Query: A Survey of Artificial Intelligence for Cognitive Radios

Relevant passages:
Genetic Algorithms in Search Optimization and Machine Learning <=SEP=> This book brings together-in an informal and tutorial fashion-the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems...

Optimization by simulated annealing <=SEP=> 

Metaheuristics in combinatorial optimization: Overview and conceptual comparison <=SEP=> The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behavior of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.

Cognitive radio: brain-empowered wireless communications <=SEP=> Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: /spl middot/ highly reliable communication whenever and wherever needed; /spl middot/ efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radio-scene analysis. 2) Channel-state estimation and predictive modeling. 3) Transmit-power control and dynamic spectrum management. This work also discusses the emergent behavior of cognitive radio.

Neural Networks: A Comprehensive Foundation <=SEP=> El profesor Haykin ha escrito un libro muy interesante, que ya lleva su segunda edición, lo cual es extrafio dentro de la gran cantidad de libros que se producen en la actualidad. Es fácil encontrar en la literatura contemporánea la constante referencia que se hace a este libro; sin embargo, el libro no es lo que el título indica, ya que no es un libro ortodoxo de las teorías y técnicas que son conocidas como redes neuronales (también conocidas como sistemas conexionistas o sistemas neurocomputacionales), tampoco es un libro que presente los fundamentos o bases teóricas de las redes neuronales. neuronales, así como los grandes modelos de esta área de investigación, como son los perceptrones y las redes RBFN (Radial-Basis Function Networks). La presentación de estos cuatro capítulos es excelente.

Engagement in Multimedia Training Systems <=SEP=> Two studies examined user engagement in two type multimedia training systems -a more passive medium, videotape, and a less passive medium, interactive softw Each study compared engagement in three formats: the Text format contained text and still images, the Audio format contained audio and still images, and the Video format contained audio and video images. In both studies, engagement was lower in the Text condition than in the Video condition. However, there were no differences in engagement between Text and Audio in the videotapebased training, and no differences between Audio and Video in the computer-based training.

AoA and ToA Accuracy for Antenna Arrays in Dense Multipath Channels <=SEP=> The accuracy that can be achieved in time of arrival (ToA) estimation strongly depends on the utilized signal bandwidth. In an indoor environment multipath propagation usually causes a degradation of the achievable accuracy due to the overlapping signals. A similar effect can be observed for the angle of arrival (AoA) estimation using arrays. This paper derives a closed-form equation for the Cramer-Rae lower bound (CRLB) of the achievable AoA and the ToA error variances, considering the presence of dense multipath. The Fisher information expressions for both parameters allows an evaluation of the influence of channel parameters and system parameters such as the array geometry. Our results demonstrate that the AoA estimation accuracy is strongly related to the signal bandwidth, due to the multipath influence. The theoretical results are evaluated with experimental data.

MODEL DIFFERENCES IN THE ECLIPSE MODELING FRAMEWORK <=SEP=> Increasingly, recording the various kinds of design-level structural evolution that a system undergoes throughout its entire life-cycle is gaining a fundamental importance and cannot be neglected in software modeling and development. In this respect, an interesting and useful operation between the designs of subsequent system versions is the difference management consisting in calculation, representation, and visualization. This work presents EMF Compare, an approach to model difference calculation and representation for the Eclipse modeling framework. Apart from leveraging the rank of model differences to that of first-class artifacts according to the “everything is a model” principle, the approach presents several properties which are discussed according to a conceptual framework.

A Blended Deep Learning Approach for Predicting User Intended Actions <=SEP=> User intended actions are widely seen in many areas. Forecasting these actions and taking proactive measures to optimize business outcome is a crucial step towards sustaining the steady business growth. In this work, we focus on predicting attrition, which is one of typical user intended actions. Conventional attrition predictive modeling strategies suffer a few inherent drawbacks. To overcome these limitations, we propose a novel end-to-end learning scheme to keep track of the evolution of attrition patterns for the predictive modeling. It integrates user activity logs, dynamic and static user profiles based on multi-path learning. It exploits historical user records by establishing a decaying multi-snapshot technique. And finally it employs the precedent user intentions via guiding them to the subsequent learning procedure. As a result, it addresses all disadvantages of conventional methods. We evaluate our methodology on two public data repositories and one private user usage dataset provided by Adobe Creative Cloud. The extensive experiments demonstrate that it can offer the appealing performance in comparison with several existing approaches as rated by different popular metrics. Furthermore, we introduce an advanced interpretation and visualization strategy to effectively characterize the periodicity of user activity logs. It can help to pinpoint important factors that are critical to user attrition and retention and thus suggests actionable improvement targets for business practice. Our work will provide useful insights into the prediction and elucidation of other user intended actions as well.

5G security architecture and light weight security authentication <=SEP=> The concept of future 5G (the 5th generation of mobile networks) widely spreads in industry, but the related standards are still unclear. The definition of 5G is to provide adequate RF coverage, more bits/Hz and to interconnect all wireless heterogeneous networks to provide seamless, consistent telecom experience to the user. In this paper we have surveyed 5G network architecture and proposed a multi-tier security architecture and a lightweight authentication measure for 5G. The proposed scheme aims at achieving fast authentication and minimizing the packet transmission overhead without compromising the security requirements.

OINT C LOUDS <=SEP=> Three-dimensional geometric data offer an excellent domain for studying representation learning and generative modeling. In this paper, we look at geometric data represented as point clouds. We introduce a deep autoencoder (AE) network with state-of-the-art reconstruction quality and generalization ability. The learned representations outperform existing methods on 3D recognition tasks and enable basic shape editing via simple algebraic manipulations, such as semantic part editing, shape analogies and shape interpolation. We perform a thorough study of different generative models including: GANs operating on the raw point clouds, significantly improved GANs trained in the fixed latent space of our AEs and, Gaussian mixture models (GMM). For our quantitative evaluation we propose measures of sample fidelity and diversity based on matchings between sets of point clouds. Interestingly, our careful evaluation of generalization, fidelity and diversity reveals that GMMs trained in the latent space of our AEs produce the best results.

Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization <=SEP=> Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color & spatial controls, all using a single feed-forward neural network.

Enterprise systems implementation and accounting benefits <=SEP=> Purpose – The aim of this paper is to examine the accounting benefits involved in adopting enterprise systems, but also to comprehend the underlying causes that motivate their adoption. Design/methodology/approach – This paper presents evidence from a survey of 73 companies which implemented ES on the related benefits to their accounting information provision and practices. Findings – Empirical evidence confirms a number of benefits derived from the application of ES that focus on the following dimensions: organisation, operations, management and IT infrastructure. These benefits are related both to the reasons leading to the implementation of ES and to the relevant selection of ES modules. Originality/value – The corresponding results from this survey can then be used as a basis for establishing the best approach in order to fully exploit the potential of future ES applications in accounting practice.

Multiscale Energy and Eigenspace Approach to Detection and Localization of Myocardial Infarction <=SEP=> In this paper, a novel technique on a multiscale energy and eigenspace (MEES) approach is proposed for the detection and localization of myocardial infarction (MI) from multilead electrocardiogram (ECG). Wavelet decomposition of multilead ECG signals grossly segments the clinical components at different subbands. In MI, pathological characteristics such as hypercute T-wave, inversion of T-wave, changes in ST elevation, or pathological Q-wave are seen in ECG signals. This pathological information alters the covariance structures of multiscale multivariate matrices at different scales and the corresponding eigenvalues. The clinically relevant components can be captured by eigenvalues. In this study, multiscale wavelet energies and eigenvalues of multiscale covariance matrices are used as diagnostic features. Support vector machines (SVMs) with both linear and radial basis function (RBF) kernel and K-nearest neighbor are used as classifiers. Datasets, which include healthy control, and various types of MI, such as anterior, anteriolateral, anterioseptal, inferior, inferiolateral, and inferioposterio-lateral, from the PTB diagnostic ECG database are used for evaluation. The results show that the proposed technique can successfully detect the MI pathologies. The MEES approach also helps localize different types of MIs. For MI detection, the accuracy, the sensitivity, and the specificity values are 96%, 93%, and 99% respectively. The localization accuracy is 99.58%, using a multiclass SVM classifier with RBF kernel.

Secure Logging as a Service—Delegating Log Management to the Cloud <=SEP=> Securely maintaining log records over extended periods of time is very important to the proper functioning of any organization. Integrity of the log files and that of the logging process need to be ensured at all times. In addition, as log files often contain sensitive information, confidentiality and privacy of log records are equally important. However, deploying a secure logging infrastructure involves substantial capital expenses that many organizations may find overwhelming. Delegating log management to the cloud appears to be a viable cost saving measure. In this paper, we identify the challenges for a secure cloud-based log management service and propose a framework for doing the same.

Fast-and-Light Stochastic ADMM <=SEP=> The alternating direction method of multipliers (ADMM) is a powerful optimization solver in machine learning. Recently, stochastic ADMM has been integrated with variance reduction methods for stochastic gradient, leading to SAG-ADMM and SDCA-ADMM that have fast convergence rates and low iteration complexities. However, their space requirements can still be high. In this paper, we propose an integration of ADMM with the method of stochastic variance reduced gradient (SVRG). Unlike another recent integration attempt called SCAS-ADMM, the proposed algorithm retains the fast convergence benefits of SAG-ADMM and SDCA-ADMM, but is more advantageous in that its storage requirement is very low, even independent of the sample size n. Experimental results demonstrate that it is as fast as SAG-ADMM and SDCA-ADMM, much faster than SCAS-ADMM, and can be used on much bigger data sets.

Revenue : 229 B Revenue : ? Birth Year : ? Height : ? Weight : 70 <=SEP=> Knowledge bases (KB) are often represented as a collection of facts in the form (head, predicate, tail), where head and tail are entities while predicate is a binary relationship that links the two. It is a well-known fact that knowledge bases are far from complete, and hence the plethora of research on KB completion methods, specifically on link prediction. However, though frequently ignored, these repositories also contain numerical facts. Numerical facts link entities to numerical values via numerical predicates; e.g., (Paris, latitude, 48.8). Likewise, numerical facts also suffer from the incompleteness problem. To address this issue, we introduce the numerical attribute prediction problem. This problem involves a new type of query where the relationship is a numerical predicate. Consequently, and contrary to link prediction, the answer to this query is a numerical value. We argue that the numerical values associated with entities explain, to some extent, the relational structure of the knowledge base. Therefore, we leverage knowledge base embedding methods to learn representations that are useful predictors for the numerical attributes. An extensive set of experiments on benchmark versions of Freebase and Yago show that our approaches largely outperform sensible baselines. We make the datasets available under a permissive BSD-3 license.

Severely suppressed bone turnover: a potential complication of alendronate therapy. <=SEP=> Alendronate, an inhibitor of bone resorption, is widely used in osteoporosis treatment. However, concerns have been raised about potential oversuppression of bone turnover during long-term use. We report on nine patients who sustained spontaneous nonspinal fractures while on alendronate therapy, six of whom displayed either delayed or absent fracture healing for 3 months to 2 yr during therapy. Histomorphometric analysis of the cancellous bone showed markedly suppressed bone formation, with reduced or absent osteoblastic surface in most patients. Osteoclastic surface was low or low-normal in eight patients, and eroded surface was decreased in four. Matrix synthesis was markedly diminished, with absence of double-tetracycline label and absent or reduced single-tetracycline label in all patients. The same trend was seen in the intracortical and endocortical surfaces. Our findings raise the possibility that severe suppression of bone turnover may develop during long-term alendronate therapy, resulting in increased susceptibility to, and delayed healing of, nonspinal fractures. Although coadministration of estrogen or glucocorticoids appears to be a predisposing factor, this apparent complication can also occur with monotherapy. Our observations emphasize the need for increased awareness and monitoring for the potential development of excessive suppression of bone turnover during long-term alendronate therapy.

Risk factors for shoulder and elbow injuries in adolescent baseball pitchers. <=SEP=> BACKGROUND
There is little evidence supporting current safety recommendations for adolescent pitchers.


HYPOTHESIS
Pitching practices of adolescent pitchers without history of arm injury will be significantly different from those of adolescent pitchers who required shoulder or elbow surgery.


STUDY DESIGN
Case control study; Level of evidence, 3.


METHODS
Ninety-five adolescent pitchers who had shoulder or elbow surgery and 45 adolescent pitchers who never had a significant pitching-related injury completed a survey. Responses were compared between the 2 groups using t tests and chi(2) analyses. Multivariable logistic regression models were developed to identify the risk factors.


RESULTS
The injured group pitched significantly more months per year, games per year, innings per game, pitches per game, pitches per year, and warm-up pitches before a game. These pitchers were more frequently starting pitchers, pitched in more showcases, pitched with higher velocity, and pitched more often with arm pain and fatigue. They also used anti-inflammatory drugs and ice more frequently to prevent an injury. Although the groups were age matched, the injured group was taller and heavier. There were no significant differences regarding private pitching instruction, coach's chief concern, pitcher's self-rating, exercise programs, stretching practices, relieving frequency, pitch type frequency, or age at which pitch types were first thrown.


CONCLUSION
Pitching practices were significantly different between the groups. The factors with the strongest associations with injury were overuse and fatigue. High pitch velocity and participation in showcases were also associated with increased risk for injury.


CLINICAL RELEVANCE
New recommendations were made based on these results. Adherence to the recommendations may reduce the incidence of significant injury to adolescent pitchers.

Christopher Alexander's fifteen properties: Toward developing evaluation metrics for security visualizations <=SEP=> The objective of security data visualizations is to help cyber analysts to perceive trends and patterns, and gain insights into security data. Sound and systematic evaluations of security data visualizations are rarely performed, in part due to the lack of proper quantitative and qualitative measures. In this paper, we present a novel evaluation approach for security visualization based on Christopher Alexander's fifteen properties of living structures. Alexander's fifteen properties are derived from various visual patterns that appear in nature. Each property represents the guidelines for good design. We believe that using these fundamental properties have the potential for building a more robust evaluation. Each property offers essential qualities that enable better analytical reasoning. We demonstrate how to use Alexander's properties to evaluate security related visualizations. We derive a set of visualization-specific properties for evaluation, developed based on Alexander's original properties.

Influence Maximization for Complementary Goods: Why Parties Fail to Cooperate? <=SEP=> We consider the problem where companies provide different types of products and want to promote their products through viral marketing simultaneously. Most previous works assume products are purely competitive. Different from them, our work considers that each product has a pairwise relationship which can be from strongly competitive to strongly complementary to each other's product. The problem is to maximize the spread size with the presence of different opponents with different relationships on the network. We propose Interacting Influence Maximization (IIM) game to model such problems by extending the model of the Competitive Influence Maximization (CIM) game studied by previous works, which considers purely competitive relationship. As for the theoretical approach, we prove that the Nash equilibrium of highly complementary products of different companies may still be very inefficient due to the selfishness of companies. We do so by introducing a well-known concept in game theory, called Price of Stability (PoS) of the extensive-form game. We prove that in any k selfish players symmetric complementary IIM game, the overall spread of the products can be reduced to as less as 1/k of the optimal spread. Since companies may fail to cooperate with one another, we propose different competitive objective functions that companies may consider and deal with separately. We propose a scalable strategy for maximizing influence differences, called TOPBOSS that is guaranteed to beat the first player in a single-round two-player second-move game. In the experiment, we first propose a learning method to learn the ILT model, which we propose for IIM game, from both synthetic and real data to validate the effectiveness of ILT. We then exhibit that the performance of several heuristic strategies in the traditional influence maximization problem can be improved by acquiring the knowledge of the existence of competitive/complementary products in the network. Finally, we compare the TOPBOSS with different heuristic algorithms in real data and demonstrate the merits of TOPBOSS.

The Polar Parallel Coordinates Method for Time-Series Data Visualization <=SEP=> Parallel coordinates is a very important visualization method, but the dimensions it can express are limited by the length or width of the screen. In this paper, we present the polar parallel coordinates method. First, we define the polar parallel coordinates as the coordinate whose axes exist between the same degree and show the expression of the axis. We give the algorithms for the polar parallel coordinates generation and data shown in the polar parallel coordinates. Then we point out two feathers of the polar parallel coordinates. It can express more axes than parallel coordinates and reflect the characteristics of time-series data. Based on this, we compare the parallel coordinates with the polar parallel coordinates by the time-series data visualization.

Deep neural architectures for large scale android malware analysis <=SEP=> Android is arguably the most widely used mobile operating system in the world. Due to its widespead use and huge user base, it has attracted a lot of attention from the unsavory crowd of malware writers. Traditionally, techniques to counter such malicious software involved manually analyzing code and figuring out whether it was malicious or benign. However, due to the immense pace at which newer malware families are surfacing, such an approach is no longer feasible. Machine learning offers a way to tackle this issue of speed by automating the classification task. While several efforts have been made to use traditional machine learning techniques to Android malware detection, no reasonable effort has been made to utilize the newer, deep learning models in this domain. In this paper, we apply several deep learning models including fully connected, convolutional and recurrent neural networks as well as autoencoders and deep belief networks to detect Android malware from a large scale dataset of more than 55 GBs of Android malware. Further, we apply Bayesian machine learning to this problem domain to see how it fares with the deep learning based models while also providing insights into the dataset. We show that we are able to achieve better results using these models as compared to the state-of-the-art approaches. Our best model gets an F1 score of 0.986 with an AUC of 0.983 as compared to the existing best F1 score of 0.875 and AUC of 0.953.

Empirically Evaluating the Quality of Automatically Generated and Manually Written Test Suites <=SEP=> The creation, execution, and maintenance of tests are some of the most expensive tasks in software development. To help reduce the cost, automated test generation tools can be used to assist and guide developers in creating test cases. Yet, the tests that automated tools produce range from simple skeletons to fully executable test suites, hence their complexity and quality vary. This paper compares the complexity and quality of test suites created by sophisticated automated test generation tools to that of developer-written test suites. The empirical study in this paper examines ten real-world programs with existing test suites and applies two state-of-the-art automated test generation tools. The study measures the resulting test suite quality in terms of code coverage and fault-finding capability. On average, manual tests covered 31.5% of the branches while the automated tools covered 31.8% of the branches. In terms of mutation score, the tests generated by automated tools had an average mutation score of 39.8% compared to the average mutation score of 42.1% for manually written tests. Even though automatically created tests often contain more lines of source code than those written by developers, this paper's empirical results reveal that test generation tools can provide value by creating high quality test suites while reducing the cost and effort needed for testing.

Ranking scientific publications: the effect of nonlinearity <=SEP=> Ranking the significance of scientific publications is a long-standing challenge. The network-based analysis is a natural and common approach for evaluating the scientific credit of papers. Although the number of citations has been widely used as a metric to rank papers, recently some iterative processes such as the well-known PageRank algorithm have been applied to the citation networks to address this problem. In this paper, we introduce nonlinearity to the PageRank algorithm when aggregating resources from different nodes to further enhance the effect of important papers. The validation of our method is performed on the data of American Physical Society (APS) journals. The results indicate that the nonlinearity improves the performance of the PageRank algorithm in terms of ranking effectiveness, as well as robustness against malicious manipulations. Although the nonlinearity analysis is based on the PageRank algorithm, it can be easily extended to other iterative ranking algorithms and similar improvements are expected.

GradNets: Dynamic Interpolation Between Neural Architectures <=SEP=> In machine learning, there is a fundamental trade-off between ease of optimization and expressive power. Neural Networks, in particular, have enormous expressive power and yet are notoriously challenging to train. The nature of that optimization challenge changes over the course of learning. Traditionally in deep learning, one makes a static trade-off between the needs of early and late optimization. In this paper, we investigate a novel framework, GradNets, for dynamically adapting architectures during training to get the benefits of both. For example, we can gradually transition from linear to non-linear networks, deterministic to stochastic computation, shallow to deep architectures, or even simple downsampling to fully differentiable attention mechanisms. Benefits include increased accuracy, easier convergence with more complex architectures, solutions to test-time execution of batch normalization, and the ability to train networks of up to 200 layers.

A Survey on NoSQL Stores <=SEP=> Recent demands for storing and querying big data have revealed various shortcomings of traditional relational database systems. This, in turn, has led to the emergence of a new kind of complementary nonrelational data store, named as NoSQL. This survey mainly aims at elucidating the design decisions of NoSQL stores with regard to the four nonorthogonal design principles of distributed database systems: data model, consistency model, data partitioning, and the CAP theorem. For each principle, its available strategies and corresponding features, strengths, and drawbacks are explained. Furthermore, various implementations of each strategy are exemplified and crystallized through a collection of representative academic and industrial NoSQL technologies. Finally, we disclose some existing challenges in developing effective NoSQL stores, which need attention of the research community, application designers, and architects.

Distributed Manufacturing Scheduling Using Intelligent Agents <=SEP=> reflect the temporal relationships between manufacturing activities and the capacity limitations of a set of shared resources. The allocation also affects a schedule's opti-mality with respect to criteria such as cost, lateness, or throughput. The globalization of manufacturing makes such optimization increasingly important. To survive in this competitive market, manufacturing enterprises must increase their productivity and profitability through greater shop floor agility. Agent-based manufacturing scheduling systems are a promising way to provide this optimization. Scheduling problems are not exclusive to manufacturing systems. Similar situations happen routinely in publishing houses, universities, hospitals, airports, transportation companies, and so on. Scheduling problems are typically NP-hard; that is, finding an optimal solution is impossible without using an essentially enumerative algorithm , and the computation time increases exponentially with the problem size. Manufacturing scheduling is one of the most difficult scheduling problems. A well-known manufacturing scheduling problem is classical job shop scheduling, which involves a set of jobs and a set of machines. Each machine can handle at most one job at a time. Each job consists of a chain of operations , each of which must be processed during an uninterrupted time period of given length on a given machine. The purpose is to find a best schedule—that is, an allocation of the operations to time intervals on the machines that has the minimum duration required to complete all jobs. The total possible solutions for this problem with n jobs and m machines is (n!) m. The problem becomes even more complex when it includes other variables. Additional resources In a real manufacturing enterprise, production managers or human schedulers must consider all kinds of manufacturing resources, not just jobs and machines. For example, a classical job shop scheduling problem with n jobs, m machines, and k operators could have ((n!) m) k possible solutions. Simultaneous planning and scheduling Traditional approaches separating process planning and scheduling can obtain suboptimal solutions at two separate phases. Global optimization of a manufacturing system is only possible when process planning and scheduling are integrated. However, this makes the scheduling problem much more difficult to solve. Unforeseen dynamic situations In a job shop manufacturing environment, things rarely go as expected. Scheduled jobs get canceled and new jobs are inserted. Certain resources become unavailable and additional resources are introduced. A scheduled task takes more or less time than anticipated, and tasks arrive early or late. Other uncertainties include power system failures, …

A Compact Wideband Dual-Polarized Slot Antenna With Five Resonances <=SEP=> This letter presents the design and analysis of a compact dual-polarized slot antenna using a triple-mode stepped slot-line resonator and a dual-mode cross-shaped monopole. The orthogonal polarization is achieved by feeding the two ports symmetrically at the opposite positions of the slot. All the five resonance frequencies can be flexibly adjusted by tuning different dimension parameters, so by increasing the frequency difference between the five resonance modes, a very wide common operating band (1.63–2.93 GHz) is obtained and also with a high port isolation (< $-$32 dB). Finally, by loading two grounded lumped capacitors on the cusp sections of the monopole, its second resonance frequency can be independently reconfigured.

Online social networking addiction among college students in Singapore: Comorbidity with behavioral addiction and affective disorder. <=SEP=> PURPOSE OF THE RESEARCH
This study aimed to determine the prevalence of addiction to social networking sites/platforms (SNS) and its comorbidity with other behavioral addiction and affective disorder among college students in Singapore. 1110 college students (age: M=21.46, SD=1.80) in Singapore completed measures assessing online social networking, unhealthy food intake and shopping addiction as well as depression, anxiety and mania. Descriptive analyses were conducted to investigate the prevalence and comorbidity of behavioral addiction and affective disorder. Chi-square tests were used to examine gender differences.


THE PRINCIPAL RESULTS
The prevalence rates of SNS, food and shopping addiction were 29.5%, 4.7% and 9.3% respectively for the total sample. SNS addiction was found to co-occur with food addiction (3%), shopping addiction (5%), and both food and shopping addiction (1%). The comorbidity rates of SNS addiction and affective disorder were 21% for depression, 27.7% for anxiety, and 26.1% for mania. Compared with the total sample, students with SNS addiction reported higher comorbidity rates with other behavioral addiction and affective disorder. In general, females as compared to males reported higher comorbidity rates of SNS addiction and affective disorder.


MAJOR CONCLUSIONS
SNS addiction has a high prevalence rate among college students in Singapore. Students with SNS addiction were vulnerable to experience other behavior addiction as well as affective disorder, especially among females.