bluuebunny/crossref_metadata_2025 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.
SafeBound: A Practical System for Generating Cardinality Bounds
Authors: Deeds Kyle B. , Suciu Dan , Balazinska Magdalena
Published: May 2023

Recent work has reemphasized the importance of cardinality estimates for query optimization. While new techniques have continuously improved in accuracy over time, they still generally allow for under-estimates which often lead optimizers to make overly optimistic decisions. This can be very costly for expensive queries. An alternative approach to estimation is cardinality bounding, also called pessimistic cardinality estimation, where the cardinality estimator provides guaranteed upper bounds of the true cardinality. By never underestimating, this approach allows the optimizer to avoid potentially inefficient plans. However, existing pessimistic cardinality estimators are not yet practical: they use very limited statistics on the data, and cannot handle predicates. In this paper, we introduce SafeBound, the first practical system for generating cardinality bounds. SafeBound builds on a recent theoretical work that uses degree sequences on join attributes to compute cardinality bounds, extends this framework with predicates, introduces a practical compression method for the degree sequences, and implements an efficient inference algorithm. Across four workloads, SafeBound achieves up to 80% lower end-to-end runtimes than PostgreSQL, and is on par or better than state of the art ML-based estimators and pessimistic cardinality estimators, by improving the runtime of the expensive queries. It also saves up to 500x in query planning time, and uses up to 6.8x less space compared to state of the art cardinality estimation methods.
Efficient Approximate Nearest Neighbor Search in Multi-dimensional Databases
Authors: Peng Yun , Choi Byron , Chan Tsz Nam , Yang Jianye , Xu Jianliang
Published: May 2023

Approximate nearest neighbor (ANN) search is a fundamental search in multi-dimensional databases, which has numerous real-world applications, such as image retrieval, recommendation, entity resolution, and sequence matching. Proximity graph (PG) has been the state-of-the-art index for ANN search. However, the search on existing PGs either suffers from a high time complexity or has no performance guarantee on the search result. In this paper, we propose a novel τ-monotonic graph (τ- MG) to address the limitations. The novelty of τ-MG lies in a τ-monotonic property. Based on this property, we prove that if the distance between a query q and its nearest neighbor is less than a constant τ, the search on τ-MG guarantees to find the exact nearest neighbor of q and the time complexity of the search is smaller than all existing PG-based methods. For index construction efficiency, we propose an approximate variant of τ-MG, namely τ-monotonic neighborhood graph (τ- MNG), which only requires the neighborhood of each node to be τ-monotonic. We further propose an optimization to reduce the number of distance computations in search. Our extensive experiments show that our techniques outperform all existing methods on well-known real-world datasets.
Detecting Logic Bugs of Join Optimizations in DBMS
Authors: Tang Xiu , Wu Sai , Zhang Dongxiang , Li Feifei , Chen Gang
Published: May 2023

Generation-based testing techniques have shown their effectiveness in detecting logic bugs of DBMS, which are often caused by improper implementation of query optimizers. Nonetheless, existing generation-based debug tools are limited to single-table queries and there is a substantial research gap regarding multi-table queries with join operators. In this paper, we propose TQS, a novel testing framework targeted at detecting logic bugs derived by queries involving multi-table joins. Given a target DBMS, TQS achieves the goal with two key components: Data-guided Schema and Query Generation (DSG) and Knowledge-guided Query Space Exploration (KQE). DSG addresses the key challenge of multi-table query debugging: how to generate ground-truth (query, result) pairs for verification. It adopts the database normalization technique to generate a testing schema and maintains a bitmap index for result tracking. To improve debug efficiency, DSG also artificially inserts some noises into the generated data. To avoid repetitive query space search, KQE forms the problem as isomorphic graph set discovery and combines the graph embedding and weighted random walk for query generation. We evaluated TQS on four popular DBMSs: MySQL, MariaDB, TiDB and PolarDB. Experimental results show that TQS is effective in finding logic bugs of join optimization in database management systems. It successfully detected 115 bugs within 24 hours, including 31 bugs in MySQL, 30 in MariaDB, 31 in TiDB, and 23 in PolarDB respectively.
TreeSensing: Linearly Compressing Sketches with Flexibility
Authors: Liu Zirui , Zhang Yixin , Zhu Yifan , Zhang Ruwen , Yang Tong , Xie Kun , Wang Sha , Li Tao , Cui Bin
Published: May 2023

A Sketch is an excellent probabilistic data structure, which records the approximate statistics of data streams. Linear additivity is an important property of sketches. This paper studies how to keep the linear property after sketch compression. Most existing compression methods do not keep the linear property. We propose TreeSensing, an accurate, efficient, and flexible framework to linearly compress sketches. In TreeSensing, we first separate a sketch into two parts according to counter values. For the sketch with small counters, we propose a technique called TreeEncoding to compress it into a hierarchical structure. For the sketch with large counters, we propose a technique called SketchSensing to compress it using compressive sensing. We theoretically analyze the accuracy of TreeSensing. We use TreeSensing to compress 7 sketches and conduct two end-to-end experiments: distributed measurement and distributed machine learning. Experimental results show that TreeSensing outperforms prior art on both accuracy and efficiency, which achieves up to 100× smaller error and 5.1× higher speed than state-of-the-art Cluster-Reduce. All related codes are open-sourced.
MorphStream: Adaptive Scheduling for Scalable Transactional Stream Processing on Multicores
Authors: Mao Yancan , Zhao Jianjun , Zhang Shuhao , Liu Haikun , Markl Volker
Published: May 2023

Transactional stream processing engines (TSPEs) differ significantly in their designs, but all rely on non- adaptive scheduling strategies for processing concurrent state transactions. Subsequently, none exploit multicore parallelism to its full potential due to complex workload dependencies. This paper introduces MorphStream, which adopts a novel approach by decomposing scheduling strategies into three dimensions and then strives to make the right decision along each dimension, based on analyzing the decision trade-offs under varying workload characteristics. Compared to the state-of-the-art, MorphStream achieves up to 3.4 times higher throughput and 69.1% lower processing latency for handling real-world use cases with complex and dynamically changing workload dependencies.
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.
T-FSM: A Task-Based System for Massively Parallel Frequent Subgraph Pattern Mining from a Big Graph
Authors: Yuan Lyuheng , Yan Da , Qu Wenwen , Adhikari Saugat , Khalil Jalal , Long Cheng , Wang Xiaoling
Published: May 2023

Finding frequent subgraph patterns in a big graph is an important problem with many applications such as classifying chemical compounds and building indexes to speed up graph queries. Since this problem is NP-hard, some recent parallel systems have been developed to accelerate the mining. However, they often have a huge memory cost, very long running time, suboptimal load balancing, and possibly inaccurate results. In this paper, we propose an efficient system called T-FSM for parallel mining of frequent subgraph patterns in a big graph. T-FSM adopts a novel task-based execution engine design to ensure high concurrency, bounded memory consumption, and effective load balancing. It also supports a new anti-monotonic frequentness measure called Fraction-Score, which is more accurate than the widely used MNI measure. Our experiments show that T-FSM is orders of magnitude faster than SOTA systems for frequent subgraph pattern mining. Our system code has been released at https://github.com/lyuheng/T-FSM.
Effective and Efficient PageRank-based Positioning for Graph Visualization
Authors: Zhang Shiqi , Yang Renchi , Xiao Xiaokui , Yan Xiao , Tang Bo
Published: May 2023

Graph visualization is a vital component in many real-world applications (e.g., social network analysis, web mining, and bioinformatics) that enables users to unearth crucial insights from complex data. Lying in the core of graph visualization is the node distance measure, which determines how the nodes are placed on the screen. A favorable node distance measure should be informative in reflecting the full structural information between nodes and effective in optimizing visual aesthetics. However, existing node distance measures yield sub-par visualization quality as they fall short of these requirements. Moreover, most existing measures are computationally inefficient, incurring a long response time when visualizing large graphs. To overcome such deficiencies, we propose a new node distance measure, PDist, geared towards graph visualization by exploiting a well-known node proximity measure,personalized PageRank. Moreover, we propose an efficient algorithm Tau-Push for estimating PDist under both single- and multi-level visualization settings. With several carefully-designed techniques, TauPush offers non-trivial theoretical guarantees for estimation accuracy and computation complexity. Extensive experiments show that our proposal significantly outperforms 13 state-of-the-art graph visualization solutions on 12 real-world graphs in terms of both efficiency and effectiveness (including aesthetic criteria and user feedback). In particular, our proposal can interactively produce satisfactory visualizations within one second for billion-edge graphs.
Double-Anonymous Sketch: Achieving Top-K-fairness for Finding Global Top-K Frequent Items
Authors: Zhao Yikai , Han Wenchen , Zhong Zheng , Zhang Yinda , Yang Tong , Cui Bin
Published: May 2023

Finding top-K frequent items has been a hot topic in data stream processing in recent years, which has a wide range of applications. However, most of existing sketch algorithms focuses on finding local top-K in a single data stream. In this paper, we work on finding global top-K in multiple disjoint data streams. We find that directly deploying prior sketch algorithms is often unfair under global scenarios, which will degrade the accuracy of global top-K. We define top-K-fairness and show that it is important for finding global top-K. To achieve top-K-fairness, we propose a new sketch framework, called the Double-Anonymous sketch. The process of finding global top-K items is similar to that of paper reviewing and democratic elections. In these scenarios, double-anonymity is often an effective strategy to achieve top-K-fairness. We also propose two techniques, hot panning, and early freezing, to further improve the accuracy. We theoretically prove that the Double-Anonymous sketch achieves top-K-fairnesswhile keeping high accuracy. We perform extensive experiments to verify top-K-fairness in the scenario of disjoint data streams. The experimental results show that the Double-Anonymous sketch's error is up to 129 times (60 times on average) smaller than the state-of-the-art. All the related source code is open-sourced and available at Github.
JoinSketch: A Sketch Algorithm for Accurate and Unbiased Inner-Product Estimation
Authors: Wang Feiyu , Chen Qizhi , Li Yuanpeng , Yang Tong , Tu Yaofeng , Yu Lian , Cui Bin
Published: May 2023

Inner-product estimation is the base of many important tasks in a variety of big data scenarios, including measuring similarity of streams in data stream processing, estimating join size in database, and analyzing cosine similarity in various applications. Sketch, as a class of probability algorithms, is promising in inner-product estimation. However, existing sketch solutions suffer from low accuracy due to their neglect of the high skewness of real data. In this paper, we design a new sketch algorithm for accurate and unbiased inner-product estimation, namely JoinSketch. To improve accuracy, JoinSketch consists of multiple components, and records items with different frequency in different components. We theoretically prove that JoinSketch is unbiased, and has lower variance compared with the well-known AGMS and Fast-AGMS sketch. The experimental results show that JoinSketch improves the accuracy by 10 times in average while maintaining a comparable speed. All code is open-sourced at Github.
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.
Unicorn: A Unified Multi-tasking Model for Supporting Matching Tasks in Data Integration
Authors: Tu Jianhong , Fan Ju , Tang Nan , Wang Peng , Li Guoliang , Du Xiaoyong , Jia Xiaofeng , Gao Song
Published: May 2023

Data matching - which decides whether two data elements (e.g., string, tuple, column, or knowledge graph entity) are the "same" (a.k.a. a match) - is a key concept in data integration, such as entity matching and schema matching. The widely used practice is to build task-specific or even dataset-specific solutions, which are hard to generalize and disable the opportunities of knowledge sharing that can be learned from different datasets and multiple tasks. In this paper, we propose Unicorn, a unified model for generally supporting common data matching tasks. Unicorn can enable knowledge sharing by learning from multiple tasks and multiple datasets, and can also support zero-shot prediction for new tasks with zero labeled matching/non-matching pairs. However, building such a unified model is challenging due to heterogeneous formats of input data elements and various matching semantics of multiple tasks. To address the challenges, Unicorn employs one generic Encoder that converts any pair of data elements (a, b) into a learned representation, and uses a Matcher, which is a binary classifier, to decide whether a matches b. To align matching semantics of multiple tasks, Unicorn adopts a mixture-of-experts model that enhances the learned representation into a better representation. We conduct extensive experiments using 20 datasets on seven well-studied data matching tasks, and find that our unified model can achieve better performance on most tasks and on average, compared with the state-of-the-art specific models trained for ad-hoc tasks and datasets separately. Moreover, Unicorn can also well serve new matching tasks with zero-shot learning.
Making It Tractable to Catch Duplicates and Conflicts in Graphs
Authors: Fan Wenfei , Fu Wenzhi , Jin Ruochun , Liu Muyang , Lu Ping , Tian Chao
Published: May 2023

This paper proposes an approach for entity resolution (ER) and conflict resolution (CR) in large-scale graphs. It is based on a class of Graph Cleaning Rules (GCRs), which support the primitives of relational data cleaning rules, and may embed machine learning classifiers as predicates. As opposed to previous graph rules, GCRs are defined with a dual graph pattern to accommodate irregular structures of schemaless graphs, and adopt patterns of a star form to reduce the complexity. We show that the satisfiability, implication and validation problems are all in polynomial time (PTIME) for GCRs, as opposed to the intractability of these classical problems for previous graph dependencies. We develop a parallel algorithm to discover GCRs by combining the generations of patterns and predicates, and a parallel PTIME algorithm for "deep" ER and CR by recursively applying the mined GCRs. We show that these algorithms guarantee to reduce runtime when more processors are used. Using real-life and synthetic graphs, we experimentally verify that rule discovery and error detection with GCRs are substantially faster than with previous graph dependencies, with improved accuracy.
BALANCE: Bayesian Linear Attribution for Root Cause Localization
Authors: Chen Chaoyu , Yu Hang , Lei Zhichao , Li Jianguo , Ren Shaokang , Zhang Tingkai , Hu Silin , Wang Jianchao , Shi Wenhui
Published: May 2023

Root Cause Analysis (RCA) plays an indispensable role in distributed data system maintenance and operations, as it bridges the gap between fault detection and system recovery. Existing works mainly study multidimensional localization or graph-based root cause localization. This paper opens up the possibilities of exploiting the recently developed framework of explainable AI (XAI) for the purpose of RCA. In particular, we propose BALANCE (BAyesian Linear AttributioN for root CausE localization), which formulates the problem of RCA through the lens of attribution in XAI and seeks to explain the anomalies in the target KPIs by the behavior of the candidate root causes. BALANCE consists of three innovative components. First, we propose a Bayesian multicollinear feature selection (BMFS) model to predict the target KPIs given the candidate root causes in a forward manner while promoting sparsity and concurrently paying attention to the correlation between the candidate root causes. Second, we introduce attribution analysis to compute the attribution score for each candidate in a backward manner. Third, we merge the estimated root causes related to each KPI if there are multiple KPIs. We extensively evaluate the proposed BALANCE method on one synthesis dataset as well as three real-world RCA tasks, that is, bad SQL localization, container fault localization, and fault type diagnosis for Exathlon. Results show that BALANCE outperforms the state-of-the-art (SOTA) methods in terms of accuracy with the least amount of running time, and achieves at least 6% notably higher accuracy than SOTA methods for real tasks. BALANCE has been deployed to production to tackle real-world RCA problems, and the online results further advocate its usage for real-time diagnosis in distributed data systems.
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.
When Private Blockchain Meets Deterministic Database
Authors: Lai Ziliang , Liu Chris , Lo Eric
Published: May 2023

Private blockchain as a replicated transactional system shares many commonalities with distributed database. However, the intimacy between private blockchain and deterministic database has never been studied. In essence, private blockchain and deterministic database both ensure replica consistency by determinism. In this paper, we present a comprehensive analysis to uncover the connections between private blockchain and deterministic database. While private blockchains have started to pursue deterministic transaction executions recently, deterministic databases have already studied deterministic concurrency control protocols for almost a decade. This motivates us to propose Harmony, a novel deterministic concurrency control protocol designed for blockchain use. We use Harmony to build a new relational blockchain, namely HarmonyBC, which features low abort rates, hotspot resiliency, and inter-block parallelism, all of which are especially important to disk-oriented blockchain. Empirical results on Smallbank, YCSB, and TPC-C show that HarmonyBC offers 2.0x to 3.5x throughput better than the state-of-the-art private blockchains.
dsJSON: A Distributed SQL JSON Processor
Authors: Saeedan Majid , Eldawy Ahmed , Zhao Zhijia
Published: May 2023

The popularity of JSON as a data interchange format resulted in big amounts of datasets available for processing. Users would like to analyze this data using SQL queries but existing distributed systems limit their users to only two specific formats, JSONLine and GeoJSON. The complexity of JSON schema makes it challenging to parse arbitrary files in a modern distributed system while producing records with unified schema that can be processed with SQL. To address these challenges, this paper introduces dsJSON, a state-of-the-art distributed JSON processor that overcomes limitations in existing systems and scales to big and complex data. dsJSON introduces the projection tree, a novel data structure that applies selective parsing of nested attributes to produce records that are ready for SQL processors. The key objective of the projection tree is to parse a big JSON file in parallel to produce records with a unified schema that can be processed with SQL. dsJSON is integrated into SparkSQL which enables users to run arbitrary SQL queries on complex JSON files. It also pushes projection and filter down into the parser for full integration between the parser and the processor. Experiments on up-to two terabytes of real data show that dsJSON performs several times faster than existing systems. It can also efficiently parse extremely large files not supported by existing distributed parsers
Pontus: Finding Waves in Data Streams
Authors: Zhang Zhengxin , Li Qing , Duan Guanglin , Zhao Dan , Xiao Jingyu , Xie Guorui , Jiang Yong
Published: May 2023

The bumps and dips in data streams are valuable patterns for data mining and networking scenarios such as online advertising and botnet detection. In this paper, we define the wave, a data stream pattern with a serious deviation from the stable arrival rate for a period of time. We then propose Pontus, an efficient framework for wave detection and estimation. In Pontus, a lightweight data structure is utilized for the preliminary processing of incoming packets in the data plane to take advantage of its high processing speed; then, the powerful control plane carries out computationally intensive wave detection and estimation. In particular, we propose the Multi-Stage Progressive Tracking strategy which detects waves in stages and removes any disqualified items promptly to save memory. Hash collisions are addressed by a Stage Variance Maximization technique to reduce estimation error. Moreover, we prove the theoretical error bound and establish upper bounds of false positive and false negative. Experiment results show that the software version of Pontus can achieve around 97% F1-Score even under scarce memory when baselines fail. Furthermore, the implemented prototype of Pontus based on P4 achieves 842x higher throughput than the baseline strawman solution.
FEAST: A Communication-efficient Federated Feature Selection Framework for Relational Data
Authors: Fu Rui , Wu Yuncheng , Xu Quanqing , Zhang Meihui
Published: May 2023

Vertical federated learning (VFL) is an emerging paradigm for cross-silo organizations to build more accurate machine learning (ML) models. In this setting, multiple organizations (i.e., parties) hold the same set of samples with different features. However, different parties may have redundant or highly correlated features, leading to inefficient and ineffective VFL model training. Effective feature selection in VFL is therefore essential to mitigate such a problem and improve model effectiveness, as well as computation and communication efficiency. To this end, in this paper, we propose a federated feature selection framework, called FEAST, which leverages conditional mutual information (CMI) to select more informative features while having low redundancy. Furthermore, we design a communication-efficient method to reduce the information exchanged among the parties while protecting the parties' raw data. Extensive experiments on four real-world datasets demonstrate that the proposed framework achieves state-of-the-art performance in terms of accuracy, communication and computation costs.
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.
Initial Electrodeposition Behavior of Chromium from Hydrate-Melt Based Trivalent Chromium Baths
Authors: Katori Haruki , Kitada Atsushi , Fukami Kazuhiro , Murase Kuniaki
Published: May 2023

Trivalent chromium electrodeposition is expected to substitute the conventional hard chromium electroplating that requires harmful hexavalent chromium. Recently, we revealed that crystalline chromium, which is effective for hard chromium properties, can be electrodeposited from trivalent chromium baths using chloride-based hydrate-melts. Herein, we investigated the initial behavior of the trivalent chromium electrodeposition by in situ analyses using electrochemical quartz crystal microbalance (EQCM) and ex situ characterization of resulting electrodeposits. In the very initial stage of electrolysis, proton reduction proceeds preferentially, resulting in chromium hydroxide precipitation on the electrode due to the local pH increase. Chromium reduction was found to require a few seconds of induction time to start. The transient was interpreted by the Sand equation which also indicated proton depletion near the cathode. In the hydrate-melts, due to the depletion of free water, the high proton mobility due to Grotthuss mechanism is lost, resulting in the suppression of hydrogen evolution after the induction time. This explains why chromium electrodeposits are obtained at extremely high current efficiencies of 60%–80%. Additionally, the proton reduction of the initial electrolysis stage may lead to negative effects, for example, impairing adhesion of chromium electrodeposits.
Review—Electrochemical Biosensors for Interleukins: Electrode Materials
Authors: Yahia Yahia A. , Tawfik Mohamed W. , Fritzsche Wolfgang , M. E. Azzazy Hassan
Published: May 2023

Interleukins gained great interest as potential biomarkers for autoimmune diseases, cancers, and viral infections. Challenges faced in the detection of interleukins include their minute concentrations in biological specimens, the need to generate quantitative results, and multi-analyte measurement for differential diagnosis. The properties of the material of the working electrode are crucial for signal transduction. Consequently, different working electrode materials were investigated to select the ones which generate the highest signal and ensure the reproducibility of the results and the robustness of the biosensor. In this review, the interleukin electrochemical biosensors developed since 2015 were classified according to the material of the working electrode into noble metals, ceramics, and carbon-based materials. The advantages and limitations of each working electrode material are discussed. Designs and performance criteria of interleukin electrochemical biosensors developed since 2015 are compared with reference to the type of working electrode material including those made of hybrid ones.
The Effects of Ionic Liquid Addition on the Heat of Polymerization of Additive Manufacturing Resins
Authors: Dobyns Breanna M. , Reichert W. Matthew , Duranty Edward
Published: May 2023

Ionic liquids (ILs) are utilized for a wide range of applications today due to their low vapor pressure, near infinite combination of cations and anions with varying chemical functionalities, organic and inorganic species solubility, as well as their superb thermal and chemical stabilities. In this work, four ILs are used as additives for 3D printing resins to add flexibility to the final printed parts. The ILs of interest are 1-n-butyl-3-methylimidazole bis(trifluoromethanesulfonyl)imide ([C4mim][NTf2]) and 2-(1-methylimidazolium)-ethyl methacrylate chloride ([mimEMA][Cl]), which were synthesized in-house, as well as tetraethylammonium bis(trifluoromethanesulfonyl)imide ([TEA][NTf2]) and Butyltrimethylammonium bis(trifluoromethanesulfonyl)imide ([BTMA][NTf2], which were purchased and used as received. These ILs were added to commercial Formlabs CV4 resin from 10 to 50 wt%. The change in temperature of polymerization is characterized via container-less acoustic levitation and FLIR thermography and the heat capacity is characterized with differential scanning calorimetry (DSC). From these two measurements, the enthalpy of polymerization was calculated via a simple equation relating the change in droplet temperature to the reaction enthalpy equation and compared to literature values. The degradation temperature of each was also characterized via thermal gravimetric analysis (TGA). Each IL lowered the enthalpy of polymerization while the substances with [NTf2] counterions raised the decomposition temperature and the only substance containing Cl anions, [mimEMA][Cl], lowered the decomposition temperature.
Tuning the Selectivity of Electrocatalytic CO2 Reduction Reaction via Anion-driven Nanostructuring of Copper Electrodeposits
Authors: Suresh Cini M. , Mudgil Manjeet , Choudhari Prafulla Prabhakarrao , Ingole Pravin P.
Published: May 2023

Electrochemical CO2 reduction (ECR) to value-added products is one of the potential ways to utilise CO2 as a feedstock, thereby decreasing its level in the atmosphere as it has harmful repercussions on planet Earth. Copper (Cu)-nanostructures have demonstrated a great potential to convert CO2 into valuable higher-end hydrocarbons electrochemically but with poor selectivity. Therefore, novel strategies to tune Cu-based electrocatalysts’ activity and selectivity toward multi-carbon products, particularly at low overpotential, are highly desirable. In the present work, we report an atom-economic strategy to tune the physicochemical properties and the electrocatalytic activity of Cu-nanostructures towards ECR. The Cu-nanostructures synthesized via pulse electrodeposition from an electrolyte bath containing Cu-precursor salts with varying anions (viz. acetates, nitrates, sulphates, and chlorides) are investigated for their effect on the physicochemical properties and the ECR performance. The Cu-electrodeposits from Cu-chloride, having cubic morphology, exposed Cu(100) facets, higher Cu+ content and enhanced electrochemical active surface area demonstrated the best ECR performance depicting good selectivity for ethylene formation.
Hematologic Cancer Detection Using White Blood Cancerous Cells Empowered with Transfer Learning and Image Processing
Authors: Nasir Muhammad Umar , Khan Muhammad Farhan , Khan Muhammad Adnan , Zubair Muhammad , Abbas Sagheer , Alharbi Meshal , Akhtaruzzaman Md
Published: May 2023

Lymphoma and leukemia are fatal syndromes of cancer that cause other diseases and affect all types of age groups including male and female, and disastrous and fatal blood cancer causes an increased savvier death ratio. Both lymphoma and leukemia are associated with the damage and rise of immature lymphocytes, monocytes, neutrophils, and eosinophil cells. So, in the health sector, the early prediction and treatment of blood cancer is a major issue for survival rates. Nowadays, there are various manual techniques to analyze and predict blood cancer using the microscopic medical reports of white blood cell images, which is very steady for prediction and causes a major ratio of deaths. Manual prediction and analysis of eosinophils, lymphocytes, monocytes, and neutrophils are very difficult and time-consuming. In previous studies, they used numerous deep learning and machine learning techniques to predict blood cancer, but there are still some limitations in these studies. So, in this article, we propose a model of deep learning empowered with transfer learning and indulge in image processing techniques to improve the prediction results. The proposed transfer learning model empowered with image processing incorporates different levels of prediction, analysis, and learning procedures and employs different learning criteria like learning rate and epochs. The proposed model used numerous transfer learning models with varying parameters for each model and cloud techniques to choose the best prediction model, and the proposed model used an extensive set of performance techniques and procedures to predict the white blood cells which cause cancer to incorporate image processing techniques. So, after extensive procedures of AlexNet, MobileNet, and ResNet with both image processing and without image processing techniques with numerous learning criteria, the stochastic gradient descent momentum incorporated with AlexNet is outperformed with the highest prediction accuracy of 97.3% and the misclassification rate is 2.7% with image processing technique. The proposed model gives good results and can be applied for smart diagnosing of blood cancer using eosinophils, lymphocytes, monocytes, and neutrophils.
Experimental Study of Penetration Capability of an Annular-Shaped Charge under Multipoint Synchronous Initiation
Authors: Jie Liu , Qing Hong , Jiandong Gao , Xi Chen , Zhonghua Du
Published: May 2023

The large-distance penetration capability of an annular-shaped charge under multipoint synchronous initiation was experimentally investigated. For different annular charge structures and different multipoint initiation settings, incident angle, collision type of detonation waves, and overpressure on the shaped charge’s liner at adjacent initiation points were analyzed using the detonation wave propagation theory. Furthermore, the shaped charge’s penetration capability at a standoff distance of 1–15 m from a 20 mm thick 45# steel plate (the target) was determined through penetration experiments. It was found that the annular cutter formed by the 500 mm diameter annular shaped charge (charge width: 100 mm) completely penetrated the target for a large distance, namely a distance of 150 times the charge width. The ratio of the width of the ring gap penetrated by the annular cutter to the liner’s initial radial width was about 1.07; this indicated that the annular cutter’s width was equivalent to the liner’s radial width for the above flight distance, which in turn implied that the annular cutter had superior circular degree. The annular cutter formed in the case of Mach reflection had better circular degree, integrity, and penetration capability than that formed by regular reflection.
Psychometric Evaluation of the Postoperative Recovery Profile
Authors: Jakobsson Jenny
Published: May 2023

Aim. To further evaluate the postoperative recovery profile regarding its psychometric properties. Background. The postoperative recovery profile is an instrument for the self-assessment of general postoperative recovery that has received increased attention within nursing research. However, psychometric evaluation during development was sparse. Design. Psychometric evaluation was done using classical test theory. Method. Data quality, targeting, reliability, and scaling assumptions were measured. In addition, confirmatory factor analysis was used to evaluate construct validity. Data collection was made during 2011–2013. Result. Data derived from this study showed acceptable quality; however, item distribution was skewed, with ceiling effects in the majority of items. Cronbach’s alpha showed high internal consistency. Item-total correlations indicated unidimensionality, whereas six items demonstrated high correlations pointing at redundancy. The confirmatory factor analysis confirmed problems related to dimensionality as the five proposed dimensions were highly correlated with each other. Furthermore, items were largely uncorrelated with the designated dimensions. Conclusion. This study shows that the postoperative recovery profile needs to be further developed to serve as a robust instrument within nursing as well as medical research. Arguably, values from the instrument should not be calculated at a dimensional level for the time being because of discriminant validity issues.
Control-IQ Technology Positively Impacts Patient Reported Outcome Measures and Glycemic Control in Youth with Type 1 Diabetes in a Real-World Setting
Authors: Zuijdwijk Caroline , Courtney Jennilea , Mitsakakis Nicholas , Hayawi Lamia , Sutherland Stephanie , Newhook Dennis , Ahmet Alexandra , Goldbloom Ellen B. , Khatchadourian Karine , Lawrence Sarah
Published: May 2023

Objective. To determine the impact of the t:slim X2 insulin pump with Control-IQ technology on the quality of life and glycemic control in youth with type 1 diabetes (T1D) and their parents in a real-world setting. Research Design and Methods. We conducted a single-center, prospective study on pediatric patients (6–18 years old) with T1D using a Tandem t:slim X2 pump and initiating Control-IQ technology as part of routine care. Youth (≥8 years) and parents completed validated patient-reported outcome measures (PROMs) at baseline and the end of the study (16 weeks). Glycemic control measures were recorded at baseline and every 4 weeks until the end of the study. Results. Fifty-nine youth participated; the median (IQR) age was 13.8 (11.1, 15.7) years, and T1D duration was 6.3 (3.1, 8.4) years. INSPIRE scores (evaluating expectations (baseline) and impact (post) of Control-IQ technology) were favorable, unchanged at the end of the study for youth, and lower for parents (

p
=
0.04

). Other PROM scores improved by the end of the study with mean (95% CI) differences for youth and parents, respectively, as follows: Diabetes Impact and Device Satisfaction (DIDS) Scale Diabetes Impact −1.08 (−1.51, −0.64) (

p
<
0.001

) and −1.41 (−1.96, −0.87) (

p
<
0.001

); DIDS Scale Device Satisfaction +0.43 (0.11, 0.74) (

p
=
0.01

) and +0.58 (0.31, 0.85) (

p
<
0.001

); Hypoglycemia Fear Survey −4.41 (−7.65, −1.17) (

p
=
0.01

) and −7.64 (−11.66, −3.62) (

p
<
0.001

); and WHO-5 Well-Being Index +5.10 (−1.40, 11.6) (

p
=
0.12

) and +9.60 (3.40, 15.8) (

p
=
0.003

). The mean time in range increased from 52.6% at baseline to 62.6% (

p
<
0.001

) at 4 weeks, sustained to 16 weeks. Conclusions. Initiation of Control-IQ technology in a real-world setting significantly reduced the impact of diabetes on daily life while simultaneously improving glycemic control. Trial Registration. This trial is registered with ClinicalTrials.gov Identifier NCT04838561 (https://www.clinicaltrials.gov/ct2/show/NCT04838561?term=Control-IQ&cond=Type+1+Diabetes&cntry=CA&draw=2&rank=1).