Disciplined autonomy: How business analytics complements customer involvement for digital innovation

The rise of big data and the fluid boundaries of digital products are driving companies to use business analytics (BA) to power their customer involvement. The complementarity view offers unique competence to generate value from BA because capability complementarity is less likely to be replicated or imitated. Unlike prior studies on BA-enabled value realization, our research investigates the interactions of BA and customer involvement capabilities using the complementarity view. We tested our model using data collected from 317 IT companies in China. Our results suggest that BA value realization requires both a top-down mechanism in which BA skills provide global guidance for alignment with a company’s goals and a bottom-up mechanism in which BA culture empowers local autonomy for adaptation to ever-changing needs. Our BA-complemented mechanisms provide research and practice with a way to concurrently use BA and customer involvement capabilities to address the duality of digital innovation. We further suggest that BA skills are necessary but insufficient for digital innovation because BA culture demonstrates a stronger effect in complementing organizations’ existing capabilities than BA skills do

Throughput improvement for multi-hop UAV relaying

Unmanned aerial vehicle (UAV) relaying is one of the main technologies for UAV communications. It uses UAVs as relays in the sky to provide reliable wireless connection between remote users. In this paper, we consider a multi-hop UAV relaying system. To improve the spectrum efficiency of the system, we maximize the average end-to-end throughput from the source to the destination by jointly optimizing the bandwidth allocated to each hop, the transmit power for the source and relays, and the trajectories of the UAVs, subject to constraints on the total spectrum bandwidth, the average and peak transmit power, the UAV mobility and collision avoidance, and the information-causality of multi-hop relaying. The formulated optimization is non-convex. We propose an efficient algorithm to approximate and solve it, using the alternating optimization and successive convex optimization methods. Numerical results show that the proposed optimization significantly outperforms other benchmark schemes, verifying the effectiveness of our scheme

Research on Application of Single Chip Microcomputer in Modern Communication System

The application of single chip microcomputer in modern communication system is deeply studied. Firstly, the main types and characteristics of microcontroller are described in detail, including microcontroller classified according to microprocessor architecture, memory type and use environment. Then, it discusses the main application fields of microcontroller in wireless communication, wired communication and optical communication, and analyzes its practical application in these fields. On this basis, the main challenges and problems encountered in modern communication systems are discussed, such as the complexity of design and production, power consumption, compatibility and expansibility. Finally, the solutions to these challenges and problems are put forward, and the future development trend of single-chip microcomputer in modern communication system is discussed

Inductive Meta-path Learning for Schema-complex Heterogeneous Information Networks

Heterogeneous Information Networks (HINs) are information networks with multiple types of nodes and edges. The concept of meta-path, i.e., a sequence of entity types and relation types connecting two entities, is proposed to provide the meta-level explainable semantics for various HIN tasks. Traditionally, meta-paths are primarily used for schema-simple HINs, e.g., bibliographic networks with only a few entity types, where meta-paths are often enumerated with domain knowledge. However, the adoption of meta-paths for schema-complex HINs, such as knowledge bases (KBs) with hundreds of entity and relation types, has been limited due to the computational complexity associated with meta-path enumeration. Additionally, effectively assessing meta-paths requires enumerating relevant path instances, which adds further complexity to the meta-path learning process. To address these challenges, we propose SchemaWalk, an inductive meta-path learning framework for schema-complex HINs. We represent meta-paths with schema-level representations to support the learning of the scores of meta-paths for varying relations, mitigating the need of exhaustive path instance enumeration for each relation. Further, we design a reinforcement-learning based path-finding agent, which directly navigates the network schema (i.e., schema graph) to learn policies for establishing meta-paths with high coverage and confidence for multiple relations. Extensive experiments on real data sets demonstrate the effectiveness of our proposed paradigm

Photometric redshift estimation of galaxies in the DESI Legacy Imaging Surveys

The accurate estimation of photometric redshifts plays a crucial role in accomplishing science objectives of the large survey projects. The template-fitting and machine learning are the two main types of methods applied currently. Based on the training set obtained by cross-correlating the DESI Legacy Imaging Surveys DR9 galaxy catalogue and SDSS DR16 galaxy catalogue, the two kinds of methods are used and optimized, such as EAZY for template-fitting approach and CATBOOST for machine learning. Then the created models are tested by the cross-matched samples of the DESI Legacy Imaging SurveysDR9 galaxy catalogue with LAMOST DR7, GAMA DR3 and WiggleZ galaxy catalogues. Moreover three machine learning methods (CATBOOST, Multi-Layer Perceptron and Random Forest) are compared, CATBOOST shows its superiority for our case. By feature selection and optimization of model parameters, CATBOOST can obtain higher accuracy with optical and infrared photometric information, the best performance ($MSE=0.0032$, $\sigma_{NMAD}=0.0156$ and $O=0.88$ per cent) with $g \le 24.0$, $r \le 23.4$ and $z \le 22.5$ is achieved. But EAZY can provide more accurate photometric redshift estimation for high redshift galaxies, especially beyond the redhisft range of training sample. Finally, we finish the redshift estimation of all DESI DR9 galaxies with CATBOOST and EAZY, which will contribute to the further study of galaxies and their properties.Comment: Accepted for publication in MNRAS. 14 pages, 9 figures, 11 table