FedGeo: Privacy-Preserving User Next Location Prediction with Federated Learning

A User Next Location Prediction (UNLP) task, which predicts the next location that a user will move to given his/her trajectory, is an indispensable task for a wide range of applications. Previous studies using large-scale trajectory datasets in a single server have achieved remarkable performance in UNLP task. However, in real-world applications, legal and ethical issues have been raised regarding privacy concerns leading to restrictions against sharing human trajectory datasets to any other server. In response, Federated Learning (FL) has emerged to address the personal privacy issue by collaboratively training multiple clients (i.e., users) and then aggregating them. While previous studies employed FL for UNLP, they are still unable to achieve reliable performance because of the heterogeneity of clients' mobility. To tackle this problem, we propose the Federated Learning for Geographic Information (FedGeo), a FL framework specialized for UNLP, which alleviates the heterogeneity of clients' mobility and guarantees personal privacy protection. Firstly, we incorporate prior global geographic adjacency information to the local client model, since the spatial correlation between locations is trained partially in each client who has only a heterogeneous subset of the overall trajectories in FL. We also introduce a novel aggregation method that minimizes the gap between client models to solve the problem of client drift caused by differences between client models when learning with their heterogeneous data. Lastly, we probabilistically exclude clients with extremely heterogeneous data from the FL process by focusing on clients who visit relatively diverse locations. We show that FedGeo is superior to other FL methods for model performance in UNLP task. We also validated our model in a real-world application using our own customers' mobile phones and the FL agent system.Comment: Accepted at 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2023

Probing the Ion Transport Properties of Ultrashort Carbon Nanotubes Integrated with Supported Lipid Bilayers via Electrochemical Analysis.

Supported lipid bilayers (SLBs) are commonly used to investigate interactions between cell membranes and their environment. These model platforms can be formed on electrode surfaces and analyzed using electrochemical methods for bioapplications. Carbon nanotube porins (CNTPs) integrated with SLBs have emerged as promising artificial ion channel platforms. In this study, we present the integration and ion transport characterization of CNTPs in in vivo environments. We combine experimental and simulation data obtained from electrochemical analysis to analyze the membrane resistance of the equivalent circuits. Our results show that carrying CNTPs on a gold electrode results in high conductance for monovalent cations (K+ and Na+) and low conductance for divalent cations (Ca2+)

Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential Recommendation

This paper investigates Cross-Domain Sequential Recommendation (CDSR), a promising method that uses information from multiple domains (more than three) to generate accurate and diverse recommendations, and takes into account the sequential nature of user interactions. The effectiveness of these systems often depends on the complex interplay among the multiple domains. In this dynamic landscape, the problem of negative transfer arises, where heterogeneous knowledge between dissimilar domains leads to performance degradation due to differences in user preferences across these domains. As a remedy, we propose a new CDSR framework that addresses the problem of negative transfer by assessing the extent of negative transfer from one domain to another and adaptively assigning low weight values to the corresponding prediction losses. To this end, the amount of negative transfer is estimated by measuring the marginal contribution of each domain to model performance based on a cooperative game theory. In addition, a hierarchical contrastive learning approach that incorporates information from the sequence of coarse-level categories into that of fine-level categories (e.g., item level) when implementing contrastive learning was developed to mitigate negative transfer. Despite the potentially low relevance between domains at the fine-level, there may be higher relevance at the category level due to its generalised and broader preferences. We show that our model is superior to prior works in terms of model performance on two real-world datasets across ten different domains.Comment: Accepted at 32nd ACM International Conference on Information and Knowledge Management (CIKM 2023

Morphological Evolution and Dealloying During Corrosion of Ni20Cr (wt.%) in Molten FLiNaK Salts

The dealloying corrosion behavior of the FCC Ni20Cr (wt%) in molten LiF-NaF-KF (FLiNaK) salts at 600 °C under varying applied potentials was investigated. Using in-operando electrochemical techniques and a multi-modal suite of characterization methods, we connect electrochemical potential, thermodynamic stability, and electro-dissolution kinetics to the corrosion morphologies. Notably, under certain potential regimes, a micron-scale bicontinuous structure, characterized by a network of interconnected pores and ligaments riched with the composition of the more noble (MN) element, becomes prominent. At other potentials both MN and less noble (LN) elements dealloy but at different rates. The dealloying process consists of lattice and grain boundary diffusion of Cr to the metal/salt interface, interphase Cr oxidation, accompanied by surface diffusion of Ni to form interconnected ligaments. At higher potentials, the bicontinuous porous structure undergoes further surface coarsening. Concurrently, Cr(II), Cr(III), and Ni(II) begin to dissolve, with the dissolution of Ni occurring at a significantly slower rate. When solid-state transport of Cr is exceeded by the interfacial rates, dealloying depths are limited

Defect Interaction and Deformation in Graphene

Interactions between defects in graphene and the lattice distortion and electronic charge localization induced by the defect interactions are studied by tight-binding (TB) calculations using the recently developed three-center TB potential model. The interaction between two 5–7 Stone–Wales defects gliding along the zig-zag (ZZ) direction of graphene, which has been observed by experiment, is studied at first to validate the TB calculations. Reconstructed divacancy defect pairs and di-adatom defect pairs separated along the glide ZZ and armchair (AC) directions in graphene, respectively, are then studied. We show that the characteristics (i.e., attractive or repulsive) and the strength of interactions between these defects are dependent on the type of defects and on the direction and distance of the defect separation on graphene. Although elastic interaction due to graphene lattice distortion induced by the defect has significant contribution to the total interaction energy, redistribution of electron charges caused by the defects also plays an important role in the defect–defect interaction

Leveraging Big Data To Manage Transport Operations

This is the poster of LEVERAGING BIG DATA TO MANAGE TRANSPORT OPERATIONS (LeMO) project

CHARACTERISATION OF THE BARRIERS AND LIMITATIONS ON UTILISATION OF BIG DATA IN TRANSPORT: THE LEMO PROJECT

The transport sector has continuously collected and analysed massive amounts of data, such as data from timetables, traffic news and air schedules. However, recent developments in the quantity, complexity and availability of such big data collected from and about transport systems, together with advances in information and communication technology, are presenting new opportunities to create more efficient and smarter transport and traffic systems for people and freight (Akerkar 2013). Also, ‘opening up’ data in transport by making it more widely available, and linking it with data from other sectors, is the part of the European strategy to improve transparency and encourage economic growth (Akerkar 2018)