A Resource Allocation Algorithm for Ultra-Dense Networks Based on Deep Reinforcement Learning

The resource optimization of ultra-dense networks (UDNs) is critical to meet the huge demand of users for wireless data traffic. But the mainstream optimization algorithms have many problems, such as the poor optimization effect, and high computing load. This paper puts forward a wireless resource allocation algorithm based on deep reinforcement learning (DRL), which aims to maximize the total throughput of the entire network and transform the resource allocation problem into a deep Q-learning process. To effectively allocate resources in UDNs, the DRL algorithm was introduced to improve the allocation efficiency of wireless resources; the authors adopted the resource allocation strategy of the deep Q-network (DQN), and employed empirical repetition and target network to overcome the instability and divergence of the results caused by the previous network state, and to solve the overestimation of the Q value. Simulation results show that the proposed algorithm can maximize the total throughput of the network, while making the network more energy-efficient and stable. Thus, it is very meaningful to introduce the DRL to the research of UDN resource allocation

Sitting Posture Recognition Using a Spiking Neural Network

To increase the quality of citizens' lives, we designed a personalized smart chair system to recognize sitting behaviors. The system can receive surface pressure data from the designed sensor and provide feedback for guiding the user towards proper sitting postures. We used a liquid state machine and a logistic regression classifier to construct a spiking neural network for classifying 15 sitting postures. To allow this system to read our pressure data into the spiking neurons, we designed an algorithm to encode map-like data into cosine-rank sparsity data. The experimental results consisting of 15 sitting postures from 19 participants show that the prediction precision of our SNN is 88.52%

Phylogenomic evidence for the origin of obligately anaerobic anammox bacteria around the great oxidation event

Funding: This work is funded by the National Science Foundation of China (92051113), the Hong Kong Research Grants Council Area of Excellence Scheme (AoE/M-403/16), the Direct Grant of CUHK (4053495), the Hong Kong Research Grants Council (RGC) General Research Fund (GRF) (14110820), and The CUHK Impact Postdoctoral Fellowship Scheme to (S. W.).The anaerobic ammonium oxidation (anammox) bacteria can transform ammonium and nitrite to dinitrogen gas, and this obligate anaerobic process accounts for up to half of the global nitrogen loss in surface environments. Yet its origin and evolution, which may give important insights into the biogeochemistry of early Earth, remains enigmatic. Here, we performed comprehensive phylogenomic and molecular clock analysis of anammox bacteria within the phylum Planctomycetes. After accommodating the uncertainties and factors influencing time estimates, which includes implementing both a traditional cyanobacteria-based and a recently developed mitochondria-based molecular dating approach, we estimated a consistent origin of anammox bacteria at early Proterozoic and most likely around the so-called Great Oxidation Event (GOE; 2.32 to 2.5 billion years ago [Ga]) which fundamentally changed global biogeochemical cycles. We further showed that during the origin of anammox bacteria, genes involved in oxidative stress adaptation, bioenergetics and anammox granules formation were recruited, which might have contributed to their survival on an increasingly oxic Earth. Our findings suggest the rising levels of atmospheric oxygen, which made nitrite increasingly available, was a potential driving force for the emergence of anammox bacteria. This is one of the first studies that link the GOE to the evolution of obligate anaerobic bacteria.Publisher PDFPeer reviewe

Follicular dendritic cell sarcoma: a report of six cases and a review of the Chinese literature

<p>Abstract</p> <p>Goals</p> <p>The main purpose of this study is to broaden the clinicopathological spectrum and increase recognition of follicular dendritic cell sarcoma (FDCS) through analysis of the clinical and pathological features of 50 cases.</p> <p>Methods</p> <p>The clinicopathological features of total 50 cases of FDCS were analyzed including a review of 44 cases reported in Chinese literature before October 2009 and six original cases from the pathology files conducted by the authors.</p> <p>Results</p> <p>The youngest patient came under observation in this study is only seven years old. Including the cases contributed by the authors, our literary review indicated that male dominated the tumor cases (M: F = 3: 2). 28 cases (56%) present with this disease in extranodal sites. Tumor cells demonstrated positive staining for the follicular dendritic cell markers CD21 (47/49), CD35 (43/45), CD23 (20/23) and CD68 (23/25). In situ hybridization for Epstein-Barr virus-encoded RNA was performed in 10 cases. Nevertheless, EBV expression was absent in all these cases. The follow-up analysis of all cases shows that 26 (81.2%) patients were alive and disease free; 6 (18.8%) patients were alive with recurrent disease or metastasis; and nobody had died of this disease at the time of last follow-up.</p> <p>Conclusions</p> <p>The diagnosis of the FDCS is based on the findings of morphology and immunohistochemistry. The FDCS occurred in China should be viewed and treated as a low-grade sarcoma, and the role of the EBV in the pathogenesis of this tumor is still uncertain. There is a possibility that the tumor might be racial or geographic correlated, because most cases were reported from Eastern Asia area; it's particular the case of the liver or spleen tumor.</p

Physiological Characteristics and Nonparametric Test for Master-Slave Driving Task’s Mental Workload Evaluation in Mountain Area Highway at Night

With the rapid development of advanced mobile intelligent terminals, driving tasks are diverse, and new traffic safety problems occur. We propose a new research on physiological characteristics and nonparametric tests for the master-slave driving task, especially for evaluation of drivers’ mental workload in mountain area highway in nighttime scenario. First, we establish the experimental platform based driving simulator and design the master-slave driving task. Second, based on the physiological data and subjective evaluation for mental workload, we use statistical methods to composite the physical changes evolution analysis in a driving simulator. Finally, we finished nonparametric test of the drivers’ psychological load and road test. The results show that in compassion with the daytime scenario, drivers should pay much effort to driving skills and risk identification in the nighttime scenario. Thus, in the same driving condition, drivers should bear the higher level of mental workload, and it has been subjected to even greater pressures and intensity of emotions. Document type: Articl