title: DRN: A Deep Reinforcement Learning Framework for News Recommendation

abstract: In this paper, we propose a novel Deep Reinforcement Learning framework for news recommendation. Online personalized news recommendation is a highly challenging problem due to the dynamic nature of news features and user preferences. Although some online recommendation models have been proposed to address the dynamic nature of news recommendation, these methods have three major issues. First, they only try to model current reward (e.g., Click Through Rate). Second, very few work consider to use user feedback other than click / no click labels (e.g., how frequent user returns) to help improve recommendation. Third, these methods tend to keep recommending similar news to users, which may cause users to get bored. Therefore, to address the aforementioned challenges, we propose a Deep Q-Learning based recommendation framework, which can model future reward explicitly. We further consider user return pattern as a supplement to click / no click label in order to capture more user feedback information. In addition, an effective exploration strategy is incorporated to find new attractive news for users. Extensive experiments are conducted on the offline dataset and online production environment of Chinese Bing News and have shown the superior performance of our methods.


Keep Your Bearings: Lightly-Supervised Information Extraction with Ladder Networks That Avoids Semantic Drift


Methodology proposal of the creation of competency models and competency model for the position of a sales manager in an industrial organisation using the AHP method and Saaty's method of determining weights


title: Debunking the myth of the hard-to-reach farmer: effective communication on udder health.

abstract: Worldwide, programs to control mastitis are implemented using different strategies to reach farmers. Even though education materials and best practices may be technically optimal, they need to be used to be successful. Thus, effective communication with farmers is essential in order to change their behavior and to improve their farm management. During a Dutch national mastitis control program, a substantial number of farmers seemed to be hard to reach with information on udder health. Consequently, this study was designed to provide insight into the attitude and motivation of such farmers. In the period of October 2007 to July 2008, 24 in-depth, semistructured interviews were conducted with farmers whose veterinarians considered to be difficult to approach with advice on udder health management (8 practices, 3 farmers from each practice). The interviews included questions about the farms and the farmers, their attitude and behavior regarding mastitis, and their information sources and social environment. The results show that so-called hard-to-reach farmers were not always badly informed about udder health and did not always experience problems with mastitis. These ostensibly unreachable farmers were not a homogeneous group, but rather could be divided into 4 categories based on their trust in external information sources regarding mastitis and their orientation toward the outside world: proactivists, do-it-yourselfers, wait-and-see-ers, and reclusive traditionalists. There are ample opportunities to reach hard-to-reach farmers, provided that the communication strategies are tailored to their specific needs. There is especially much to gain in communication with do-it-yourselfers and wait-and-see-ers, but this demands a more proactive role on the part of veterinarians and extension specialists. Different types of farmers need to be approached in different ways and through different channels with information on udder health. Consequently, this study can contribute to the optimization of future programs designed to control and prevent diseases.


title: Virtual empathy: Positive and negative impacts of going online upon empathy in young adults

abstract: We tested if going online displaces face-to-face time and negatively impacts empathy.Internet usage, face-to-face time, and empathy were measured in 1390 young adults.Going online had little impact upon empathy and improved face-to-face time.Video gaming reduced empathy in both sexes but did not reduce face-to-face time.Virtual empathy was positively correlated with, but lower than, real-world empathy. People can show empathic responses to others online, but at the same time empathy has been declining in young people since technology-based communication has become prevalent. Displacement of face-to-face time by online activities would be expected to negatively impact empathic skills. Since there is little direct empirical research on this topic, the present study sought to determine the nature of the relationship between Internet usage and empathy. More than 1000 young adults completed an anonymous online questionnaire that asked about daily media usage, real-world empathy, virtual empathy, social support and demographic information. The results showed that, in general, going online had very small negative impacts upon cognitive and affective real-world empathy and actually improved time spent in face-to-face communication. Video gaming reduced real-world empathy in both females and males but did not reduce face-to-face time. Also, virtual empathy was positively correlated with real-world empathy, although virtual empathy scores were lower than real-world empathy scores for both sexes. Finally, both real-world empathy and virtual empathy are positively related to social support but real-world empathy demonstrated a 5-6 times stronger relationship. The findings show that spending time online does not displace face-to-face time nor reduce real-world empathy, and suggest that perhaps the lack of nonverbal cues in the online world contributes to overall lower levels of virtual empathy compared to the real world. The negative effects of being online upon empathy appear to be due to specific activities such as video gaming rather than total quantity of online time.


title: Iterative Side-Channel Cube Attack on KeeLoq

abstract: KeeLoq is a 528-round lightweight block cipher which has a 64-bit secret key and a 32-bit block length. The cube attack, proposed by Dinur and Shamir, is a new type of attacking method. In this paper, we investigate the security of KeeLoq against iterative side-channel cube attack which is an enhanced attack scheme. Based on structure of typical block ciphers, we give the model of iterative side-channel cube attack. Using the traditional single-bit leakage model, we assume that the attacker can exactly possess the information of one bit leakage after round 23. The new attack model costs a data complexity of 211.00 chosen plaintexts to recover the 23-bit key of KeeLoq. Our attack will reduce the key searching space to 241 by considering an error-free bit from internal states.


title: Learning values across many orders of magnitude

abstract: Most learning algorithms are not invariant to the scale of the function that is being approximated. We propose to adaptively normalize the targets used in learning. This is useful in value-based reinforcement learning, where the magnitude of appropriate value approximations can change over time when we update the policy of behavior. Our main motivation is prior work on learning to play Atari games, where the rewards were all clipped to a predetermined range. This clipping facilitates learning across many different games with a single learning algorithm, but a clipped reward function can result in qualitatively different behavior. Using the adaptive normalization we can remove this domain-specific heuristic without diminishing overall performance.


title: Memristor-Based Cellular Nonlinear/Neural Network: Design, Analysis, and Applications

abstract: Cellular nonlinear/neural network (CNN) has been recognized as a powerful massively parallel architecture capable of solving complex engineering problems by performing trillions of analog operations per second. The memristor was theoretically predicted in the late seventies, but it garnered nascent research interest due to the recent much-acclaimed discovery of nanocrossbar memories by engineers at the Hewlett-Packard Laboratory. The memristor is expected to be co-integrated with nanoscale CMOS technology to revolutionize conventional von Neumann as well as neuromorphic computing. In this paper, a compact CNN model based on memristors is presented along with its performance analysis and applications. In the new CNN design, the memristor bridge circuit acts as the synaptic circuit element and substitutes the complex multiplication circuit used in traditional CNN architectures. In addition, the negative differential resistance and nonlinear current–voltage characteristics of the memristor have been leveraged to replace the linear resistor in conventional CNNs. The proposed CNN design has several merits, for example, high density, nonvolatility, and programmability of synaptic weights. The proposed memristor-based CNN design operations for implementing several image processing functions are illustrated through simulation and contrasted with conventional CNNs. Monte-Carlo simulation has been used to demonstrate the behavior of the proposed CNN due to the variations in memristor synaptic weights.


title: Image Inpainting Approaches - A Review

abstract: Image inpainting is an ancient art. There may bedifferent factors which cause deterioration of images includingenvironmental factors, chemical processing, improper storageand more. Inpainting is the process which restores thedeteriorated parts of the image. This paper proposes two majortypes of inpainting approaches i.e. 2D image inpainting anddepth map inpainting. Image inpainting refers to recovercorrupted parts of images by applying structural, textural orboth the methods simultaneously. Whereas, depth mapinpainting mainly used to improve the 3D visualization effect byimproving the depth map associated with scene. Some of theimage inpainting approaches can also be applied to depth maps. This paper gives overview of such approaches as well as thosewhich are specifically meant for depth maps.


abstract: Some arguments are probably valid: Syllogistic reasoning as communication Michael Tessler, Noah D. Goodman {mtessler, ngoodman}@stanford.edu Department of Psychology, Stanford University Abstract Syllogistic reasoning lies at the intriguing intersection of natu- ral and formal reasoning, of language and logic. Syllogisms comprise a formal system of reasoning yet use natural lan- guage quantifiers, and invite natural language conclusions. How can we make sense of the interplay between logic and language? We develop a computational-level theory that con- siders reasoning over concrete situations, constructed proba- bilistically by sampling. The base model can be enriched to consider the pragmatics of natural language arguments. The model predictions are compared with behavioral data from a recent meta-analysis. The flexibility of the model is then ex- plored in a data set of syllogisms using the generalized quan- tifiers most and few. We conclude by relating our model to two extant theories of syllogistic reasoning – Mental Models and Probability Heuristics. Keywords: Reasoning; language; QUD; Bayesian model Figure 1: How will the reasoner interpret the experimenter’s argument? Consider for a moment that your friend tells you: “Every- one in my office has the flu and, you know, some people with this flu are out for weeks.” Do you respond with “Everyone in your office has the flu.” Do you respond with “Pardon me, there is no inference I can draw from what you just said.” Or do you respond “I hope your officemates are not out for weeks and I hope you don’t get sick either.” The first response – while true – does not go beyond the premises; the second response attempts to go beyond the premises by strict classical logic, and fails; the final response goes beyond the premises, to offer a conclusion which is probably useful and probably true. This cartoon illustrates a critical dimension along which cognitive theories of reason- ing differ: whether the core and ideal of reasoning is deduc- tive validity or probabilistic support. A separate dimension concerns the extent to which principles of natural language— pragmatics and semantics—are necessary for understanding reasoning tasks. In this paper, we explore the idea that the formalism of probabilistic pragmatics can provide insight into how people reason with syllogisms. The form of the argument above resembles a syllogism: a two-sentence argument used to relate two properties (or terms: A, C) via a middle term (B); the relations used in syl- logisms are quantifiers. Fit into a formal syllogistic form, this argument would read: All officemates are out with the flu Some out with the flu are out for weeks Therefore, some officemates are out for weeks The full space of syllogistic arguments is derived by shuffling the ordering of the terms in a sentence (“All A are B” vs. “All B are A”) and changing the quantifier (all, some, none, not all 1 ). Most syllogisms have no valid conclusion, i.e. there 1 For distinctiveness, we will refer to the quantifiers as such. Note is no relation between A & C which is true in every situa- tion in which the premises are true. This is the case with the argument above. Often in these cases, however, people are perfectly comfortable drawing some conclusion. A recent meta-analysis of syllogistic reasoning showed that over the population, the proper production of no valid conclusion re- sponses for invalid arguments ranged from 76% to 12%. For valid arguments, the accuracy of producing valid conclusions ranged from 90% to 1% (Khemlani & Johnson-Laird, 2012): people do not seem to find drawing deductively valid conclu- sions particularly natural. Perhaps because of this divergence between human behav- ior and deductive logic, syllogistic reasoning has been a topic of interest in cognitive psychology for over a hundred years (St¨orring, 1908), and before that in philosophy, dating back to Aristotle. Syllogisms are undoubtedly logical; indeed, the syllogistic form was the only formal system of logic for mil- lennia. At the same time, the arguments use natural language quantifiers and invite natural language conclusions; precisely pinning down the meaning and use of quantifiers has been an ongoing area of inquiry since Aristotle (e.g. Horn, 1989). Many theories of syllogistic reasoning take deduction as a given and try to explain reasoning errors as a matter of noise during cognition. Errors, then, may arise from im- proper use of deductive rules or biased construction of log- ical models. Many other kinds of reasoning, however, can be well-explained as probabilistic inference under uncertainty. Probability theory provides a natural description of a world in which you don’t know exactly how many people are in the that in sentence-form, the last two quantifiers are typically presented as “No A are B” and “Some A are not B”, respectively.


title: Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review

abstract: Abstract In recent years, deep convolutional neural networks (CNNs) have shown record-shattering performance in a variety of computer vision problems, such as visual object recognition, detection and segmentation. These methods have also been utilised in medical image analysis domain for lesion segmentation, anatomical segmentation and classification. We present an extensive literature review of CNN techniques applied in brain magnetic resonance imaging (MRI) analysis, focusing on the architectures, pre-processing, data-preparation and post-processing strategies available in these works. The aim of this study is three-fold. Our primary goal is to report how different CNN architectures have evolved, discuss state-of-the-art strategies, condense their results obtained using public datasets and examine their pros and cons. Second, this paper is intended to be a detailed reference of the research activity in deep CNN for brain MRI analysis. Finally, we present a perspective on the future of CNNs in which we hint some of the research directions in subsequent years.


title: The sky is not the limit: multitasking across GitHub projects

abstract: Software development has always inherently required multitasking: developers switch between coding, reviewing, testing, designing, and meeting with colleagues. The advent of software ecosystems like GitHub has enabled something new: the ability to easily switch between projects. Developers also have social incentives to contribute to many projects; prolific contributors gain social recognition and (eventually) economic rewards. Multitasking, however, comes at a cognitive cost: frequent context-switches can lead to distraction, sub-standard work, and even greater stress. In this paper, we gather ecosystem-level data on a group of programmers working on a large collection of projects. We develop models and methods for measuring the rate and breadth of a developers' context-switching behavior, and we study how context-switching affects their productivity. We also survey developers to understand the reasons for and perceptions of multitasking. We find that the most common reason for multitasking is interrelationships and dependencies between projects. Notably, we find that the rate of switching and breadth (number of projects) of a developer's work matter. Developers who work on many projects have higher productivity if they focus on few projects per day. Developers that switch projects too much during the course of a day have lower productivity as they work on more projects overall. Despite these findings, developers perceptions of the benefits of multitasking are varied.


title: User-adaptive information visualization: using eye gaze data to infer visualization tasks and user cognitive abilities

abstract: Information Visualization systems have traditionally followed a one-size-fits-all model, typically ignoring an individual user's needs, abilities and preferences. However, recent research has indicated that visualization performance could be improved by adapting aspects of the visualization to each individual user. To this end, this paper presents research aimed at supporting the design of novel user-adaptive visualization systems. In particular, we discuss results on using information on user eye gaze patterns while interacting with a given visualization to predict the user's visualization tasks, as well as user cognitive abilities including perceptual speed, visual working memory, and verbal working memory. We show that such predictions are significantly better than a baseline classifier even during the early stages of visualization usage. These findings are discussed in view of designing visualization systems that can adapt to each individual user in real-time.


title: Addressing fake news: Open standards & easy identification

abstract: Fake news proliferation and distrust in news media has become an endemic problem in the American society and has aided in the corruption of civil political discourse. The formation of a standard and subsequent implementation using automated algorithmic processes will provide unbiased ratings and evaluations of online news media truthfulness in the form of easy-to-understand scores. This easily accessible information will aid in the reduction of fake news proliferation and help produce a more informed electorate.


title: Algorithms on evolving graphs

abstract: Motivated by applications that concern graphs that are evolving and massive in nature, we define a new general framework for computing with such graphs. In our framework, the graph changes over time and an algorithm can only track these changes by explicitly probing the graph. This framework captures the inherent tradeoff between the complexity of maintaining an up-to-date view of the graph and the quality of results computed with the available view. We apply this framework to two classical graph connectivity problems, namely, path connectivity and minimum spanning trees, and obtain efficient algorithms.


Modulation of 6-gingerolin Antidepressant-like Effects: An Investigation of Serotonergic System in Mice Model


title: Automated Regularization Parameter Selection in Multi-Scale Total Variation Models for Image Restoration

abstract: Multi-scale total variation models for image restoration are introduced. The models utilize a spatially dependent regularization parameter in order to enhance image regions containing details while still sufficiently smoothing homogeneous features. The fully automated adjustment strategy of the regularization parameter is based on local variance estimators. For robustness reasons, the decision on the acceptance or rejection of a local parameter value relies on a confidence interval technique based on the expected maximal local variance estimate. In order to improve the performance of the initial algorithm a generalized hierarchical decomposition of the restored image is used. The corresponding subproblems are solved by a superlinearly convergent algorithm based on Fenchel-duality and inexact semismooth Newton techniques. The paper ends by a report on numerical tests, a qualitative study of the proposed adjustment scheme and a comparison with popular total variation based restoration methods.


title: Hierarchical Power Control in Cognitive Networks

abstract: We investigate the interactive behavior and strategic decision-making between multiple secondary users (SUs) and primary users (PUs), both of which are end-to-end performance aware in cognitive networks. A Stackelberg game is utilized to formulate the spectrum utilization maximization problem after the complex interference situation is analyzed. Especially, an interference power cap (IPC) function predefined by PUs as a pricing function is introduced into the utility function design of SUs to guarantee QoS of PUs, as well as to decouple constraints. Further, an asymmetric information situation can be formed by considering PUs as leaders who employ the optimal water-filling algorithm, and the closed-form power strategy of SUs can be derived. And accordingly, SUs as followers can observe the available information to do more foresighted decision by learning. What is more, we prove the optimality and existence of the deceived solutions. Numerical results demonstrate that the proposed distributed algorithm provides more spectrum revenue and better QoS guarantees to PUs with limited iterations.


title: Anatomy of a web-scale resale market: a data mining approach

abstract: Reuse and remarketing of content and products is an integral part of the internet. As E-commerce has grown, online resale and secondary markets form a significant part of the commerce space. The intentions and methods for reselling are diverse. In this paper, we study an instance of such markets that affords interesting data at large scale for mining purposes to understand the properties and patterns of this online market.As part of knowledge discovery of such a market, we first formally propose criteria to reveal unseen resale behaviors by elastic matching identification (EMI) based on the account transfer and item similarity properties of transactions. Then, we present a large-scale system that leverages MapReduce paradigm to mine millions of online resale activities from petabyte scale heterogeneous e-commerce data. With the collected data, we show that the number of resale activities leads to a power law distribution with a 'long tail', where a significant share of users only resell in very low numbers and a large portion of resales come from a small number of highly active resellers. We further conduct a comprehensive empirical study from different aspects of resales, including the temporal, spatial patterns, user demographics, reputation and the content of sale postings. Based on these observations, we explore the features related to "successful" resale transactions and evaluate if they can be predictable. We also discuss uses of this information mining for business insights and user experience on a real-world online marketplace.


title: Report of the American Psychiatric Association Task Force on Treatment of Gender Identity Disorder

abstract: Both the diagnosis and treatment of Gender Identity Disorder (GID) are controversial. Although linked, they are separate issues and the DSM does not evaluate treatments. The Board of Trustees (BOT) of the American Psychiatric Association (APA), therefore, formed a Task Force charged to perform a critical review of the literature on the treatment of GID at different ages, to assess the quality of evidence pertaining to treatment, and to prepare a report that included an opinion as to whether or not sufficient credible literature exists for development of treatment recommendations by the APA. The literature on treatment of gender dysphoria in individuals with disorders of sex development was also assessed. The completed report was accepted by the BOT on September 11, 2011. The quality of evidence pertaining to most aspects of treatment in all subgroups was determined to be low; however, areas of broad clinical consensus were identified and were deemed sufficient to support recommendations for treatment in all subgroups. With subjective improvement as the primary outcome measure, current evidence was judged sufficient to support recommendations for adults in the form of an evidence-based APA Practice Guideline with gaps in the empirical data supplemented by clinical consensus. The report recommends that the APA take steps beyond drafting treatment recommendations. These include issuing position statements to clarify the APA’s position regarding the medical necessity of treatments for GID, the ethical bounds of treatments of gender variant minors, and the rights of persons of any age who are gender variant, transgender or transsexual.


title: A Nonisolated Three-Port DC–DC Converter and Three-Domain Control Method for PV-Battery Power Systems

abstract: In order to interface one photovoltaic (PV) port, one bidirectional battery port, and one load port of a PV-battery dc power system, a novel nonisolated three-port dc/dc converter named boost bidirectional buck converter (B3C) and its control method based on three-domain control are proposed in this paper. The power flow and operating principles of the proposed B3C are analyzed in detail, and then, the dc voltage relation between three ports is deduced. The proposed converter features high integration and single-stage power conversion from both PV and battery ports to the load port, thus leading to high efficiency. The current of all three ports is continuous; hence, the electromagnetic noise can be reduced. Furthermore, the control and modulation method for B3C has been proposed for realizing maximum power point tracking (MPPT), battery management, and bus voltage regulation simultaneously. The operation can be transited between conductance mode and MPPT mode automatically according to the load power. Finally, experimental verifications are given to illustrate the feasibility and effectiveness of the proposed topology and control method.


title: A Comprehensive Review of Image Smoothing Techniques

abstract: Smoothing is often used to reduce noise within an image or to produce a less pixelated image. Image smoothing is a key technology of image enhancement, which can remove noise in images. So, it is a necessary functional module in various image-processing software. Excellent smoothing algorithm can both remove various noises and preserve details. This paper analyzed some image smoothing algorithms. These algorithms have the ability of preserving details, such as gradient weighting filtering, self-adaptive median filtering, robust smoothing and edge preserving filtering. Appropriate choice of such techniques is greatly influenced by the imaging modality, task at hand and viewing conditions. This paper will provide an overview of underlying concepts, along with algorithms commonly used for image smoothing.


title: An adaptive RFID middleware for supporting metaphysical data independence

abstract: Sensor devices produce data that are unreliable, low-level, and seldom able to be used directly by applications. In this paper, we propose metaphysical data independence (MDI), a layer of independence that shields applications from the challenges that arise when interacting directly with sensor devices. The key philosophy behind MDI is that applications do not deal with any aspect of physical device data, but rather interface with a high-level reconstruction of the physical world created by a sensor infrastructure. As a concrete instantiation of MDI in such a sensor infrastructure, we detail MDI-SMURF, a Radio Frequency Identification (RFID) middleware system that alleviates issues associated with using RFID data through adaptive techniques based on a novel statistical framework.


title: Learning Concept Taxonomies from Multi-modal Data

abstract: We study the problem of automatically building hypernym taxonomies from textual and visual data. Previous works in taxonomy induction generally ignore the increasingly prominent visual data, which encode important perceptual semantics. Instead, we propose a probabilistic model for taxonomy induction by jointly leveraging text and images. To avoid hand-crafted feature engineering, we design end-to-end features based on distributed representations of images and words. The model is discriminatively trained given a small set of existing ontologies and is capable of building full taxonomies from scratch for a collection of unseen conceptual label items with associated images. We evaluate our model and features on the WordNet hierarchies, where our system outperforms previous approaches by a large gap.


title: Development of a Facebook Addiction Scale.

abstract: The Bergen Facebook Addiction Scale (BFAS), initially a pool of 18 items, three reflecting each of the six core elements of addiction (salience, mood modification, tolerance, withdrawal, conflict, and relapse), was constructed and administered to 423 students together with several other standardized self-report scales (Addictive Tendencies Scale, Online Sociability Scale, Facebook Attitude Scale, NEO–FFI, BIS/BAS scales, and Sleep questions). That item within each of the six addiction elements with the highest corrected item-total correlation was retained in the final scale. The factor structure of the scale was good (RMSEA = .046, CFI = .99) and coefficient alpha was .83. The 3-week test-retest reliability coefficient was .82. The scores converged with scores for other scales of Facebook activity. Also, they were positively related to Neuroticism and Extraversion, and negatively related to Conscientiousness. High scores on the new scale were associated with delayed bedtimes and rising times.


title: Quantum-inspired Evolutionary Algorithm for a Class of Combinatorial Optimization

abstract: This paper proposes a novel evolutionary algorithm inspired by quantum computing, called a quantum-inspired evolutionary algorithm (QEA), which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Like other evolutionary algorithms, QEA is also characterized by the representation of the individual, evaluation function, and population dynamics. However, instead of binary, numeric, or symbolic representation, QEA uses a Q-bit, defined as the smallest unit of information, for the probabilistic representation and a Q-bit individual as a string of Q-bits. A Q-gate is introduced as a variation operator to drive the individuals toward better solutions. To demonstrate its effectiveness and applicability, experiments were carried out on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that QEA performs well, even with a small population, without premature convergence as compared to the conventional genetic algorithm.


title: A frequency agile microstrip antenna

abstract: This paper describes a novel technique for increasing the operational frequency bandwidth of microstrip patch antennas, by using varactor diodes at the radiating edges of the structure. Theoretical and experimental results confirm that bandwidths of the order of 30% are achievable compared to 1-2% for the unloaded patch radiator.


title: Robust and sparse estimation of tensor decompositions

abstract: We propose novel tensor decomposition methods that advocate both properties of sparsity and robustness to outliers. The sparsity enables us to extract some essential features from a big data that are easily interpretable. The robustness ensures the resistance to outliers that appear commonly in high-dimensional data. We first propose a method that generalizes the ridge regression in M-estimation framework for tensor decompositions. The other approach we propose combines the least absolute deviation (LAD) regression and the least absolute shrinkage operator (LASSO) for the CANDECOMP/PARAFAC (CP) tensor decompositions. We also formulate various robust tensor decomposition methods using different loss functions. The simulation study shows that our robust-sparse methods outperform other general tensor decomposition methods in the presence of outliers.


title: Web-scale named entity recognition

abstract: Automatic recognition of named entities such as people, places, organizations, books, and movies across the entire web presents a number of challenges, both of scale and scope. Data for training general named entity recognizers is difficult to come by, and efficient machine learning methods are required once we have found hundreds of millions of labeled observations. We present an implemented system that addresses these issues, including a method for automatically generating training data, and a multi-class online classification training method that learns to recognize not only high level categories such as place and person, but also more fine-grained categories such as soccer players, birds, and universities. The resulting system gives precision and recall performance comparable to that obtained for more limited entity types in much more structured domains such as company recognition in newswire, even though web documents often lack consistent capitalization and grammatical sentence construction.


title: Effects of storage temperature and duration on release of antimony and bisphenol A from polyethylene terephthalate drinking water bottles of China.

abstract: Abstract We investigated effects of storage temperature and duration on release of antimony (Sb) and bisphenol A (BPA) from 16 brands of polyethylene terephthalate (PET) drinking water bottles in China. After 1-week storage, Sb release increased from 1.88–8.32 ng/L at 4 °C, to 2.10–18.4 ng/L at 25 °C and to 20.3–2604 ng/L at 70 °C. The corresponding releases for BPA were less at 0.26–18.7, 0.62–22.6, and 2.89–38.9 ng/L. Both Sb and BPA release increased with storage duration up to 4-week, but their releasing rates decreased with storage time, indicating that Sb and BPA release from PET bottles may become stable under long term storage. Human health risk was evaluated based on the worst case, i.e., storage at 70 °C for 4-week. Chronic daily intake (CDI) caused by BPA release was below USEPA regulation, Sb release in one brand exceeded USEPA regulated CDI (400 ng/kg bw/d) with values of 409 and 1430 ng/kg bw/d for adult and children.


title: Model checking SystemC designs using timed automata

abstract: SystemC is widely used for modeling and simulation in hardware/software co-design. Due to the lack of a complete formal semantics, it is not possible to verify SystemC designs. In this paper, we present an approach to overcome this problem by defining the semantics of SystemC by a mapping from SystemC designs into the well-defined semantics of Uppaal timed automata. The informally defined behavior and the structure of SystemC designs are completely preserved in the generated Uppaal models. The resulting Uppaal models allow us to use the Uppaal model checker and the Uppaal tool suite, including simulation and visualization tools. The model checker can be used to verify important properties such as liveness, deadlock freedom or compliance with timing constraints. We have implemented the presented transformation, applied it to two examples and verified liveness, safety and timing properties by model checking, thus showing the applicability of our approach in practice.


Industry 4 . 0 is coming : the role of ambition in digital adoption by micro-businesses


Finding hidden hierarchy in social networks


title: Autonomous 6D-docking and manipulation with non-stationary-base using self-reconfigurable modular robots

abstract: Aggregation of self-reconfigurable robotic modules can potentially offer many advantages for robotic locomotion and manipulation. The resulting system could be more reliable and fault-tolerant and provide the necessary flexibility for new tasks and environments. However, self-aggregation of modules is a challenging task, especially when the alignment of the docking parties in a 3D environment involves both position and orientation (6D), since the bases of docking may be non-stationary (e.g., floating in space, underwater, or moving along the ground), and the end-effectors may have accumulated uncertainties due to many dynamically-established connections between modules. This paper presents a new framework for docking in such a context and describes a solution for sensor-guided self-reconfiguration and manipulation with non-fixed bases. The main contributions of the paper include a realistic experiment setting for 6D docking where a modular manipulator is floating or rotating in space with a reaction wheel and searches and docks with a target module using vision. The movement of the docking parties is a combination of floating and manipulation, and the precision of the docking is guided by a sensor located at the tip of the docking interface. The docking itself is planned and executed by a real-time algorithm with a theoretical convergence boundary. This new framework has been tested in a high-fidelity physics-based simulator, as well as by real robotic modules based on SuperBot. Experimental results have shown an average success rate of more than 86.7 percent in a variety of different 6D-docking scenarios.


title: Facilitating online privacy on eCommerce websites: an Australian experience

abstract: As traditional organizations using their websites for eCommerce transactions are increasing at an exponential rate, privacy concerns of users are also on the rise. To gain an insight into these concerns, existing policies and legislation, we conducted the research reported in this paper, in 2003. To augment the literature synthesis, a multiple case study analysis was conducted, based on six large organisations in Australia. Our research findings suggested that in the Australian context, an online privacy policy (OPP) on the website which complies with the Privacy Act, supported by few best practices are reasonably able to address online privacy concerns. However, these findings are restricted in time frame, indicative and relevant in the Australian context. Nevertheless, we hope to stimulate academic research enquiry and discussion forums through this research.


title: Towards a better collaboration of static and dynamic analyses for testing concurrent programs

abstract: Testing concurrent programs remains a difficult task due to the non-deterministic nature of concurrent executions. Many approaches have been proposed to combine static and dynamic analysis to reduce the complexity of uncovering potential concurrency bugs. However, the existing collaboration schemes only provide a limited mechanism for exchanging relevant information between the two analyses. For example, alias information only flows from the static analysis module to the dynamic analysis module at the beginning of the dynamic analysis. Therefore, we cannot fully exploit the advantages of each type of analysis. Motivated by this observation, in this paper we present a new testing technique which enables a tighter collaboration between static analysis and dynamic analysis. In this collaboration scheme, static analysis and dynamic analysis interact iteratively throughout the whole testing process. Static analysis uses coarse-grained analysis to guide the dynamic analysis to concentrate on the relevant search space, while dynamic analysis collects concrete runtime information during the guided exploration. The runtime information provided by the dynamic analysis helps the static analysis to refine its coarse-grained analysis and provides better guidance on dynamic analysis. Currently, our implementation consists of a static analysis module based on Soot and a dynamic analysis module based on JPF (Java PathFinder).


title: When private set intersection meets big data: an efficient and scalable protocol

abstract: Large scale data processing brings new challenges to the design of privacy-preserving protocols: how to meet the increasing requirements of speed and throughput of modern applications, and how to scale up smoothly when data being protected is big. Efficiency and scalability become critical criteria for privacy preserving protocols in the age of Big Data. In this paper, we present a new Private Set Intersection (PSI) protocol that is extremely efficient and highly scalable compared with existing protocols. The protocol is based on a novel approach that we call oblivious Bloom intersection. It has linear complexity and relies mostly on efficient symmetric key operations. It has high scalability due to the fact that most operations can be parallelized easily. The protocol has two versions: a basic protocol and an enhanced protocol, the security of the two variants is analyzed and proved in the semi-honest model and the malicious model respectively. A prototype of the basic protocol has been built. We report the result of performance evaluation and compare it against the two previously fastest PSI protocols. Our protocol is orders of magnitude faster than these two protocols. To compute the intersection of two million-element sets, our protocol needs only 41 seconds (80-bit security) and 339 seconds (256-bit security) on moderate hardware in parallel mode.


title: THE RESPONSE OF NATURAL MICROBIAL POPULATIONS IN SEAWATER TO ORGANIC ENRICHMENT1

abstract: When the heterotrophic potential technique is applied to marine situations, its analytical value is frequently impaired. In the open ocean, uptake responses often fail to develop despite use of a variety of 14C labeled substrates. Elsewhere, the incidence of uninterpretable kinetic uptake patterns is excessively high. However, when the period of exposure to organic enrichment is extended to 24 hr or more, natural populations from coastal areas develop measurable and analytically useful uptake patterns. Besides providing an attractive source of test cells for bioassay purposes, this shift in uptake behavior can be exploited for studying the dynamics of heterotrophic behavior. An assessment of the marine environment in terms of these and related observations is provided.


title: Compact Multi-Band H-Shaped Slot Antenna

abstract: A compact triple-band H-shaped slot antenna fed by microstrip coupling is proposed. Four resonant modes are excited, including a monopole mode, a slot mode, and their higher-order modes, to cover GPS (1.575 GHz) and Wi-Fi (2.4-2.485 GHz and 5.15-5.85 GHz), respectively. Sensitivity study of the slot geometry upon the resonant modes have been conducted. The measured gains at these four resonant frequencies are 0.2 dBi, 3.5 dBi, 2.37 dBi, and 3.7 dBi, respectively, and the total efficiencies are -2.5 dB, -1.07 dB, -3.06 dB, and -2.7 dB, respectively. The size of this slot antenna is only 0.24λ0×0.034λ0, where λ0 is the free-space wavelength at 1.575 GHz, hence is suitable to install on notebook PC's and handheld devices.


title: Working memory in children with reading disabilities

abstract: This study investigated associations between working memory (measured by complex memory tasks) and both reading and mathematics abilities, as well as the possible mediating factors of fluid intelligence, verbal abilities, short-term memory (STM), and phonological awareness, in a sample of 46 6- to 11-year-olds with reading disabilities. As a whole, the sample was characterized by deficits in complex memory and visuospatial STM and by low IQ scores; language, phonological STM, and phonological awareness abilities fell in the low average range. Severity of reading difficulties within the sample was significantly associated with complex memory, language, and phonological awareness abilities, whereas poor mathematics abilities were linked with complex memory, phonological STM, and phonological awareness scores. These findings suggest that working memory skills indexed by complex memory tasks represent an important constraint on the acquisition of skill and knowledge in reading and mathematics. Possible mechanisms for the contribution of working memory to learning, and the implications for educational practice, are considered.


title: Multi-Task Active Learning for Linguistic Annotations

abstract: We extend the classical single-task active learning (AL) approach. In the multi-task active learning (MTAL) paradigm, we select examples for several annotation tasks rather than for a single one as usually done in the context of AL. We introduce two MTAL metaprotocols, alternating selection and rank combination, and propose a method to implement them in practice. We experiment with a twotask annotation scenario that includes named entity and syntactic parse tree annotations on three different corpora. MTAL outperforms random selection and a stronger baseline, onesided example selection, in which one task is pursued using AL and the selected examples are provided also to the other task.


title: DOCUMENT RETRIEVAL USING A PROBABILISTIC KNOWLEDGE MODEL

abstract: We are interested in enhancing information retrieval methods by incorporating domain knowledge. In this paper, we present a new document retrieval framework that learns a probabilistic knowledge model and exploits this model to improve document retrieval. The knowledge model is represented by a network of associations among concepts defining key domain entities and is extracted from a corpus of documents or from a curated domain knowledge base. This knowledge model is then used to perform concept-related probabilistic inferences using link analysis methods and applied to the task of document retrieval. We evaluate this new framework on two biomedical datasets and show that this novel knowledge-based approach outperforms the state-of-art Lemur/Indri document retrieval method.


title: Laboratory design for wireless network attacks

abstract: There has been an increased awareness on the importance of a hands-on laboratory component in information security education in recent years. We developed a series of laboratory exercises for wireless network security. These laboratory exercises introduce to the students the following wireless network attacks: Wardriving, Eavesdropping, WEP Key Cracking/Decryption, Man in the Middle, MAC Spoofing, ARP Cache Poisoning, and ARP Request Replay. Open source tools such as Aircrack-ng, Cain and Abel, and Mac Makeup are used in these laboratory exercises to demonstrate how these attacks are conducted. The laboratory exercises were presented to two information security related courses at this university. The student feedback was very positive. Future work will include refining the laboratory design, conducting evaluation of the exercises extensively, developing more laboratory exercises for wireless network security and developing laboratory exercises for the Linux platform.


title: Detection and Mitigation of Congestion in SDN Enabled Data Center Networks: A Survey

abstract: Large-scale data centers play a vital role in supporting the ever growing demand for computation and storage. These data centers often employ commodity hardware switches with shallow buffers arranged in a multi-rooted tree topology. Many-to-one traffic communication pattern is often observed in a data center whereby many servers simultaneously send requested data to an aggregation server. This may result in excessive congestion on the bottleneck switch, overwhelming the shallow switch buffer and resulting in large number of packet drops. Legacy transmission control protocol (TCP) treats a packet drop as an indication of severe congestion in the path and forces the sender to drastically reduce its sending rate. This leads to the phenomena of TCP incast resulting in severe throughput collapse deteriorating the performance of the data center. Many solutions have been proposed in literature that addresses the TCP incast issue by either tweaking TCP parameters or proposing a modified/custom version of TCP to be used in data centers. Recently, software-defined networking (SDN) has gained the attention of researchers due to the ease of centralized control and programmability of the network. The centralized nature of SDN presents an opportunity to address the TCP incast issue in data center networks in an efficient manner. This paper provides a snapshot of research efforts that leverage the capabilities of SDN to mitigate the effect of congestion in data centers. We provide a taxonomy of the existing solutions, discuss complementary concepts and highlight their relative strengths along with their shortcomings.


title: Variational Bayes with Synthetic Likelihood

abstract: Synthetic likelihood is an attractive approach to likelihood-free inference when an approximately Gaussian summary statistic for the data, informative for inference about the parameters, is available. The synthetic likelihood method derives an approximate likelihood function from a plug-in normal density estimate for the summary statistic, with plug-in mean and covariance matrix obtained by Monte Carlo simulation from the model. In this article, we develop alternatives to Markov chain Monte Carlo implementations of Bayesian synthetic likelihoods with reduced computational overheads. Our approach uses stochastic gradient variational inference methods for posterior approximation in the synthetic likelihood context, employing unbiased estimates of the log likelihood. We compare the new method with a related likelihood free variational inference technique in the literature, while at the same time improving the implementation of that approach in a number of ways. These new algorithms are feasible to implement in situations which are challenging for conventional approximate Bayesian computation (ABC) methods, in terms of the dimensionality of the parameter and summary statistic.


title: Performance of Using Antenna Arrays to Generate and Receive mm-Wave Orbital-Angular-Momentum Beams

abstract: Generation and detection of millimeter-wave carrying orbital angular momentum (OAM) have been of growing interest. In this paper, we evaluate patch antenna arrays with different arrangements as OAM generators and receivers by simulation. We compare beam evolution processes and steering performance for circular and ring-antenna-array based OAM links. Mode purity of the generated OAM +1 beam fluctuates between 10% and 99% for ring antenna arrays with 10 cm diameters while it remains >99% for circular antenna arrays. Compared to a ring-antenna-array based link, the circular-antenna- array based link could have an ~10 dB lower power loss at the distance up to 0.5m. We also show that a 5cm diameter circular antenna array could steer an OAM +1 beam up to 80o with a mode purity degradation of <1%, while ring antenna arrays have ~10% mode purity degradation. OAM spectrum analysis shows both the lattice shape and the boundary shape of an antenna array could cause power leakage to harmonic OAM orders. Such power leakages would increase as the designed OAM order or the lattice period d increases, while it would decrease as the array diameter D increases.


title: The h Index : Help or Hype?

abstract: Three years ago a bibliometric index for the qualification of a person's scientific output was proposed by Hirsch, the so-called h index. This is an integer number which combines the number of papers of an author and the number of citations they gathered. Thus the h index is an indicator for both the productivity and the impact of a scientist. This paper presents the properties of the h index and the great attention it attracted within a short time. Numerous other indices, claimed to be better than the original, were proposed in the meantime. These developments are discussed critically.


title: Inductive design of maturity models: applying the Rasch algorithm for design science research

abstract: Maturity models are an established means to systematically document and guide the development of organizations using archetypal capability levels. Often, these models lack a sound foundation and/or are derived on the basis of an arbitrary design method. In order to foster the design of relevant and rigorous artifacts, this paper presents a method for maturity model construction that applies the Rasch algorithm and cluster analysis as a sound methodical foundation. The Rasch algorithm is widely used to improve scholarly intelligence and attainment tests. In order to demonstrate the application of the proposed method and to evaluate its usability and applicability, we present a design exemplar in the business intelligence domain.


title: Deep Subspace Clustering Networks

abstract: We present a novel deep neural network architecture for unsupervised subspace clustering. This architecture is built upon deep auto-encoders, which non-linearly map the input data into a latent space. Our key idea is to introduce a novel self-expressive layer between the encoder and the decoder to mimic the "self-expressiveness" property that has proven effective in traditional subspace clustering. Being differentiable, our new self-expressive layer provides a simple but effective way to learn pairwise affinities between all data points through a standard back-propagation procedure. Being nonlinear, our neural-network based method is able to cluster data points having complex (often nonlinear) structures. We further propose pre-training and fine-tuning strategies that let us effectively learn the parameters of our subspace clustering networks. Our experiments show that the proposed method significantly outperforms the state-of-the-art unsupervised subspace clustering methods.


title: Position based cryptography with location privacy: A step for Fog Computing

abstract: Abstract Fog Computing, which has the advantage of both the Cloud Computing and the Internet of Things, has changed our daily lives. One important property of the Fog Computing is location awareness. To guarantee this property in a practical hostile environment, location verification protocols are always desired. However, few location verification protocols can protect the location privacy of the location prover, who attempts to prove that he or she is at some position or in some region. Even worse, to the best of our knowledge, no location verification protocol has considered how to protect the location privacy against the verifiers of the protocol, who verify whether the prover is telling the truth. In this paper, we solve this problem by giving constructions of secure positioning protocol with location privacy in the bounded retrieval model. More precisely, we first explore how to define location privacy, which requires that nearly all parties except the prover, including verifiers and outside attackers, cannot learn any additional information about the exact position of the prover, for secure positioning protocol in the bounded retrieval model. Then we illustrate how to construct protocols satisfying this definition. In particular, we present a construction in the one-dimension setting, which explains the basic idea of our construction, and a construction in the three-dimensions setting, which can capture most practical scenarios. Amazingly, privacy is achieved without introducing additional computation overhead, and our system is as efficient as the one constructed in Chandran et al. (CRYPTO 2009). Besides, based on our proposed secure positioning protocols with location privacy, we also build more advanced cryptographic protocols exploiting location verification, e.g., position based key exchange, position based multi-party computation, position based public key infrastructure, etc., all of which still preserve the location privacy. Finally, we also give a proof of concept implementation of our system, which demonstrates that our system is quite practical in practice.


title: A study on cloud based Internet of Things: CloudIoT

abstract: The Next Revolution in the era of computing will be changing in comparison to traditional desktop. Many objects surrounds the human users will be on the network in one form or in another form in the Cloud Computing and Internet of Things framework. Cloud Computing and Internet of Things are two different technologies, these are into our daily life. Most of the surveys discussed the literature work on Internet of Things and Cloud separately. This paper presents the need for integration of Cloud and Internet of Things, an agent-oriented and Cloud assisted on Cloud IoT paradigm which based upon the layered reference architecture. Reference architecture for agent-oriented vision and Cloud-assisted is proposed, a Cloud based IoT paradigm applications scenario is described that have been presented in the literature, and Finally identified and discussed about open issues and future directions.


title: Motorcycle's lateral stability issues: Comparison of methods for dynamic modelling of roll angle

abstract: This paper proposes a simulation scenario in order to compare a proposed model of a motorcycle in cornering condition. The behaviours obtained by considering a linearized roll dynamic and a nonlinear roll dynamic have been investigated in order to evaluate their use in designing automatic stability controls of a motorcycle. The former model shows a linear dependency on the roll angle and the steer angle. The second model starts from the former and removes the roll angle linearization. The comparative analysis of the results shows how the linearized model is able to describe adequately the cornering dynamics for sufficiently large values of roll angle. These performances are suitable for designing a motorcycle active safety device controlling the lateral dynamics for preventing lateral falls.


title: Multi-Service Highly Sensitive Rectifier for Enhanced RF Energy Scavenging

abstract: Due to the growing implications of energy costs and carbon footprints, the need to adopt inexpensive, green energy harvesting strategies are of paramount importance for the long-term conservation of the environment and the global economy. To address this, the feasibility of harvesting low power density ambient RF energy simultaneously from multiple sources is examined. A high efficiency multi-resonant rectifier is proposed, which operates at two frequency bands (478-496 and 852-869 MHz) and exhibits favorable impedance matching over a broad input power range (-40 to -10 dBm). Simulation and experimental results of input reflection coefficient and rectified output power are in excellent agreement, demonstrating the usefulness of this innovative low-power rectification technique. Measurement results indicate an effective efficiency of 54.3%, and an output DC voltage of 772.8 mV is achieved for a multi-tone input power of -10 dBm. Furthermore, the measured output DC power from harvesting RF energy from multiple services concurrently exhibits a 3.14 and 7.24 fold increase over single frequency rectification at 490 and 860 MHz respectively. Therefore, the proposed multi-service highly sensitive rectifier is a promising technique for providing a sustainable energy source for low power applications in urban environments.


title: Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN

abstract: Semantic matching, which aims to determine the matching degree between two texts, is a fundamental problem for many NLP applications. Recently, deep learning approach has been applied to this problem and significant improvements have been achieved. In this paper, we propose to view the generation of the global interaction between two texts as a recursive process: i.e. the interaction of two texts at each position is a composition of the interactions between their prefixes as well as the word level interaction at the current position. Based on this idea, we propose a novel deep architecture, namely Match-SRNN, to model the recursive matching structure. Firstly, a tensor is constructed to capture the word level interactions. Then a spatial RNN is applied to integrate the local interactions recursively, with importance determined by four types of gates. Finally, the matching score is calculated based on the global interaction. We show that, after degenerated to the exact matching scenario, Match-SRNN can approximate the dynamic programming process of longest common subsequence. Thus, there exists a clear interpretation for Match-SRNN. Our experiments on two semantic matching tasks showed the effectiveness of Match-SRNN, and its ability of visualizing the learned matching structure.


title: A segmental CRF approach to large vocabulary continuous speech recognition

abstract: This paper proposes a segmental conditional random field framework for large vocabulary continuous speech recognition. Fundamental to this approach is the use of acoustic detectors as the basic input, and the automatic construction of a versatile set of segment-level features. The detector streams operate at multiple time scales (frame, phone, multi-phone, syllable or word) and are combined at the word level in the CRF training and decoding processes. A key aspect of our approach is that features are defined at the word level, and are naturally geared to explain long span phenomena such as formant trajectories, duration, and syllable stress patterns. Generalization to unseen words is possible through the use of decomposable consistency features [1], [2], and our framework allows for the joint or separate discriminative training of the acoustic and language models. An initial evaluation of this framework with voice search data from the Bing Mobile (BM) application results in a 2% absolute improvement over an HMM baseline.


title: Wireless Network Coding via Modified 802.11 MAC/PHY: Design and Implementation on SDR

abstract: Network coding (NC), in principle, is a Layer-3 innovation that improves network throughput in wired networks for multicast/broadcast scenarios. Due to the fundamental differences between wired and wireless networks, extending NC to wireless networks generates several new and significant practical challenges. Two-way information exchange (both symmetric and asymmetric) between a pair of 802.11 sources/sinks using an intermediate relay node is a canonical scenario for evaluating the effectiveness of Wireless Network Coding (WNC) in a practical setting. Our primary objective in this work is to suggest pragmatic and novel modifications at the MAC and PHY layers of the 802.11 protocol stack on a Software Radio (SORA) platform to support WNC and obtain achievable throughput estimates via lab-scale experiments. Our results show that network coding (at the MAC or PHY layer) increases system throughput-typically by 20-30%.


Model Uncertainty for Adversarial Examples using Dropouts


title: Radiomics: Extracting more information from medical images using advanced feature analysis

abstract: Solid cancers are spatially and temporally heterogeneous. This limits the use of invasive biopsy based molecular assays but gives huge potential for medical imaging, which has the ability to capture intra-tumoural heterogeneity in a non-invasive way. During the past decades, medical imaging innovations with new hardware, new imaging agents and standardised protocols, allows the field to move towards quantitative imaging. Therefore, also the development of automated and reproducible analysis methodologies to extract more information from image-based features is a requirement. Radiomics – the high-throughput extraction of large amounts of image features from radiographic images – addresses this problem and is one of the approaches that hold great promises but need further validation in multi-centric settings and in the laboratory.


title: Wireless Magnetic Sensor Node for Vehicle Detection With Optical Wake-Up

abstract: Vehicle detectors provide essential information about parking occupancy and traffic flow. To cover large areas that lack a suitable electrical infrastructure, wired sensors networks are impractical because of their high deployment and maintenance costs. Wireless sensor networks (WSNs) with autonomous sensor nodes can be more economical. Vehicle detectors intended for a WSN should be small, sturdy, low power, cost-effective, and easy to install and maintain. Currently available vehicle detectors based on inductive loops, ultrasound, infrared, or magnetic sensors do not fulfill the requirements above, which has led to the search for alternative solutions. This paper presents a vehicle detector which includes a magnetic and an optical sensor and is intended as sensor node for use with a WSN. Magnetic sensors based on magnetoresistors are very sensitive and can detect the magnetic anomaly in the Earth's magnetic field that results from the presence of a car, but their continuous operation would drain more than 1.5 mA at 3 V, hence limiting the autonomy of a battery-supplied sensor node. Passive, low-power optical sensors can detect the shadow cast by car that covers them, but are prone to false detections. The use of optical triggering to wake-up a magnetic sensor, combined with power-efficient event-based software, yields a simple, compact, reliable, low-power sensor node for vehicle detection whose quiescent current drain is 5.5 μA. This approach of using a low-power sensor to trigger a second more specific sensor can be applied to other autonomous sensor nodes.


title: Conflating Capacity & Authority: Why We're Asking the Wrong Question in the Adolescent Decision‐Making Debate

abstract: Whether adolescents should be allowed to make their own medical decisions has been a topic of discussion in bioethics for at least two decades now. Are adolescents sufficiently capacitated to make their own medical decisions? Is the mature-minor doctrine, an uncommon legal exception to the rule of parental decision-making authority, something we should expand or eliminate? Bioethicists have dealt with the curious liminality of adolescents—their being neither children nor adults—in a variety of ways. However, recently there has been a trend to rely heavily, and often exclusively, on emerging neuroscientific and psychological data to answer these questions. Using data from magnetic resonance imaging and functional MRI studies on the adolescent brain, authors have argued both that the adolescent brain isn't sufficiently mature to broadly confer capacity on this population and that the adolescent brain is sufficiently mature to assume adolescent capacity. Scholars then accept these data as sufficient for concluding that adolescents should or should not have decision-making authority. Two critical mistakes are being made here. The first is the expectation that neuroscience or psychology is or will be able to answer all our questions about capacity. The second, and more concerning, mistake is the conflation of decision-making capacity with decision-making authority.


title: A Survey on Nearest Neighbor Search Methods

abstract: ABSTRACT Nowadays, the need to techniques, approaches, and algorithms to search on data is increased due to improvements in computer science and increasing amount of information. This ever increasing information volume has led to time and computation complexity. Recently, different methods to solve such problems are proposed. Among the others, nearest neighbor search is one of the best techniques to this end which is focused by many researchers. Different techniques are used for nearest neighbor search. In addition to put an end to some complexities, variety of these techniques has made them suitable for different applications such as pattern recognition, searching in multimedia data, information retrieval, databases, data mining, and computational geometry to name but a few. In this paper, by opening a new view to this problem, a comprehensive evaluation on structures, techniques and different algorithms in this field is done and a new categorization of techniques in NNS is presented. This categorization is consists of seven groups: Weighted, Reductional, Additive, Reverse, Continuous, Principal Axis and Other techniques which are studied, evaluated and compared in this paper. Complexity of used structures, techniques and their algorithms are discussed, as well.


title: Automatic Targetless Extrinsic Calibration of a 3D Lidar and Camera by Maximizing Mutual Information

abstract: This paper reports on a mutual information (MI) based algorithm for automatic extrinsic calibration of a 3D laser scanner and optical camera system. By using MI as the registration criterion, our method is able to work in situ without the need for any specific calibration targets, which makes it practical for in-field calibration. The calibration parameters are estimated by maximizing the mutual information obtained between the sensor-measured surface intensities. We calculate the Cramer-Rao-Lower-Bound (CRLB) and show that the sample variance of the estimated parameters empirically approaches the CRLB for a sufficient number of views. Furthermore, we compare the calibration results to independent ground-truth and observe that the mean error also empirically approaches to zero as the number of views are increased. This indicates that the proposed algorithm, in the limiting case, calculates a minimum variance unbiased (MVUB) estimate of the calibration parameters. Experimental results are presented for data collected by a vehicle mounted with a 3D laser scanner and an omnidirectional camera system.


title: Document classification on neural networks using only positive examples (poster session)

abstract: In this paper, we show how a simple feedforward neural network can be trained to filter documents when only positive information is available, and that this method seems to be superior to more standard methods, such as tf-idf retrieval based on an "average vector". A novel experimental finding that retrieval is enhanced substantially in this context by carrying out a certain kind of uniform transformation ("Hadamard") of the information prior to the training of the network.


title: SYBL: An Extensible Language for Controlling Elasticity in Cloud Applications

abstract: Elasticity in cloud computing is a complex problem, regarding not only resource elasticity but also quality and cost elasticity, and most importantly, the relations among the three. Therefore, existing support for controlling elasticity in complex applications, focusing solely on resource scaling, is not adequate. In this paper we present SYBL - a novel language for controlling elasticity in cloud applications - and its runtime system. SYBL allows specifying in detail elasticity monitoring, constraints, and strategies at different levels of cloud applications, including the whole application, application component, and within application component code. Based on simple SYBL elasticity directives, our runtime system will perform complex elasticity controls for the client, by leveraging underlying cloud monitoring and resource management APIs. We also present a prototype implementation and experiments illustrating how SYBL can be used in real-world scenarios.


title: Resonant PZT MEMS Scanner for High-Resolution Displays

abstract: A resonant piezoelectric scanner is developed for high-resolution laser-scanning displays. A novel actuation scheme combines the principle of mechanical amplification with lead zirconate titanate (PZT) thin-film actuation. Sinusoidal actuation with 24 V at the mechanical resonance frequency of 40 kHz provides an optical scan angle of 38.5° for the 1.4-mm-wide mirror. This scanner is a significant step toward achieving full-high-definition resolution (1920 × 1080 pixels) in mobile laser projectors without the use of vacuum packaging. The reported piezoscanner requires no bulky components and consumes <; 30-mW power at maximum deflection, thus providing significant power and size advantages, compared with reported electromagnetic and electrostatic scanners. Interferometry measurements show that the dynamic deformation is at acceptable levels for a large fraction of the mirror and can be improved further for diffraction-limited performance at full resolution. A design variation with a segmented electrode pair illustrated that reliable angle sensing can be achieved with PZT for closed-loop control of the scanner.


Tracking the Pequod Along The Oregon Trail: The Influence of Parkman’s Narrative on Imagery and Characters in Moby-Dick


title: Understanding and using the Implicit Association Test: II. Method variables and construct validity

abstract: This review of 122 research reports (184 independent samples, 14,900 subjects) found average r = .274 for prediction of behavioral, judgment, and physiological measures by Implicit Association Test (IAT) measures. Parallel explicit (i.e., self-report) measures, available in 156 of these samples (13,068 subjects), also predicted effectively (average r = .361), but with much greater variability of effect size. Predictive validity of self-report was impaired for socially sensitive topics, for which impression management may distort self-report responses. For 32 samples with criterion measures involving Black–White interracial behavior, predictive validity of IAT measures significantly exceeded that of self-report measures. Both IAT and self-report measures displayed incremental validity, with each measure predicting criterion variance beyond that predicted by the other. The more highly IAT and self-report measures were intercorrelated, the greater was the predictive validity of each.


title: Blood flow restricted exercise for athletes: A review of available evidence.

abstract: OBJECTIVES ::: This study aimed to collate current evidence regarding the efficacy of various blood flow restriction (BFR) strategies for well-trained athletes, and to provide insight regarding how such strategies can be used by these populations. ::: ::: ::: DESIGN ::: Review article. ::: ::: ::: METHODS ::: Studies that had investigated the acute or adaptive responses to BFR interventions in athletic participants were identified from searches in MEDLINE (PubMed), SPORTDiscus (EBSCO) and Google Scholar databases up to April 2015. The reference lists of identified papers were also examined for relevant studies. ::: ::: ::: RESULTS ::: Twelve papers were identified from 11 separate investigations that had assessed acute and adaptive responses to BFR in athletic cohorts. Of these, 7 papers observed enhanced hypertrophic and/or strength responses and 2 reported alterations in the acute responses to low-load resistance exercise when combined with BFR. One paper had examined the adaptive responses to moderate-load resistance training with BFR, 1 noted improved training responses to low-work rate BFR cardiovascular exercise, and 1 reported on a case of injury following BFR exercise in an athlete. ::: ::: ::: CONCLUSIONS ::: Current evidence suggests that low-load resistance training with BFR can enhance muscle hypertrophy and strength in well-trained athletes, who would not normally benefit from using light loads. For healthy athletes, low-load BFR resistance training performed in conjunction with normal high-load training may provide an additional stimulus for muscular development. As low-load BFR resistance exercise does not appear to cause measureable muscle damage, supplementing normal high-load training using this novel strategy may elicit beneficial muscular responses in healthy athletes.


title: A Critical Review of the Bacterial Baptism Hypothesis and the Impact of Cesarean Delivery on the Infant Microbiome

abstract: Numerous studies suggest that infants delivered by cesarean section are at a greater risk of non-communicable diseases than their vaginal counterparts. In particular, epidemiological studies have linked Cesarean delivery with increased rates of asthma, allergies, autoimmune disorders, and obesity. Mode of delivery has also been associated with differences in the infant microbiome. It has been suggested that these differences are attributable to the "bacterial baptism" of vaginal birth, which is bypassed in cesarean deliveries, and that the abnormal establishment of the early-life microbiome is the mediator of later-life adverse outcomes observed in cesarean delivered infants. This has led to the increasingly popular practice of "vaginal seeding": the iatrogenic transfer of vaginal microbiota to the neonate to promote establishment of a "normal" infant microbiome. In this review, we summarize and critically appraise the current evidence for a causal association between Cesarean delivery and neonatal dysbiosis. We suggest that, while Cesarean delivery is certainly associated with alterations in the infant microbiome, the lack of exposure to vaginal microbiota is unlikely to be a major contributing factor. Instead, it is likely that indication for Cesarean delivery, intrapartum antibiotic administration, absence of labor, differences in breastfeeding behaviors, maternal obesity, and gestational age are major drivers of the Cesarean delivery microbial phenotype. We, therefore, call into question the rationale for "vaginal seeding" and support calls for the halting of this practice until robust evidence of need, efficacy, and safety is available.


title: Fully Redundant Decimal Arithmetic

abstract: Hardware implementation of all the basic radix-10 arithmetic operations is evolving as a new trend in the design and implementation of general purpose digital processors. Redundant representation of partial products and remainders is common in the multiplication and division hardware algorithms, respectively. Carry-free implementation of the more frequent add/subtract operations, with the byproduct of enhancing the speed of multiplication and division, is possible with redundant number representation. However, conversion of redundant results to conventional representations entails slow carry propagation that can be avoided if the results are kept in redundant format for later use as operands of other arithmetic operations. Given that redundant decimal representations, contrary to redundant binary, do not necessarily require extra storage, we are motivated to develop a framework for fully redundant decimal arithmetic, where all operands and results belong to the same redundant decimal number system and can be stored and later used as operands of further decimal operations. In this paper, we present a new faster decimal signed digit add/sub unit and show how it can be efficiently used in the design of decimal multipliers and dividers, where all operands and results are represented with the same redundant digit set [–7, 7].


title: Requirements prioritization techniques and different aspects for prioritization a systematic literature review protocol

abstract: Requirements prioritization (RP) is considered as an important part of software requirements engineering in which requirements are ranked to develop high-quality software. Success of quality software depends on the selection of well- prioritized requirements. Different techniques are proposed and used to prioritize the software requirements. Requirements are assigned weights or ranked according to their importance and are placed in a priority list for implementation in successive releases. These techniques depend on many aspects that need to be addressed while prioritizing requirements. Requirements prioritization aspects are categorized into three major groups' technical aspects, business aspects and client aspects. Most of the existing techniques are unable to support these aspects, and it affects the quality of decision-making in the requirements prioritization process. Hence, there is a need to explore the different techniques and their support for different aspects. A comparison of the existing techniques is performed. The requirements prioritization aspects are selected to determine the current trends in the software requirements prioritization process. The issue of scalability and the business/client related aspects are the key focal points of this research paper. This paper describes the review protocol, as per guidelines of the Barbara Kitchenham, in order to conduct a systematic literature review.


title: Design of Bidirectional DC–DC Resonant Converter for Vehicle-to-Grid (V2G) Applications

abstract: In this paper, a detailed design procedure is presented for a bidirectional CLLLC-type resonant converter for a battery charging application. This converter is similar to an LLC-type resonant converter with an extra inductor and capacitor in the secondary side. Soft-switching can be ensured in all switches without additional snubber or clamp circuitry. Because of soft-switching in all switches, very high-frequency operation is possible; thus, the size of the magnetics and the filter capacitors can be made small. To reduce the size and cost of the converter, a CLLC-type resonant network is derived from the original CLLLC-type resonant network. First, in this paper, an equivalent model for the bidirectional converter is derived for the steady-state analysis. Then, the design methodology is presented for the CLLLC-type resonant converter. Design of this converter includes determining the transformer turns ratio, design of the magnetizing inductance based on ZVS condition, design of the resonant inductances and capacitances. Then, the CLLC-type resonant network is derived from the CLLLC-type resonant network. To validate the design procedure, a 3.5-kW converter was designed following the guidelines in the proposed methodology. A prototype was built and tested in the laboratory. Experimental results verified the design procedure presented.


title: A Flexible Temperature Sensor Based on Reduced Graphene Oxide for Robot Skin Used in Internet of Things

abstract: Flexible electronics, which can be distributed on any surface we need, are highly demanded in the development of Internet of Things (IoT), robot technology and electronic skins. Temperature is a fundamental physical parameter, and it is an important indicator in many applications. Therefore, a flexible temperature sensor is required. Here, we report a simple method to fabricate three lightweight, low-cost and flexible temperature sensors, whose sensitive materials are reduced graphene oxide (r-GO), single-walled carbon nanotubes (SWCNTs) and multi-wall carbon nanotubes (MWCNTs). By comparing linearity, sensitive and repeatability, we found that the r-GO temperature sensor had the most balanced performance. Furthermore, the r-GO temperature sensor showed good mechanical properties and it could be bent in different angles with negligible resistance change. In addition, the performance of the r-GO temperature sensor remained stable under different kinds of pressure and was unaffected by surrounding environments, like humidity or other gases, because of the insulating layer on its sensitive layer. The easy-fabricated process and economy, together with the remarkable performance of the r-GO temperature sensor, suggest that it is suitable for use as a robot skin or used in the environment of IoT.


Learning Statistical Scripts with LSTM Recurrent Neural Networks


title: Action-Graph Games

abstract: Representing and reasoning with games becomes difficult once they involve large numbers of actions and players, because the space requirement for utility functions can grow unmanageably. Action-Graph Games (AGGs) are a fully-expressive game representation that can compactly express utility functions with structure such as context-specific independence, anonymity, and additivity. We show that AGGs can be used to compactly represent all games that are compact when represented as graphical games, symmetric games, anonymous games, congestion games, and polymatrix games, as well as games that require exponential space under all of these existing representations. We give a polynomial-time algorithm for computing a player's expected utility under an arbitrary mixed-strategy profile, and show how to use this algorithm to achieve exponential speedups of existing methods for computing sample Nash equilibria. We present results of experiments showing that using AGGs leads to a dramatic increase in the size of games accessible to computational analysis.2


title: Effects of core strength training using stable versus unstable surfaces on physical fitness in adolescents: a randomized controlled trial

abstract: It has been demonstrated that core strength training is an effective means to enhance trunk muscle strength (TMS) and proxies of physical fitness in youth. Of note, cross-sectional studies revealed that the inclusion of unstable elements in core strengthening exercises produced increases in trunk muscle activity and thus provide potential extra training stimuli for performance enhancement. Thus, utilizing unstable surfaces during core strength training may even produce larger performance gains. However, the effects of core strength training using unstable surfaces are unresolved in youth. This randomized controlled study specifically investigated the effects of core strength training performed on stable surfaces (CSTS) compared to unstable surfaces (CSTU) on physical fitness in school-aged children. Twenty-seven (14 girls, 13 boys) healthy subjects (mean age: 14 ± 1 years, age range: 13–15 years) were randomly assigned to a CSTS (n = 13) or a CSTU (n = 14) group. Both training programs lasted 6 weeks (2 sessions/week) and included frontal, dorsal, and lateral core exercises. During CSTU, these exercises were conducted on unstable surfaces (e.g., TOGU© DYNAIR CUSSIONS, THERA-BAND© STABILITY TRAINER). Significant main effects of Time (pre vs. post) were observed for the TMS tests (8-22%, f = 0.47-0.76), the jumping sideways test (4-5%, f = 1.07), and the Y balance test (2-3%, f = 0.46-0.49). Trends towards significance were found for the standing long jump test (1-3%, f = 0.39) and the stand-and-reach test (0-2%, f = 0.39). We could not detect any significant main effects of Group. Significant Time x Group interactions were detected for the stand-and-reach test in favour of the CSTU group (2%, f = 0.54). Core strength training resulted in significant increases in proxies of physical fitness in adolescents. However, CSTU as compared to CSTS had only limited additional effects (i.e., stand-and-reach test). Consequently, if the goal of training is to enhance physical fitness, then CSTU has limited advantages over CSTS. ClinicalTrials.gov Identifier: NCT02290457 Registered 13 November 2014.


title: Analysis and prediction of Ε-customers' behavior by mining clickstream data

abstract: In a regular retail shop the behavior of customers may yield a lot to the shop assistant. However, when it comes to online shopping it is not possible to see and analyze customer behavior such as facial mimics, products they check or touch etc. In this case, clickstreams or the mouse movements of e-customers may provide some hints about their buying behavior. In this study, we have presented a model to analyze clickstreams of e-customers and extract information and make predictions about their shopping behavior on a digital market place. After collecting data from an e-commerce market in Turkey, we performed a data mining application and extracted online customers' behavior patterns about buying or not. The model we present predicts whether customers will or will not buy their items added to shopping baskets on a digital market place. For the analysis, decision tree and multi-layer neural network prediction data mining models have been used. Findings have been discussed in the conclusion.


On the Planck Scale and Structures of Matter


Malware encryption schemes - rerandomizable ciphertexts encrypted using environmental keys


title: GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs

abstract: We propose a new network architecture, Gated Attention Networks (GaAN), for learning on graphs. Unlike the traditional multi-head attention mechanism, which equally consumes all attention heads, GaAN uses a convolutional sub-network to control each attention head's importance. We demonstrate the effectiveness of GaAN on the inductive node classification problem. Moreover, with GaAN as a building block, we construct the Graph Gated Recurrent Unit (GGRU) to address the traffic speed forecasting problem. Extensive experiments on three real-world datasets show that our GaAN framework achieves state-of-the-art results on both tasks.


title: Mining Weighted Association Rules for Fuzzy Quantitative Items

abstract: During the last ten years, data mining, also known as knowledge discovery in databases, has established its position as a prominent and important research area. Mining association rules is one of the important research problems in data mining. Many algorithms have been proposed to find association rules in large databases containing both categorical and quantitative attributes. We generalize this to the case where part of attributes are given weights to reflect their importance to the user. In this paper, we introduce the problem of mining weighted quantitative association rules based on fuzzy approach. Using the fuzzy set concept, the discovered rules are more understandable to a human. We propose two different definitions of weighted support: with and with- out normalization. In the normalized case, a subset of a frequent itemset may not be frequent, and we cannot generate candidate k-itemsets simply from the frequent (k-1)-itemsets. We tackle this problem by using the concept of z-potential frequent subset for each candidate itemset. We give an algorithm for mining such quantitative association rules. Fi- nally, we describe the results of using this approach on a real-life dataset.


title: Thompson Sampling For Stochastic Bandits with Graph Feedback

abstract: We present a novel extension of Thompson Sampling for stochastic sequential decision problems with graph feedback, even when the graph structure itself is unknown and/or changing. We provide theoretical guarantees on the Bayesian regret of the algorithm, linking its performance to the underlying properties of the graph. Thompson Sampling has the advantage of being applicable without the need to construct complicated upper confidence bounds for different problems. We illustrate its performance through extensive experimental results on real and simulated networks with graph feedback. More specifically, we tested our algorithms on power law, planted partitions and Erdo s–Renyi graphs, as well as on graphs derived from Facebook and Flixster data. These all show that our algorithms clearly outperform related methods that employ upper confidence bounds, even if the latter use more information about the graph.


title: Metamaterial advances for radar and communications

abstract: Metamaterial antennas have progressed considerably in the last few years. Kymeta has demonstrated December 2014 the transmission to satellites and back using 20 and 30 GHz antennas about the size of a laptop which use meta material resonators to effect the phase shifting. A clear explanation of how this antenna works is given. Echo dyne and PARC, a Xerox Co., have developed meta material arrays for radar. The Army Research Laboratory in Adelphi MD has funded the development of a meta material 250–505 MHZ antenna with a λ/20 thickness. Complementing this a conventional tightly coupled dipole antenna (TCDA) has been developed which provides a 20:1 bandwidth with a λ /40 thickness. Target cloaking has been demonstrated at microwaves using meta materials. With cloaking the electromagnetic wave signal transmitted by a radar goes around the target making it invisible. It has also been demonstrated at L-band with a 50% bandwidth using fractals. Stealthing by absorption using a thin flexible and stretchable meta materials sheet has been demonstrated to give 6 dB absorption over the band from 8–10 GHz, larger absorption over a narrower band. Using fractals material < 1 mm thick simulation gave a 10 dB reduction in backscatter from 2 to 20 GHz, ∼20 dB reduction from 9 to 15 GHz. Good results were obtained for large incidence angles and both polarizations. Using meta material one can focus 6X beyond diffraction limit at 0.38 μm (Moore's Law marches on); 40X diffraction limit, λ /80, at 375 MHz. Has been used in cell phones to provide antennas 5X smaller (1/10th λ) having 700 MHz-2.7 GHz bandwidth. Provided isolation between antennas having 2.5 cm separation equivalent to 1m separation. Used for phased array wide angle impedance matching (WAIM). It has been found that n-doped graphene has a negative index of refraction.


title: Color-to-Grayscale: Does the Method Matter in Image Recognition?

abstract: In image recognition it is often assumed the method used to convert color images to grayscale has little impact on recognition performance. We compare thirteen different grayscale algorithms with four types of image descriptors and demonstrate that this assumption is wrong: not all color-to-grayscale algorithms work equally well, even when using descriptors that are robust to changes in illumination. These methods are tested using a modern descriptor-based image recognition framework, on face, object, and texture datasets, with relatively few training instances. We identify a simple method that generally works best for face and object recognition, and two that work well for recognizing textures.


title: Discrete Bayesian Network Classifiers: A Survey

abstract: We have had to wait over 30 years since the naive Bayes model was first introduced in 1960 for the so-called Bayesian network classifiers to resurge. Based on Bayesian networks, these classifiers have many strengths, like model interpretability, accommodation to complex data and classification problem settings, existence of efficient algorithms for learning and classification tasks, and successful applicability in real-world problems. In this article, we survey the whole set of discrete Bayesian network classifiers devised to date, organized in increasing order of structure complexity: naive Bayes, selective naive Bayes, seminaive Bayes, one-dependence Bayesian classifiers, k-dependence Bayesian classifiers, Bayesian network-augmented naive Bayes, Markov blanket-based Bayesian classifier, unrestricted Bayesian classifiers, and Bayesian multinets. Issues of feature subset selection and generative and discriminative structure and parameter learning are also covered.


title: Resilient multi-user beamforming WLANs: Mobility, interference, and imperfect CSI

abstract: In this paper we present the design of CHRoME, a downlink multi-user beamforming (MUBF) protocol that addresses the inherent sensitivity of multi-stream systems to mobility, inter-stream interference, and imperfect channel state information. Our contributions are: (i) a technique for accurately selecting the downlink bit rate in the presence of inter-stream interference via a custom multi-user probe and feedback signal, immediately preceding data transmission, and (ii) a fast retransmission scheme that exploits liberated antenna resources to increase the expected per-user signal-to-interference-plus-noise ratio (SINR) and retransmit without having to re-sound the channel. We implement each mechanism and evaluate via a combination of indoor over-the-air experiments and trace-driven emulation. We demonstrate that CHRoME increases the resilience of MUBF systems to inter-stream interference and achieves multi-fold throughput gains compared to IEEE 802.11ac.


title: Fria: fast and robust instance alignment

abstract: This paper proposes Fria, a fast and robust instance alignment framework across two independently built knowledge bases (KBs). Our objective is two-fold: (1) to design an effective instance similarity measure and (2) to build a fast and robust alignment framework. Specifically, Fria consists of two-phases. Fria first achieves high-precision alignment for seed matches which have strong evidence for aligning. To obtain high-recall alignment, Fria then divides non-matched instances according to the types identified from seeds, and gives additional chances to the same-typed instances to be matched. Experimental results show that Fria is fast and robust, by achieving comparable accuracy to state-of-the-arts and a 10-times speed up.


title: A Broadband Dual-Polarized Omnidirectional Antenna for Base Stations

abstract: A broadband vertically/horizontally dual-polarized omnidirectional antenna is proposed for mobile communications. The dual-polarized antenna is a combination of a modified low-profile monopole for vertical polarization (VP) and a circular planar loop for horizontal polarization (HP). The modified low-profile monopole is a circular folded patch shorted by four tubes while the circular loop consists of four half-wavelength arc dipoles. The dual-polarized omnidirectional antenna achieves a bandwidth of ~ 25% (1.7-2.2 GHz) with an isolation of around 40 dB. The gain variations in the horizontal plane are less than 2.5 dB for VP and 1.5 dB for HP. An eight-element dual-polarized antenna array is developed for base station applications. The antenna gains of the array for both VP and HP are ~ 8 dBi with a difference of less than 1 dB. The beamwidths in the vertical plane are 9±1° for VP and 9±1.5° for HP. The cross-polarization levels in the horizontal plane for both VP and HP are lower than -20 dB.