Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Radar Signal Feature Extraction Algorithm Based on Zernike Moment Bi-spectrum

The phase of radar signal can reflect the intra-pulse modulation feature.The phase information can be retained completely while extracting the radar signal features by means of bi-spectrum.Aiming at the problems of the current bi-spectrum feature extraction methods such as ordinary bi-spectrum,cross-items and redundancy feature dimensions,this paper puts forward a new extraction method of radar signal intra-pulse feature based on bi-spectrum secondary feature.Firstly,the bi-spectrum is converted to gray image and the amplitude of bi-spectrum is denoted by gray value;secondly,the bi-spectrum secondary feature is extracted by means of image processing technique,in order to extract the Zernike orthogonal moment feature set of different orders to reflect the complicated radar signal intra-pulse features;finally,the Zernike moment feature set is compared with Hu-invariant moment feature set which is widely used for feature extraction,and the result shows that the algorithm presented in this paper has better performance of radar signal classification and recognition.
Developmental Alterations in Brain Network Asymmetry in 3- to 9-Month Infants with Congenital Sensorineural Hearing Loss

Auditory exposure plays crucial roles in shaping healthy brain development and generating lateralization of functional network organization. However, little is known about whether and how an initial lack of auditory exposure in early infancy may disrupt development of functional network lateralization. We addressed this issue by recruiting 55 infants with congenital sensorineural hearing loss (SNHL) and 60 typically developing (TD) controls. Resting-state fNIRS imaging data were acquired to construct hemispheric cerebral networks, and graph theory was applied to quantify the topological characteristics of hemispheric networks. The infants with SNHL exhibited efficient small-world characteristic within each hemispheric network, however, the lateralization of functional network efficiency was substantially disrupted. Compared with TD infants with significantly increased network efficiency lateralized toward left hemisphere with age, the SNHL infants did not exhibit the emergence and development of such cerebral lateralization. Furthermore, the increased leftward asymmetry in nodal efficiency with age was found in TD but not in SNHL infants. Interestingly, the degree of hearing loss had no correlation with lateralization strength in the SNHL group. These results suggest that SNHL infants exhibited disrupted development of cortical lateralization in functional network organization, and highlight the importance of auditory stimulation-promoted multisensory functional integration in early infancy.
Pore-forming activity of S. pneumoniae pneumolysin disrupts the paracellular localization of the epithelial adherens junction protein E-cadherin

ABSTRACT Streptococcus pneumoniae, a common cause of community-acquired bacterial pneumonia, can cross the respiratory epithelial barrier to cause lethal septicemia and meningitis. S. pneumoniae pore-forming toxin pneumolysin (PLY) triggers robust neutrophil (PMN) infiltration that promotes bacterial transepithelial migration in vitro and disseminated disease in mice. Apical infection of polarized respiratory epithelial monolayers by S. pneumoniae at a multiplicity of infection (MOI) of 20 resulted in recruitment of PMNs, loss of 50% of the monolayer, and PMN-dependent bacterial translocation. Reducing the MOI to 2 decreased PMN recruitment two-fold and preserved the monolayer, but apical-to-basolateral translocation of S. pneumoniae remained relatively efficient. At both MOI of 2 and 20, PLY was required for maximal PMN recruitment and bacterial translocation. Co-infection by wild-type S. pneumoniae restored translocation by a PLY-deficient mutant, indicating that PLY can act in trans. Investigating the contribution of S. pneumoniae infection on apical junction complexes in the absence of PMN transmigration, we found that S. pneumoniae infection triggered the cleavage and mislocalization of the adherens junction (AJ) protein E-cadherin. This disruption was PLY-dependent at MOI of 2 and was recapitulated by purified PLY, requiring its pore-forming activity. In contrast, at MOI of 20, E-cadherin disruption was independent of PLY, indicating that S. pneumoniae encodes multiple means to disrupt epithelial integrity. This disruption was insufficient to promote bacterial translocation in the absence of PMNs. Thus, S. pneumoniae triggers cleavage and mislocalization of E-cadherin through PLY-dependent and -independent mechanisms, but maximal bacterial translocation across epithelial monolayers requires PLY-dependent neutrophil transmigration.
Dominant negative and directional dysregulation of Polycomb function in EZH2-mutant human growth disorders

Weaver syndrome (WS) is a developmental overgrowth and intellectual disability disorder caused by heterozygous mutations in EZH2. EZH2 encodes the enzymatic subunit of Polycomb Repressive Complex 2 (PRC2) which mediates methylation of histone H3 lysine residue 27 (H3K27). Although PRC2 has been extensively studied at the molecular level, the effects of WS-associated EZH2 mutations on cells remain poorly understood. In this study, we expressed WS-associated EZH2 variants in mouse embryonic stem cells and found that they have dominant negative effects on Polycomb repressive function. These EZH2 variants decrease H3K27me2/3 and increase H3K27ac levels at intergenic regions, causing global chromatin decompaction and transcriptional upregulation of sensitive Polycomb target genes. Interestingly, a contrasting EZH2 variant linked to growth restriction caused opposing changes in H3K27 methylation and acetylation, and repressed the same cohort of Polycomb target genes. Our study provides insights into the molecular mechanisms of EZH2-mutant human growth disorders, revealing the opposing changes in chromatin and transcription associated with Weaver syndrome and growth restriction, respectively.
An Efficient Algorithm for Distance-based Structural Graph Clustering

Structural graph clustering (SCAN) is a classic graph clustering algorithm. In SCAN, a key step is to compute the structural similarity between vertices according to the overlap ratio of one-hop neighborhoods. Given two vertices u and v, existing studies only consider the case when u and v are neighbors. However, the structural similarity between non-neighboring vertices in SCAN is always zero, and using only one-hop neighbors on weighted graphs discards the weights on each edge. Both may not reflect the true closeness of two vertices and may fail to return high-quality clustering results. To tackle this issue, we define and study the distance-based structural graph clustering problem. Given a distance threshold d and two vertices u and v, the structural similarity between u and v is defined as the ratio of their respective neighbors within a distance of no more than d. We show that the newly defined distance-based SCAN achieves better clustering results compared to the vanilla version of SCAN. However, the new definition brings challenges in the computation of final clustering results. To tackle this efficiency issue, we propose DistanceSCAN, an efficient approximate algorithm for solving the distance-based SCAN problem. The main idea of DistanceSCAN is to use all-distances bottom-k sketches (ADS) to speed up the computation of similarities. Given the ADS, we can derive the similarity between two vertices with a bounded cost of O(k). However, to ensure that the estimated similarity has an approximation guarantee, the value of k still needs to be set to as large as thousands. This brings high computational costs when computing the similarities between neighboring vertices. To tackle this issue, we further construct histograms to prune the structural similarity computations of vertices pairs. Extensive experiments on real datasets validate the effectiveness and efficiency of DistanceSCAN.
Time2State: An Unsupervised Framework for Inferring the Latent States in Time Series Data

Time series data from monitoring applications reflect the physical or logical states of the objects, which may produce time series of distinguishable characteristics in different states. Thus, time series data can usually be split into different segments, each reflecting a state of the objects. These states carry rich high-level semantic information, e.g., run, walk, or jump, which helps people better understand the behaviour of the monitored objects. Nevertheless, these states are latent and hard to discover, because the characteristic of time series is complicated and the computational cost is high. This paper develops an efficient and effective unsupervised approach for inferring the latent states of massive multivariate time data. To reduce the computational cost, we present Time2State, a scalable framework that utilizes a sliding window and an encoder to greatly reduce the length of raw time series. To train the encoder, we propose a novel unsupervised loss function, LSE-Loss. Extensive experiments show that compared to the state-of-the-art time series representation learning methods of the same kind, LSE-Loss brings a performance improvement of up to 15% in accuracy.
The decision model of the intuitionistic fuzzy group bid evaluation for urban infrastructure projects considering social costs

In the construction of urban infrastructure projects, the ecology, economy, and society of surrounding areas will suffer from negative influences, which will eventually be transformed into economic loss, thus social costs are formed. The current bid evaluation mechanism has not yet taken social costs into account, while the present bid evaluation model for construction projects has ignored experts' group character and fuzziness. Based on the review of literature, this paper attempts to apply the intuitionistic fuzzy group decision model to urban infrastructure projects considering social costs, and to establish the bid evaluation indicator system for urban infrastructure projects considering social costs. This paper introduces the intuitionistic fuzzy entropy, the relative entropy, the intuitionistic fuzzy cross entropy, the intuitionistic fuzzy hybrid averaging operator, and the intuitionistic fuzzy weighted averaging operator to solve respectively attribute (indicator) weights, expert weights, and the i.
Total chromatic number of graphs of high degree, II

Abstract We prove Theorem 1: suppose G is a simple graph of order n having Δ(G) = n − k where k ≥ 5 and n ≥ max (13, 3k −3). If G contains an independent set of k − 3 vertices, then the TCC (Total Colouring Conjecture) is true. Applying Theorem 1, we also prove that the TCC is true for any simple graph G of order n having Δ(G) = n −5. The latter result together with some earlier results confirm that the TCC is true for all simple graphs whose maximum degree is at most four and for all simple graphs of order n having maximum degree at least n − 5.
Enterprise Modelling of Rule Networks in Organisational Collaborations

Organisations and their activities are largely controlled by different types of rules. Rules can have particular importance for organisational collaborations, for example in healthcare, in which negative effects of rules at different levels are identified. Rules have interdependencies with other rules, forming rule networks, which may be examined using systems and organisation theory. Rules can also be modelled through enterprise modelling. However, present models seem to have limited support for representing rule networks and understanding the emergence of unforeseen and undesired effects on the boundaries of the action space of collaborating organisational units. This gap will be addressed through the design of a modelling language with an accompanying method and tools, using Design Science Research.
ST4ML: Machine Learning Oriented Spatio-Temporal Data Processing at Scale

Data scientists and researchers utilize enormous spatio-temporal data and build machine learning models to solve practical problems in diverse domains including intelligent transportation, urban planning, epidemic prediction, and many more. Extracting application-specific features from big spatio-temporal data poses system requirements of heterogeneous data support, efficient and scalable computing over spatial and temporal dimensions, as well as a user-friendly programming interface. This paper presents ST4ML, a distributed spatio-temporal data processing system to support scalable machine-learning-oriented applications. We propose a three-stage pipelining computing framework, namely "selection-conversion-extraction" to abstract the distributed computing flow and implement it based on Apache Spark. To the best of our knowledge, ST4ML is the first of its kind to realize our design considerations. Extensive experiments with real-world datasets evidence that ST4ML outperforms straightforward extensions of existing ST data processing systems by up to an order of magnitude. ST4ML is open-sourced at https://github.com/Panrong/st4ml.
Transaction Scheduling: From Conflicts to Runtime Conflicts

This paper studies how to improve the performance of main memory multicore OLTP systems for executing transactions with conflicts. A promising approach is to partition transaction workloads into mutually conflict-free clusters, and distribute the clusters to different cores for concurrent execution. We show that if transactions in each cluster are properly scheduled, transactions that are traditionally considered conflicting can be executed without conflicts at runtime. In light of this, we propose to schedule transactions and reduce runtime conflicts, instead of partitioning based on the conventional notion of conflicts. We formulate the transaction scheduling problem to minimize runtime conflicts, and show that the problem is NP-complete. This said, we develop an efficient scheduling algorithm to improve parallelism. Moreover, for transactions that are not packed in batches, we show that runtime conflict analysis also helps reduce conflict penalties, by proposing a proactive deferring method. Using standard and enhanced benchmarks, we show that on average our scheduling and proactive deferring methods improve the throughput of existing partitioners and concurrency control protocols by 131% and 109%, respectively, up to 294% and 152%.
Discovering Similarity Inclusion Dependencies

Inclusion dependencies (INDs) are a well-known type of data dependency, specifying that the values of one column are contained in those of another column. INDs can be used for various purposes, such as foreign-key candidate selection or join partner discovery. The traditional notion of INDs is based on clean data, where the dependencies hold without exceptions. Unfortunately, data often contain errors, preventing otherwise valid INDs from being discovered. A typical response to this problem is to relax the dependency definition using a similarity measure to account for minor data errors, such as typos or different formatting. While this relaxation is known for functional dependencies, for inclusion dependencies no such relaxation has been defined. We formally introduce similarity inclusion dependencies, which relax the inclusion by demanding the existence only of sufficiently similar values. Similarity inclusion dependencies can fulfill traditional IND use cases, such as foreign-key candidate discovery, even in the presence of dirty data. We present Sawfish, the first algorithm to discover all similarity inclusion dependencies in a given dataset efficiently. Our algorithm combines approaches for the discovery of traditional INDs and string similarity joins with a novel sliding-window approach and lazy candidate validation. Our experimental evaluation shows that Sawfish can outperform a baseline by a factor of up to 6.5.
AWARE: Workload-aware, Redundancy-exploiting Linear Algebra

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

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
IcebergHT: High Performance Hash Tables Through Stability and Low Associativity

Modern hash table designs for DRAM and PMEM strive to minimize space while maximizing speed. The most important factor in speed is the number of cache lines accessed during updates and queries. On PMEM, there is an additional consideration, which is to minimize the number of writes, because on PMEM writes are more expensive than reads. This paper proposes two design objectives, stability and low-associativity, that enable us to build hash tables that minimize cache-line accesses for all operations. A hash table is stable if it does not move items around, and a hash table has low associativity if there are only a few locations where an item can be stored. Low associativity ensures that queries need to examine only a few memory locations, and stability ensures that insertions write to very few cache lines. Stability also simplifies concurrency and, on PMEM, crash safety. We present IcebergHT, a fast, concurrent, space-efficient, and crash-safe (for PMEM) hash table based on the design principles of stability and low associativity. IcebergHT combines in-memory metadata with a new hashing technique, iceberg hashing, that is (1) space efficient, (2) stable, and (3) supports low associativity. In contrast, existing hash-tables either modify numerous cache lines during insertions (e.g. cuckoo hashing), access numerous cache lines during queries (e.g. linear probing), or waste space (e.g. chaining). Moreover, the combination of (1)-(3) yields several emergent benefits: IcebergHT scales better than other hash tables, has excellent performance, and supports crash-safety on PMEM. Our benchmarks show that IcebergHT has excellent performance both in DRAM and PMEM. In PMEM, IcebergHT insertions are 50% to 3× faster than state-of-the-art PMEM hash tables, such as Dash and CLHT, and queries are 20% to 2× faster. IcebergHT space overhead is 17%, whereas Dash and CLHT have space overheads of 2× and 3×, respectively. IcebergHT also scaled linearly throughout our experiments and is crash safe. In DRAM, IcebergHT outperforms state-of-the-art hash tables libcuckoo and CLHT by almost 2× on insertions while offering good query throughput and much better space efficiency.
Polaris: Enabling Transaction Priority in Optimistic Concurrency Control

Transaction priority is a critical feature for real-world database systems. Under high contention, certain classes of transactions should be given a higher chance to commit than others. Such a prioritization mechanism is commonly implemented in locking-based concurrency control protocols as some lock scheduling mechanisms, but it is rarely supported in the world of optimistic concurrency control. We present Polaris, an optimistic concurrency control protocol that supports multiple priority levels. To enforce priority, Polaris introduces a minimal amount of pessimism through a lightweight reservation mechanism. The protocol is fully optimistic among transactions within the same priority level and preserves the high throughput advantage of optimistic protocols. Our evaluation with YCSB workload shows that Polaris can make the p999 tail latency of high-priority transactions 13x lower than that of low-priority ones. With an abort-aware priority assignment policy, Polaris can deliver 1.9x higher throughput and 17x lower tail latency compared to Silo for high-contention workloads.
LiteHST: A Tree Embedding based Method for Similarity Search

Similarity search is getting increasingly useful in real applications. This paper focuses on the in-memory similarity search, i.e., the range query and k nearest neighbor (kNN) query, under arbitrary metric spaces, where the only known information is the distance function to measure the similarity between two objects. Although lots of research has studied this problem, the query efficiency of existing solutions is still unsatisfactory. To further improve the query efficiency, we are inspired by the tree embeddings, which map each object into a unique leaf of a well-structured tree solely based on the distances. Unlike existing embedding techniques (e.g., Lipschitz embeddings and pivot mapping) for similarity search, where an extra multi-dimensional index is needed to index the embedding space (e.g., Lp metrics), we directly use this tree to answer similarity search. This seems to be promising, but it is challenging to tailor tree embeddings for efficient similarity search. Specifically, we present a novel index called LiteHST, which is based on the most popular tree embedding (HST) and heavily customized for similarity search in the node structure and storage scheme. We propose a new construction algorithm with lower time complexity than existing methods and prove the optimality of LiteHST in the distance bound. Based on this new index, we also design optimization techniques that heavily reduce the number of distance computations and hence save running time. Finally, extensive experiments demonstrate that our solution outperforms the state-of-the-art in the query efficiency by a large margin.