Hierarchical Radio Resource Optimization for Heterogeneous Networks with Enhanced Inter-cell Interference Coordination (eICIC)

Interference is a major performance bottleneck in Heterogeneous Network (HetNet) due to its multi-tier topological structure. We propose almost blank resource block (ABRB) for interference control in HetNet. When an ABRB is scheduled in a macro BS, a resource block (RB) with blank payload is transmitted and this eliminates the interference from this macro BS to the pico BSs. We study a two timescale hierarchical radio resource management (RRM) scheme for HetNet with dynamic ABRB control. The long term controls, such as dynamic ABRB, are adaptive to the large scale fading at a RRM server for co-Tier and cross-Tier interference control. The short term control (user scheduling) is adaptive to the local channel state information within each BS to exploit the multi-user diversity. The two timescale optimization problem is challenging due to the exponentially large solution space. We exploit the sparsity in the interference graph of the HetNet topology and derive structural properties for the optimal ABRB control. Based on that, we propose a two timescale alternative optimization solution for the user scheduling and ABRB control. The solution has low complexity and is asymptotically optimal at high SNR. Simulations show that the proposed solution has significant gain over various baselines.Comment: 14 pages, 8 figure

Nationality Classification Using Name Embeddings

Nationality identification unlocks important demographic information, with many applications in biomedical and sociological research. Existing name-based nationality classifiers use name substrings as features and are trained on small, unrepresentative sets of labeled names, typically extracted from Wikipedia. As a result, these methods achieve limited performance and cannot support fine-grained classification. We exploit the phenomena of homophily in communication patterns to learn name embeddings, a new representation that encodes gender, ethnicity, and nationality which is readily applicable to building classifiers and other systems. Through our analysis of 57M contact lists from a major Internet company, we are able to design a fine-grained nationality classifier covering 39 groups representing over 90% of the world population. In an evaluation against other published systems over 13 common classes, our F1 score (0.795) is substantial better than our closest competitor Ethnea (0.580). To the best of our knowledge, this is the most accurate, fine-grained nationality classifier available. As a social media application, we apply our classifiers to the followers of major Twitter celebrities over six different domains. We demonstrate stark differences in the ethnicities of the followers of Trump and Obama, and in the sports and entertainments favored by different groups. Finally, we identify an anomalous political figure whose presumably inflated following appears largely incapable of reading the language he posts in.Comment: 10 pages, 9 figures, 4 table, accepted by CIKM 2017, Demo and free API: www.name-prism.co

Causal conditional hidden Markov model for multimodal traffic prediction

Multimodal traffic flow can reflect the health of the transportation system, and its prediction is crucial to urban traffic management. Recent works overemphasize spatio-temporal correlations of traffic flow, ignoring the physical concepts that lead to the generation of observations and their causal relationship. Spatio-temporal correlations are considered unstable under the influence of different conditions, and spurious correlations may exist in observations. In this paper, we analyze the physical concepts affecting the generation of multimode traffic flow from the perspective of the observation generation principle and propose a Causal Conditional Hidden Markov Model (CCHMM) to predict multimodal traffic flow. In the latent variables inference stage, a posterior network disentangles the causal representations of the concepts of interest from conditional information and observations, and a causal propagation module mines their causal relationship. In the data generation stage, a prior network samples the causal latent variables from the prior distribution and feeds them into the generator to generate multimodal traffic flow. We use a mutually supervised training method for the prior and posterior to enhance the identifiability of the model. Experiments on real-world datasets show that CCHMM can effectively disentangle causal representations of concepts of interest and identify causality, and accurately predict multimodal traffic flow.Comment: 8 pages, 5 figure

Changes in element accumulation, phenolic metabolism, and antioxidative enzyme activities in the red-skin roots of Panax ginseng

AbstractBackgroundRed-skin root disease has seriously decreased the quality and production of Panax ginseng (ginseng).MethodsTo explore the disease's origin, comparative analysis was performed in different parts of the plant, particularly the epidermis, cortex, and/or fibrous roots of 5-yr-old healthy and diseased red-skin ginseng. The inorganic element composition, phenolic compound concentration, reactive oxidation system, antioxidant concentrations such as ascorbate and glutathione, activities of enzymes related to phenolic metabolism and oxidation, and antioxidative system particularly the ascorbate–glutathione cycle were examined using conventional methods.ResultsAluminum (Al), iron (Fe), magnesium, and phosphorus were increased, whereas manganese was unchanged and calcium was decreased in the epidermis and fibrous root of red-skin ginseng, which also contained higher levels of phenolic compounds, higher activities of the phenolic compound-synthesizing enzyme phenylalanine ammonia-lyase and the phenolic compound oxidation-related enzymes guaiacol peroxidase and polyphenoloxidase. As the substrate of guaiacol peroxidase, higher levels of H2O2 and correspondingly higher activities of superoxide dismutase and catalase were found in red-skin ginseng. Increased levels of ascorbate and glutathione; increased activities of l-galactose 1-dehydrogenase, ascorbate peroxidase, ascorbic acid oxidase, and glutathione reductase; and lower activities of dehydroascorbate reductase, monodehydroascorbate reductase, and glutathione peroxidase were found in red-skin ginseng. Glutathione-S-transferase activity remained constant.ConclusionHence, higher element accumulation, particularly Al and Fe, activated multiple enzymes related to accumulation of phenolic compounds and their oxidation. This might contribute to red-skin symptoms in ginseng. It is proposed that antioxidant and antioxidative enzymes, especially those involved in ascorbate–glutathione cycles, are activated to protect against phenolic compound oxidation

Evolution of palladium sulfide phases during thermal treatments and consequences for acetylene hydrogenation

We thank Diamond Light Source for beamline access B18 (SP15151-5) and are grateful to the expertise and help provided by Dr Emma Gibson (UK Catalysis Hub, Harwell) and Diego Gianolio (Beamline Scientist on B18) whilst data collecting. XPS data collection was performed at the EPSRC National Facility for XPS (‘HarwellXPS’), operated by Cardiff University and UCL, under contract No. PR16195. We would also like to thank Prof. Philip R. Davies for helpful discussions on XPS data analysis. This work was partly supported by the National Key Research and Development Program of China (2016YFB0301601), National Natural Science Foundation of China.Peer reviewedPostprin

Palladium phosphide nanoparticles as highly selective catalysts for the selective hydrogenation of acetylene

This work was supported by the National Key Research and Development Program of China (2016YFB0301601), National Natural Science Foundation of China.Peer reviewedPostprin

Adsorbate-Induced Structural Evolution of Pd Catalyst for Selective Hydrogenation of Acetylene

ACKNOWLEDGMENT: This work was financially supported by National Natural Science Foundation of China (21908002), project funded by China Postdoctoral Science Foundation (2019M660416, 2020T130045) and the Fundamental Research Funds for the Central Universities (buctrc201921, JD2004, XK1802-6). We would like to thank the UK catalysis Hub for help collecting the XAS.Peer reviewedPostprin

Tetragonal Mexican-Hat Dispersion and Switchable Half-Metal State with Multiple Anisotropic Weyl Fermions in Penta-Graphene

In past decades, the ever-expanding library of 2D carbon allotropes has yielded a broad range of exotic properties for the future carbon-based electronics. However, the known allotropes are all intrinsic nonmagnetic due to the paired valence electrons configuration. Based on the reported 2D carbon structure database and first-principles calculations, herein we demonstrate that inherent ferromagnetism can be obtained in the prominent allotrope, penta-graphene, which has an unique Mexican-hat valence band edge, giving rise to van Hove singularities and electronic instability. Induced by modest hole-doping, being achievable in electrolyte gate, the semiconducting pentagraphene can transform into different ferromagnetic half-metals with room temperature stability and switchable spin directions. In particular, multiple anisotropic Weyl states, including type-I and type-II Weyl cones and hybrid quasi Weyl nodal loop, can be found in a sizable energy window of spin-down half-metal under proper strains. These findings not only identify a promising carbon allotrope to obtain the inherent magnetism for carbon-based spintronic devices, but highlight the possibility to realize different Weyl states by combining the electronic and mechanical means as well