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In spectrum sharing systems based on spectrum trading, user locations are vital for the efficiency of dynamic channel reuse. However, both primary users (PUs) and secondary users (SUs) undertake the risk of location information leakage: a malicious PU may illegally collect SUs’ location information to manipulate market decisions; a malicious SU would threat a PU’s operational privacy by inferring the PU’s location through seemingly inoffensive queries. To protect both PUs’ and SUs’ location information in spectrum trading, a bilateral privacy preservation framework is introduced in this paper. A game-theoretic approach based on the Stackelberg model is proposed to achieve the tradeoff between the privacy-preserving level and user utility. With the proposed approach, both PUs and SUs can maximize their utilities while maintaining their location privacy to desired levels. Simulation results demonstrate that the proposed approach can effectively enhance user utility gain and strengthen user privacy guarantee by flexibly adjusting their privacy levels in practice.

Herein, a heterostructure of MoS2/CdS/TiO2 nanotube arrays is constructed by successive ionic layer adsorption/reaction of CdS and dipping-assembly of MoS2 on the vertically oriented TiO2 nanotubes prepared by anodization method. The heterostructure is extensively investigated in terms of the composition, morphology, structural, and optical properties. It is observed that the co-modification of TiO2 nanotube arrays with CdS and MoS2 significantly enhances the visible light response and boosts the facile photo-generated electrons’ transfer and charge separation efficiencies. Therefore, the prepared heterostructure photo-anode exhibits significantly enhanced photoelectrochemical properties including photocurrent, open circuit potential, and photo-conversion efficiencies compared with that of pure TiO2 nanotube arrays and CdS/TiO2 nanotube systems. Interestingly, it is found that the MoS2 coatings on the surface of CdS/TiO2 nanotube arrays greatly enhance their photoelectrochemical stability. The presented work demonstrates that MoS2/CdS/TiO2 nanotube heterostructures are a promising material for improved photoelectrochemical properties. A heterostructure of MoS2/CdS/TiO2 nanotube arrays was constructed by successive ionic layer adsorption/reaction of CdS and dipping-assembly of MoS2 on the vertically oriented TiO2 nanotube arrays, and their improved photoelectrochemical properties were demonstrated

Learning object detectors from weak image annotations is an important yet challenging problem. Many weakly supervised approaches formulate the task as a multiple instance learning problem, where each image is represented as a bag of instances. For predicting the score for each object that occurs in an image, existing MIL based approaches tend to select the instance that responds more strongly to a specific class, which, however, overlooks the contextual information. Besides, objects often exhibit dramatic variations such as scaling and transformations, which makes them hard to detect. In this paper, we propose the weakly supervised group mask network (WSGMN), which mainly has two distinctive properties: (i) it exploits the relations among regions to generate community instances, which contain context information and are robust to object variations. (ii) It generates a mask for each label group, and utilizes these masks to dynamically select the feature information of the most useful community instances for recognizing specific objects. Extensive experiments on several benchmark datasets demonstrate the effectiveness of WSGMN on the tasks of weakly supervised object detection.

Herein, a flame-assisted pyrolysis technique is developed for one-step in-situ construction of Cu2O/Cu/TiO2 nanotube arrays. The structure and morphology of the obtained samples are systematically characterized. Interestingly, UV–vis absorption spectroscopy reveals that the introduction of Cu2O and Cu considerably enhance light response of TiO2 nanotube arrays in the visible region. Moreover, the composited electrode shows enhanced photo-electrochemical activity. Compared with blank TiO2 nanotube arrays, not only the photocurrent and photo-voltage of composited electrodes are improved but also the stability is also enhanced. And, the maximum photo-conversion efficiency for composited electrode presents 4.71 folds larger than that of blank TiO2 electrode. This enhancement is due to the faster charge separation/transportation ability of Cu and visible light response of Cu2O. This research develops a novel and facile method for the in-situ fabrication of Cu2O/Cu/TiO2 nanotube arrays and demonstrates their improved utilization of solar energy.

Cooperative relaying is highly affected by the way that the source and relays are incentivized. However, the existing studies have not paid enough attention to the impacts of negotiation power and information availability of the players on the network performance. In this paper, two incentive mechanisms for cooperative relaying are first proposed to explore the influence of negotiation power, wherein the source holds either strong or weak negotiation power. In these two cases, the source posts take-it-or-leave-it offers for the relays and has to negotiate possible deals with the relays, respectively. The relay selection rules and the optimal amounts of relaying service and rewards are derived for each mechanism, respectively. Another two incentive mechanisms are also proposed to explore the influence of information availability, wherein the source has either weakly or strongly incomplete information about the relays. In these two cases, the source acquires the number of relays belonging to each type and the probability of each relay belonging to a certain type, respectively. The relay selection rules and the optimal contract offers are derived for the two mechanisms, respectively. The numerical results are provided to verify the theoretical analyses and demonstrate the effectiveness of the proposed mechanisms.

A ternary composites of MoO3/Ag/TiO2 nanotube arrays were synthesized by in-situ annealing of TiO2 nanotube arrays impregnated with AgNO3 over MoO3 powders. During the annealing process, the crystallization of the TiO2 nanotubes, the thermo-decomposition of AgNO3 to Ag nanoparticles, and the sublimation of MoO3 occur jointly. The photoelectrochemical measurements of the resultants indicate that MoO3/Ag/TiO2 nanotube arrays present better photoelectrochemical properties compared with Ag/TiO2 nanotube arrays and pristine TiO2 nanotube arrays. Especially, the highest photocurrent and open circuit voltage are up to 21.29 μA/cm2 and 0.058 V under visible light irradiation, whereas 1.77 and 3.87 times larger than those of TiO2 nanotube arrays, respectively. Superior photoelectrochemical stability and larger photo-conversion efficiency of the ternary composites are also demonstrated. The improved photoelectrochemical properties are related to the close interfacial contact among MoO3, Ag, and TiO2 as well as the surface plasma resonance of Ag in the ternary composites, which broaden the range of light response and enhance the efficiency of charge separation. This study provides a skillful solution to construct TiO2-based composite materials and demonstrates it is an unique architecture to promote the visible light driven photocatalytic application of TiO2.

Herein, a series of TiO2/Bi0.5Na0.5TiO3 heterojunctions with different mass ratio are first constructed under ultrasound irradiation, which shows superior sonocatalytic and photoelectrochemical properties. The improved performance is attributed to the internal electric field and close interfacial contact of TiO2 and Bi0.5Na0.5TiO3, which promote the transfer of photoinduced electrons from TiO2 to Bi0.5Na0.5TiO3. The spontaneous polarization of ferroelectrics in the heterostructures can be adjusted by ultrasonic treatment durations, which further influences the internal electric field. The TiO2/Bi0.5Na0.5TiO3 sample with mass ratios of 5:1 and ultrasonic treatment for 15 min presents the highest sonocatalytic degradation of Rhodamine B. Moreover, the sonocatalytic activity also shows a close relevant to the ultrasonic temperature and frequency of degradation process. And, the •OH radicals are mainly responsible for Rhodamine B sonodegradation. This work provides a novel TiO2/Bi0.5Na0.5TiO3 heterojunctions and promotes the application of ferroelectrics modified materials in sono-/photo-catalysis.

This paper addresses the problem of adaptive fixed-time attitude stabilization for uncertain rigid spacecraft with inertia uncertainties, external disturbances, actuator saturations, and faults. A nonsingular fixed-time sliding mode surface is constructed so that the settling time of the established surface is independent of the system initial states. By employing an exponential function in the controller design, an adaptive fixed-time control scheme is proposed to shorten the time during which the system states reach the sliding mode surface. With the proposed control method, the information on the bound of the lumped uncertainty is not needed in prior but estimated by the designed update laws. The fixed-time convergence of both the attitude and angular velocity is established, and comparative simulations are presented to illustrate the effectiveness of the proposed control scheme.

Different numbers of N/Ti3+ codoped triphasic TiO2 layer are deposited on TiO2 nanotube arrays by hydrothermal reaction of TiN and TiO2 nanotube arrays. The crystal structure, morphology, and composition of the as-prepared samples and their photoelectrochemical properties are investigated and discussed. There are obviously red shift of the optical edge and enhanced visible light absorption for the resulting samples in the ultraviolet–visible diffuse reflectance spectroscopy. Besides, the photoelectrochemical tests indicate that the N/Ti3+ codoped triphasic TiO2-TiO2 nanotube array samples exhibit an enhanced photocurrent density and photovoltage under visible light irradiation. The maximum photoconversion efficiency of N/Ti3+ codoped triphasic TiO2-TiO2 nanotube array sample is 2.1 times higher than that of pristine TiO2 nanotube arrays. In addition, reduced impedance, higher carrier density, and excellent stability of photoelectrochemical performance of N/Ti3+ codoped triphasic TiO2-TiO2 nanotube array samples are also demonstrated. Finally, a plausible mechanism for the charge migration and separation on N/Ti3+ codoped triphasic TiO2-TiO2 nanotube array samples is proposed. This study provides a novel strategy to design and fabricate visible-light-driven TiO2 based composites and promote their applications in the field of solar energy utilization.

A paradigm for material synthesis is demonstrated under the guidance of machine learning as well as the designed wet-lab experiments. This work begins with a series of well-tailored experiments, including the synthesis of C/N-codoped TiO2 nanoparticles with exposed anatase {001} facets and its photocatalytic performance evaluations. In advance of applying the machine learning, the underlying synergistic effects are revealed and expounded in detail purely based on wet-lab experiments. Then, the idea of active learning is adopted by implementing a multi-step workflow to iterate the experiments around preferred synthesis conditions, and unravel the optimum synthesis parameters with the assistance of machine learning. In this work, the prediction models are built on a solid physical and chemical understanding, rather than a ‘black box approach’. To this end, the designed experiments as well as the bootstrapping technique together guarantee a good prediction with less training. The compatibility of the artificial neural network and wet-lab experiment also endows the current strategy with several innate advantages. Moreover, controlled experiments with different starting materials were carried out. The results suggest that, in an effort to ensure more accurate predictions, further restrictions could be placed on data collection prior to the stage of introducing machine learning, and the design of wet-lab experiment plays a crucial role in facilitating machine learning. Lastly, the future prospect for machine learning-assisted material synthesis is briefly discussed based on the current work.

In this paper, a barrier Lyapunov function (BLF)-based backstepping control design is proposed for uncertain rigid spacecraft with both input and output constraints. A modified BLF (MBLF) is constructed to extend the application scope of the traditional logarithmic BLF. Through using the MBLFs in each step of the backstepping design, an adaptive constrained control scheme is presented to guarantee the tracking performance and the constraint requirement of spacecraft systems, and the differentiation of the virtual control is avoided with the employment of the tracking differentiator. The uncertainty bounds are estimated by designing adaptive update laws, such that no prior knowledge is required on the bound of the lumped uncertainty including input saturation and faults. Numerical simulations demonstrate the effectiveness of the proposed scheme.

Machine learning models especially deep neural network models have shown great potential in making decisions when analyzing clustered or longitudinal data. However, lack of model transparency is a major concern in risk sensitive domains such as social science and medical diagnosis. Despite the early success of explaining machine learning models, there is a lack of explanation methods that can be applied to any predictors on clustered data since most of the existing models assume that all observations are independent of each other. In this paper, we address this deficiency and propose to use a linear mixed model to mimic the local behavior of any complex model on clustered data, which can also improve the fidelity of the explanation method to the complex models. We apply our method to explain several models including a deep neural network model on two tasks including movie recommendation and medical record diagnosis. Experiment results show that our model outperforms the baseline models on several metrics such as fidelity and exactness.

As a novel type of carbon materials, graphynes possesses the merits of high carrier mobility and large surface areas, etc. However, to date, the main research of graphynes is focused on theoretical calculation whereas few strategies have been reported for the fabrication of graphynes. In this work, a facile method named ultrasound-promoted synthesis was developed to fabricate γ-graphyne using PhBr6 and CaC2 as the raw materials. The reaction system in argon atmosphere ultrasonically vibrated for 24 h in the ultrasonic bath at a power of 180 W and frequency of 53 kHz. The structure, morphology, and component of the obtained samples were detected by X-ray diffraction, Raman spectroscopy, X-ray photoelectron spectroscopy, FT-IR spectra, scanning electron microscopy, transmission electron microscopy, and the selected area electron diffraction. The electrochemical and photoelectrochemical measurements indicate that γ-graphyne can be used as superior electrode mateirals in supercapacitor and photoelectrochemical catalysis. From the results of galvanostatic charge/discharge measurements, the γ-graphyne supercapacitor delivers a maximum specific capacitance of 81 F/g at 0.2 A/g and a capacitance retention rate of 87.5% after 5000 cycles at 3 A/g. Moreover, UV-vis light photoelectrochemical response and high carrier density are observed for γ-graphyne. It is also demonstrated that the charge-transfer resistance is low by Tafel slopes and Nyquist plots. This work puts forward a new and facile strategy for the fabrication of γ-graphyne and explores its application in the field of solar energy conversion and storage.

A series of γ-graphyne/TiO2 nanotube array heterostructures are first synthesized by a facile and environmental-friendly drop-coating method. The prepared heterostructures is completely investigated by a collection of characterizations. Interestingly, the unique C-O-Ti linkage between γ-graphyne and TiO2 nanotube arrays is verified by both X-ray photoelectron spectroscopy and Fourier transform infrared analysis. After modification of γ-graphyne, the maximum transient photo-current and photo-potential of TiO2 nanotube arrays are improved by 2.2 and 1.3 folds, respectively. Moreover, a maximum of 3.64 and 1.35 folds enhancements are severally verified for the photoelectrocatalytic degradation of levofloxacin and rhodamine B over the heterostructures compared to that of TiO2 nanotube arrays. Furtherly, the heterostructures also show superior photoelectrocatalytic performance on nitrogen fixation and oxygen evolution than TiO2 nanotube arrays. The crystal, morphological, photoelectrochemical, and photoelectrocatalytic stability of the heterostructures are confirmed. This work sheds light on designing γ-graphyne modified composites for photoelectrochemical and photoelectrocatalytic application.

Advances in malware development have led to the widespread use of attacker toolkits that do not leave any trace in the local filesystem. This negatively impacts traditional investigative procedures that rely on filesystem analysis to reconstruct attacker activities. As a solution, memory forensics has replaced filesystem analysis in these scenarios. Unfortunately, existing memory forensics tools leave many capabilities inaccessible to all but the most experienced investigators, who are well versed in operating systems internals and reverse engineering. The goal of the research described in this paper is to make investigation of one of the greatest threats that organizations face, userland keyloggers, less error-prone and less dependent on manual reverse engineering. To accomplish this, we have added significant new capabilities to HookTracer, which is an engine capable of emulating code discovered in a physical memory captures and recording all actions taken by the emulated code. Based on this work, we present new memory forensics capabilities, embodied in a new Volatility plugin, hooktracer_messagehooks, that uses Hooktracer to automatically decide whether a hook in memory is associated with a malicious keylogger or benign software. We also include a detailed case study that illustrates our technique’s ability to successfully analyze very sophisticated keyloggers, such as Turla.

Cooperative relaying in wireless networks is strongly affected by the way that the source and relays are incentivized. However, existing studies have not paid enough attention to the influence of negotiation power of the involved parties. In this paper, two incentive mechanisms for cooperative relaying are proposed, wherein the source possesses different degrees of negotiation power. One mechanism is for the source with strong negotiation power posting a series of take-it-or- leave-it contract offers for the relays while the relays are not entitled to negotiate the counteroffers, and the other mechanism is for the source with weak negotiation power while the relays confer substantial negotiation power, i.e., are capable of doing business with the source by crafting more profitable deals. The relay selection rules, the optimal amounts of relaying service, and the optimal rewards for the relays, are derived for the proposed mechanisms, respectively. A distributed algorithm is further proposed to iteratively obtain the optimal solution for the second mechanism. A case study is also presented to show the influence of negotiation power on the behaviors of the participants and the efficiency and distribution of profits. Numerical results are provided to verify the theoretical analyses of the proposed mechanisms.

Deep convolutional neural networks (CNNs) have shown superior performance on the task of single-label image classification. However, the applicability of CNNs to multi-label images still remains an open problem, mainly because of two reasons. First, each image is usually treated as an inseparable entity and represented as one instance, which mixes the visual information corresponding to different labels. Second, the correlations amongst labels are often overlooked. To address these limitations, we propose a deep multi-modal CNN for multi-instance multi-label image classification, called MMCNN-MIML. By combining CNNs with multi-instance multi-label (MIML) learning, our model represents each image as a bag of instances for image classification and inherits the merits of both CNNs and MIML. In particular, MMCNN-MIML has three main appealing properties: 1) it can automatically generate instance representations for MIML by exploiting the architecture of CNNs; 2) it takes advantage of the label correlations by grouping labels in its later layers; and 3) it incorporates the textual context of label groups to generate multi-modal instances, which are effective in discriminating visually similar objects belonging to different groups. Empirical studies on several benchmark multi-label image data sets show that MMCNN-MIML significantly outperforms the state-of-the-art baselines on multi-label image classification tasks.

In this paper, a finite-time adaptive control method is developed for attitude stabilization of rigid spacecraft with inertia uncertainties and external disturbances. A non-singular fast terminal sliding surface is proposed to circumvent the singularity problem. Then, a finite-time controller is designed to guarantee that the attitude and angular velocity converge into a small region within a finite time. By using an adaptive update laws, no prior knowledge on the bound of uncertainty is needed. The effectiveness of the proposed method is verified through comparative simulation results.

In this paper, a network consisting of a source- destination pair and multiple relays in the presence of a smart jammer who can launch multi- channel jamming attacks is considered, where the direct link between the source and destination does not exist. A game-theoretic framework is proposed to analyze the conflict between the jammer and the legitimate nodes, i.e., the source and the relays, and the cooperation among the legitimate nodes. Specifically, a two-level Stackelberg game is formulated, where the jammer as a leader combats against the legitimate nodes by allocating the jamming powers at the upper level, and the relays as leaders cooperate with the source by selling forwarding powers at the lower level after observing the strategy of the jammer. The Stackelberg equilibrium of the proposed game is derived and analyzed. An algorithm is further designed to obtain the optimal jamming power allocation. Numerical results are provided to verify the theoretical analysis and show the effectiveness of the proposed algorithm.

Collaborative filtering (CF) approaches, which provide recommendations based on ratings or purchase history, perform well for users and items with sufficient interactions. However, CF approaches suffer from the cold-start problem for users and items with few ratings. Hybrid recommender systems that combine collaborative filtering and content-based approaches have been proved as an effective way to alleviate the cold-start issue. Integrating contents from multiple heterogeneous data sources such as reviews and product images is challenging for two reasons. Firstly, mapping contents in different modalities from the original feature space to a joint lower-dimensional space is difficult since they have intrinsically different characteristics and statistical properties, such as sparse texts and dense images. Secondly, most algorithms only use content features as the prior knowledge to improve the estimation of user and item profiles but the ratings do not directly provide feedback to guide feature extraction. To tackle these challenges, we propose a tightly-coupled deep network model for fusing heterogeneous modalities, to avoid tedious feature extraction in specific domains, and to enable two-way information propagation from both content and rating information. Experiments on large-scale Amazon product data in book and movie domains demonstrate the effectiveness of the proposed model for cold-start recommendation.

CdS photocatalysts loaded with Ag2S and MoS2 as dual co-catalysts were prepared via a one-step in situ hydrothermal method using CdCl2·2.5H2O, H2MoO4, AgNO3·9H2O, and CS(NH2)2 as the raw materials. The as-prepared photocatalysts were characterized by X-ray diffraction, scanning electron microscopy, transmission electron micrograph, X-ray photoelectron spectroscopy, photoluminescence spectra, and Ultraviolet–Visible diffuse reflectance spectroscopy (UV–Vis DRS). The highest photocatalytic degradation rate was achieved for Ag2S/MoS2/CdS composites (87 %) compared to MoS2/CdS composites (66 %) and CdS (62 %) under visible light illumination for 60 min. The recycled photocatalytic experiments showed that the photocatalytic stability of CdS was improved with the introduction of Ag2S and MoS2. The improved photocatalytic performance of Ag2S/MoS2/CdS composites can be ascribed to the red shift of absorption edge, enhanced light absorption intensity, and the increased separation of the photoinduced electron–hole pairs, which was attributed to the synergetic effect of MoS2 and Ag2S on CdS. It was proven that Ag2S and MoS2 can act as effective dual co-catalysts to enhance the photocatalytic degradation activity of CdS.

In this paper, an adaptive finite-time fault-tolerant control scheme is proposed for the attitude stabilization of rigid spacecrafts. A first-order command filter is presented at the second step of the backstepping design to approximate the derivative of the virtual control, such that the singularity problem caused by the differentiation of the virtual control is avoided. Then, an adaptive fuzzy finite-time backstepping controller is developed to achieve the finite-time attitude stabilization subject to inertia uncertainty, external disturbance, actuator saturation, and faults. Through using an error transformation, the prescribed performance boundary is incorporated into the controller design to guarantee the prescribed performance of the system output. Numerical simulations demonstrate the effectiveness of the proposed scheme.

In this paper, secure transmission in a two-tier heterogeneous network, consisting of a macrocell and a set of small cells, is considered, where an eavesdropper attempts to wiretap legitimate macrocell users. A game-theoretic framework is proposed to enhance the security of the macrocell while guaranteeing user satisfaction of the small cells. Specifically, a two-level Stackelberg game is formulated, where the macro base station as a follower employs multiple small base stations (SBSs) to jam the eavesdropper at the top level, and each employed SBS as a leader requires offloading service from multiple helping SBSs in its cluster at the bottom level if needed. Two types of objectives are investigated at the bottom level, one with the priority given to user satisfaction over the leader's profit, and the other to balance between user satisfaction and the leader's profit, respectively. The Stackelberg equilibrium of the proposed game is also derived. Numerical results verify the analysis and show that employing the existing SBSs is a promising approach to enhancing security while satisfying the user demands of the small cells.

With the growing number of wireless and mobile devices ingrained into our daily lives, more and more people are interacting with online services that adopt recommender systems to suggest movies, news and points of interest. The private demographics of users such as age and gender in online recommender systems are very useful for many applications such as personalized ads, social study and marketing. However, users do not always provide details in their online profiles due to privacy concern. Most existing approaches can infer user private attributes based on sufficient interaction history but could fail for new users with few ratings. In this paper, we present a novel preference elicitation method, with which a recommender system asks cold-start users to rate selected items adaptively and infer the demographics rapidly via a few interactions. Specifically, latent user profiles are learned across the tasks of demographic inference and rating prediction simultaneously, which enables knowledge transfer through the two related tasks and improves the prediction accuracy for both tasks. The proposed method can also facilitate the understanding of the tradeoff between user privacy and the utility of personalization. Experimental results on real-world datasets demonstrate the performance of the proposed method in terms of the accuracy of both demographics inference and rating prediction.

Needle-shaped dye-sensitized solar cells are developed using anodized Ti wire and carbon fiber with Pt nanoparticle. The model cell is composed of N719 dye-sensitized TiO2 nanotubes/Ti wire as working electrode, Pt nanoparticle/carbon fiber as a counter electrode and I−/I3− electrolyte encased in a capillary glass tube with internal diameter of 1.0 mm and 40 mm in length. The as-prepared DSSC looks like a needle leaf of pine tree. The energy conversion efficiency is 2.21% which can be up to 3.07% after TiCl4 post-treatment of photoanodes. In addition, higher current and output power can be obtained by parallel connection. Pt nanoparticle/carbon fiber is firstly used as counter electrode and the cyclic voltammetries investigation show that Pt nanoparticle/carbon fiber possess higher catalytic activity than Pt wire. Such type DSSC is TCO free and may be capable of achieving a long distance transport of photocurrent and harvesting all light from any direction in surroundings to generate electricity. It has been demonstrated that needle-shaped structure may be a good choice for a solar cell.

Variations of deep neural networks such as convolutional neural network (CNN) have been successfully applied to image denoising. The goal is to automatically learn a mapping from a noisy image to a clean image given training data consisting of pairs of noisy and clean images. Most existing CNN models for image denoising have many layers. In such cases, the models involve a large amount of parameters and are computationally expensive to train. In this paper, we develop a dilated residual CNN for Gaussian image denoising. Compared with the recently proposed residual denoiser, our method can achieve comparable performance with less computational cost. Specifically, we enlarge receptive field by adopting dilated convolution in residual network, and the dilation factor is set to a certain value. We utilize appropriate zero padding to make the dimension of the output the same as the input. It has been proven that the expansion of receptive field can boost the CNN performance in image classification, and we further demonstrate that it can also lead to competitive performance for denoising problem. Moreover, we present a formula to calculate receptive field size when dilated convolution is incorporated. Thus, the change of receptive field can be interpreted mathematically. To validate the efficacy of our approach, we conduct extensive experiments for both gray and color image denoising with specific or randomized noise levels. Both of the quantitative measurements and the visual results of denoising are promising comparing with state-of-the-art baselines., Comment: camera ready, 8 pages, accepted to IEEE ICTAI 2017

Si–W codoped TiO2 nanotubes were synthesized using a one-step anodization process with the presence of silicotungstic acid in the electrolyte. X-ray photoelectron spectroscopy results indicated that the content of Si and W in the TiO2 nanotubes was proportional to the concentration of the silicotungstic acid in the solution. More absorption in the visible light region was observed for the resulted samples from the UV–vis diffuse reflectance spectra. An enhanced photoelectrochemical visible response was demonstrated for the codoped samples and the photocurrent was 2.5 times larger than that of pristine TiO2 nanotubes under visible light irradiation. More negative flat band potential and a higher carrier density were associated with the introduction of dopants Si and W. This work demonstrated a feasible and simple method to fabricate doped TiO2 with controlled doping content based on the corresponding additives in the anodization electrolyte. Keywords: Anodization, Si–W codoping, Titania nanotubes, Visible light, Photoelectrochemistry

Silicon-doped TiO2 thin films were fabricated by annealing titanium metal sheet embedded in SiO2 powders and characterized by X-ray photoemission spectroscopy and photoelectrochemical measurements. The results showed that the content of silicon in the doped TiO2 thin films was proportional to the annealing time and temperature. Enhanced visible light response, more negative flat band potential and higher carrier density were demonstrated by the electrochemical measurement. The technique proposed in this paper can be also applicable to fabricate other doped TiO2 thin films based on the corresponding oxide bath. Keywords: Titania, Si-doped, Thermal treatment, Visible light response, Photoelectrochemical

Summary Recommendation systems are emerging as an important business application with significant economic impact. Currently popular systems include Amazon's book recommendations, Netflix's movie recommendations and Pandora's music recommendations. We address the problem of estimating probabilities associated with recommendation system data by using non-parametric kernel smoothing. In our estimation we interpret missing items as randomly censored observations of preference relations and obtain efficient computation schemes by using combinatorial properties of generating functions. We demonstrate our approach with several case-studies involving real world movie recommendation data. The results are comparable with state of the art techniques while also providing probabilistic preference estimates outside the scope of traditional recommender systems.

A homing mechanism is required for repositioning as a system performs tasks repeatedly. By examining the effect of poor repositioning on the tracking performance of iterative learning control, this paper develops a varying-order learning approach for the performance improvement. Through varying-order learning, the resultant system output trajectory is ensured to follow a given trajectory with a lowered error bound, in comparison with the conventional fixed-order method. A discrete-time initial rectifying action is introduced in the formed varying-order learning algorithm, and a sufficient condition for convergence is derived. An implementable scheme is presented based on the proposed approach, and illustrated by numerical results of two examples of robotic manipulators.

An efficient synthesis of 3-aryl-3-hydroxy-2-(1H-indol-3-yl)-1-phenyl-1-propanone via the cleavage of epoxides with indole promoted by ultrasound irradiation was carried out in good yields by using montmorillonite K10-ZnCl 2 within 2–5 h at room temperature. This method provides several advantages such as operational simplicity, high yield and mild conditions.

In consumer video conferencing, lighting conditions are usually not ideal thus the image qualities are poor. Lighting affects image quality on two aspects: brightness and skin tone. While there has been much research on improving the brightness of the captured images including contrast enhancement and noise removal (which can be thought of as components for brightness improvement), little attention has been paid to the skin tone aspect. In contrast, it is a common knowledge for professional stage lighting designers that lighting affects not only the brightness but also the color tone which plays a critical role in the perceived look of the host and the mood of the stage scene. Inspired by stage lighting design, we propose an active lighting system which automatically adjusts the lighting so that the image looks visually appealing. The system consists of computer controllable light emitting diode light sources of different colors so that it improves not only the brightness but also the skin tone of the face. Given that there is no quantitative formula on what makes a good skin tone, we use a data driven approach to learn a good skin tone model from a collection of photographs taken by professional photographers. We have developed a working system and conducted user studies to validate our approach.

This paper presents a framework to restore the 2D content printed on documents in the presence of geometric distortion and non-uniform illumination. Compared with textbased document imaging approaches that correct distortion to a level necessary to obtain sufficiently readable text or to facilitate optical character recognition (OCR), our work targets nontextual documents where the original printed content is desired. To achieve this goal, our framework acquires a 3D scan of the document's surface together with a high-resolution image. Conformal mapping is used to rectify geometric distortion by mapping the 3D surface back to a plane while minimizing angular distortion. This conformal "deskewing" assumes no parametric model of the document's surface and is suitable for arbitrary distortions. Illumination correction is performed by using the 3D shape to distinguish content gradient edges from illumination gradient edges in the high-resolution image. Integration is performed using only the content edges to obtain a reflectance image with significantly less illumination artifacts. This approach makes no assumptions about light sources and their positions. The results from the geometric and photometric correction are combined to produce the final output.

In this paper, we propose a novel framework called space-time light field rendering, which allows continuous exploration of a dynamic scene in both space and time. Compared to existing light field capture/rendering systems, it offers the capability of using unsynchronized video inputs and the added freedom of controlling the visualization in the temporal domain, such as smooth slow motion and temporal integration. In order to synthesize novel views from any viewpoint at any time instant, we develop a two-stage rendering algorithm. We first interpolate in the temporal domain to generate globally synchronized images using a robust spatial-temporal image registration algorithm followed by edge-preserving image morphing. We then interpolate these software-synchronized images in the spatial domain to synthesize the final view. In addition, we introduce a very accurate and robust algorithm to estimate subframe temporal offsets among input video sequences. Experimental results from unsynchronized videos with or without time stamps show that our approach is capable of maintaining photorealistic quality from a variety of real scenes.

This paper presents adaptive repetitive learning control for trajectory tracking of uncertain robotic manipulators. Through the introduction of a novel Lyapunov-like function, the proposed method only requires the system to start from where it stopped at the last cycle, and avoids the strict requirement for initial repositioning for all the cycles. In addition, it is more applicable, as it only requires the variables to be learned in an iteration-independent manner, rather than satisfying the periodicity requirement in a number of the conventional methods. With the adoption of fully saturated learning, all the signals in the closed loop are guaranteed to be bounded, and the iterative trajectories are proven to follow the profiles of desired trajectories over the entire operation interval. The effectiveness of the proposed method is shown through extensive numerical simulation results.

This paper addresses the problem of periodic disturbance rejection arising from rotational plants following periodic trajectories. Repetitive control is a method usually employed in solving such a problem. The reported analysis results give sufficient conditions for stability of the controlled systems in the frequency domain. In this paper, discrete-time variable-structure repetitive control (VSRC) is proposed, where the periodicity features are taken into account and no disturbance estimate is involved explicitly. The presented VSRC control scheme ensures robust stability of the resultant quasi-sliding-mode dynamics to parameter variations and exogenous disturbances. The complete rejection of periodic disturbances and the perfect tracking of periodic trajectories are achieved. Time-domain sufficient conditions for the robust stability are presented by which the controller parameters can be chosen. A numerical simulation is conducted to illustrate the validity of the developed method.

In the competitive environment of the internet, retaining and growing one's user base is of major concern to most web services. Furthermore, the economic model of many web services is allowing free access to most content, and generating revenue through advertising. This unique model requires securing user time on a site rather than the purchase of good which makes it crucially important to create new kinds of metrics and solutions for growth and retention efforts for web services. In this work, we address this problem by proposing a new retention metric for web services by concentrating on the rate of user return. We further apply predictive analysis to the proposed retention metric on a service, as a means for characterizing lost customers. Finally, we set up a simple yet effective framework to evaluate a multitude of factors that contribute to user return. Specifically, we define the problem of return time prediction for free web services. Our solution is based on the Cox's proportional hazard model from survival analysis. The hazard based approach offers several benefits including the ability to work with censored data, to model the dynamics in user return rates, and to easily incorporate different types of covariates in the model. We compare the performance of our hazard based model in predicting the user return time and in categorizing users into buckets based on their predicted return time, against several baseline regression and classification methods and find the hazard based approach to be superior.

Most available results on iterative learning control address trajectory tracking problem for systems without time delay. The effect of time delay on tracking performance is not yet fully understood. This paper is concerned with iterative learning control design for the systems with delayed states, where the system class under consideration is confined by a defined relative degree. A learning control scheme is proposed to overcome the uncertainties in delay times and=or in system parameters. Robustness of the learning control is established in the presence of initial function errors. Furthermore, uniform convergence of system outputs to the desired trajectory is ensured if the initial function at each cycle is set to align with the desired initial function. Validity of the results is demonstrated through numerical simulations.

ZnO/CdS core/shell nanorods (NRs) constructed of ZnO NR cores and CdS coating shells were fabricated by hydrothermally growing ZnO NRs and pulsed laser depositing CdS coatings on the grown ZnO NRs. The fabricated ZnO/CdS NRs were characterized by field-emission scanning electron microscopy, transmission electron microscopy, selected-area electron diffraction, X-ray diffraction, and Raman backsattering spectroscopy. The optical and photoelectrochemical properties were evaluated and their correlation was studied. The ZnO cores and CdS shells are both in a hexagonal wurtzite structure with c-axis orientation. Compared with the bare ZnO NRs, the heterostructured ZnO/CdS NRs exhibit improved optical properties including strong visible light absorption and quenched photoluminescence. The former facilitates the generation of electron-hole pairs by visible optical excitation, and the latter reveals the suppressed radiative recombination of photogenerated charges and hence efficient charge transfer and separation....

In this paper, an ideal integral error dynamics based control design method is proposed for linear servo systems. Two cases are detailed, which in one case dose not use any disturbance compensation, while other case the compensation is involved. The control performance for both cases is analyzed, respectively. Numerical simulation and practical experiment are carried out, and the obtained results are presented to demonstrate effectiveness of the proposed ideal integral error dynamics based control method.

This paper proposes a unified architecture of time-varying neural networks for implementing unknown time-varying mappings. The methodology of iterative learning is applied for the network training, and a modified iterative learning least squares algorithm is presented. Under the assumption of bounded input signals, convergence results of the proposed learning algorithm are given. In order to realize periodic mappings, periodic neural networks are characterized and a periodic learning algorithm is presented for training such neural networks.

This paper presents the design of an iterative learning controller for a class of uncertain time-varying nonlinear systems in the presence of initial state errors. Through the introduction of initial rectified attractors and a finite-time dead-zone, a neural network iterative learning controller is designed with the proposed learning mechanism for the time-varying neural network training. The complete tracking is shown to be able to be realized over a pre-specified time-interval. In particular, all the signals in the closed-loop are guaranteed to be bounded, while the error between the system state and the desired one is ensured to converge to zero within a pre-specified time horizon. Moreover, the error is always confined to the dead-zone, which is well-governed by the introduced initial rectified attractor. Numeric simulation is carried out to demonstrate effectiveness of the proposed method.

In this paper, the problem of chaotic secure communication is addressed from an iterative learning perspective, where the plaintext spans over a pre-specified finite-time interval. In the cryptographic scheme, the plaintext is modulated through chaotic parameter modulation, and then chaotically masked by a nonlinear mechanism. An efficient learning algorithm is exploited for decryption, and the sufficient convergence condition is derived by which the learning gain can be chosen. Furthermore, two fundamental techniques are given in order to enhance security. Case studies are carried out to demonstrate the effectiveness of the proposed method.

Through reducing the boundary layer, higher control accuracy can be achieved when applying robust control techniques. However, chattering phenomenon may occur. This paper presents a nonlinear robust controller design in which a dynamic boundary layer is introduced. Theoretical results are presented for characterizing the tracking performance. In particular, the tracking error is forced to converge to the pre-specified boundary layer so that the control accuracy is achieved, while the chattering phenomenon is alleviated. The effectiveness of the proposed control method is demonstrated by the presented numerical results.

We introduce a new dissimilarity function for ranked lists, the expected weighted Hoeffding distance, that has several advantages over current dissimilarity measures for ranked search results. First, it is easily customized for users who pay varying degrees of attention to websites at different ranks. Second, unlike existing measures such as generalized Kendall's tau, it is based on a true metric, preserving meaningful embeddings when visualization techniques like multi-dimensional scaling are applied. Third, our measure can effectively handle partial or missing rank information while retaining a probabilistic interpretation. Finally, the measure can be made computationally tractable and we give a highly efficient algorithm for computing it. We then apply our new metric with multi-dimensional scaling to visualize and explore relationships between the result sets from different search engines, showing how the weighted Hoeffding distance can distinguish important differences in search engine behavior that are not apparent with other rank-distance metrics. Such visualizations are highly effective at summarizing and analyzing insights on which search engines to use, what search strategies users can employ, and how search results evolve over time. We demonstrate our techniques using a collection of popular search engines, a representative set of queries, and frequently used query manipulation methods.

This paper presents a neural network framework for implementing unknown time-varying mappings. A unified architecture of time-varying neural networks is proposed, and the methodology of iterative learning is used for the network training. Convergence results of the iterative learning least squares algorithm are derived under assumption of bounded input signals. Periodic neural networks are explored as well to be used as periodic function approximation tools.

Photographs taken of the same scene often look very different, due to various conditions such as the time of day, the camera characteristics, and subsequent processing of the image. Prime examples are the countless photographs of urban centers taken throughout history. In this paper we present an approach to match the appearance between photographs that removes effects such as different camera settings, illumination, fading ink and paper discoloration over time, and digitization artifacts. Global histogram matching techniques are inadequate for appearance matching of complex scenes where background, light, and shadow can vary drastically, making correspondence a difficult problem. We alleviate this correspondence problem by registering photographs to 3D models of the scene. In addition, by estimating the calendar date and time of day, we can additionally remove the effect of drastic lighting and shadow differences between the photographs. We present results for the case of urban scenes, and show that our method allows for realistic visualizations by blending information from multiple photographs without color-matching artifacts.

Synthesis of the glycolurils catalyzed by potassium hydroxide was carried out in 17–75% yield at 40 °C in EtOH under ultrasound irradiation. Compared to the method using stirring, the main advantage of the present procedure is milder conditions and shorter reaction time.