Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graphs

In recent years, ride-hailing services have been increasingly prevalent, as they provide huge convenience for passengers. As a fundamental problem, the timely prediction of passenger demands in different regions is vital for effective traffic flow control and route planning. As both spatial and temporal patterns are indispensable passenger demand prediction, relevant research has evolved from pure time series to graph-structured data for modeling historical passenger demand data, where a snapshot graph is constructed for each time slot by connecting region nodes via different relational edges (origin-destination relationship, geographical distance, etc.). Consequently, the spatiotemporal passenger demand records naturally carry dynamic patterns in the constructed graphs, where the edges also encode important information about the directions and volume (i.e., weights) of passenger demands between two connected regions. aspects in the graph-structure data. representation for DDW is the key to solve the prediction problem. However, existing graph-based solutions fail to simultaneously consider those three crucial aspects of dynamic, directed, and weighted graphs, leading to limited expressiveness when learning graph representations for passenger demand prediction. Therefore, we propose a novel spatiotemporal graph attention network, namely Gallat ( G raph prediction with all at tention) as a solution. In Gallat, by comprehensively incorporating those three intrinsic properties of dynamic directed and weighted graphs, we build three attention layers to fully capture the spatiotemporal dependencies among different regions across all historical time slots. Moreover, the model employs a subtask to conduct pretraining so that it can obtain accurate results more quickly. We evaluate the proposed model on real-world datasets, and our experimental results demonstrate that Gallat outperforms the state-of-the-art approaches.

sGrapp: Butterfly Approximation in Streaming Graphs

We study the fundamental problem of butterfly (i.e., (2,2)-bicliques) counting in bipartite streaming graphs. Similar to triangles in unipartite graphs, enumerating butterflies is crucial in understanding the structure of bipartite graphs. This benefits many applications where studying the cohesion in a graph shaped data is of particular interest. Examples include investigating the structure of computational graphs or input graphs to the algorithms, as well as dynamic phenomena and analytic tasks over complex real graphs. Butterfly counting is computationally expensive, and known techniques do not scale to large graphs; the problem is even harder in streaming graphs. In this article, following a data-driven methodology, we first conduct an empirical analysis to uncover temporal organizing principles of butterflies in real streaming graphs and then we introduce an approximate adaptive window-based algorithm, sGrapp, for counting butterflies as well as its optimized version sGrapp-x. sGrapp is designed to operate efficiently and effectively over any graph stream with any temporal behavior. Experimental studies of sGrapp and sGrapp-x show superior performance in terms of both accuracy and efficiency.

Sequential-Knowledge-Aware Next POI Recommendation: A Meta-Learning Approach

Accurately recommending the next point of interest (POI) has become a fundamental problem with the rapid growth of location-based social networks. However, sparse, imbalanced check-in data and diverse user check-in patterns pose severe challenges for POI recommendation tasks. Knowledge-aware models are known to be primary in leveraging these problems. However, as most knowledge graphs are constructed statically, sequential information is yet integrated. In this work, we propose a meta-learned sequential-knowledge-aware recommender (Meta-SKR), which utilizes sequential, spatio-temporal, and social knowledge to recommend the next POI for a location-based social network user. The framework mainly contains four modules. First, in the graph construction module, a novel type of knowledge graph—the sequential knowledge graph, which is sensitive to the check-in order of POIs—is built to model users’ check-in patterns. To deal with the problem of data sparsity, a meta-learning module based on latent embedding optimization is then introduced to generate user-conditioned parameters of the subsequent sequential-knowledge-aware embedding module, where representation vectors of entities (nodes) and relations (edges) are learned. In this embedding module, gated recurrent units are adapted to distill intra- and inter-sequential knowledge graph information. We also design a novel knowledge-aware attention mechanism to capture information surrounding a given node. Finally, POI recommendation is provided by inferring potential links of knowledge graphs in the prediction module. Evaluations on three real-world check-in datasets show that Meta-SKR can achieve high recommendation accuracy even with sparse data.

Topic Difficulty: Collection and Query Formulation Effects

Several recent studies have explored the interaction effects between topics, systems, corpora, and components when measuring retrieval effectiveness. However, all of these previous studies assume that a topic or information need is represented by a single query. In reality, users routinely reformulate queries to satisfy an information need. In recent years, there has been renewed interest in the notion of “query variations” which are essentially multiple user formulations for an information need. Like many retrieval models, some queries are highly effective while others are not. This is often an artifact of the collection being searched which might be more or less sensitive to word choice. Users rarely have perfect knowledge about the underlying collection, and so finding queries that work is often a trial-and-error process. In this work, we explore the fundamental problem of system interaction effects between collections, ranking models, and queries. To answer this important question, we formalize the analysis using ANalysis Of VAriance (ANOVA) models to measure multiple components effects across collections and topics by nesting multiple query variations within each topic. Our findings show that query formulations have a comparable effect size of the topic factor itself, which is known to be the factor with the greatest effect size in prior ANOVA studies. Both topic and formulation have a substantially larger effect size than any other factor, including the ranking algorithms and, surprisingly, even query expansion. This finding reinforces the importance of further research in understanding the role of query rewriting in IR related tasks.

An Approach to Risk Assessment and Threat Prediction for Complex Object Security Based on a Predicative Self-Configuring Neural System

We identified a set of methods for solving risk assessment problems by forecasting an incident of complex object security based on incident monitoring. The solving problem approach includes the following steps: building and training a classification model using the C4.5 algorithm, a decision tree creation, risk assessment system development, and incident prediction. The last system is a predicative self-configuring neural system that includes a SCNN (self-configuring neural network), an RNN (recurrent neural network), and a predicative model that allows for determining the risk and forecasting the probability of an incident for an object. We proposed and developed: a mathematical model of a neural system; a SCNN architecture, where, for the first time, the fundamental problem of teaching a perceptron SCNN was solved without a teacher by adapting thresholds of activation functions of RNN neurons and a special learning algorithm; and a predicative model that includes a fuzzy output system with a membership function of current incidents of the considered object, which belongs to three fuzzy sets, namely “low risk”, “medium risk”, and “high risk”. For the first time, we gave the definition of the base class of an object’s prediction and SCNN, and the fundamental problem of teaching a perceptron SCNN was solved without a teacher. We propose an approach to neural system implementation for multiple incidents of complex object security. The results of experimental studies of the forecasting error at the level of 2.41% were obtained.

Does Aid Drive Migration? Evidence from a Shift-Share Instrument∗

Foreign aid payments have been a key policy response by Global North countries to reduce increased migration flows from the Global South. In this article, we contribute to the literature on the relationship between aid and international migration flows and estimate the contemporaneous effect of bilateral aid payments on bilateral, international migration flows. The fundamental problem in analyzing this relationship is endogeneity, or reverse causality. To address this issue and achieve causal inference, we use a shift-share, or Bartik, instrument. Examining migration flows between 198 origin countries and 16 OECD destination countries over 36 years (1980−2015), we find a positive relationship between aid and migration. A ten-percent increase in aid payments will increase migration by roughly 2 percent. We further document non-linearity in the relationship between aid and migration and find an inverted U-shaped relationship between aid and migration flows. The findings presented here have implications for the design of bilateral and multilateral aid policies and for achieving various United Nations Sustainable Development Goals by stressing the importance of a better coordination between aid and immigration policies.

Interactions in ternary bismuth-containing molybdate systems M2MoO4-Bi2(MoO4)3-Zr(MoO4)2 in the subsolidus region

A fundamental problem in materials science consists in establishing a relationship between the chemical composition, structure, and properties of materials. This issue can be solved through the study of multicomponent systems and the directed synthesis of promising compounds. Of practical interest here are active dielectrics that are based on complex oxide compounds, specifically molybdates. Among complex molybdates and tungstates, ternary caged molybdates of the following structural types are of greatest importance: nasicon, perovskite, langbeinite, etc. Due to their widely varying elemental and quantitative compositions, such molybdates are convenient models for structural and chemical design, as well as the establishment of “composition–structure– properties” genetic relationships. Bismuth-containing complex molybdate systems exhibit the formation of phases having ferro-piezoelectric, ionic, and other properties. In this work, the Rb2MoO4–Bi2(MoO4)3–Zr(MoO4)2 ter nary salt system was studied for the first time using the method of intersecting sections in the subsolidus region (450–650 ℃). To this end, quasibinary sections were identified; triangulation was performed. Ternary molybdates Rb5BiZr(MoO4)6 and Rb2BiZr2(MoO4)6,5 were formed in the system using a ceramic technology. These compounds are isostructural to the previously obtained REE molybdates (M5LnZr(MoO4)6) but contain trivalent bismuth instead of rare earth elements. The structure of Rb5BiZr(MoO4)6 was adjusted via the Rietveld refinement technique using the TOPAS 4.2 software package. The ternary molybdate crystallizes in a trigonal system, with the following unit cell parameters of the R`3c space group: a = 10.7756(2) and c = 39.0464(7) Å. According to the studies of thermal properties exhibited by M5BiZr(MoO4)6, these ternary molybdates undergo the first-order phase transition in the temperature range of 450–600 ºC. The IR and Raman spectra of M5BiZr(MoO4)6 reveal the crystallization of ternary molybdates in the R`3c space group. The conducted comparative characterization of M2MoO4–Bi2(MoO4)3–Zr(MoO4)2 phase diagrams suggests that the phase equilibria of these systems depend on the nature of molybdates of monovalent elements.

Properties of the compressibility and transport motion in wall-bounded turbulent flows at Mach 8

The compressibility effect and transport motion in highspeed turbulent boundary layer (TBL) is a fundamental problem because they dominate the average and statistical characteristics. Using the statistical methods and flow visualization technology, flat-plate TBLs at [Formula: see text] with high- and low-wall temperatures, [Formula: see text] and 1.9, are investigated based on the direct numerical simulation (DNS) datasets. Compared with previous studies, this study considers relative higher Mach number and strong cold wall temperature condition at the same time. First, the turbulent Mach number and turbulent intensity show that the compressibility effects are enhanced by the cooling process. Second, the high-order statistical moments and structure parameters confirm cold wall that causes stronger compressibility and the corresponding increased intensities of local streamwise and wall-normal transport motions. Finally, for uncovering the relationship between the compressibility effect and turbulent transport, more in-depth visualization analyses of velocity streaks are performed. It is found that ‘knot-like’ structures are generated when cooling the wall, and they lead to stronger intermittent, which results in the rapid increase of local compressibility effect and the wall-normal transport motion. Our research sheds light on providing a theoretical basis for further understanding the compressibility effects of TBL at high Mach number.

Open Challenges and Research Roadmap: Visual Representations aiding Organizational Leadership in Decision Making for New Product Development at the Early Innovation Stage

Innovation is a cornerstone for an organization’s survival and success in the global competitive landscape in the VUCA world. The New Product Development (NPD) process is a crucial part of the portfolio and Innovation Management (IM) process. The leadership of an organization has a disproportionate impact on the outcome of innovation activities. Their involvement in IM and NPD is critical for success, considering they make strategic decisions to allocate resources for business growth. The leadership team demands a holistic picture of ideas before making decisions at early stages. The leadership challenge in decision making is that they have a limited time to make decisions by understanding many related aspects and insights quickly. The visual approaches have been vital in management practices to understand the situation and aid in decision-making by supporting cognitive processes. The fundamental problem in using visual representation is hidden expectations of leadership teams to represent needed elements to aid in strategic decision-making by leadership at the early stage of innovation. Also, the configuration of elements and interplay is another issue. The core challenge lies in understanding the effectiveness of currently used visual representations and then improving them by identifying needed elements and their configuration and placement in the visual representation. The paper takes literature review, expert interviews, and brainstorming approaches to distill the challenges to the concrete research questions and objectives. Providing solutions to the open research questions and challenges can significantly enhance the quality and speed of innovation-related decision-making.

Open Challenges and Research Roadmap: Visual Representations aiding Organizational Leadership in Decision Making for New Product Development at the Early Innovation Stage

Innovation is a cornerstone for an organization’s survival and success in the global competitive landscape in the VUCA world. The New Product Development (NPD) process is a crucial part of the portfolio and Innovation Management (IM) process. The leadership of an organization has a disproportionate impact on the outcome of innovation activities. Their involvement in IM and NPD is critical for success, considering they make strategic decisions to allocate resources for business growth. The leadership team demands a holistic picture of ideas before making decisions at early stages. The leadership challenge in decision making is that they have a limited time to make decisions by understanding many related aspects and insights quickly. The visual approaches have been vital in management practices to understand the situation and aid in decision-making by supporting cognitive processes. The fundamental problem in using visual representation is hidden expectations of leadership teams to represent needed elements to aid in strategic decision-making by leadership at the early stage of innovation. Also, the configuration of elements and interplay is another issue. The core challenge lies in understanding the effectiveness of currently used visual representations and then improving them by identifying needed elements and their configuration and placement in the visual representation. The paper takes literature review, expert interviews, and brainstorming approaches to distill the challenges to the concrete research questions and objectives. Providing solutions to the open research questions and challenges can significantly enhance the quality and speed of innovation-related decision-making.