Social Data Offloading in D2D-Enhanced Cellular Networks by Network Formation Games

Recently, cellular networks are severely overloaded by social-based services, such as YouTube, Facebook and Twitter, in which thousands of clients subscribe a common content provider (e.g., a popular singer) and download his/her content updates all the time. Offloading such traffic through complementary networks, such as a delay tolerant network formed by device-to-device (D2D) communications between mobile subscribers, is a promising solution to reduce the cellular burdens. In the existing solutions, mobile users are assumed to be volunteers who selfishlessly deliver the content to every other user in proximity while moving. However, practical users are selfish and they will evaluate their individual payoffs in the D2D sharing process, which may highly influence the network performance compared to the case of selfishless users. In this paper, we take user selfishness into consideration and propose a network formation game to capture the dynamic characteristics of selfish behaviors. In the proposed game, we provide the utility function of each user and specify the conditions under which the subscribers are guaranteed to converge to a stable network. Then, we propose a practical network formation algorithm in which the users can decide their D2D sharing strategies based on their historical records. Simulation results show that user selfishness can highly degrade the efficiency of data offloading, compared with ideal volunteer users. Also, the decrease caused by user selfishness can be highly affected by the cost ratio between the cellular transmission and D2D transmission, the access delays, and mobility patterns

Hybrid Comfort: 3D Printing Interwoven

Under the concept of Maker Movement, apparel researchers and designers are exploring the potentials of three-dimensional printing (3DP) and seeking ways to take the advantages of 3DP and apply it to wearable products. This design case study aimed to integrate 3DP textiles in a beach vest to allow new properties and functions to emerge with aesthetics, and explore the properties of 3D textiles by manipulating the structure of TPU materials using the FDM 3DP method. The hybrid 3DP textile was developed by mimicking and integrating the structures of traditional woven and knitted fabrics, tried to take advantages of both fabrics. The final 3DP textile structure evaluations suggested some expected properties (e.g., flexible, strong), while revealed new properties (e.g., porous, cushioning). Further, the specialty 3D printed TPU material was unique in its resilient and flexible properties, and fit the functions of the man’s beach vest design

Smooth Dynamic

With technology advancement and product customization demands, more people with mobility issues are seeking specialty assistive tools to help facilitate and manage their daily lives. This case study adopts research through design methodology and explores the explores the workflow approach in developing wearable assistive glove for female wheelchair users using 3D CAD modeling program, 3D scanning, and 3DP technology. The assistive glove was developed using both and 3D printed nylon filament. It consists of a custom fit glove and a 3D printed nylon wrist protection portion. Friction pads are custom designed on fit model to support the potential hand overuse in the pushrim gripping motion. Heat dissipation is also considered through incorporating a 3D printed textile inset on the glove dorsal side. Key findings reflect challenges in manipulating 3DP materials for assistive tool development and virtual fit evaluation in the 3D CAD modeling process

Sensitivity analysis of Monte Carlo model of a gantry-mounted passively scattered proton system

PURPOSE: This study aimed to present guidance on the correlation between treatment nozzle and proton source parameters, and dose distribution of a passive double scattering compact proton therapy unit, known as Mevion S250. METHODS: All 24 beam options were modeled using the MCNPX MC code. The calculated physical dose for pristine peak, profiles, and spread out Bragg peak (SOBP) were benchmarked with the measured data. Track-averaged LET (LET RESULTS: For the physical dose distribution, the MCNPX MC model matched measurements data for all the options to within 2 mm and 2% criterion. The Mevion S250 was found to have a LET CONCLUSIONS: This study revealed the importance of considering detailed beam parameters, and identifying those that resulted in large effects on the physical dose distribution and LETs for a compact proton therapy machine

DAMM: Directionality-Aware Mixture Model Parallel Sampling for Efficient Dynamical System Learning

The Linear Parameter Varying Dynamical System (LPV-DS) is a promising framework for learning stable time-invariant motion policies in robot control. By employing statistical modeling and semi-definite optimization, LPV-DS encodes complex motions via non-linear DS, ensuring the robustness and stability of the system. However, the current LPV-DS scheme faces challenges in accurately interpreting trajectory data while maintaining model efficiency and computational efficiency. To address these limitations, we propose the Directionality-aware Mixture Model (DAMM), a new statistical model that leverages Riemannian metric on $d$-dimensional sphere $\mathbb{S}^d$, and efficiently incorporates non-Euclidean directional information with position. Additionally, we introduce a hybrid Markov chain Monte Carlo method that combines the Gibbs Sampling and the Split/Merge Proposal, facilitating parallel computation and enabling faster inference for near real-time learning performance. Through extensive empirical validation, we demonstrate that the improved LPV-DS framework with DAMM is capable of producing physically-meaningful representations of the trajectory data and improved performance of the generated DS while showcasing significantly enhanced learning speed compared to its previous iterations

Molecular Joint Representation Learning via Multi-modal Information

In recent years, artificial intelligence has played an important role on accelerating the whole process of drug discovery. Various of molecular representation schemes of different modals (e.g. textual sequence or graph) are developed. By digitally encoding them, different chemical information can be learned through corresponding network structures. Molecular graphs and Simplified Molecular Input Line Entry System (SMILES) are popular means for molecular representation learning in current. Previous works have done attempts by combining both of them to solve the problem of specific information loss in single-modal representation on various tasks. To further fusing such multi-modal imformation, the correspondence between learned chemical feature from different representation should be considered. To realize this, we propose a novel framework of molecular joint representation learning via Multi-Modal information of SMILES and molecular Graphs, called MMSG. We improve the self-attention mechanism by introducing bond level graph representation as attention bias in Transformer to reinforce feature correspondence between multi-modal information. We further propose a Bidirectional Message Communication Graph Neural Network (BMC GNN) to strengthen the information flow aggregated from graphs for further combination. Numerous experiments on public property prediction datasets have demonstrated the effectiveness of our model