Machine Learning Inspired Energy-Efficient Hybrid Precoding for MmWave Massive MIMO Systems

Hybrid precoding is a promising technique for mmWave massive MIMO systems, as it can considerably reduce the number of required radio-frequency (RF) chains without obvious performance loss. However, most of the existing hybrid precoding schemes require a complicated phase shifter network, which still involves high energy consumption. In this paper, we propose an energy-efficient hybrid precoding architecture, where the analog part is realized by a small number of switches and inverters instead of a large number of high-resolution phase shifters. Our analysis proves that the performance gap between the proposed hybrid precoding architecture and the traditional one is small and keeps constant when the number of antennas goes to infinity. Then, inspired by the cross-entropy (CE) optimization developed in machine learning, we propose an adaptive CE (ACE)-based hybrid precoding scheme for this new architecture. It aims to adaptively update the probability distributions of the elements in hybrid precoder by minimizing the CE, which can generate a solution close to the optimal one with a sufficiently high probability. Simulation results verify that our scheme can achieve the near-optimal sum-rate performance and much higher energy efficiency than traditional schemes.Comment: This paper has been accepted by IEEE ICC 2017. The simulation codes are provided to reproduce the results in this paper at: http://oa.ee.tsinghua.edu.cn/dailinglong/publications/publications.htm

Mining association language patterns using a distributional semantic model for negative life event classification

AbstractPurposeNegative life events, such as the death of a family member, an argument with a spouse or the loss of a job, play an important role in triggering depressive episodes. Therefore, it is worthwhile to develop psychiatric services that can automatically identify such events. This study describes the use of association language patterns, i.e., meaningful combinations of words (e.g., <loss, job>), as features to classify sentences with negative life events into predefined categories (e.g., Family, Love, Work).MethodsThis study proposes a framework that combines a supervised data mining algorithm and an unsupervised distributional semantic model to discover association language patterns. The data mining algorithm, called association rule mining, was used to generate a set of seed patterns by incrementally associating frequently co-occurring words from a small corpus of sentences labeled with negative life events. The distributional semantic model was then used to discover more patterns similar to the seed patterns from a large, unlabeled web corpus.ResultsThe experimental results showed that association language patterns were significant features for negative life event classification. Additionally, the unsupervised distributional semantic model was not only able to improve the level of performance but also to reduce the reliance of the classification process on the availability of a large, labeled corpus

When Web 3.0 Meets Reality: A Hyperdimensional Fractal Polytope P2P Ecosystems

Web 3.0 opens the world of new existence of the crypto-network-entity, which is independently defined by the public key pairs for entities and the connection to the Web 3.0 cyberspace. In this paper, we first discover a spacetime coordinate system based on fractal polytope in any dimensions with discrete time offered by blockchain and consensus. Second, the novel network entities and functions are defined to make use of hyperdimensional deterministic switching and routing protocols and blockchain-enabled mutual authentication. In addition to spacetime network architecture, we also define a multi-tier identity scheme which extends the native Web 3.0 crypto-network-entity to outer cyber and physical world, offering legal-compliant anonymity and linkability to all derived identifiers of entities. In this way, we unify the holistic Web 3.0 network based on persistent spacetime and its entity extension to our cyber and physical world

The effects of postintubation hypertension in severe traumatic brain injury

Introduction. The effect of post-intubation hypertension in severe traumatic brain injury (TBI) patients remains uncertain. We aimed to determine the relationship between post-intubation hypertension (mean arterial pressure (MAP) > 110mmHg) and outcomes in severe TBI. Methods. In this retrospective cohort study, adults who presented with isolated TBI and a MAP 70mmHg were assessed. Data were retrieved from our institutional trauma registry and the admission list of our neurosurgical intensive care unit (ICU). Results. We enrolled 126 patients, 81 of whom had a MAP 110 mmHg after intubation and were assigned to group 1; 45 patients who had a MAP > 110 mmHg were assigned to group 2. Only age (P = 0.008), heart rate (HR; P = 0.036), and MAP before intubation (P 110 mmHg, P < 0.034, OR 3.119, 95% CI 1.087–8.953). Conclusion. Post-intubation hypertension was associated with longer ventilator-dependent and ICU stays in patients with severe TBI