Analysis and Optimization of GNN-Based Recommender Systems on Persistent Memory

Graph neural networks (GNNs), which have emerged as an effective method for handling machine learning tasks on graphs, bring a new approach to building recommender systems, where the task of recommendation can be formulated as the link prediction problem on user-item bipartite graphs. Training GNN-based recommender systems (GNNRecSys) on large graphs incurs a large memory footprint, easily exceeding the DRAM capacity on a typical server. Existing solutions resort to distributed subgraph training, which is inefficient due to the high cost of dynamically constructing subgraphs and significant redundancy across subgraphs. The emerging persistent memory technologies provide a significantly larger memory capacity than DRAMs at an affordable cost, making single-machine GNNRecSys training feasible, which eliminates the inefficiencies in distributed training. One major concern of using persistent memory devices for GNNRecSys is their relatively low bandwidth compared with DRAMs. This limitation can be particularly detrimental to achieving high performance for GNNRecSys workloads since their dominant compute kernels are sparse and memory access intensive. To understand whether persistent memory is a good fit for GNNRecSys training, we perform an in-depth characterization of GNNRecSys workloads and a comprehensive analysis of their performance on a persistent memory device, namely, Intel Optane. Based on the analysis, we provide guidance on how to configure Optane for GNNRecSys workloads. Furthermore, we present techniques for large-batch training to fully realize the advantages of single-machine GNNRecSys training. Our experiment results show that with the tuned batch size and optimal system configuration, Optane-based single-machine GNNRecSys training outperforms distributed training by a large margin, especially when handling deep GNN models

The possibility of leptonic CP-violation measurement with JUNO

The existence of CP-violation in the leptonic sector is one of the most important issues for modern science. Neutrino physics is a key to the solution of this problem. JUNO (under construction) is the near future of neutrino physics. However CP-violation is not a priority for the current scientific program. We estimate the capability of $\delta_{\rm CP}$ measurement, assuming a combination of the JUNO detector and a superconductive cyclotron as the antineutrino source. This method of measuring CP-violation is an alternative to conventional beam experiments. A significance level of 3$\sigma$ can be reached for 22% of the $\delta_{\rm CP}$ range. The accuracy of measurement lies between 8$^{\rm o}$ and 22$^{\rm o}$. It is shown that the dominant influence on the result is the uncertainty in the mixing angle $\Theta_{23}$

Intrinsically stretchable and transparent thin-film transistors based on printable silver nanowires, carbon nanotubes and an elastomeric dielectric.

Thin-film field-effect transistor is a fundamental component behind various mordern electronics. The development of stretchable electronics poses fundamental challenges in developing new electronic materials for stretchable thin-film transistors that are mechanically compliant and solution processable. Here we report the fabrication of transparent thin-film transistors that behave like an elastomer film. The entire fabrication is carried out by solution-based techniques, and the resulting devices exhibit a mobility of ∼30 cm(2) V(-1) s(-1), on/off ratio of 10(3)-10(4), switching current >100 μA, transconductance >50 μS and relative low operating voltages. The devices can be stretched by up to 50% strain and subjected to 500 cycles of repeated stretching to 20% strain without significant loss in electrical property. The thin-film transistors are also used to drive organic light-emitting diodes. The approach and results represent an important progress toward the development of stretchable active-matrix displays

Efficient Deep Reinforcement Learning via Adaptive Policy Transfer

Transfer Learning (TL) has shown great potential to accelerate Reinforcement Learning (RL) by leveraging prior knowledge from past learned policies of relevant tasks. Existing transfer approaches either explicitly computes the similarity between tasks or select appropriate source policies to provide guided explorations for the target task. However, how to directly optimize the target policy by alternatively utilizing knowledge from appropriate source policies without explicitly measuring the similarity is currently missing. In this paper, we propose a novel Policy Transfer Framework (PTF) to accelerate RL by taking advantage of this idea. Our framework learns when and which source policy is the best to reuse for the target policy and when to terminate it by modeling multi-policy transfer as the option learning problem. PTF can be easily combined with existing deep RL approaches. Experimental results show it significantly accelerates the learning process and surpasses state-of-the-art policy transfer methods in terms of learning efficiency and final performance in both discrete and continuous action spaces.Comment: Accepted by IJCAI'202

The effects of tai chi on markers of atherosclerosis, lower-limb physical function, and cognitive ability in adults aged over 60: A randomized controlled trial

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Objective: The purpose of this study was to investigate the effects of Tai Chi (TC) on arterial stiffness, physical function of lower-limb, and cognitive ability in adults aged over 60. Methods: This study was a prospective and randomized 12-week intervention trial with three repeated measurements (baseline, 6, and 12 weeks). Sixty healthy adults who met the inclusion criteria were randomly allocated into three training conditions (TC-24, TC-42, and TC-56) matched by gender, with 20 participants (10 males, 10 females) in each of the three groups. We measured the following health outcomes, including markers of atherosclerosis, physical function (leg power, and static and dynamic balance) of lower-limb, and cognitive ability. Results: When all three TC groups (p \u3c 0.05) have showed significant improvements on these outcomes but overall cognitive ability at 6 or 12 weeks training period, TC-56 appears to have superior effects on arterial stiffness and static/dynamic balance in the present study. Conclusions: Study results of the present study add to growing body of evidence regarding therapeutic TC for health promotion and disease prevention in aging population. Future studies should further determine whether TC-42 and TC-56 are beneficial for other non-Chinese populations, with rigorous research design and follow-up assessment