1,878 research outputs found
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
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