Aggregate View Research Articles

К РЕАТИВНЫЕ ИНДУСТРИИ В ЭКОНОМИКЕ ДАННЫХ: СУЩНОСТЬ И ПЕРСПЕКТИВЫ

The rapid growth of the share of creative industries in the GDP of the Russian economy must be ensured in the Na - tional Project “Data Economy and Digital Transformation of the state”. The analytical base of the research is the results of de - velopments by leading Russian analytical centers (Agency for Strategic Initiatives, NAFI, HSE). The study presents the concep- tual foundations of the essence of creative industries from the perspective of their development and management in the data economy; the concepts of “digital economy” and “data economy” are differentiated. Results: 1. The essence of creative indus- tries is revealed in the understanding of aggregation of objective and subjective data. The study of creative industries as a combination of three areas (the economy of impressions; intellectual property objects; labor organization) allows us to identify the importance of subjective data (“impressions”) for the development of creative industries; to develop the architecture of the labor market of creative industries based on remote types of employment, involving (taking into account) citizens with disabilities. 2. For the concept of “impression”, characteristics are highlighted for use in the management of organizations of creative industries. 3. The approaches to statistical observation and research of creative industries from the point of view of aggregation of objective (statistical) and subjective data are clarified.

Radial analysis and scaling of housing prices in French urban areas

Urban scaling laws summarize how attributes evolve with city size. However, one limitation concerns the aggregate view of this approach, which leads to neglecting the internal structure of cities. This is an important issue regarding housing prices, given their significant variations across space. Based on a dataset compiling millions of real estate transactions over the period 2017–2021, we investigate the regularities of the radial (center-periphery) profiles of housing prices across cities, with respect to their size. Results are threefold. First, they corroborate prior findings in the urban scaling literature stating that largest cities agglomerate higher housing prices. Second, we find that housing price radial profiles scale in three dimensions with the power 1/5 of city population. After rescaling, great regularities between radial profiles can be observed, although some locational amenities have a significant impact on prices. Third, it appears that our approach with rescaled profiles fails to explain housing price variations in the city center across cities. In fact, prices near the city center rise much faster with city size than in the periphery. This has strong implications for low-income households seeking homeownership because prohibitive prices in the center may contribute to pushing them out into peripheral locations, especially in large cities.

Summarized Causal Explanations For Aggregate Views

SQL queries with group-by and average are frequently used and plotted as bar charts in several data analysis applications. Understanding the reasons behind the results in such an aggregate view may be a highly nontrivial and time-consuming task, especially for large datasets with multiple attributes. Hence, generating automated explanations for aggregate views can allow users to gain better insights into the results while saving time in data analysis. When providing explanations for such views, it is paramount to ensure that they are succinct yet comprehensive, reveal different types of insights that hold for different aggregate answers in the view, and, most importantly, they reflect reality and arm users to make informed data-driven decisions, i.e., the explanations do not only consider correlations but are causal. In this paper, we present CauSumX, a framework for generating summarized causal explanations for the entire aggregate view. Using background knowledge captured in a causal DAG, CauSumX finds the most effective causal treatments for different groups in the view. We formally define the framework and the optimization problem, study its complexity, and devise an efficient algorithm using the Apriori algorithm, LP rounding, and several optimizations. We experimentally show that our system generates useful summarized causal explanations compared to prior work and scales well for large high-dimensional data.

Adaptive Feature Learning for Unbiased Scene Graph Generation.

Scene Graph Generation (SGG) aims to detect all objects and identify their pairwise relationships in the scene. Recently, tremendous progress has been made in exploring better context relationship representations. Previous work mainly focuses on contextual information aggregation and uses de-biasing strategies on samples to eliminate the preference for head predicates. However, there remain challenges caused by indeterminate feature training. Overlooking the label confusion problem in feature training easily results in a messy feature distribution among the confused categories, thereby affecting the prediction of predicates. To alleviate the aforementioned problem, in this paper, we focus on enhancing predicate representation learning. Firstly, we propose a novel Adaptive Message Passing (AMP) network to dynamically conduct information propagation among neighbors. AMP provides discriminating representations for neighbor nodes under the view of de-noising and adaptive aggregation. Furthermore, we construct a feature-assisted training paradigm alongside the predicate classification branch, guiding predicate feature learning to the corresponding feature space. Moreover, to alleviate biased prediction caused by the long-tailed class distribution and the interference of confused labels, we design a Bi-level Curriculum learning scheme (BiC). The BiC separately considers the training from the feature learning and de-biasing levels, preserving discriminating representations of different predicates while resisting biased predictions. Results on multiple SGG datasets show that our proposed method AMP-BiC has superior comprehensive performance, demonstrating its effectiveness.

Abstract 19210: Interpretation of Deep Learning Model That Identifies Regional Myocardial Dysfunction From the Surface Ecg

Introduction: Cardiac wall motion abnormalities (WMA) are key prognostic indicators for major cardiovascular events and mortality, yet they are poorly detected by current ECG screening tools such as Q waves. Deep learning (DL) models have improved analysis of complex data and novel tools may provide interpretation of these models. Hypothesis: ECG features beyond the Q wave may be identified by analyzing an ECG-trained DL model to improve WMA detection over standard ECG measurements. Methods: We developed a DL neural network using 36,964 unique patients (62.9±16.9 years, 47% female, 58% white) labeled by natural language processing of echocardiography reports within 60 days. To understand how the CNN model identifies WMAs from the ECG input, we implemented Shapley Additive exPlanations (SHAP) and DeepExplainer on R-wave aligned beats over 300ms surrounding the QRS complexes to identify hot-spots across the ECG. We then retrained classifiers on 60mg windows of the ECG to identify which timepoints produced the greatest performance (ablation method). Results: Probing the CNN revealed that ECG regions throughout the QRS and T-waves identified WMA. Analysis output shows that such ECG regions arose throughout the QRS and T-waves, and not just at early regions corresponding to Q waves (Panel A) . An aggregate view of the ECG reports summarizes the most discriminatory ECG regions for all patients (Panel B) . Stepwise analysis of limited windows within aligned ECG beats also confirmed this analysis (Panel C) . We found that the highest performance window was 60-120ms (AUC 0.814), containing most of the QRS, with the lowest performance in the window of 0-60 ms (AUC 0.699) and 0.20-0.80ms (AUC 0.707). Conclusions: The DL model show promise in accurately detecting WMAs and provides key insights into critical ECG regions for WMA detection beyond the Q wave. Further analysis of WMA detection with these expanded regions is needed.

Totality of Evidence of the effectiveness of repurposed therapies for COVID-19: Can we use Real-World Studies alongside Randomised Controlled Trials?

Rapid and robust strategies to evaluate the efficacy and effectiveness of novel and existing pharmacotherapeutic interventions (repurposed treatments) in future pandemics are required. Observational 'Real-World Studies' (RWS) can report more quickly than randomised controlled trials (RCTs) and would have value were they to yield reliable results. Both RCTs and RWS were deployed during the COVID-19 pandemic. Comparing results between them offers a unique opportunity to determine the potential value and contribution of each. A learning review of these parallel evidence channels in COVID19, based on quantitative modelling, can help improve speed and reliability in the evaluation of repurposed therapeutics in a future pandemic. Analysis of all-cause mortality data from 249 observational RWS and RCTs across eight treatment regimens for COVID-19 showed that RWS yield more heterogeneous results, and generally over-estimate the effect size subsequently seen in RCTs. This is explained in part by a few study factors: the presence of RWS that are imbalanced for age, gender and disease severity, and those reporting mortality at 2 weeks or less. Smaller studies of either type contributed negligibly. Analysis of evidence generated sequentially during the pandemic indicated that larger RCTs drive our ability to make conclusive decisions regarding clinical benefit of each treatment, with limited inference drawn from RWS. These results suggest that when evaluating therapies in future pandemics, 1) large RCTs, especially platform studies, be deployed early; 2) any RWS should be large and should have adequate matching of known confounders and long follow-up; 3) reporting standards and data standards for primary endpoints, explanatory factors and key subgroups should be improved. In addition, 4) appropriate incentives should be in place to enable access to patient-level data; and 5) an overall aggregate view of all available results should be available at any given time.

Condensing Meaning

In this chapter we address the challenge of interpreting and teaching complex infographics of the kind read and viewed in secondary school science in Australia. Inspired by the work of Bateman and his colleagues we adopt a complementary bottom-up and top-down perspective – analysing infographics bottom-up in terms of general gestalt grouping principles (for both micro- and macro-groups) and interpreting images top-down with respect to abductions based on modelling of the relevant field (as activity, item, and property), in Systemic Functional Linguistic theory (SFL). We then compare two infographics in detail, considering how mitosis is intermodally construed as activity and composition in verbiage and image. The key finding arising from this analysis is that in spite of the complexity of the infographics, considerable information has to be abduced, drawing on additional knowledge of the field – information that the co-text in the textbooks accompanying these infographics may not make explicit. This highlights the importance of multimodal literacy pedagogy as a crucial part of science education. We show how such a pedagogy can address the complexity of reading infographics in conjunction with verbal co-text. In this kind of carefully scaffolded methodology, the synoptic view of complex knowledge aggregation provided by infographics can play a significant role in the dynamics of knowledge building.

Impact of Airports on Landside Industrial Development: A Case Study of Brisbane Airport

Numerous studies have established strong connections between air traffic and economic growth at the regional level. However, without consideration of individual airports’ immediate vicinities, this aggregate view can lead to an overly simplistic conclusion about the role of airports in their urban environment. The impact of airports on local economic development is evaluated through a case study of Brisbane Airport by using primary data sourced from semi-structured interviews and a survey. The results demonstrate that the role of airports as transport hubs is not the dominant mechanism that drives industries to locate in airport areas. Rather, the local urban context is a dominant driver for development in and around airports. Based on the survey and interview results, urbanization economies have the highest significance on the landside industrial location decision, with a mean of 3.53 in terms of importance level. The findings provide a reference for policymakers regarding investment in airport expansion or construction, as well as a refinement of the understanding of the local economic impact of airports.

Widely Distributed Radar Imaging: Unmediated ADMM Based Approach

This paper presents a novel approach to reconstruct a unique image of an observed scene via synthetic apertures (SA) generated by employing widely distributed radar sensors. The problem is posed as a constrained optimization problem in which the global image which represents the aggregate view of the sensors is a decision variable. While the problem is designed to promote a sparse solution for the global image, it is constrained such that a relationship with local images that can be reconstructed using the measurements at each sensor is respected. Two problem formulations are introduced by stipulating two different establishments of that relationship. The proposed formulations are designed according to consensus ADMM (CADMM) and sharing ADMM (SADMM), and their solutions are provided accordingly as iterative algorithms. We drive the explicit variable updates for each algorithm in addition to the recommended scheme for hybrid parallel implementation on the distributed sensors and a central processing unit. Our algorithms are validated and their performance is evaluated by exploiting the Civilian Vehicles Dome dataset to realize different scenarios of practical relevance. Experimental results show the effectiveness of the proposed algorithms, especially in cases with limited measurements.

User-centered visual explorer of in-process comparison in spatiotemporal space

We propose a user-centered visual explorer (UcVE) for progressive comparing multiple visualization units in spatiotemporal space. We create unique unit visualization with the customizable aggregated view based on the visual metaphor of flower bursts. Each visualization unit is encoded with the abstraction of spatiotemporal properties. To reduce user cognition load, UcVE allows users to visualize, save, and track in-the-process exploration results. In coordination of storage sequence and block tracking views, UcVE can facilitate comparison with multiple visualization units concurrently, selected from historical and current exploration results. UcVE offers a flexible geo-based layout, with aggregation functions and temporal views of the timeline with categorized events, to maximize the user’s exploration capabilities. Finally, we demonstrate the usefulness by using COVID-19 datasets, case studies with different user scenarios, and expert feedback.Graphical abstract

Critiquing the rationales for using comparative judgement: a call for clarity

ABSTRACT Comparative judgment is gaining popularity as an assessment tool, including for high-stakes testing purposes, despite relatively little research on the use of the technique. Advocates claim two main rationales for its use: that comparative judgment is valid because humans are better at comparative than absolute judgment, and because it distils the aggregate view of expert judges. We explore these contentions. We argue that the psychological underpinnings used to justify the method are superficially treated in the literature. We conceptualise and critique the notion that comparative judgment is ‘intrinsically valid’ due to its use of expert judges. We conclude that the rationales as presented by the comparative judgment literature are incomplete and inconsistent. We recommend that future work should clarify its position regarding the psychological underpinnings of comparative judgment, and if necessary present a more compelling case; for example, by integrating the comparative judgment literature with evidence from other fields.

LIP: Local Importance-Based Pooling

Spatial downsampling layers are favored in convolutional neural networks (CNNs) to downscale feature maps for larger receptive fields and less memory consumption. However, for visual recognition tasks, these layers might lose discriminative details due to improper pooling strategies. In this paper, we present a unified framework (LAN) over the common downsampling layers (e.g., average pooling, max pooling, and strided convolution) from a view of local aggregation based on importance. In this LAN framework, we analyze the issues of these widely-used pooling layers and figure out the criteria of designing an effective downsampling layer. Based on this analysis, we propose a simple, general, and effective pooling operation based on local importance modeling, termed as Local Importance-based Pooling (LIP). LIP is able to enhance discriminative features during the downsampling procedure by learning adaptive importance weights based on inputs. To further modulate different pooling windows for more effective pooling, we present the improved version of LIP, termed LIP++, by introducing an explicit margin term and efficient logit modules. Our LIP++ can yield consistent accuracy improvement over the original LIP yet with a smaller computational cost. Extensive experiments show that our presented LIP method consistently yields notable gains with different CNN architectures on the image classification task. In the challenging MS COCO dataset, detectors with our LIP-ResNets as backbones obtain a consistent performance improvement over the vanilla ResNets on both bounding box detection and instance segmentation. Finally, we also verify the effectiveness of LIP on the tasks of pose estimation and semantic segmentation, demonstrating its generalization to the dense prediction task.

Role of Biodiversity in Ecological Calendars and Its Implications for Food Sovereignty: Empirical Assessment of the Resilience of Indicator Species to Anthropogenic Climate Change.

Ecological calendars are knowledge systems based on close observation of one's habitat to measure and give meaning to time, thereby providing anticipatory capacity for livelihood activities and contributing to food sovereignty. They rely upon seasonal indicators that integrate biophysical and ecological phenomena (e.g., first snowfall, last frost, blossoming of a tree species; seasonal appearance of an animal or plant) with locally grounded cultural meaning and value systems. These context‐specific relationships have enabled Indigenous and rural societies to anticipate weather and other seasonal processes in their environment. However, anthropogenic climate change could undermine ecological calendars due to adverse impacts on specific indicators species, but this issue remains unexplored. We address this knowledge gap by examining how anthropogenic climate change might affect selected species (birds, fish, and mammals) that are seasonal and key to Indigenous food systems in two Western Arctic communities. We leverage existing dietary animal datasets to which we apply a novel methodology for assessing organismal vulnerability to climate change. The methodology uses intrinsic species traits such as physiological tolerances, genetic variability, and life history traits to generate an empirical and integrative assessment of vulnerability for any given species. Subsequently, an aggregate view of vulnerability across calendar species is achieved through comparative statistical analysis across species both within and between communities. This exercise permits the first quantitative assessment of the continued relevance and effective use of an ecological calendar, thus demonstrating that food sovereignty and livelihood security is enhanced by biodiversity of indicator species.

Combining Operative Train Simulation with Logistics Simulation in SUMO

Rail freight logistics is usually planned and analyzed using a macroscopic aggregated view on railway networks and train operations. As a result, disjoint tools have developed for simulating train operations which requires a detailed representation of track assets as well as the signaling architecture and supply chain networks in logistics analyzing the flow of goods where mode-specific capacity and traffic situations are incorporated in an aggregated manner. However, integrating the two areas could help evaluating railway-specific operative implications (such as conflicts and consequent delays) on the level of transport chains and thus single transport units instead of trains or network areas. The simulation tool SUMO is identified to meet criteria from both disciplines. It is shown how a respective methodology can be realized in SUMO to create such a simulation model. A use case of northwestern Germany shows by the means of exemplary container trajectories that the two simulative approaches can be merged.

Animation Design of Multisensor Data Fusion Based on Optimized AVOD Algorithm

With the development of artificial intelligence, Internet of things, machine learning, and many other technologies, animation design task based on algorithm theory has become a research hotspot in the field. In recent years, perception technology has gradually become the key technology of animation design, and it is also the key research content in the current field. Whether the perception system can design animation quickly and accurately is the key of research. Compared with other algorithms, using AVOD (Aggregate view object detection) algorithm for animation design has obvious advantages. The original AVOD algorithm has some problems, such as low clustering efficiency, insufficient depth of feature extraction network, and occupying a large amount of memory. Based on this, this paper proposes to use the googleNet network and initial model of k-means++ to extract features and establish an optimized AVOD algorithm. At the same time, in order to illustrate the effectiveness of the optimization method, two typical cases are introduced to provide scientific guidance and reference for the research in this field.

Towards situational aware cyber-physical systems: A security-enhancing use case of blockchain-based digital twins

The complexity of cyberattacks in Cyber-Physical Systems (CPSs) calls for a mechanism that can evaluate critical infrastructures’ operational behaviour and security without affecting the operation of live systems. In this regard, Digital Twins (DTs) provide actionable insights through monitoring, simulating, predicting, and optimizing the state of CPSs. Through the use cases, including system testing and training, detecting system misconfigurations, and security testing, DTs strengthen the security of CPSs throughout the product lifecycle. However, such benefits of DTs depend on an assumption about data integrity and security. Data trustworthiness becomes more critical while integrating multiple components among different DTs owned by various stakeholders to provide an aggregated view of the complex physical system. This article envisions a blockchain-based DT framework as Trusted Twins for Securing Cyber-Physical Systems (TTS-CPS). With the automotive industry as a CPS use case, we demonstrate the viability of the TTS-CPS framework through a proof of concept. To utilize reliable system specification data for building the process knowledge of DTs, we ensure the trustworthiness of data-generating sources through Integrity Checking Mechanisms (ICMs). Additionally, Safety and Security (S&S) rules evaluated during simulation are stored and retrieved from the blockchain, thereby establishing more understanding and confidence in the decisions made by the underlying systems. Finally, we perform formal verification of the TTS-CPS.

Modelling the demand for educational tourism: do dynamic effect, university quality and competitor countries play a role?

PurposeThis study aims to develop an educational tourism demand model, particularly in respect to dynamic effects, university quality (QU) and competitor countries. Educational tourism has been identified as a new tourism sub-sector with high potential, and is thus expected to boost economic growth and sustainability.Design/methodology/approachThis study reviews the literature on the determinants of educational tourism demand. Even though the existing literature is intensively discussed, mostly focusing on the educational tourism demand from an individual consumer's perspective, this study makes an innovation in line with the aggregate demand view. The study uses data that consist of the enrolment of international students from 47 home countries who studied in Malaysia from 2008 to 2017. The study utilised the dynamic panel method of analysis.FindingsThis study affirms that income per capita, educational tourism price, price of competitor countries and quality of universities based on accredited programmes and world university ranking are the determinants of educational tourism demand in both the short and the long term. Also, a dynamic effect exists in educational tourism demand.Research limitations/implicationsThe results imply that government should take the quality of services for existing students, price decisions and QU into account to promote the country as a tertiary education hub and achieve sustainable development.Originality/valueResearch on the determinants of the demand for educational tourism is rare in terms of macro data, and this study includes the roles of QU, competitor countries and dynamic effects.

523 The Development of an Institutional Study Start-up Tracker for Industry Sponsored Clinical Trials

OBJECTIVES/GOALS: Duke University developed a Project Tracker system to provide transparency into the complex study start-up process for Industry sponsored clinical trials. Partnering with process owners, investigators, research teams, and IT developers, we aimed to reduce timelines for IRB approval, Contract Execution, and Billing/Calendar setup. METHODS/STUDY POPULATION: In 2019, a partnership of administrative stakeholders and IT developers began engaging with the primary populations involved in study start-up: research study teams (STs) and research administrators (RAs). A series of workgroups and feedback sessions revealed common themes with a slightly different scope: STs were interested in the progress of individual projects awaiting approval whereas RAs needed an aggregate view of start-up metrics over time and a way to help troubleshoot delays for individuals. Both groups were hindered by data captured across multiple systems, limited understanding of the entire approval process, and an absence of reliable indicators for delays or outstanding requirements. RESULTS/ANTICIPATED RESULTS: In mid-2021, we delivered a Project Tracker system comprised of two main components, accessible via Dukes myRESEARCHhome portal: 1) a Tableau metrics dashboard for RAs that aggregates retrospective data quarterly, visualizing time to completion of approval workflow milestones with defined outliers, filterable by investigator, unit, or sponsor. 2) a project status tracker for STs (with a complementary view for RAs) that consolidates status information for projects awaiting approval, visualizing the process in a linear fashion with clear indicators of progress and steps awaiting study team action. This involved defining the milestones for each workflow, mapping milestones to application variables, combining the variables in a common data mart, and developing the interfaces with user-centered design. DISCUSSION/SIGNIFICANCE: Given its recent implementation, the impact of the tool on start-up timelines cannot yet be determined. However, our development process led to key insights: a simplified process visualization limited to key milestones allows STs and RAs to understand responsibilities, address delays and data discrepancies, and reinforce institutional process.

Alkane solubilization by surfactants: Aggregate view and size analysis based on cryo-TEM

In this study the aggregation behavior for solubilization of alkanes by surfactants (rhamnolipid, sodium dodecyl benzene sulfonate (SDBS), Triton X-100) were investigated with a focus on the morphology and particle size distribution of the aggregates. The powerful tool of cryo-transmission electron microscopy was used to view the aggregates and obtain the size, which was compared to the size data obtained by the method of dynamic light scattering. The aggregation behavior at both sub-CMC and hyper-CMC concentrations was investigated. The results show that the morphology of the aggregates at both sub-CMC and hyper-CMC concentrations is spherical or spheroidal with a size smaller than 20 nm. No layered structure and no difference in transparency are observed in the aggregates, indicating micelle-type structure. The particle size distribution curve shows only one peak and it is well depicted by log-normal distribution. For all of the surfactants, the size of the aggregates at surfactant concentrations below CMC is larger than that at the concentrations above CMC, which is caused by the difference in excess of the surfactants at the surface of the aggregates. The size of the aggregates formed by anionic surfactants (rhamnolipid and SDBS) and hexadecane is smaller than that formed by nonionic Triton-X-100 and hexadecane and the effect of surfactant concentration on aggregate particle size is more significant for the anionic surfactants. The size of the rhamnolipid-dodecane aggregates is larger than that of the rhamnolipid-hexadecane aggregates at rhamnolipid concentrations above CMC, indicating that the chain length of the alkane also has an effect on the aggregate size.

Biomineralization from the Perspective of Ion Aggregation: Calcium Phosphate Nucleation in the Physiological Environment.

Biomineralization is an important process of bone tissue generation. Calcium (Ca) and phosphate (P) ions aggregate and nucleate under the regulation of biomolecules at the initial mineralization stage. Due to the complexity of the physiological environment, the movement behavior and mineralization mechanism of calcium and phosphate ions, as well as the effect of biomolecules on them, are not clear. In this study, computer simulations and experimental verification were applied to investigate the characteristics of the initial biomineralization from the view of ion aggregation and nucleation. The results prove that P ions play a more important role in mineralization than Ca ions. The guanidyl group and surrounding carboxyl terminal groups are a potential excellent nucleation domain on proteins. The interval distribution of acidic/basic residues on protein is more conductive to the formation of large Ca and P ions clusters. The involvement of protein could increase the probability of hydroxyapatite phase precipitation, especially in the presence of a helical conformation. The detailed information on Ca and P ions behavior provided by the computer simulations is helpful for further understanding the mechanism of biomineralization, which will promote the development of bone repair materials to the biomimetic mineralized materials.