Computing User Reputation in a Social Network of Web 2.0

In the Web 2.0 era, people not only read web contents but create, upload, view, share and evaluate all contents on the web. This leads us to introduce a new type of social network based on user activity and content metadata. We notice that we can determine the quality of related contents using this new social network. Based on this observation, we introduce a user evaluation algorithm for user-generated video sharing website. First, we make a social network of users from video contents and related social activities such as subscription, uploading or favorite. We then use a modified PageRank algorithm to compute user reputation from the social network. We re-calculate the content scores using user reputations and compare the results with a standard BM25 result. We apply the proposed approach to YouTube and demonstrate that the user reputation is closely related to the number of subscriptions and the number of uploaded contents. Furthermore, we show that the new ranking results relied on the user reputation is better than the standard BM25 approach by experiments

RuO2-coated MoS2 Nanosheets as Cathode Catalysts for High Efficiency LiO2 Batteries

© 2019 Korean Chemical Society, Seoul & Wiley-VCH Verlag GmbH & Co. KGaA, WeinheimDespite their potential for high capacity, lithium oxygen (Li–O2) batteries still suffer from the low round-trip energy efficiency and limited cycle life, mainly due to the slow decomposition of discharge products. Therefore, developing efficient catalysts is a key issue for the practical application of Li-O2 batteries. Ruthenium oxide (RuO2) is one of the most efficient catalysts developed thus far for lithium-air batteries. However, the high price and limited availability of Ru prohibits its large-scale use in practical device fabrications. Recently, molybdenum disulfide (MoS2) has been actively investigated in various catalytic systems, taking advantage of its two-dimensional (2D) structure and catalytic activities. However, the low electrical conductivity of MoS2 limits the realization of fully operative MoS2-based catalysts on its own. In this report, RuO2-coated MoS2 nanosheets (RuO2/MoS2) are prepared and implemented as cathode catalysts for Li–O2 batteries. In this hybrid structure, RuO2 and MoS2 complement each other; the poor electrical conductivity of MoS2 is overcome by the nearly conformal coating of conducting RuO2, while 2D MoS2 nanosheets act as excellent supports for RuO2 catalysts and also contribute to the overall catalytic activities. These combined features result in excellent cathode performance, including improved efficiency and cycling lifetimes, with significantly reduced amounts of precious RuO

Free Standing Reduced Graphene Oxide Film Cathodes for Lithium Ion Batteries

We report the fabrication and electrochemical activity of free-standing reduced graphene oxide (RGO) films as cathode materials for lithium ion batteries. The conducting additive and binder-free RGO electrodes with different oxygen contents were assembled by a simple vacuum filtration process from aqueous RGO colloids prepared with the aid of cationic surfactants. The gravimetric capacity of RGO film cathodes showed clear dependence on the oxygen contents controlled by the thermal reduction process. The capacity increased with the increase of the amount of oxygen functional groups, indicating that the main lithium capturing mechanism of RGO cathodes is Li⁺ ion interaction with the surface oxygen functionalities. The hydroxyl groups (C–OH) as well as carbon–oxygen double bonds have been identified as the lithiation-active species. The RGO cathodes achieved excellent rate capability due to the fast surface Faradaic reaction, suggesting that self-supported RGO films are promising cathodes for high power application. The graphene oxide (GO)/RGO composite films showed inferior performance to those of RGO only. The poor electronic conductivity of GO might result in inefficient utilization of redox active oxygen functional groups despite the higher oxygen content and higher theoretical capacity of GO/RGO composite films. Further optimization on the amount of oxygen functional groups for higher capacity and better electronic conductivity would lead to the development of RGO based high energy-high power cathodes

ALP: Data Augmentation Using Lexicalized PCFGs for Few-Shot Text Classification

Data augmentation has been an important ingredient for boosting performances of learned models. Prior data augmentation methods for few-shot text classification have led to great performance boosts. However, they have not been designed to capture the intricate compositional structure of natural language. As a result, they fail to generate samples with plausible and diverse sentence structures. Motivated by this, we present the data Augmentation using Lexicalized Probabilistic context-free grammars (ALP) that generates augmented samples with diverse syntactic structures with plausible grammar. The lexicalized PCFG parse trees consider both the constituents and dependencies to produce a syntactic frame that maximizes a variety of word choices in a syntactically preservable manner without specific domain experts. Experiments on few-shot text classification tasks demonstrate that ALP enhances many state-of-the-art classification methods. As a second contribution, we delve into the train-val splitting methodologies when a data augmentation method comes into play. We argue empirically that the traditional splitting of training and validation sets is sub-optimal compared to our novel augmentation-based splitting strategies that further expand the training split with the same number of labeled data. Taken together, our contributions on the data augmentation strategies yield a strong training recipe for few-shot text classification tasks

Ruthenium-Based Electrocatalysts Supported on Reduced Graphene Oxide for Lithium-Air Batteries

Ruthenium-based nanomaterials supported on reduced graphene oxide (rGO) have been investigated as air cathodes in non-aqueous electrolyte Li-air cells using a TEGDME-LiCF₃SO₃ electrolyte. Homogeneously distributed metallic ruthenium and hydrated ruthenium oxide (RuO₂·0.64H₂O), deposited exclusively on rGO, have been synthesized with average size below 2.5 nm. The synthesized hybrid materials of Ru-based nanoparticles supported on rGO efficiently functioned as electrocatalysts for Li₂O₂ oxidation reactions, maintaining cycling stability for 30 cycles without sign of TEGDME-LiCF₃SO₃ electrolyte decomposition. Specifically, RuO₂·0.64H₂O-rGO hybrids were superior to Ru-rGO hybrids in catalyzing the OER reaction, significantly reducing the average charge potential to ∼3.7 V at the high current density of 500 mA g^–1 and high specific capacity of 5000 mAh g^–1

Biomimetic Selective Ion Transport through Graphene Oxide Membranes Functionalized with Ion Recognizing Peptides

Membranes that differentiate ions are being actively developed to meet the needs in separation, sensing, biomedical, and water treatment technologies. Biomimetic approaches that combine bioinspired functional molecules with solid state supports offer great potential for imitating the functions and principles of biological ion channels. Here we report the design and fabrication of biomimetic graphene oxide (GO) based membranes functionalized with a peptide motif that has the capabilities for selective recognition and transport. The peptide, which has ion binding affinity to Co²⁺ ions, was adopted to enable the ion selective filtration capability and was then anchored on a GO surface. The resulting GO-based membranes show remarkable ion selectivity toward the specific ion of interest, for the transport across the membranes as in the biological ion channels. Ion recognition capability of this peptide motif successfully translates into ion specificity for selective transport. This study provides a new avenue for developing artificial ion channels via a synergistic combination of biomimetic recognition chemistry, with a novel nanoplatform such as GO

Study on the Catalytic Activity of Noble Metal Nanoparticles on Reduced Graphene Oxide for Oxygen Evolution Reactions in Lithium–Air Batteries

Among many challenges present in Li–air batteries, one of the main reasons of low efficiency is the high charge overpotential due to the slow oxygen evolution reaction (OER). Here, we present systematic evaluation of Pt, Pd, and Ru nanoparticles supported on rGO as OER electrocatalysts in Li–air cell cathodes with LiCF₃SO₃–tetra­(ethylene glycol) dimethyl ether (TEGDME) salt-electrolyte system. All of the noble metals explored could lower the charge overpotentials, and among them, Ru-rGO hybrids exhibited the most stable cycling performance and the lowest charge overpotentials. Role of Ru nanoparticles in boosting oxidation kinetics of the discharge products were investigated. Apparent behavior of Ru nanoparticles was different from the conventional electrocatalysts that lower activation barrier through electron transfer, because the major contribution of Ru nanoparticles in lowering charge overpotential is to control the nature of the discharge products. Ru nanoparticles facilitated thin film-like or nanoparticulate Li₂O₂ formation during oxygen reduction reaction (ORR), which decomposes at lower potentials during charge, although the conventional role as electrocatalysts during OER cannot be ruled out. Pt-and Pd-rGO hybrids showed fluctuating potential profiles during the cycling. Although Pt- and Pd-rGO decomposed the electrolyte after electrochemical cycling, no electrolyte instability was observed with Ru-rGO hybrids. This study provides the possibility of screening selective electrocatalysts for Li–air cells while maintaining electrolyte stability