Multiangle social network recommendation algorithms and similarity network evaluation

Multiangle social network recommendation algorithms (MSN) and a new assessmentmethod, called similarity network evaluation (SNE), are both proposed. From the viewpoint of six dimensions, the MSN are classified into six algorithms, including user-based algorithmfromresource point (UBR), user-based algorithmfromtag point (UBT), resource-based algorithm fromtag point (RBT), resource-based algorithm from user point (RBU), tag-based algorithm from resource point (TBR), and tag-based algorithm from user point (TBU). Compared with the traditional recall/precision (RP) method, the SNE is more simple, effective, and visualized. The simulation results show that TBR and UBR are the best algorithms, RBU and TBU are the worst ones, and UBT and RBT are in the medium levels

Multi-Drone-Cell 3D Trajectory Planning and Resource Allocation for Drone-Assisted Radio Access Networks

Equipped with communication modules, drones can perform as drone-cells (DCs) that provide on-demand communication services to users in various scenarios, such as traffic monitoring, Internet of things (IoT) data collections, and temporal communication provisioning. As the aerial relay nodes between terrestrial users and base stations (BSs), DCs are leveraged to extend wireless connections for uncovered users of radio access networks (RAN), which forms the drone-assisted RAN (DA-RAN). In DA-RAN, the communication coverage, quality-of-service (QoS) performance and deployment flexibility can be improved due to the line-of-sight DC-to-ground (D2G) wireless links and the dynamic deployment capabilities of DCs. Considering the special mobility pattern, channel model, energy consumption, and other features of DCs, it is essential yet challenging to design the flying trajectories and resource allocation schemes for DA-RAN. In specific, given the emerging D2G communication models and dynamic deployment capability of DCs, new DC deployment strategies are required by DA-RAN. Moreover, to exploit the fully controlled mobility of DCs and promote the user fairness, the flying trajectories of DCs and the D2G communications must be jointly optimized. Further, to serve the high-mobility users (e.g. vehicular users) whose mobility patterns are hard to be modeled, both the trajectory planning and resource allocation schemes for DA-RAN should be re-designed to adapt to the variations of terrestrial traffic. To address the above challenges, in this thesis, we propose a DA-RAN architecture in which multiple DCs are leveraged to relay data between BSs and terrestrial users. Based on the theoretical analyses of the D2G communication, DC energy consumption, and DC mobility features, the deployment, trajectory planning and communication resource allocation of multiple DCs are jointly investigated for both quasi-static and high-mobility users. We first analyze the communication coverage, drone-to-BS (D2B) backhaul link quality, and optimal flying height of the DC according to the state-of-the-art drone-to-user (D2U) and D2B channel models. We then formulate the multi-DC three-dimensional (3D) deployment problem with the objective of maximizing the ratio of effectively covered users while guaranteeing D2B link qualities. To solve the problem, a per-drone iterated particle swarm optimization (DI-PSO) algorithm is proposed, which prevents the large particle searching space and the high violating probability of constraints existing in the pure PSO based algorithm. Simulations show that the DI-PSO algorithm can achieve higher coverage ratio with less complexity comparing to the pure PSO based algorithm. Secondly, to improve overall network performance and the fairness among edge and central users, we design 3D trajectories for multiple DCs in DA-RAN. The multi-DC 3D trajectory planning and scheduling is formulated as a mixed integer non-linear programming (MINLP) problem with the objective of maximizing the average D2U throughput. To address the non-convexity and NP-hardness of the MINLP problem due to the 3D trajectory, we first decouple the MINLP problem into multiple integer linear programming and quasi-convex sub-problems in which user association, D2U communication scheduling, horizontal trajectories and flying heights of DBSs are respectively optimized. Then, we design a multi-DC 3D trajectory planning and scheduling algorithm to solve the sub-problems iteratively based on the block coordinate descent (BCD) method. A k-means-based initial trajectory generation scheme and a search-based start slot scheduling scheme are also designed to improve network performance and control mutual interference between DCs, respectively. Compared with the static DBS deployment, the proposed trajectory planning scheme can achieve much lower average value and standard deviation of D2U pathloss, which indicate the improvements of network throughput and user fairness. Thirdly, considering the highly dynamic and uncertain environment composed by high-mobility users, we propose a hierarchical deep reinforcement learning (DRL) based multi-DC trajectory planning and resource allocation (HDRLTPRA) scheme for high-mobility users. The objective is to maximize the accumulative network throughput while satisfying user fairness, DC power consumption, and DC-to-ground link quality constraints. To address the high uncertainties of environment, we decouple the multi-DC TPRA problem into two hierarchical sub-problems, i.e., the higher-level global trajectory planning sub-problem and the lower-level local TPRA sub-problem. First, the global trajectory planning sub-problem is to address trajectory planning for multiple DCs in the RAN over a long time period. To solve the sub-problem, we propose a multi-agent DRL based global trajectory planning (MARL-GTP) algorithm in which the non-stationary state space caused by multi-DC environment is addressed by the multi-agent fingerprint technique. Second, based on the global trajectory planning results, the local TPRA (LTPRA) sub-problem is investigated independently for each DC to control the movement and transmit power allocation based on the real-time user traffic variations. A deep deterministic policy gradient based LTPRA (DDPG-LTPRA) algorithm is then proposed to solve the LTPRA sub-problem. With the two algorithms addressing both sub-problems at different decision granularities, the multi-DC TPRA problem can be resolved by the HDRLTPRA scheme. Simulation results show that 40% network throughput improvement can be achieved by the proposed HDRLTPRA scheme over the non-learning-based TPRA scheme. In summary, we have investigated the multi-DC 3D deployment, trajectory planning and communication resource allocation in DA-RAN considering different user mobility patterns in this thesis. The proposed schemes and theoretical results should provide useful guidelines for future research in DC trajectory planning, resource allocation, as well as the real deployment of DCs in complex environments with diversified users

Relational-Interdependence and Life Transitions in College: Study Abroad, First-Year, and International Students

The current study examined the sojourner adjustment of U.S. college students studying abroad, international college students studying in the States, and first-year students adjusting to life in the first semester of their undergraduate careers. An online survey was distributed to 412 college students; it included the Sojourner Adjustment Measure (SAM), the Lifelong Learning Scale (WielkLLS), the Relational-Interdependent Self-Construal Scale (RISC), the Brief HEXACO Inventory of Personality, and the Social Media Use Integration Scale (SMUIS). The purpose of the study was to explore the relationships among major emerging adulthood transitions and various measures of adjustment to college. Results suggest that students tend to report different levels of adjustment at various stages of their academic careers, whether study abroad participants (US and international) or first-year students coming to college for the first tim

Effective Structured Prompting by Meta-Learning and Representative Verbalizer

Prompt tuning for pre-trained masked language models (MLM) has shown promising performance in natural language processing tasks with few labeled examples. It tunes a prompt for the downstream task, and a verbalizer is used to bridge the predicted token and label prediction. Due to the limited training data, prompt initialization is crucial for prompt tuning. Recently, MetaPrompting (Hou et al., 2022) uses meta-learning to learn a shared initialization for all task-specific prompts. However, a single initialization is insufficient to obtain good prompts for all tasks and samples when the tasks are complex. Moreover, MetaPrompting requires tuning the whole MLM, causing a heavy burden on computation and memory as the MLM is usually large. To address these issues, we use a prompt pool to extract more task knowledge and construct instance-dependent prompts via attention. We further propose a novel soft verbalizer (RepVerb) which constructs label embedding from feature embeddings directly. Combining meta-learning the prompt pool and RepVerb, we propose MetaPrompter for effective structured prompting. MetaPrompter is parameter-efficient as only the pool is required to be tuned. Experimental results demonstrate that MetaPrompter performs better than the recent state-of-the-arts and RepVerb outperforms existing soft verbalizers.Comment: Accepted at ICML 202

The Crust and Upper Mantle Structure of Central and West Antarctica From Bayesian Inversion of Rayleigh Wave and Receiver Functions

We construct a new seismic model for central and West Antarctica by jointly inverting Rayleigh wave phase and group velocities along with P wave receiver functions. Ambient noise tomography exploiting data from more than 200 seismic stations deployed over the past 18 years is used to construct Rayleigh wave phase and group velocity dispersion maps. Comparison between the ambient noise phase velocity maps with those constructed using teleseismic earthquakes confirms the accuracy of both results. These maps, together with P receiver function waveforms, are used to construct a new 3-D shear velocity (Vs) model for the crust and uppermost mantle using a Bayesian Monte Carlo algorithm. The new 3-D seismic model shows the dichotomy of the tectonically active West Antarctica (WANT) and the stable and ancient East Antarctica (EANT). In WANT, the model exhibits a slow uppermost mantle along the Transantarctic Mountains (TAMs) front, interpreted as the thermal effect from Cenozoic rifting. Beneath the southern TAMs, the slow uppermost mantle extends horizontally beneath the traditionally recognized EANT, hypothesized to be associated with lithospheric delamination. Thin crust and lithosphere observed along the Amundsen Sea coast and extending into the interior suggest involvement of these areas in Cenozoic rifting. EANT, with its relatively thick and cold crust and lithosphere marked by high Vs, displays a slower Vs anomaly beneath the Gamburtsev Subglacial Mountains in the uppermost mantle, which we hypothesize may be the signature of a compositionally anomalous body, perhaps remnant from a continental collision

Neutron yield studies in JET H-modes

The measured D-D neutron rate of NBI heated JET baseline and hybrid H-modes in Deuterium is found to be between approximately 50% and 100% of the neutron rate expected from the TRANSP code, depending on plasma parameters. A number of candidate explanations, such as fuel dilution, errors in beam penetration and effectively available beam power have been excluded. As the neutron rate in JET is dominated by beam-plasma interactions, the 'neutron deficit' may be caused by a yet unidentified form of fast particle redistribution. Modelling, which assumes fast particle transport to be responsible for the deficit, indicates that such redistribution would have to happen at time scales faster than the slowing down time and the energy confinement time. Sawteeth and ELMs are found to make no significant contribution to the deficit. There is also no obvious correlation with MHD activity measured using magnetic probes at the tokamak vessel walls. Modelling of fast particle orbits in the 3D fields of NTM's shows that realistically sized islands can contribute only a few % to the deficit