in this framework, a set of attentive weight matrices are introduced to each attribute field for modeling obviously different relationships with its neighboring attribute fields. Further, we derive from the Bayesian viewpoint a novel Attentive Regularization method for imposing adaptive regularization strengths on different pairs of attribute fields, based on the informativeness of their relationship, which is calculated using both data-driven information and functional dependency (FD) knowledge. Such adaptive regularization facilitates each attribute field to learn discriminative and diversified representations for more effective predictive analytics. We also develop a feature attribution method for supporting more interpretable predictions We validate the effectiveness of our ATT-Reg on six real-world datasets. Extensive experimental results show that ATT-Reg achieves significant improvement over state-of-the-art graph models, attentive models as well as regularization methods and supports an excellent degree of interpretation.
Efficient Tree-SVD for Subset Node Embedding over Large Dynamic Graphs
Authors: Du Xinyu , Zhang Xingyi , Wang Sibo , Huang Zengfeng 
Published: May 2023

Subset embedding is the task to learn low-dimensional representations for a subset of nodes according to the graph topology. It has applications when we focus on a subset of users, e.g., young adults, and aim to make better recommendations for these target users. In real-world scenarios, graphs are dynamically changing. Thus, it is more desirable to dynamically maintain the subset embeddings to reflect graph updates. The state-of-the-art methods, e.g., DynPPE, still adopt a hashing-based method, while hashing-based solutions are shown to be less effective than matrix factorization (MF)-based methods in existing studies. At the same time, MF-based methods in the literature are too expensive to update the embedding when the graph changes, making them inapplicable on dynamic graphs.
Motivated by this, we present Tree-SVD, an efficient and effective MF-based method for dynamic subset embedding. If we simply maintain the whole proximity matrix, then we need to re-do the MF, e.g., truncated Singular Value Decomposition (SVD), on the whole matrix after graph updates, which is prohibitive. To tackle this issue, our main idea is to do hierarchical SVD (HSVD) on the proximity matrix of the given subset, which vertically divides the proximity matrix into multiple sub-matrices, and then repeatedly do SVD on sub-matrices and merge the intermediate results to obtain the final embedding. We first present Tree-SVD, which combines a sparse randomized SVD with an HSVD. Our theoretical analysis shows that our Tree-SVD gains the efficiency of sparse randomized SVD and the flexibility of the HSVD with theoretical guarantees. To further reduce update costs, we present a lazy-update strategy. In this strategy, we only update sub-matrices that changes remarkably in terms of the Frobenius norm. We present theoretical analysis to show the guarantees with our lazy-update strategy. Extensive experiments show the efficiency and effectiveness of Tree-SVD on node classification and link prediction tasks.
FlexMoE: Scaling Large-scale Sparse Pre-trained Model Training via Dynamic Device Placement
Authors: Nie Xiaonan , Miao Xupeng , Wang Zilong , Yang Zichao , Xue Jilong , Ma Lingxiao , Cao Gang , Cui Bin 
Published: May 2023

With the increasing data volume, there is a trend of using large-scale pre-trained models to store the knowledge into an enormous number of model parameters. The training of these models is composed of lots of dense algebras, requiring a huge amount of hardware resources. Recently, sparsely-gated Mixture-of-Experts (MoEs) are becoming more popular and have demonstrated impressive pretraining scalability in various downstream tasks. However, such a sparse conditional computation may not be effective as expected in practical systems due to the routing imbalance and fluctuation problems. Generally, MoEs are becoming a new data analytics paradigm in the data life cycle and suffering from unique challenges at scales, complexities, and granularities never before possible.
In this paper, we propose a novel DNN training framework, FlexMoE, which systematically and transparently address the inefficiency caused by dynamic dataflow. We first present an empirical analysis on the problems and opportunities of training MoE models, which motivates us to overcome the routing imbalance and fluctuation problems by a dynamic expert management and device placement mechanism. Then we introduce a novel scheduling module over the existing DNN runtime to monitor the data flow, make the scheduling plans, and dynamically adjust the model-to-hardware mapping guided by the real-time data traffic. A simple but efficient heuristic algorithm is exploited to dynamically optimize the device placement during training. We have conducted experiments on both NLP models (e.g., BERT and GPT) and vision models (e.g., Swin). And results show FlexMoE can achieve superior performance compared with existing systems on real-world workloads --- FlexMoE outperforms DeepSpeed by 1.70x on average and up to 2.10x, and outperforms FasterMoE by 1.30x on average and up to 1.45x.
Oncogenic Transformation Drives DNA Methylation Loss and Transcriptional Activation at Transposable Element Loci
Authors: Kanholm Tomas , Rentia Uzma , Hadley Melissa , Karlow Jennifer A. , Cox Olivia L. , Diab Noor , Bendall Matthew L. , Dawson Tyson , McDonald James I. , Xie Wenbing , Crandall Keith A. , Burns Kathleen H. , Baylin Stephen B. , Easwaran Hari , Chiappinelli Katherine B. 
Published: May 2023

Abstract

Transposable elements (TE) are typically silenced by DNA methylation and repressive histone modifications in differentiated healthy human tissues. However, TE expression increases in a wide range of cancers and is correlated with global hypomethylation of cancer genomes. We assessed expression and DNA methylation of TEs in fibroblast cells that were serially transduced with hTERT, SV40, and HRASR24C to immortalize and then transform them, modeling the different steps of the tumorigenesis process. RNA sequencing and whole-genome bisulfite sequencing were performed at each stage of transformation. TE expression significantly increased as cells progressed through transformation, with the largest increase in expression after the final stage of transformation, consistent with data from human tumors. The upregulated TEs were dominated by endogenous retroviruses [long terminal repeats (LTR)]. Most differentially methylated regions (DMR) in all stages were hypomethylated, with the greatest hypomethylation in the final stage of transformation. A majority of the DMRs overlapped TEs from the RepeatMasker database, indicating that TEs are preferentially demethylated. Many hypomethylated TEs displayed a concordant increase in expression. Demethylation began during immortalization and continued into transformation, while upregulation of TE transcription occurred in transformation. Numerous LTR elements upregulated in the model were also identified in The Cancer Genome Atlas datasets of breast, colon, and prostate cancer. Overall, these findings indicate that TEs, specifically endogenous retroviruses, are demethylated and transcribed during transformation.

Significance:
Analysis of epigenetic and transcriptional changes in a transformation model reveals that transposable element expression and methylation are dysregulated during oncogenic transformation.
An Old Dog Has a New Trick: Somatic Exonic Deletions inRUNX1Are Frequent in AML
Authors: Chakraborty Joyeeta , Stengel Kristy R. 
Published: May 2023

SummarySomatic loss-of-function RUNX1 mutations in acute myeloid leukemia (AML) include missense, nonsense, and frameshift mutations, whereas germline RUNX1 variants in RUNX1-FPDMM also include large exonic deletions. Alternative variant detection approaches revealed that large exonic deletions in RUNX1 are also common in sporadic AML, which has implications for patient stratification and therapeutic decision-making.See related article by Eriksson et al., p. 2826
Mirar de cerca el horror. Los ejércitos de Evelio Rosero y Laberinto de Eduardo Antonio Parra
Authors: Oliver Felipe 
Published: May 2023

Las novelas Los ejércitos (2007) del colombiano Evelio Rosero y Laberinto (2019) del mexicano Eduardo Antonio Parra narran la devastación de toda una comunidad a manos de grupos armados vinculados con el crimen organizado. Desde la mirada de un profesor escolar, ambas obras revelan la indefensión de la población civil ante la violencia aparatosa e insensible que se cierne sobre ellos. Este trabajo analiza desde la mirada la representación de la violencia presente en ambas novelas. ¿Cómo o de qué manera la acción de mirar vulnera implica y expone a los testigos de la masacre? ¿Cuál es el valor o la funcionalidad del maestro escolar como testigo privilegiado de la destrucción de la comunidad?
Role of Family Risk and of Pre-Reading Auditory and Neurostructural Measures in Predicting Reading Outcome
Authors: Blockmans Lauren , Golestani Narly , Dalboni da Rocha Josué Luiz , Wouters Jan , Ghesquière Pol , Vandermosten Maaike 
Published: May 2023

Abstract
Some children who develop dyslexia show pre-reading auditory and speech processing difficulties. Furthermore, left auditory cortex structure might be related to family risk for dyslexia rather than to reading outcome. However, it remains unclear to what extent auditory and speech processing and auditory cortex structure mediate the relationship between family risk and reading. In the current longitudinal study, we investigated the role of family risk (measured using parental reading questionnaires) and of pre-reading auditory measures in predicting third grade word reading. We measured auditory and speech processing in 162 pre-readers varying in family risk. In 129 of them, we also acquired structural magnetic resonance imaging (MRI). We quantified surface area and duplication patterns of the bilateral transverse temporal gyri (TTG(s)), and surface area of the bilateral planum temporale (PT). We found effects of pre-reading auditory and speech processing, surface area of the left first TTG and of bilateral PT and of left TTG duplication pattern on later reading. Higher pre-reading values on these measures were predictive of better word reading. Although we also found some evidence for an effect of family risk on auditory and speech processing, these latter measures did not mediate the strong relationship between family risk and later reading. Our study shows the importance of pre-reading auditory and speech processing and of auditory cortex anatomy for later reading. A better understanding of such interrelations during reading development will facilitate early diagnosis and intervention, which can be especially important given the continuity of family risk in the general population.
Impact of HAT2CH2 Score on the Development of No-Reflow Phenomenon in Patients With ST-Segment Elevation Myocardial Infarction
Authors: Harbalıoğlu Hazar , Genç Ömer , Alıcı Gökhan , Quisi Alaa , Yıldırım Abdullah 
Published: May 2023

The HAT2CH2 score [Hypertension (1 point), Age > 75 years (1 point), Stroke/Transient ischemic attack (2 points), Chronic obstructive pulmonary disease (1 point), and Heart failure (2 points)] was originally developed to predict the occurrence of new-onset atrial fibrillation. The aim of the present study was to examine whether this score could predict the development of no-reflow phenomenon (NR) in ST-segment elevation myocardial infarction (STEMI) patients undergoing primary percutaneous coronary intervention (pPCI). Patients (n = 1552) with STEMI were consecutively enrolled in this single-center retrospective study. The SYNTAX score (SXscore) and HAT2CH2 score were calculated. The presence of thrombolysis in myocardial infarction (TIMI) score ≤2, without significant residual stenosis and mechanical obstruction, indicated the presence of NR. The HAT2CH2 score was significantly higher in the NR (+) group compared with the NR (−) group [2.29 ± 1.43 vs 1.46 ± 1.24, p < .001]. In multivariable logistic regression analysis, the HAT2CH2 score [OR = 1.585, p < .001] and SXscore [OR = 1.028, p = .017] were found to be independent predictors of NR. Receiver operating characteristic curve analysis showed that the HAT2CH2 score with a cutoff value of >2 determined NR, with 50.2% sensitivity and 79.4% specificity (AUC = .669, p < .001). In conclusion, the HAT2CH2 score may facilitate risk stratification in estimating NR in STEMI patients undergoing pPCI.
Effects of nanosecond laser ablation parameters on surface modification of carbon fiber reinforced polymer composites
Authors: İplikçi Hande , Barisik Murat , Türkdoğan Ceren , Martin Seçkin , Yeke Melisa , Nuhoğlu Kaan , Esenoğlu Gözde , Tanoğlu Metin , Aktaş Engin , Dehneliler Serkan , İriş Mehmet Erdem 
Published: May 2023

Removal of contaminants and top polymer layer from the surface of carbon-fiber-reinforced polymer (CFRP) composites is critical for high-quality adhesive-joining with direct bonding to the reinforcing fiber constituents. Surface treatment with a laser beam provides selective removal of the polymer matrix without damaging the fibers and increasing the wettability. However, inhomogeneous thermal properties of CFRP make control of laser ablation difficult as the laser energy absorbed by the carbon fibers is converted into heat and transmitted through the fiber structures during the laser operation. In this study, the effect of scanning speed and laser power on nanosecond laser surface treatment was characterized by scanning electron microscope images and wetting angle measurements. Low scanning speeds allowed laser energy to be conducted as thermal energy through the fibers, which resulted in less epoxy matrix removal and substantial thermal damage. Low laser power partially degraded the epoxy the surface while the high power damaged the carbon fibers. For the studied CFRP specimens consisting of unidirectional [45/0/−45/90]2s stacking of carbon/epoxy prepregs (HexPly®-M91), 100 mJ/mm2 generated by 10 m/s scanning speed and 30 W power appeared as optimum processing parameters for the complete removal of epoxy matrix from the top surface with mostly undamaged carbon fibers and super hydrophilic surface condition.
Performance analysis of recycled carbon fiber under recycling process parameters optimized using response surface methodology
Authors: Cheng Huanbo , Wang Huafeng , Zhou Jinhu , Guo Lijun , Wang Qingze , Tang Mingxi 
Published: May 2023

The recycling of high-performance carbon fiber from carbon fiber-reinforced polymer (CFRP) wastes have great economic value and environmental significance. Based on the principle of thermally activated oxide semiconductors, the resin matrix decomposition of process model was established by response surface methodology (RSM). The morphology, elements, functional groups and mechanical properties of recycled carbon fiber were investigated. The results indicated that the degradation was positively correlated with time and temperature, and the degree of influence was significant, the effect of O2 flow rate and concentration were not significant. The influence of process parameters on resin decomposition was as follows shown: temperature > time > O2 concentration > O2 flow rate. The actual degradation was 96.12 wt.% under temperature of 520°C, time of 23 min, O2 concentration of 80% and flow rate of 180 mL/min. The recycled carbon fiber (rCF) was compared with the original carbon fiber the surface roughness of rCF was increased, and without carbon deposition on the surface. The content of C element and C–C bond on the surface of rCF were significantly increased, and functional group of COOH was produced. The tensile strength of rCF was maintained above 99%, the Young’s modulus was maintained at 92%, and the interfacial shear strength was maintained at 85%.
Integrative analysis of tertiary lymphoid structures and immune microenvironment in patients with esophageal carcinoma
Authors: Yao Yuanshan , Xuan Haojie , Wang Jing , Gong Libao , Gao Wen 
Published: May 2023

Background: Esophageal squamous cell carcinoma (ESCC) is one of the most common upper gastrointestinal malignancies worldwide. Tertiary lymphoid structures (TLS) are tumor-infiltrating immune cells aggregates coupled with stromal cells which are similar to secondary lymphoid organs. The objective of this study is to explore the predictive effects of two common genes associated with TLS models on prognosis and immunotherapy effects in ESCC patients. Methods: Clinical information for ESCC patients in the TCGA(The Cancer Genome Altas) cohort and GSE 53625 were collected. All of the samples were classified as either high score group or low score group based on two TLS signatures, and the association between TLS signatures and survival, clinical indicators, genomic burden, stemness indices analysis, tumor microenvironment and immunotherapy response were performed. Furthermore, the mature TLS was also assessed in ESCC tissue microarray. Results: In our study, we quantified the score of TLS_9 and TLS_12, respectively, reflecting the different statuses of TLS (TLS_9 = B and T cells in TLSs; TLS_12 = neogenesis of TLSs). Subsequently, we explored the effect of TLS score on ESCC tumor microenvironment quantified by multiple algorithms. We found that a correlation analysis indicated that TLS_9 and TLS_12 were all positively correlated with CD8+ T cell, NK cells, CD4+ T cells, M1 macrophages and so on. Meanwhile, some cells present a different correlation pattern of TLS_9 and TLS_12, including activated CD4+ memory T cells and Tgd cells. Immune-related analysis revealed that the TLS_12 and TLS_9 scores were all positively correlated with immune dysfunction, yet negatively correlated with immune exclusion. Following this, the biological roles of TLS_9 and TLS_12 scores were investigated. Also, we noticed that the TLS score could significantly affect the CAFs infiltration and be associated with the genomic burden and tumor stemness. In addition, we explored the prognostic value of mature TLS through tissue microarray (TMA). Our result displayed ESCC patients with the presence of mature TLS had a better prognosis than ESCC patients without it. Conclusions: Our study indicated that ESCC patients with the presence of TLS had better outcomes and an inflamed immune microenvironment. In addition, both TLS-9 and TLS-12 gene signatures could be used as potential biomarkers for the immunotherapy of ESCC patients.