Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment

Understanding mobile traffic patterns of large scale cellular towers in urban environment is extremely valuable for Internet service providers, mobile users, and government managers of modern metropolis. This paper aims at extracting and modeling the traffic patterns of large scale towers deployed in a metropolitan city. To achieve this goal, we need to address several challenges, including lack of appropriate tools for processing large scale traffic measurement data, unknown traffic patterns, as well as handling complicated factors of urban ecology and human behaviors that affect traffic patterns. Our core contribution is a powerful model which combines three dimensional information (time, locations of towers, and traffic frequency spectrum) to extract and model the traffic patterns of thousands of cellular towers. Our empirical analysis reveals the following important observations. First, only five basic time-domain traffic patterns exist among the 9,600 cellular towers. Second, each of the extracted traffic pattern maps to one type of geographical locations related to urban ecology, including residential area, business district, transport, entertainment, and comprehensive area. Third, our frequency-domain traffic spectrum analysis suggests that the traffic of any tower among the 9,600 can be constructed using a linear combination of four primary components corresponding to human activity behaviors. We believe that the proposed traffic patterns extraction and modeling methodology, combined with the empirical analysis on the mobile traffic, pave the way toward a deep understanding of the traffic patterns of large scale cellular towers in modern metropolis.Comment: To appear at IMC 201

Optimal relaying in heterogeneous delay tolerant networks

In Delay Tolerant Networks (DTNs), there exists only intermittent connectivity between communication sources and destinations. In order to provide successful communication services for these challenged networks, a variety of relaying and routing algorithms have been proposed with the assumption that nodes are homogeneous in terms of contact rates and delivery costs. However, various applications of DTN have shown that mobile nodes should be divided into different classes in terms of their energy requirements and communication ability, and real application data have revealed the heterogeneous contact rates between node pairs. In this paper, we design an optimal relaying scheme for DTNs, which takes into account nodes’ heterogeneous contact rates and delivery costs when selecting relays to minimise the delivery cost while satisfying the required message delivery probability. Extensive results based on real traces demonstrate that our relaying scheme requires the least delivery cost and achieves the largest maximum delivery probability, compared with the schemes that neglect nodes’ heterogeneity

Vertical vs. Adiabatic Ionization Energies in Solution and Gas-Phase: Probing Ionization-Induced Reorganization in Conformationally-Mobile Bichromophoric Actuators Using Photoelectron Spectroscopy, Electrochemistry and Theory

Ionization-induced structural and conformational reorganization in various π-stacked dimers and covalently linked bichromophores is relevant to many processes in biological systems and functional materials. In this work, we examine the role of structural, conformational, and solvent reorganization in a set of conformationally mobile bichromophoric donors, using a combination of gas-phase photoelectron spectroscopy, solution-phase electrochemistry, and density functional theory (DFT) calculations. Photoelectron spectral analysis yields both adiabatic and vertical ionization energies (AIE/VIE), which are compared with measured (adiabatic) solution-phase oxidation potentials (Eox). Importantly, we find a strong correlation of Eox with AIE, but not VIE, reflecting variations in the attendant structural/conformational reorganization upon ionization. A careful comparison of the experimental data with the DFT calculations allowed us to probe the extent of charge stabilization in the gas phase and solution and to parse the reorganizational energy into its various components. This study highlights the importance of a synergistic approach of experiment and theory to study ionization-induced structural and conformational reorganization

Fabrication of a NiFe Alloy Oxide Catalyst via Surface Reconstruction for Selective Hydrodeoxygenation of Fatty Acid to Fatty Alcohol

Traditional NiFe alloy catalyst (NiFe AC) possesses low alcohol selectivity for the hydrodeoxygenation (HDO) of fatty acid due to its excessive deoxygenation into alkane. Herein, we innovatively provide the NiFe alloy oxide catalyst (NiFe AOC) to suppress the adsorption of aldehyde, which is the crucial intermediate of objective product alcohol converting into a side product, via the steric hindrance of lattice oxygen to inhibit the further conversion of alcohol. NiFe AOC reaches 100% conversion of lauric acid with 90% selectivity to lauryl alcohol. Kinetic analysis indicated that the apparent activation energy of side reaction increases by 71.1 kJ/mol for NiFe AOC relative to NiFe AC, evidencing the inhibition for the conversion of objective product alcohol into alkane for NiFe AOC. Furthermore, DFT calculation also suggests that the activation energy of the side reaction increases by 0.33 eV on NiFe AOC compared to NiFe AC. In addition, used NiFe AOC can be totally regenerated via surface reconstruction during the reduction-reoxidation treatment. However, overoxidation inducing NiFe surface phase separation weakened the synergistic interaction of Ni-Fe bimetallic sites and further decreased the catalytic activity

Harnessing Neural Unit Dynamics for Effective and Scalable Class-Incremental Learning

Class-incremental learning (CIL) aims to train a model to learn new classes from non-stationary data streams without forgetting old ones. In this paper, we propose a new kind of connectionist model by tailoring neural unit dynamics that adapt the behavior of neural networks for CIL. In each training session, it introduces a supervisory mechanism to guide network expansion whose growth size is compactly commensurate with the intrinsic complexity of a newly arriving task. This constructs a near-minimal network while allowing the model to expand its capacity when cannot sufficiently hold new classes. At inference time, it automatically reactivates the required neural units to retrieve knowledge and leaves the remaining inactivated to prevent interference. We name our model AutoActivator, which is effective and scalable. To gain insights into the neural unit dynamics, we theoretically analyze the model's convergence property via a universal approximation theorem on learning sequential mappings, which is under-explored in the CIL community. Experiments show that our method achieves strong CIL performance in rehearsal-free and minimal-expansion settings with different backbones.Comment: Accepted to ICML 202

Dynamic Prediction and Optimization of Energy Consumption in Mining Equipment Using Mobile Computing Platforms

With the increasing energy consumption in the mining industry, the effective prediction and optimization of energy consumption in mining equipment have become pressing challenges. Traditional energy consumption prediction methods suffer from data processing delays and the fixed nature of monitoring devices, making them inadequate for meeting the real-time and flexible demands of modern mining operations. The advent of mobile computing platforms has introduced new possibilities for the dynamic prediction and optimization of energy consumption in mining equipment. In recent years, energy consumption prediction techniques based on mobile computing platforms have gained significant attention, enabling realtime data acquisition and analysis for a more precise understanding of energy consumption patterns and the implementation of efficient optimization strategies. However, existing studies predominantly focus on conventional models and methodologies, lacking effective mechanisms to capture spatiotemporal dynamics and optimize energy consumption accordingly. In this study, a spatiotemporal gated graph convolutional prediction model was proposed for the dynamic prediction of energy consumption in mining equipment based on a mobile computing platform. Additionally, an energy consumption optimization strategy was explored using the prediction results. This study provides a novel approach to energy consumption optimization in mining equipment, offering both theoretical significance and practical value

Multi-View Class Incremental Learning

Multi-view learning (MVL) has gained great success in integrating information from multiple perspectives of a dataset to improve downstream task performance. To make MVL methods more practical in an open-ended environment, this paper investigates a novel paradigm called multi-view class incremental learning (MVCIL), where a single model incrementally classifies new classes from a continual stream of views, requiring no access to earlier views of data. However, MVCIL is challenged by the catastrophic forgetting of old information and the interference with learning new concepts. To address this, we first develop a randomization-based representation learning technique serving for feature extraction to guarantee their separate view-optimal working states, during which multiple views belonging to a class are presented sequentially; Then, we integrate them one by one in the orthogonality fusion subspace spanned by the extracted features; Finally, we introduce selective weight consolidation for learning-without-forgetting decision-making while encountering new classes. Extensive experiments on synthetic and real-world datasets validate the effectiveness of our approach.Comment: 34 pages,4 figures. Under revie