Showing papers by "Hong Kong University of Science and Technology published in 2017"
TL;DR: A comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management is provided in this paper, where a set of issues, challenges, and future research directions for MEC are discussed.
Abstract: Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized mobile cloud computing toward mobile edge computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discuss a set of issues, challenges, and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.
2,992 citations
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TL;DR: A comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management and recent standardization efforts on MEC are introduced.
Abstract: Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also present a research outlook consisting of a set of promising directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.
2,289 citations
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TL;DR: A unified model architecture of StarGAN allows simultaneous training of multiple datasets with different domains within a single network, which leads to StarGAN's superior quality of translated images compared to existing models as well as the novel capability of flexibly translating an input image to any desired target domain.
Abstract: Recent studies have shown remarkable success in image-to-image translation for two domains. However, existing approaches have limited scalability and robustness in handling more than two domains, since different models should be built independently for every pair of image domains. To address this limitation, we propose StarGAN, a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model. Such a unified model architecture of StarGAN allows simultaneous training of multiple datasets with different domains within a single network. This leads to StarGAN's superior quality of translated images compared to existing models as well as the novel capability of flexibly translating an input image to any desired target domain. We empirically demonstrate the effectiveness of our approach on a facial attribute transfer and a facial expression synthesis tasks.
2,033 citations
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TL;DR: Multi-task learning (MTL) as mentioned in this paper is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks.
Abstract: Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks. In this paper, we give a survey for MTL from the perspective of algorithmic modeling, applications and theoretical analyses. For algorithmic modeling, we give a definition of MTL and then classify different MTL algorithms into five categories, including feature learning approach, low-rank approach, task clustering approach, task relation learning approach and decomposition approach as well as discussing the characteristics of each approach. In order to improve the performance of learning tasks further, MTL can be combined with other learning paradigms including semi-supervised learning, active learning, unsupervised learning, reinforcement learning, multi-view learning and graphical models. When the number of tasks is large or the data dimensionality is high, we review online, parallel and distributed MTL models as well as dimensionality reduction and feature hashing to reveal their computational and storage advantages. Many real-world applications use MTL to boost their performance and we review representative works in this paper. Finally, we present theoretical analyses and discuss several future directions for MTL.
1,202 citations
TL;DR: In this paper, a review of the physics of spin liquid states is presented, including spin-singlet states, which may be viewed as an extension of Fermi liquid states to Mott insulators, and they are usually classified in the category of SU(2), U(1), or Z2.
Abstract: This is an introductory review of the physics of quantum spin liquid states. Quantum magnetism is a rapidly evolving field, and recent developments reveal that the ground states and low-energy physics of frustrated spin systems may develop many exotic behaviors once we leave the regime of semiclassical approaches. The purpose of this article is to introduce these developments. The article begins by explaining how semiclassical approaches fail once quantum mechanics become important and then describe the alternative approaches for addressing the problem. Mainly spin-1/2 systems are discussed, and most of the time is spent in this article on one particular set of plausible spin liquid states in which spins are represented by fermions. These states are spin-singlet states and may be viewed as an extension of Fermi liquid states to Mott insulators, and they are usually classified in the category of so-called SU(2), U(1), or Z2 spin liquid states. A review is given of the basic theory regarding these states and the extensions of these states to include the effect of spin-orbit coupling and to higher spin (S>1/2) systems. Two other important approaches with strong influences on the understanding of spin liquid states are also introduced: (i) matrix product states and projected entangled pair states and (ii) the Kitaev honeycomb model. Experimental progress concerning spin liquid states in realistic materials, including anisotropic triangular-lattice systems [κ-(ET)2Cu2(CN)3 and EtMe3Sb[Pd(dmit)2]2], kagome-lattice system [ZnCu3(OH)6Cl2], and hyperkagome lattice system (Na4Ir3O8), is reviewed and compared against the corresponding theories.
1,108 citations
TL;DR: An overview of the majorization-minimization (MM) algorithmic framework, which can provide guidance in deriving problem-driven algorithms with low computational cost and is elaborated by a wide range of applications in signal processing, communications, and machine learning.
Abstract: This paper gives an overview of the majorization-minimization (MM) algorithmic framework, which can provide guidance in deriving problem-driven algorithms with low computational cost. A general introduction of MM is presented, including a description of the basic principle and its convergence results. The extensions, acceleration schemes, and connection to other algorithmic frameworks are also covered. To bridge the gap between theory and practice, upperbounds for a large number of basic functions, derived based on the Taylor expansion, convexity, and special inequalities, are provided as ingredients for constructing surrogate functions. With the pre-requisites established, the way of applying MM to solving specific problems is elaborated by a wide range of applications in signal processing, communications, and machine learning.
1,073 citations
TL;DR: Several device technologies for realizing normally off operation that is highly desirable for power switching applications are presented and the examples of circuit applications that can greatly benefit from the superior performance of GaN power devices are demonstrated.
Abstract: In this paper, we present a comprehensive reviewand discussion of the state-of-the-art device technology and application development of GaN-on-Si power electronics. Several device technologies for realizing normally off operation that is highly desirable for power switching applications are presented. In addition, the examples of circuit applications that can greatly benefit from the superior performance of GaN power devices are demonstrated. Comparisonwith other competingpower device technology, such as Si superjunction-MOSFET and SiC MOSFET, is also presented and analyzed. Critical issues for commercialization of GaN-on-Si power devices are discussed with regard to cost, reliability, and ease of use.
922 citations
TL;DR: The increased polymer chain dynamics under nanoconfinement significantly reduces the modulus of the conjugated polymer and largely delays the onset of crack formation under strain, and the fabricated semiconducting film can be stretched up to 100% strain without affecting mobility, retaining values comparable to that of amorphous silicon.
Abstract: Soft and conformable wearable electronics require stretchable semiconductors, but existing ones typically sacrifice charge transport mobility to achieve stretchability. We explore a concept based on the nanoconfinement of polymers to substantially improve the stretchability of polymer semiconductors, without affecting charge transport mobility. The increased polymer chain dynamics under nanoconfinement significantly reduces the modulus of the conjugated polymer and largely delays the onset of crack formation under strain. As a result, our fabricated semiconducting film can be stretched up to 100% strain without affecting mobility, retaining values comparable to that of amorphous silicon. The fully stretchable transistors exhibit high biaxial stretchability with minimal change in on current even when poked with a sharp object. We demonstrate a skinlike finger-wearable driver for a light-emitting diode.
796 citations
TL;DR: This work analyzes the wireless signal propagation model considering human activities influence and proposes a novel and truly unobtrusive detection method based on the advanced wireless technologies, which it is called as WiFall, which withdraws the need for hardware modification, environmental setup and worn or taken devices.
Abstract: Injuries that are caused by falls have been regarded as one of the major health threats to the independent living for the elderly. Conventional fall detection systems have various limitations. In this work, we first look for the correlations between different radio signal variations and activities by analyzing radio propagation model. Based on our observation, we propose WiFall, a truly unobtrusive fall detection system. WiFall employs physical layer Channel State Information (CSI) as the indicator of activities. It can detect fall of the human without hardware modification, extra environmental setup, or any wearable device. We implement WiFall on desktops equipped with commodity 802.11n NIC, and evaluate the performance in three typical indoor scenarios with several layouts of transmitter-receiver (Tx-Rx) links. In our area of interest, WiFall can achieve fall detection for a single person with high accuracy. As demonstrated by the experimental results, WiFall yields 90 percent detection precision with a false alarm rate of 15 percent on average using a one-class SVM classifier in all testing scenarios. It can also achieve average 94 percent fall detection precisions with 13 percent false alarm using Random Forest algorithm.
686 citations
TL;DR: This free-standing, adhesive, tough, and biocompatible hydrogel may be more convenient for surgical applications than adhesives that involve in situ gelation and extra agents.
Abstract: Adhesive hydrogels are attractive biomaterials for various applications, such as electronic skin, wound dressing, and wearable devices. However, fabricating a hydrogel with both adequate adhesiveness and excellent mechanical properties remains a challenge. Inspired by the adhesion mechanism of mussels, we used a two-step process to develop an adhesive and tough polydopamine-clay-polyacrylamide (PDA-clay-PAM) hydrogel. Dopamine was intercalated into clay nanosheets and limitedly oxidized between the layers, resulting in PDA-intercalated clay nanosheets containing free catechol groups. Acrylamide monomers were then added and in situ polymerized to form the hydrogel. Unlike previous single-use adhesive hydrogels, our hydrogel showed repeatable and durable adhesiveness. It adhered directly on human skin without causing an inflammatory response and was easily removed without causing damage. The adhesiveness of this hydrogel was attributed to the presence of enough free catechol groups in the hydrogel, which we...
676 citations
TL;DR: In this paper, the authors examine how mandatory disclosure of corporate social responsibility impacts firm performance and social externalities and find that mandatory CSR reporting firms experience a decrease in profitability subsequent to the mandate.
TL;DR: This paper develops an online joint radio and computational resource management algorithm for multi-user MEC systems, with the objective of minimizing the long-term average weighted sum power consumption of the mobile devices and the MEC server, subject to a task buffer stability constraint.
Abstract: Mobile-edge computing (MEC) has recently emerged as a prominent technology to liberate mobile devices from computationally intensive workloads, by offloading them to the proximate MEC server. To make offloading effective, the radio and computational resources need to be dynamically managed, to cope with the time-varying computation demands and wireless fading channels. In this paper, we develop an online joint radio and computational resource management algorithm for multi-user MEC systems, with the objective of minimizing the long-term average weighted sum power consumption of the mobile devices and the MEC server, subject to a task buffer stability constraint. Specifically, at each time slot, the optimal CPU-cycle frequencies of the mobile devices are obtained in closed forms, and the optimal transmit power and bandwidth allocation for computation offloading are determined with the Gauss-Seidel method ; while for the MEC server, both the optimal frequencies of the CPU cores and the optimal MEC server scheduling decision are derived in closed forms. Besides, a delay-improved mechanism is proposed to reduce the execution delay. Rigorous performance analysis is conducted for the proposed algorithm and its delay-improved version, indicating that the weighted sum power consumption and execution delay obey an $\left [{O\left ({1 / V}\right), O\left ({V}\right) }\right ]$ tradeoff with $V$ as a control parameter. Simulation results are provided to validate the theoretical analysis and demonstrate the impacts of various parameters.
TL;DR: In this paper, a multicolor bandgap fluorescent carbon quantum dots (MCBF-CQDs) from blue to red with quantum yield up to 75% were synthesized using a solvothermal method.
Abstract: Multicolor bandgap fluorescent carbon quantum dots (MCBF-CQDs) from blue to red with quantum yield up to 75% are synthesized using a solvothermal method. For the first time, monochrome electroluminescent light-emitting diodes (LEDs) with MCBF-CQDs directly as an active emission layer are fabricated. The maximum luminance of blue LEDs reaches 136 cd m-2 , which is the best performance for CQD-based monochrome electroluminescent LEDs.
TL;DR: An ultraviolet (UV)-pumped CQD phosphors-based warm white light-emitting diode (WLED) is realized for the first time and achieves a color rendering index of 97.
Abstract: Red emissive carbon quantum dots (R-CQDs) with quantum yield of 53% is successfully prepared. An ultraviolet (UV)-pumped CQD phosphors-based warm white light-emitting diode (WLED) is realized for the first time and achieves a color rendering index of 97. This work provides a new avenue for the exploration of low cost, environment-friendly, and high-performance CQD phosphors-based warm WLEDs.
01 Jan 2017
TL;DR: In this paper, a mapless motion planner is proposed by taking the sparse 10-dimensional range findings and the target position with respect to the mobile robot coordinate frame as input and the continuous steering commands as output.
Abstract: We present a learning-based mapless motion planner by taking the sparse 10-dimensional range findings and the target position with respect to the mobile robot coordinate frame as input and the continuous steering commands as output. Traditional motion planners for mobile ground robots with a laser range sensor mostly depend on the obstacle map of the navigation environment where both the highly precise laser sensor and the obstacle map building work of the environment are indispensable. We show that, through an asynchronous deep reinforcement learning method, a mapless motion planner can be trained end-to-end without any manually designed features and prior demonstrations. The trained planner can be directly applied in unseen virtual and real environments. The experiments show that the proposed mapless motion planner can navigate the nonholonomic mobile robot to the desired targets without colliding with any obstacles.
TL;DR: A single pure organic phosphor, namely 4-chlorobenzoyldibenzothiophene, emitting white room temperature phosphorescence with Commission Internationale de l’Éclair-age coordinates is reported, revealing that the white light emission is emerged from dual phosphorescence, which emit from the first and second excited triplet states.
Abstract: The development of single molecule white light emitters is extremely challenging for pure phosphorescent metal-free system at room temperature. Here we report a single pure organic phosphor, namely 4-chlorobenzoyldibenzothiophene, emitting white room temperature phosphorescence with Commission Internationale de l’Eclair-age coordinates of (0.33, 0.35). Experimental and theoretical investigations reveal that the white light emission is emerged from dual phosphorescence, which emit from the first and second excited triplet states. We also demonstrate the validity of the strategy to achieve metal-free pure phosphorescent single molecule white light emitters by intrasystem mixing dual room temperature phosphorescence arising from the low- and high-lying triplet states. The development of single molecule white light-emitters is extremely challenging for pure phosphorescent metal-free systems at room temperature. Here the authors show a single pure organic room temperature phosphor, 4-chlorobenzoyldibenzothiophene, utilizing the emission from both T1 and T2 states.
TL;DR: A graphene oxide conductive hydrogel is reported that simultaneously possesses high toughness, self-healability, and self-adhesiveness and can be used asSelf-adhesive bioelectronics, such as electrical stimulators to regulate cell activity and implantable electrodes for recording in vivo signals.
Abstract: A graphene oxide conductive hydrogel is reported that simultaneously possesses high toughness, self-healability, and self-adhesiveness. Inspired by the adhesion behaviors of mussels, our conductive hydrogel shows self-adhesiveness on various surfaces and soft tissues. The hydrogel can be used as self-adhesive bioelectronics, such as electrical stimulators to regulate cell activity and implantable electrodes for recording in vivo signals.
TL;DR: This paper is one of the first DL studies to forecast the short-term passenger demand of an on-demand ride service platform by examining the spatio-temporal correlations and the FCL-Net achieves the better predictive performance than traditional approaches.
Abstract: Short-term passenger demand forecasting is of great importance to the on-demand ride service platform, which can incentivize vacant cars moving from over-supply regions to over-demand regions. The spatial dependencies, temporal dependencies, and exogenous dependencies need to be considered simultaneously, however, which makes short-term passenger demand forecasting challenging. We propose a novel deep learning (DL) approach, named the fusion convolutional long short-term memory network (FCL-Net), to address these three dependencies within one end-to-end learning architecture. The model is stacked and fused by multiple convolutional long short-term memory (LSTM) layers, standard LSTM layers, and convolutional layers. The fusion of convolutional techniques and the LSTM network enables the proposed DL approach to better capture the spatio-temporal characteristics and correlations of explanatory variables. A tailored spatially aggregated random forest is employed to rank the importance of the explanatory variables. The ranking is then used for feature selection. The proposed DL approach is applied to the short-term forecasting of passenger demand under an on-demand ride service platform in Hangzhou, China. The experimental results, validated on the real-world data provided by DiDi Chuxing, show that the FCL-Net achieves the better predictive performance than traditional approaches including both classical time-series prediction models and state-of-art machine learning algorithms (e.g., artificial neural network, XGBoost, LSTM and CNN). Furthermore, the consideration of exogenous variables in addition to the passenger demand itself, such as the travel time rate, time-of-day, day-of-week, and weather conditions, is proven to be promising, since they reduce the root mean squared error (RMSE) by 48.3%. It is also interesting to find that the feature selection reduces 24.4% in the training time and leads to only the 1.8% loss in the forecasting accuracy measured by RMSE in the proposed model. This paper is one of the first DL studies to forecast the short-term passenger demand of an on-demand ride service platform by examining the spatio-temporal correlations.
TL;DR: This paper presents a short overview of the changes to the trigger and data acquisition systems during the first long shutdown of the LHC and shows the performance of the trigger system and its components based on the 2015 proton–proton collision data.
Abstract: During 2015 the ATLAS experiment recorded 3.8 fb(-1) of proton-proton collision data at a centre-of-mass energy of 13 TeV. The ATLAS trigger system is a crucial component of the experiment, respons ...
TL;DR: In this review, the newly emerged aggregation-induced emission fluorogens (AIEgens) are featured with high emission efficiency in the aggregated state, which provide unique opportunities for various sensing applications with advantages of high signal-to-noise ratio, strong photostability, and large Stokes' shift.
Abstract: Fluorescent sensors with advantages of excellent sensitivity, rapid response, and easy operation are emerging as powerful tools in environmental monitoring, biological research, and disease diagnosis. However, conventional fluorophores featured with π-planar structures usually suffer from serious self-quenching in the aggregated state, poor photostability, and small Stokes’ shift. In contrast to conventional aggregation-caused quenching (ACQ) fluorophores, the newly emerged aggregation-induced emission fluorogens (AIEgens) are featured with high emission efficiency in the aggregated state, which provide unique opportunities for various sensing applications with advantages of high signal-to-noise ratio, strong photostability, and large Stokes’ shift. In this review, we will first briefly give an introduction of the AIE concept and the turn-on sensing principles. Then, we will discuss the recent examples of AIE sensors according to types of analytes. Finally, we will give a perspective on the future develop...
04 Aug 2017
TL;DR: This paper introduces the concept of meta-graph to HIN-based recommendation, and solves the information fusion problem with a "matrix factorization + factorization machine (FM)" approach, and proposes to use FM with Group lasso (FMG) to automatically learn from the observed ratings to effectively select useful meta- graph based features.
Abstract: Heterogeneous Information Network (HIN) is a natural and general representation of data in modern large commercial recommender systems which involve heterogeneous types of data. HIN based recommenders face two problems: how to represent the high-level semantics of recommendations and how to fuse the heterogeneous information to make recommendations. In this paper, we solve the two problems by first introducing the concept of meta-graph to HIN-based recommendation, and then solving the information fusion problem with a "matrix factorization (MF) + factorization machine (FM)" approach. For the similarities generated by each meta-graph, we perform standard MF to generate latent features for both users and items. With different meta-graph based features, we propose to use FM with Group lasso (FMG) to automatically learn from the observed ratings to effectively select useful meta-graph based features. Experimental results on two real-world datasets, Amazon and Yelp, show the effectiveness of our approach compared to state-of-the-art FM and other HIN-based recommendation algorithms.
TL;DR: Topological cell clustering is established as a well-performing calorimeter signal definition for jet and missing transverse momentum reconstruction in ATLAS and is exploited to apply a local energy calibration and corrections depending on the nature of the cluster.
Abstract: The reconstruction of the signal from hadrons and jets emerging from the proton–proton collisions at the Large Hadron Collider (LHC) and entering the ATLAS calorimeters is based on a three-dimensional topological clustering of individual calorimeter cell signals. The cluster formation follows cell signal-significance patterns generated by electromagnetic and hadronic showers. In this, the clustering algorithm implicitly performs a topological noise suppression by removing cells with insignificant signals which are not in close proximity to cells with significant signals. The resulting topological cell clusters have shape and location information, which is exploited to apply a local energy calibration and corrections depending on the nature of the cluster. Topological cell clustering is established as a well-performing calorimeter signal definition for jet and missing transverse momentum reconstruction in ATLAS.
TL;DR: AIEgens-incorporated nanoparticles possess bright fluorescence and improved photostability, which is beneficial to long-term bioimaging with high contrast and spatial resolution, as well as the relevant applications in theranostics.
TL;DR: A controllable N-doping strategy to significantly improve the catalytic activity of Co3 O4 for ORR is reported and these N doped Co 3 O4 nanowires are demonstrated as an additive-free air-cathode for flexible solid-state zinc-air batteries.
Abstract: The kinetically sluggish rate of oxygen reduction reaction (ORR) on the cathode side is one of the main bottlenecks of zinc-air batteries (ZABs), and thus the search for an efficient and cost-effective catalyst for ORR is highly pursued. Co3O4 has received ever-growing interest as a promising ORR catalyst due to the unique advantages of low-cost, earth abundance and decent catalytic activity. However, owing to the poor conductivity as a result of its semiconducting nature, the ORR activity of the Co3O4 catalyst is still far below the expectation. Herein, we report a controllable N-doping strategy to significantly improve the catalytic activity of Co3O4 for ORR and demonstrate these N doped Co3O4 nanowires as an additive-free air-cathode for flexible solid-state zinc-air batteries. The results of experiments and DFT calculations reveal that the catalytic activity is promoted by the N dopant through a combined set of factors, including enhanced electronic conductivity, increased O2 adsorption strength and improved reaction kinetics. Finally, the assembly of all-solid-state ZABs based on the optimized cathode exhibit a high volumetric capacity of 98.1 mAh cm-3 and outstanding flexibility. The demonstration of such flexible ZABs provides valuable insights that point the way to the redesign of emerging portable electronics.
TL;DR: A self-healing, super-resilient hydrogel that can accelerate skin regeneration has been made using an adhesive mechanism inspired by mussels using a process that preserves PDA's catechols – substances that impart mussels with high adhesiveness – when embedded in an elastic polymer matrix.
Abstract: An ideal hydrogel for biomedical engineering should mimic the intrinsic properties of natural tissue, especially high toughness and self-healing ability, in order to withstand cyclic loading and repair skin and muscle damage. In addition, excellent cell affinity and tissue adhesiveness enable integration with the surrounding tissue after implantation. Inspired by the natural mussel adhesive mechanism, we designed a polydopamine–polyacrylamide (PDA–PAM) single network hydrogel by preventing the overoxidation of dopamine to maintain enough free catechol groups in the hydrogel. Therefore, the hydrogel possesses super stretchability, high toughness, stimuli-free self-healing ability, cell affinity and tissue adhesiveness. More remarkably, the current hydrogel can repeatedly be adhered on/stripped from a variety of surfaces for many cycles without loss of adhesion strength. Furthermore, the hydrogel can serve as an excellent platform to host various nano-building blocks, in which multiple functionalities are integrated to achieve versatile potential applications, such as magnetic and electrical therapies. A self-healing, super-resilient hydrogel that can accelerate skin regeneration has been made using an adhesive mechanism inspired by mussels. Hydrogels have similar structures to soft biological tissues and have great potential for tissue engineering applications. However, most are too fragile for use in the body and lack the ability to self-heal and adhere to tissue. Now, Xiong Lu from China's Southwest Jiaotong University and co-workers have synthesized a self-healing, super-resilient hydrogel using a process that preserves PDA's catechols – substances that impart mussels with high adhesiveness – when embedded in an elastic polymer matrix. The numerous non-covalent bonds between PDA catechols enable the hydrogel to perfectly re-form after being sliced open and help it stretch over 30 times its initial length without breaking. The material could also carry magnetic or conductive nanoparticles for future integrated healthcare applications. Inspired by mussel chemistry, a novel polydopamine–polyacrymide hydrogel simultaneously possesses super stretchability, stimuli-free self-healing properties, cell affinity and tissue adhesiveness. The current hydrogel lasts its adhesiveness for a long term, and can be repeatedly adhered on/stripped from a variety of substrates. The hydrogel can host various nano-building blocks and be tuned to magnetic and conductive hydrogels with above-mentioned properties.
TL;DR: This is the first study on the reactivity of RCS, particularly ClO•, with structurally diverse PPCPs under simulated drinking water condition and indicates that electron-donating groups promote the attack of benzene derivatives by RCS.
Abstract: The UV/chlorine process is an emerging advanced oxidation process (AOP) used for the degradation of micropollutants. However, the radical chemistry of this AOP is largely unknown for the degradation of numerous structurally diverse micropollutants in water matrices of varying quality. These issues were addressed by grouping 34 pharmaceuticals and personal care products (PPCPs) according to the radical chemistry of their degradation in the UV/chlorine process at practical PPCP concentrations (1 μg L–1) and in different water matrices. The contributions of HO• and reactive chlorine species (RCS), including Cl•, Cl2•–, and ClO•, to the degradation of different PPCPs were compound specific. RCS showed considerable reactivity with olefins and benzene derivatives, such as phenols, anilines, and alkyl-/alkoxybenzenes. A good linear relationship was found between the RCS reactivity and negative values of the Hammett ∑σp+ constant for aromatic PPCPs, indicating that electron-donating groups promote the attack of b...
TL;DR: It is shown for the first time that the catalyst can work efficiently in neutral solution (pH 7) with a record η10 of 82 mV for all noble metal-free electrocatalysts ever reported.
Abstract: Novel 3D Ni1−xCoxSe2 mesoporous nanosheet networks with tunable stoichiometry are successfully synthesized on Ni foam (Ni1−xCoxSe2 MNSN/NF with x ranging from 0 to 0.35). The collective effects of special morphological design and electronic structure engineering enable the integrated electrocatalyst to have very high activity for hydrogen evolution reaction (HER) and excellent stability in a wide pH range. Ni0.89Co0.11Se2 MNSN/NF is revealed to exhibit an overpotential (η10) of 85 mV at −10 mA cm−2 in alkaline medium (pH 14) and η10 of 52 mV in acidic solution (pH 0), which are the best among all selenide-based electrocatalysts reported thus far. In particular, it is shown for the first time that the catalyst can work efficiently in neutral solution (pH 7) with a record η10 of 82 mV for all noble metal-free electrocatalysts ever reported. Based on theoretical calculations, it is further verified that the advanced all-pH HER activity of Ni0.89Co0.11Se2 is originated from the enhanced adsorption of both H+ and H2O induced by the substitutional doping of cobalt at an optimal level. It is believed that the present work provides a valuable route for the design and synthesis of inexpensive and efficient all-pH HER electrocatalysts.
TL;DR: Benefiting from the excellent electrical conductivity, ultralight porous structure, and effective charge delocalization, the composites deliver remarkable EMI shielding performance with a shielding effectiveness (SE) of 91.9 dB and a specific SE of 3124 dB·cm3/g, both of which are the highest among those reported in the literature for carbon-based polymer composites.
Abstract: Ultralight, high-performance electromagnetic interference (EMI) shielding graphene foam (GF)/poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) composites are developed by drop coating of PEDOT:PSS on cellular-structured, freestanding GFs. To enhance the wettability and the interfacial bonds with PEDOT:PSS, GFs are functionalized with 4-dodecylbenzenesulfonic acid. The GF/PEDOT:PSS composites possess an ultralow density of 18.2 × 10–3 g/cm3 and a high porosity of 98.8%, as well as an enhanced electrical conductivity by almost 4 folds from 11.8 to 43.2 S/cm after the incorporation of the conductive PEDOT:PSS. Benefiting from the excellent electrical conductivity, ultralight porous structure, and effective charge delocalization, the composites deliver remarkable EMI shielding performance with a shielding effectiveness (SE) of 91.9 dB and a specific SE (SSE) of 3124 dB·cm3/g, both of which are the highest among those reported in the literature for carbon-based polymer composites. The excelle...
TL;DR: The results show that the introduction of highly crystalline small molecule donors into ternary OSCs is an effective means to enhance the charge transport and thus increase the active layer thickness of ternARY Oscs to make them more suitable for roll-to-roll production than previous thinner devices.
Abstract: Ternary organic solar cells (OSCs) have attracted much research attention in the past few years, as ternary organic blends can broaden the absorption range of OSCs without the use of complicated tandem cell structures. Despite their broadened absorption range, the light harvesting capability of ternary OSCs is still limited because most ternary OSCs use thin active layers of about 100 nm in thickness, which is not sufficient to absorb all photons in their spectral range and may also cause problems for future roll-to-roll mass production that requires thick active layers. In this paper, we report a highly efficient ternary OSC (11.40%) obtained by incorporating a nematic liquid crystalline small molecule (named benzodithiophene terthiophene rhodanine (BTR)) into a state-of-the-art PTB7-Th:PC71BM binary system. The addition of BTR into PTB7-Th:PC71BM was found to improve the morphology of the blend film with decreased π–π stacking distance, enlarged coherence length, and enhanced domain purity. This resulte...
04 Aug 2017
TL;DR: A Bayesian generative model called collaborative variational autoencoder (CVAE) that considers both rating and content for recommendation in multimedia scenario that is able to significantly outperform the state-of-the-art recommendation methods with more robust performance is proposed.
Abstract: Modern recommender systems usually employ collaborative filtering with rating information to recommend items to users due to its successful performance. However, because of the drawbacks of collaborative-based methods such as sparsity, cold start, etc., more attention has been drawn to hybrid methods that consider both the rating and content information. Most of the previous works in this area cannot learn a good representation from content for recommendation task or consider only text modality of the content, thus their methods are very limited in current multimedia scenario. This paper proposes a Bayesian generative model called collaborative variational autoencoder (CVAE) that considers both rating and content for recommendation in multimedia scenario. The model learns deep latent representations from content data in an unsupervised manner and also learns implicit relationships between items and users from both content and rating. Unlike previous works with denoising criteria, the proposed CVAE learns a latent distribution for content in latent space instead of observation space through an inference network and can be easily extended to other multimedia modalities other than text. Experiments show that CVAE is able to significantly outperform the state-of-the-art recommendation methods with more robust performance.