Miracle-dz/UniFAR-FTUs huggingface dataset:
Large-scale Geospatial Analytics: Problems, Challenges, and Opportunities

Geospatial analytics is an important field in many communities, including crime science, transportation science, epidemiology, ecology, and urban planning. However, with the rapid growth of big geospatial data, most of the commonly used geospatial analytic tools are not efficient (or even feasible) to support large-scale datasets. As such, domain experts have raised the concerns about the inefficiency issues for using these tools. In this tutorial, we aim to arouse the attention of database researchers for this important, emerging, database-related, and interdisciplinary topic, which consists of four parts. In the first part, we will discuss different problems and highlight the challenges for two types of geospatial analytic tools, which are (1) hotspot detection and (2) correlation analysis. In the second and third parts, we will specifically discuss two geospatial analytic tools, namely kernel density visualization (the representative hotspot detection method) and K-function (the representative correlation analysis method), respectively, and their variants. In the fourth part, we will highlight the future opportunities for this topic.
SmokedDuck Demonstration: SQLStepper

Fine-grained lineage tracks the relationships between input and output of a query, and is particularly useful in analytical applications such as query debugging, view maintenance, query explanations, and data cleaning. Prior approaches rewrite SQL queries to also track lineage, but can slow query execution in analytical engines that are designed to process complex query patterns on large datasets. Moreover, they mainly capture lineage at the logical level. SmokedDuck extends DuckDB to support fast lineage capture and querying by tracking lineage at the instruction level by leveraging the duality between lineage and data movement. In this demonstration, we show how a user can leverage operator-level lineage to understand and debug a query execution through SQLStepper: an application built on top of SmokedDuck. Users upload data and execute queries using an in-browser command line, then explore query-level and operator-level lineage visually to track down bugs.
Circular RNA Circ_0038467 promotes the maturation of miRNA-203 to increase lipopolysaccharide-induced apoptosis of chondrocytes

Abstract Circ_0038467 and miR-203 exert important functions in lipopolysaccharide (LPS)-induced inflammation, which contributes to osteoarthritis (OA). Our preliminary deep sequencing analysis revealed altered expression of Circ_0038467 and miR-203 in OA and a close correlation between them. This study was therefore to explore crosstalk between them in OA. The expression of Circ_0038467, mature miR-203, and miR-203 precursor in OA patients and controls was determined using RT-qPCR. An overexpression assay was performed to explore the role of Circ_0038467 in regulating the expression of mature miR-203 and miR-203 precursor. Cell apoptosis was analyzed by cell apoptosis assay. Circ_0038467 was upregulated in OA and positively correlated with mature miR-203 but not that of miR-203 precursor. In chondrocytes, increased expression levels of both Circ_0038467 and miR-203 were observed after LPS treatment. In chondrocytes, overexpression of Circ_0038467 increased the expression levels of mature miR-203 but not that of miR-203 precursor. Overexpression of Circ_0038467 and miR-203 increased cell apoptosis. Then, the miR-203 inhibitor reversed the effects of overexpression of Circ_0038467 on cell apoptosis. Interestingly, Circ_0038467 was detected in both the cytoplasm and nucleus. Circ_0038467 directly interacted with the precursor miR-203. Therefore, Circ_0038467 is highly expressed in OA and it may promote the production of mature miR-203 to increase apoptosis of chondrocytes induced by LPS.
Reversal of Increasing Failure Rates When Pooling Failure Data

Because mixtures of exponential distributions (with constant failure rate) have the decreasing failure rate (DFR) property, as shown by Proschan in the sixties, it is not unexpected that mixtures of distributions that have a mildly increasing failure rate (IFR) also have this property. What is, perhaps, surprising is that mixtures of IFR distributions with rapidly increasing failure rate may also behave in this manner. Two striking examples of such mixtures are presented here and illustrated graphically. This phenomenon could be the cause of concern, in practice, when it is suspected that a sample is based on pooled data.
Prospects of a cross section ratio measurement of W and Z events as a function of the jet multiplicity with early ATLAS data

Die Produktion von W und Z Bosonen zusammen mit Jets spielt eine zentrale Rolle beim ATLAS Experiment am "Large Hadron Collider (LHC)" des Europaischen Kernforschungszentrum CERN (Genf, Schweiz). Im Rahmen dieser Dissertation wurde eine Messung des Wirkungsquerschnitt-Verhaltnisses von W+Jets und Z+Jets Ereignissen in Abhangigkeit der Jetmultiplitzitat vorbereitet. Dieses Wirkungsquerschnitt-Verhaltnis wird benotigt, um die W+Jets Normierung in der Top-Quark Analyse zu verbessern und um Monte-Carlo Ereignis-Generatoren und QCD Vorhersagen daran zu testen. Hierbei wurde der Schwerpunkt auf eine Analyse erster ATLAS Daten gelegt. Die Idee ist, dass die klar selektierbaren Z+Jets Ereignisse W+Jets Ereignissen sehr ahnlich sind und daher zur Abschatzung verwendet werden konnen. Im ersten Teil der Arbeit wurden daher die Ereignisstrukturen von W und Z Ereignissen verglichen. Der Vergleich bestatigte die erwartete Ahnlichkeit. Danach wurde die Messung des Wirkungsquerschnitt-Verhaltnisses vorbereitet. Eine Abschatzung der Auswirkungungen systematischer Unsicherheiten wurde ebenfalls vorgenommen. Zuletzt wurden die W+Jets und Z+Jets Spektren von Detektor- und Rekonstruktionseffekten korrigiert, um einen direkten Vergleich von QCD Berechnungen und gemessenem Wirkunsquerschnitt-Verhaltnisses zu ermoglichen.
TERRA R-loops connect and protect sister telomeres in mitosis

Resolution of cohesion between sister telomeres in human cells depends on TRF1-mediated recruitment of the polyADP-ribosyltransferase, tankyrase to telomeres. In cells where tankyrase is deleted or the tankyrase binding site in TRF1 is mutated, sister telomeres remain cohered in mitosis. Human aged cells and ALT cancer cells naturally exhibit persistent telomere cohesion due to shortened telomeres that do not recruit sufficient TRF1/tankyrase for resolution. Persistent cohesion plays a protective role, but the mechanism by which sister telomeres remain cohered is not well understood. Here we show that telomere repeat containing RNA (TERRA) holds sister telomeres together through RNA-DNA hybrid (R-loop) structures. We show that a tankyrase-interacting partner, the RNA-binding protein C19orf43 is required for resolution of telomere cohesion and for repression of TERRA R-loops. Depletion of C19orf43 led to persistent telomere cohesion and an increase in TERRA R-loops. Overexpression of RNaseH1 counteracted persistent cohesion in C19orf43-depleted cells, as well as in aged and ALT cells. In fact, treatment of cohered telomeres in mitotic cells with RNaseH1 in situ, was sufficient to resolve sister telomere cohesion, confirming that RNA-DNA hybrids hold sister telomeres together. Consistent with a protective role for persistent telomere cohesion, depletion of C19orf43 in aged cells reduced DNA damage and significantly delayed replicative senescence. We propose that the inherent inability of shortened telomeres to recruit R-loop repressing machinery permits a controlled onset of senescence.
Visualizing scRNA-Seq Data at Population Scale with GloScope

Increasingly scRNA-Seq studies explore the heterogeneity of cell populations across different samples and its effect on an organism’s phenotype. However, relatively few bioinformatic methods have been developed which adequately address the variation between samples for such population-level analyses. We propose a framework for representing the entire single-cell profile of a sample, which we call its GloScope representation. We implement GloScope on scRNA-Seq datasets from study designs ranging from 12 to over 300 samples. These examples demonstrate how GloScope allows researchers to perform essential bioinformatic tasks at the sample-level, in particular visualization and quality control assessment.
Decoding face recognition abilities in the human brain

Why are some individuals better at recognising faces? Uncovering the neural mechanisms supporting face recognition ability has proven elusive. To tackle this challenge, we used a multi-modal data-driven approach combining neuroimaging, computational modelling, and behavioural tests. We recorded the high-density electroencephalographic brain activity of individuals with extraordinary face recognition abilities—super-recognisers—and typical recognisers in response to diverse visual stimuli. Using multivariate pattern analyses, we decoded face recognition abilities from 1 second of brain activity with up to 80% accuracy. To better understand the mechanisms subtending this decoding, we compared computations in the brains of our participants with those in artificial neural network models of vision and semantics, as well as with those involved in human judgments of shape and meaning similarity. Compared to typical recognisers, we found stronger associations between early brain computations of super-recognisers and mid-level computations of vision models as well as shape similarity judgments. Moreover, we found stronger associations between late brain representations of super-recognisers and computations of the artificial semantic model as well as meaning similarity judgments. Overall, these results indicate that important individual variations in brain processing, including neural computations extending beyond purely visual processes, support differences in face recognition abilities. They provide the first empirical evidence for an association between semantic computations and face recognition abilities. We believe that such multi-modal data-driven approaches will likely play a critical role in further revealing the complex nature of idiosyncratic face recognition in the human brain. Significance The ability to robustly recognise faces is crucial to our success as social beings. Yet, we still know little about the brain mechanisms allowing some individuals to excel at face recognition. This study builds on a sizeable neural dataset measuring the brain activity of individuals with extraordinary face recognition abilities—super-recognisers—to tackle this challenge. Using state-of-the-art computational methods, we show robust prediction of face recognition abilities in single individuals from a mere second of brain activity, and revealed specific brain computations supporting individual differences in face recognition ability. Doing so, we provide direct empirical evidence for an association between semantic computations and face recognition abilities in the human brain—a key component of prominent face recognition models.
Maximal Defective Clique Enumeration

Maximal clique enumeration is a fundamental operator in graph analysis. The model of clique, however, is typically too restrictive for real-world applications as it requires an edge for every pair of vertices. To remedy this restriction, practical graph analysis applications often resort to find relaxed cliques as alternatives. In this work, we investigate a notable relaxed clique model, called s-defective clique, which allows at most s edges to be missing. Similar to the complexity of maximal clique enumeration, the problem of enumerating all maximal s-defective cliques is also NP-hard. To solve this problem, we first develop a new polynomial-delay algorithm based on a carefully-designed reverse search technique, which can output two consecutive results within polynomial time. To achieve better practical efficiency, we propose a branch-and-bound algorithm with a novel pivoting technique. We prove that the time complexity of this algorithm depends only on O(α_sn) or O(αsδ) when using a degeneracy ordering optimization, where αs is a positive real number strictly less than 2, and δ (δ 0). We also develop several new pruning techniques to further improve the efficiency of our branch-and-bound algorithm to enumerate all relatively-large maximal s-defective cliques. In addition, we further generalize our pivot-based branch-and-bound algorithm to enumerate all maximal subgraphs satisfying a hereditary property. Here we call a graph meeting the hereditary property if all its subgraphs have the same property as itself. Finally, extensive experiments on 11 datasets demonstrate the efficiency, effectiveness, and scalability of the proposed solutions.
Motivations to Design Central Bank Digital Currency: A Conceptual Model

The nonexistence of well-structured information about the design and implementation of the Central Bank Digital Currency (CBDC) is affecting the cooperation of teams in CBDC research projects. CBDC research teams need to structure their country's motivation to regulate CBDC within a shared vision to solve the problem of the financial and technological regulation of the digital currency, which will have a significant impact, especially on the digital innovation of the digital economy ecosystem, the digital transformation of the companies, and the digital inclusion of unbanked people. This research aims to survey the key constructs of the CBDC design through the literature review, map these constructs in ArchiMate elements and propose a conceptual model that uses the key constructs to combine the motivation, the business, the application, and the technology layers of ArchiMate, providing the legal, financial, and technological viewpoints of CBDC design scope. The proposed model simplifies the collaboration between researchers and practitioners, namely central banks, policymakers, and technology providers, as a communication tool between financial, legal, and technological professionals in a CBDC research project.
Hierarchical Residual Encoding for Multiresolution Time Series Compression

Data compression is a key technique for reducing the cost of data transfer from storage to compute nodes. Increasingly, modern data scales necessitate lossy compression techniques, where exactness is sacrificed for a smaller compressed representation. One challenge in lossy compression is that different applications may have different accuracy demands. Today's compression techniques struggle in this setting either forcing the user to compress at the strictest accuracy demand, or to re-encode the data at multiple resolutions. This paper proposes a simple, but effective multiresolution compression algorithm for time series data, where a single encoding can effectively be decompressed at multiple output resolutions. There are a number of benefits over current state-of-the-art techniques for time series compression. (1) The storage footprint of this encoding is smaller than re-encoding the data at multiple resolutions. (2) Similarly, the compression latency is generally smaller than re-encoding at multiple resolutions. (3) Finally, the decompression latency of our encoding is significantly faster than single encodings at the strictest accuracy demand.
Variability of Aerosol Optical Properties at Four North American Surface Monitoring Sites

Abstract Aerosol optical properties measured over several years at surface monitoring stations located at Bondville, Illinois (BND); Lamont, Oklahoma (SGP); Sable Island, Nova Scotia (WSA); and Barrow, Alaska (BRW), have been analyzed to determine the importance of the variability in aerosol optical properties to direct aerosol radiative forcing calculations. The amount of aerosol present is of primary importance and the aerosol optical properties are of secondary importance to direct aerosol radiative forcing calculations. The mean aerosol light absorption coefficient (σap) is 10 times larger and the mean aerosol scattering coefficient (σsp) is 5 times larger at the anthropogenically influenced site at BND than at BRW. The aerosol optical properties of single scattering albedo (ωo) and hemispheric backscatter fraction (b) have variability of approximately ± 3% and ± 8%, respectively, in mean values among the four stations. To assess the importance of the variability in ωo and b on top of the atmosphere a.
A NON ADIABATIC THEORY FOR ULTRAFAST AND CATALYTIC TRANSFER OF ELECTRONS AT LOW TEMPERATURE

Electron transfer (ET) between weakly interacting molecules is a ubiquitous elementary process of chemical reactions generally well described by the Marcus theory as a thermally activated process. In the vicinity of the inversion point where the activation energy becomes small, E T is faster and should occur by quantum tunnelling at low temperature. Then, the standard adiabatic approximation used in the Marcus theory looses its validity and improvements are needed. We construct a non-adiabatic theory of E T using the complex amplitudes on each molecule of the electronic wave-functions as Kramers variables. The effective dynamics of E T is then described by a nonlinear equation with dissipative terms and colored Langevin forces globally modeling the interaction with the thermalized environment. Far from the inversion point, our model reproduces essentially the standard Marcus results but close to it, a correct description of the quantum tunnelling of the electron in its deformable environment requires to take into account nonlinearities and damping. We analyze quantum E T on the base of the recently proposed nonlinear concept of Targeted Transfer which extends but also qualitatively modifies the well-known concept of linear resonance and tunnelling. In addition, we predict and numerically confirm spectacular catalytic effects if E T occurs in the presence of a third molecule chosen very special. Then even in situations with a large energy barrier, where a direct E T between donor and acceptor cannot occur at low temperature, a weak coupling with an extra appropriately tuned (catalytic) site can trigger selectively an ultrafast ET at low temperature. New perspectives on ET and more generally on selective quantum transitions in complex systems are opened and seems to be highly relevant for biosystems (e.g. the photosynthetic reaction center).
AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting

Sensors in cyber-physical systems often capture interconnected processes and thus emit correlated time series (CTS), the forecasting of which enables important applications. The key to successful CTS forecasting is to uncover the temporal dynamics of time series and the spatial correlations among time series. Deep learning-based solutions exhibit impressive performance at discerning these aspects. In particular, automated CTS forecasting, where the design of an optimal deep learning architecture is automated, enables forecasting accuracy that surpasses what has been achieved by manual approaches. However, automated CTS solutions remain in their infancy and are only able to find optimal architectures for predefined hyperparameters and scale poorly to large-scale CTS. To overcome these limitations, we propose AutoCTS+, a joint, scalable framework, to automatically devise effective CTS forecasting models. Specifically, we encode each candidate architecture and accompanying hyperparameters into a joint graph representation. We introduce an efficient Architecture-Hyperparameter Comparator (AHC) to rank all architecture-hyperparameter pairs, and we then further evaluate the top-ranked pairs to select an architecture-hyperparameter pair as the final model. Extensive experiments on six benchmark datasets demonstrate that AutoCTS+ not only eliminates manual efforts but also is capable of better performance than manually designed and existing automatically designed CTS models. In addition, it shows excellent scalability to large CTS.
Fast Density-Based Clustering: Geometric Approach

DBSCAN is a fundamental density-based clustering algorithm with extensive applications. However, a bottleneck of DBSCAN is its O(n2) worst-case time complexity. In this paper, we propose an algorithm called GAP-DBC, which exploits the geometric relationships between points to solve this problem. GAP-DBC introduces an efficient partitioning algorithm to partition the data set with a limited number of range queries and then establishes an initial cluster structure based on the partition. GAP-DBC proceeds to iteratively refine the cluster structure by additional range queries. Finally, the cluster structure is accomplished using an iterative algorithm that utilizes the spatial relationships among points to reduce unnecessary distance calculations. We further demonstrate theoretically that GAP-DBC has an excellent guarantee in terms of computational efficiency. We conducted experiments on both synthetic and real-world data sets to evaluate the performance of GAP-DBC. The results show that our algorithm is competitive with other state-of-the-art algorithms.
Cross-provider Cloud Cost Calculation

Cloud computing grants flexible access to resources such as servers and storage to create compute infrastructures while only paying for what is used. Due to the complexity of usage-based pricing models, it is not feasible to obtain cost estimates for larger infrastructures manually. However, the available cost calculation tools mostly support calculations for one provider and are not open to importing existing infrastructure models, which makes cost estimations for large infrastructures of enterprises tedious. In this paper, we present a first version of a cross-provider cloud cost calculation model as a step towards enabling cost estimations across providers without requiring knowledge of the providers’ pricing models.
Matching Roles from Temporal Data: Why Joe Biden is not only President, but also Commander-in-Chief

We present role matching, a novel, fine-grained integrity constraint on temporal fact data, i.e., (subject, predicate, object, timestamp)-quadruples. A role is a combination of subject and predicate and can be associated with different objects as the real world evolves and the data changes over time. A role matching states that the associated object of two or more roles should always match across time. Once discovered, role matchings can serve as integrity constraints to improve data quality, for instance of structured data in Wikipedia[3]. If violated, role matchings can alert data owners or editors and thus allow them to correct the error. Finding all role matchings is challenging due both to the inherent quadratic complexity of the matching problem and the need to identify true matches based on the possibly short history of the facts observed so far. To address the first challenge, we introduce several blocking methods both for clean and dirty input data. For the second challenge, the matching stage, we show how the entity resolution method Ditto[27] can be adapted to achieve satisfactory performance for the role matching task. We evaluate our method on datasets from Wikipedia infoboxes, showing that our blocking approaches can achieve 95% recall, while maintaining a reduction ratio of more than 99.99%, even in the presence of dirty data. In the matching stage, we achieve a macro F1-score of 89% on our datasets, using automatically generated labels.