zeta-alpha-ai/NanoSCIDOCS huggingface dataset:
For the synthetic aperture radar systems, there is an increasing demand for achieving resolutions of 0.2 mtimes0.2 m. As the range resolution and system bandwidth are inversely proportional, the system bandwidth is expected to be greater than 1 GHz. Thus an antenna with wider band needs to be developed. Waveguide slot antennas have been implemented on several SAR satellites due to its inherent advantages such as high efficiency and power handling capacity, but its bandwidth is quite limited. To avoid the manufacture difficulties of the ridge waveguide, which is capable of broadening the bandwidth of slot antennas, a novel antenna element with conventional waveguide is designed. The bandwidth of VSWR les1.5 is greater than 1 GHz at X-band. To reduce the mutual coupling of closely placed antenna elements, a decoupling method with cavity-like walls inserted between adjacent elements is adopted and their effects on the performance of the antenna are summarized.
We propose a demonstration of Cayuga, a complex event monitoring system for high speed data streams. Our demonstration will show Cayuga applied to monitoring Web feeds; the demo will illustrate the expressiveness of the Cayuga query language, the scalability of its query processing engine to high stream rates, and a visualization of the internals of the query processing engine.
When querying databases, users often wish to express vague concepts, as for instance asking for the cheap hotels. This has been extensively studied in the case of relational databases. In this paper, we propose to study how such useful techniques can be adapted to NoSQL graph databases where the role of fuzziness is crucial. Such databases are indeed among the fastest-growing models for dealing with big data, especially when dealing with network data (e.g., social networks). We consider the Cypher declarative query language proposed for Neo4j which is the current leader on this market, and we present how to express fuzzy queries.
Internet and internet users are increasing day by day. Also due to rapid development of internet technology , security is becoming big issue. Intruders are monitoring comput er network continuously for attacks. A sophisticated firewall with efficient intrusion detection system (IDS) is required to pre vent computer network from attacks. A comprehensive study of litera tures proves that data mining techniques are more powerful techni que to develop IDS as a classifier. Performance of classif ier is a crucial issue in terms of its efficiency, also number of fe ature to be scanned by the IDS should also be optimized. In thi s paper two techniques C5.0 and artificial neural network (ANN) ar e utilized with feature selection. Feature selection techniques will discard some irrelevant features while C5.0 and ANN acts as a classifier to classify the data in either normal type or one of t he five types of attack.KDD99 data set is used to train and test the models ,C5.0 model with numbers of features is producing better r sults with all most 100% accuracy. Performances were also verified in terms of data partition size.
Metamaterials are typically engineered by arranging a set of small scatterers or apertures in a regular array throughout a region of space, thus obtaining some desirable bulk electromagnetic behavior. The desired property is often one that is not normally found naturally (negative refractive index, near-zero index, etc.). Over the past ten years, metamaterials have moved from being simply a theoretical concept to a field with developed and marketed applications. Three-dimensional metamaterials can be extended by arranging electrically small scatterers or holes into a two-dimensional pattern at a surface or interface. This surface version of a metamaterial has been given the name metasurface (the term metafilm has also been employed for certain structures). For many applications, metasurfaces can be used in place of metamaterials. Metasurfaces have the advantage of taking up less physical space than do full three-dimensional metamaterial structures; consequently, metasurfaces offer the possibility of less-lossy structures. In this overview paper, we discuss the theoretical basis by which metasurfaces should be characterized, and discuss their various applications. We will see how metasurfaces are distinguished from conventional frequency-selective surfaces. Metasurfaces have a wide range of potential applications in electromagnetics (ranging from low microwave to optical frequencies), including: (1) controllable “smart” surfaces, (2) miniaturized cavity resonators, (3) novel wave-guiding structures, (4) angular-independent surfaces, (5) absorbers, (6) biomedical devices, (7) terahertz switches, and (8) fluid-tunable frequency-agile materials, to name only a few. In this review, we will see that the development in recent years of such materials and/or surfaces is bringing us closer to realizing the exciting speculations made over one hundred years ago by the work of Lamb, Schuster, and Pocklington, and later by Mandel'shtam and Veselago.
In this work, we introduce a new video representation for action classification that aggregates local convolutional features across the entire spatio-temporal extent of the video. We do so by integrating state-of-the-art two-stream networks [42] with learnable spatio-temporal feature aggregation [6]. The resulting architecture is end-to-end trainable for whole-video classification. We investigate different strategies for pooling across space and time and combining signals from the different streams. We find that: (i) it is important to pool jointly across space and time, but (ii) appearance and motion streams are best aggregated into their own separate representations. Finally, we show that our representation outperforms the two-stream base architecture by a large margin (13% relative) as well as outperforms other baselines with comparable base architectures on HMDB51, UCF101, and Charades video classification benchmarks.
The design of a multiband reflector antenna for an on-the-move satellite communications terminal is presented. This antenna was designed to operate with numerous modern and future military communications satellites, which in turn requires the antenna to be capable of operation at multiple frequencies and polarizations while maintaining high aperture efficiency. Several feed antenna concepts were developed to accomplish this task, and are discussed in detail. Multiple working prototypes based on this design have been realized, with excellent performance. Measured data of individual antenna components and the complete assembly is also included
We present an iterative linear-quadratic-Gaussian method for locally-optimal feedback control of nonlinear stochastic systems subject to control constraints. Previously, similar methods have been restricted to deterministic unconstrained problems with quadratic costs. The new method constructs an affine feedback control law, obtained by minimizing a novel quadratic approximation to the optimal cost-to-go function. Global convergence is guaranteed through a Levenberg-Marquardt method; convergence in the vicinity of a local minimum is quadratic. Performance is illustrated on a limited-torque inverted pendulum problem, as well as a complex biomechanical control problem involving a stochastic model of the human arm, with 10 state dimensions and 6 muscle actuators. A Matlab implementation of the new algorithm is availabe at www.cogsci.ucsd.edu//spl sim/todorov.
The Lucia comprehension system attempts to model human comprehension by using the Soar cognitive architecture, Embodied Construction Grammar (ECG), and an incremental, word-by-word approach to grounded processing. Traditional approaches use techniques such as parallel paths and global optimization to resolve ambiguities. Here we describe how Lucia deals with lexical, grammatical, structural, and semantic ambiguities by using knowledge from the surrounding linguistic and environmental context. It uses a local repair mechanism to maintain a single path, and shows a garden path effect when local repair breaks down. Data on adding new linguistic knowledge shows that the ECG grammar grows faster than the knowledge for handling context, and that lowlevel grammar items grow faster than more general ones.
We address the problem of image search on a very large scale, where three constraints have to be considered jointly: the accuracy of the search, its efficiency, and the memory usage of the representation. We first propose a simple yet efficient way of aggregating local image descriptors into a vector of limited dimension, which can be viewed as a simplification of the Fisher kernel representation. We then show how to jointly optimize the dimension reduction and the indexing algorithm, so that it best preserves the quality of vector comparison. The evaluation shows that our approach significantly outperforms the state of the art: the search accuracy is comparable to the bag-of-features approach for an image representation that fits in 20 bytes. Searching a 10 million image dataset takes about 50ms.
Given a real world graph, how should we lay-out its edges? How can we compress it? These questions are closely related, and the typical approach so far is to find clique-like communities, like the `cavemen graph', and compress them. We show that the block-diagonal mental image of the `cavemen graph' is the wrong paradigm, in full agreement with earlier results that real world graphs have no good cuts. Instead, we propose to envision graphs as a collection of hubs connecting spokes, with super-hubs connecting the hubs, and so on, recursively. Based on the idea, we propose the Slash Burn method (burn the hubs, and slash the remaining graph into smaller connected components). Our view point has several advantages: (a) it avoids the `no good cuts' problem, (b) it gives better compression, and (c) it leads to faster execution times for matrix-vector operations, which are the back-bone of most graph processing tools. Experimental results show that our Slash Burn method consistently outperforms other methods on all datasets, giving good compression and faster running time.
The relationship between information systems (IS) and organizational strategies has been a much discussed topic with most of the prior studies taking a highly positive view of technology’s role in enabling organizational strategies. Despite this wealth of studies, there is a dearth of empirical investigations on how IS enable specific organizational strategies. Through a qualitative empirical investigation of five case organizations this research derives five organizational strategies that are specifically enabled through IS. The five strategies; (i) generic-heartland, (ii) craft-based selective, (iii) adhoc, IT-driven, (iv) corporative-orchestrated and (v) transformative provide a unique perspective of how IS enable organizational strategy.
The blockchain initially gained traction in 2008 as the technology underlying Bitcoin [105], but now has been employed in a diverse range of applications and created a global market worth over $150B as of 2017. What distinguishes blockchains from traditional distributed databases is the ability to operate in a decentralized setting without relying on a trusted third party. As such their core technical component is consensus: how to reach agreement among a group of nodes. This has been extensively studied already in the distributed systems community for closed systems, but its application to open blockchains has revitalized the field and led to a plethora of new designs. The inherent complexity of consensus protocols and their rapid and dramatic evolution makes it hard to contextualize the design landscape. We address this challenge by conducting a systematic and comprehensive study of blockchain consensus protocols. After first discussing key themes in classical consensus protocols, we describe: (i) protocols based on proof-of-work (PoW), (ii) proof-of-X (PoX) protocols that replace PoW with more energy-efficient alternatives, and (iii) hybrid protocols that are compositions or variations of classical consensus protocols. We develop a framework to evaluate their performance, security and design properties, and use it to systematize key themes in the protocol categories described above. This evaluation leads us to identify research gaps and challenges for the community to consider in future research endeavours.
A low-cost, fully-integrated antenna-in-package solution for 60 GHz phased-array systems is demonstrated. Sixteen patch antennas are integrated into a 28 mm × 28 mm ball grid array together with a flip-chip attached transmitter or receiver IC. The packages have been implemented using low temperature co-fired ceramic technology. 60 GHz interconnects, including flip-chip transitions and via structures, are optimized using full-wave simulation. Anechoic chamber measurement has shown ~ 5 dBi unit antenna gain across all four IEEE 802.15.3c channels, achieving excellent model-to-hardware correlation. The packaged transmitter and receiver ICs, mounted on evaluation boards, have demonstrated beam-steered, non-line-of-sight links with data rates up to 5.3 Gb/s.
An overview of the key challenges facing the practice of medicine today is presented along with the need for technological solutions that can "prevent" problems. Then, the development of the Wearable Motherboard/spl trade/ (Smart Shirt) as a platform for sensors and monitoring devices that can unobtrusively monitor the health and well being of individuals (directly and/or remotely) is described. This is followed by a discussion of the applications and impact of this technology in the continuum of life-from preventing SIDS to facilitating independent living for senior citizens. Finally, the future advancements in the area of wearable, yet comfortable, systems that can continue the transformation of healthcare - all aimed at enhancing the quality of life for humans - are presented.
The localization problem is to determine an assignment of coordinates to nodes in a wireless ad-hoc or sensor network that is consistent with measured pairwise node distances. Most previously proposed solutions to this problem assume that the nodes can obtain pairwise distances to other nearby nodes using some ranging technology. However, for a variety of reasons that include obstructions and lack of reliable omnidirectional ranging, this distance information is hard to obtain in practice. Even when pairwise distances between nearby nodes are known, there may not be enough information to solve the problem uniquely. This paper describes MAL, a mobile-assisted localization method which employs a mobile user to assist in measuring distances between node pairs until these distance constraints form a "globally rigid'* structure that guarantees a unique localization. We derive the required constraints on the mobile's movement and the minimum number of measurements it must collect; these constraints depend on the number of nodes visible to the mobile in a given region. We show how to guide the mobile's movement to gather a sufficient number of distance samples for node localization. We use simulations and measurements from an indoor deployment using the Cricket location system to investigate the performance of MAL, finding in real-world experiments that MAL's median pairwise distance error is less than 1.5% of the true node distance.
The work presented in the paper focuses on accuracy of models for broad-band ferrite based coaxial transmission-line transformers. Soft-ferrites are largely used in VHF/UHF components allowing band enlargement on the low-edge side. Degradation of frequency performance on the high-edge side are produced both by ferrite losses, and by parasitic capacitance due to connection to the thermal and electrical ground in high power applications. Both a circuital model for low-power applications and a scalable e.m. model for high-power applications are presented and discussed.
To understand the dynamics of optimization in deep neural networks, we develop a tool to study the evolution of the entire Hessian spectrum throughout the optimization process. Using this, we study a number of hypotheses concerning smoothness, curvature, and sharpness in the deep learning literature. We then thoroughly analyze a crucial structural feature of the spectra: in non-batch normalized networks, we observe the rapid appearance of large isolated eigenvalues in the spectrum, along with a surprising concentration of the gradient in the corresponding eigenspaces. In batch normalized networks, these two effects are almost absent. We characterize these effects, and explain how they affect optimization speed through both theory and experiments. As part of this work, we adapt advanced tools from numerical linear algebra that allow scalable and accurate estimation of the entire Hessian spectrum of ImageNet-scale neural networks; this technique may be of independent interest in other applications.
Article history: Received 27 August 2010 Received in revised form 23 December 2010 Accepted 6 January 2011 The traditional audit paradigm is outdated in the real time economy. Innovation of the traditional audit process is necessary to support real time assurance. Practitioners and academics are exploring continuous auditing as a potential successor to the traditional audit paradigm. Using technology and automation, continuous auditing methodology enhances the efficiency and effectiveness of the audit process to support real time assurance. This paper defines how continuous auditing methodology introduces innovation to practice in seven dimensions and proposes a four-stage paradigm to advance future research. In addition, we formulate a set of methodological propositions concerning the future of assurance for practitioners and academic researchers. © 2011 Elsevier Inc. All rights reserved.
Despite the burgeoning research on social entrepreneurship (SE), SE strategies remain poorly understood. Drawing on extant research on the social activism and social change, empowerment and SE models, we explore, classify and validate the strategies used by 2,334 social entrepreneurs affiliated with the world's largest SE support organization, Ashoka. The results of the topic modeling of the social entrepreneurs' strategy profiles reveal that they employed a total of 39 change-making strategies that vary across resources (material versus symbolic strategies), specificity (general versus specific strategies), and mode of participation (mass versus elite participation strategies); they also vary across fields of practice and time. Finally, we identify six meta-SE strategies-a reduction from the 39 strategies-and identify four new meta-SE strategies (i.e., system reform, physical capital development, evidence-based practices, and prototyping) that have been overlooked in prior SE research. Our findings extend and deepen the research into SE strategies and offer a comprehensive model of SE strategies that advances theory, practice and policy making.
Undoing the image formation process and therefore decomposing appearance into its intrinsic properties is a challenging task due to the under-constrained nature of this inverse problem. While significant progress has been made on inferring shape, materials and illumination from images only, progress in an unconstrained setting is still limited. We propose a convolutional neural architecture to estimate reflectance maps of specular materials in natural lighting conditions. We achieve this in an end-to-end learning formulation that directly predicts a reflectance map from the image itself. We show how to improve estimates by facilitating additional supervision in an indirect scheme that first predicts surface orientation and afterwards predicts the reflectance map by a learning-based sparse data interpolation. In order to analyze performance on this difficult task, we propose a new challenge of Specular MAterials on SHapes with complex IllumiNation (SMASHINg) using both synthetic and real images. Furthermore, we show the application of our method to a range of image editing tasks on real images.
This paper presents an overview of the possibility offered by 3D plastic printers for a quick, simple and affordable manufacturing of working filters and other passive devices such as antennas. This paper thus goes through numerous examples of passive devices made with the Fused Deposition Modeling (FDM) and material jetting (Polyjet©) technologies and will highlight how they can now be considered as a solid companion to RF designers during an optimization process up to Ku and higher bands.
We consider the following type of online variance minimization problem: In every trial t our algorithms get a covariance matrix C t and try to select a parameter vector w t−1 such that the total variance over a sequence of trials $\sum_{t=1}^{T} (\boldsymbol {w}^{t-1})^{\top} \boldsymbol {C}^{t}\boldsymbol {w}^{t-1}$ is not much larger than the total variance of the best parameter vector u chosen in hindsight. Two parameter spaces in ℝ n are considered—the probability simplex and the unit sphere. The first space is associated with the problem of minimizing risk in stock portfolios and the second space leads to an online calculation of the eigenvector with minimum eigenvalue of the total covariance matrix $\sum_{t=1}^{T} \boldsymbol {C}^{t}$ . For the first parameter space we apply the Exponentiated Gradient algorithm which is motivated with a relative entropy regularization. In the second case, the algorithm has to maintain uncertainty information over all unit directions u. For this purpose, directions are represented as dyads uu ⊤ and the uncertainty over all directions as a mixture of dyads which is a density matrix. The motivating divergence for density matrices is the quantum version of the relative entropy and the resulting algorithm is a special case of the Matrix Exponentiated Gradient algorithm. In each of the two cases we prove bounds on the additional total variance incurred by the online algorithm over the best offline parameter.
This paper presents a review of current literature on ethical theories as they relate to ethical leadership in the virtual business environment (e-ethics) and virtual project leadership. Ethical theories are reviewed in relation to virtual project management, such as participative management, Theory Y, and its relationship to utilitarianism; Kantian ethics, motivation, and trust; communitarian ethics, ethic of care and egalitarianism; Stakeholder Theory; and the use of political tactics. Challenges to e-ethical leadership are presented and responses to these issues discussed. The conclusion presents four propositions for future research. The purpose of this paper is to identify secondary literature on e-ethics and how this new area of business ethics may affect the leaders of virtual project teams. 2008 Elsevier Ltd and IPMA. All rights reserved.
Joint mining of multiple data sets can often discover interesting, novel, and reliable patterns which cannot be obtained solely from any single source. For example, in cross-market customer segmentation, a group of customers who behave similarly in multiple markets should be considered as a more coherent and more reliable cluster than clusters found in a single market. As another example, in bioinformatics, by joint mining of gene expression data and protein interaction data, we can find clusters of genes which show coherent expression patterns and also produce interacting proteins. Such clusters may be potential pathways.In this paper, we investigate a novel data mining problem, mining cross-graph quasi-cliques, which is generalized from several interesting applications such as cross-market customer segmentation and joint mining of gene expression data and protein interaction data. We build a general model for mining cross-graph quasi-cliques, show why the complete set of cross-graph quasi-cliques cannot be found by previous data mining methods, and study the complexity of the problem. While the problem is difficult, we develop an efficient algorithm, Crochet, which exploits several interesting and effective techniques and heuristics to efficaciously mine cross-graph quasi-cliques. A systematic performance study is reported on both synthetic and real data sets. We demonstrate some interesting and meaningful cross-graph quasi-cliques in bioinformatics. The experimental results also show that algorithm Crochet is efficient and scalable.
Human linguistic annotation is crucial for many natural language processing tasks but can be expensive and time-consuming. We explore the use of Amazon’s Mechanical Turk system, a significantly cheaper and faster method for collecting annotations from a broad base of paid non-expert contributors over the Web. We investigate five tasks: affect recognition, word similarity, recognizing textual entailment, event temporal ordering, and word sense disambiguation. For all five, we show high agreement between Mechanical Turk non-expert annotations and existing gold standard labels provided by expert labelers. For the task of affect recognition, we also show that using non-expert labels for training machine learning algorithms can be as effective as using gold standard annotations from experts. We propose a technique for bias correction that significantly improves annotation quality on two tasks. We conclude that many large labeling tasks can be effectively designed and carried out in this method at a fraction of the usual expense.
BACKGROUND
Individuals with dementia often experience poor quality of life (QOL) due to behavioral and psychological symptoms of dementia (BPSD). Music therapy can reduce BPSD, but most studies have focused on patients with mild to moderate dementia. We hypothesized that music intervention would have beneficial effects compared with a no-music control condition, and that interactive music intervention would have stronger effects than passive music intervention.


METHODS
Thirty-nine individuals with severe Alzheimer's disease were randomly and blindly assigned to two music intervention groups (passive or interactive) and a no-music Control group. Music intervention involved individualized music. Short-term effects were evaluated via emotional response and stress levels measured with the autonomic nerve index and the Faces Scale. Long-term effects were evaluated by BPSD changes using the Behavioral Pathology in Alzheimer's Disease (BEHAVE-AD) Rating Scale.


RESULTS
Passive and interactive music interventions caused short-term parasympathetic dominance. Interactive intervention caused the greatest improvement in emotional state. Greater long-term reduction in BPSD was observed following interactive intervention, compared with passive music intervention and a no-music control condition.


CONCLUSION
Music intervention can reduce stress in individuals with severe dementia, with interactive interventions exhibiting the strongest beneficial effects. Since interactive music intervention can restore residual cognitive and emotional function, this approach may be useful for aiding severe dementia patients' relationships with others and improving QOL. The registration number of the trial and the name of the trial registry are UMIN000008801 and "Examination of Effective Nursing Intervention for Music Therapy for Severe Dementia Elderly Person" respectively.
With the existence of many large transaction databases, the huge amounts of data, the high scalability of distributed systems, and the easy partition and distribution of a centralized database, it is important to inuestzgate eficient methods for distributed mining of association rules. This study discloses some interesting relationships between locally large and globally large itemsets and proposes an interesting distributed association rule mining algorithm, FDM (Fast Distributed Mining of association rules), which generates a small number of candidate sets and substantially reduces the number of messages to be passed at mining association rules. Our performance study shows that FDM has a superior performance over the direct application of a typical sequential algorithm. Further performance enhancement leads to a few variations of the algorithm.
The mathematical problems and their solutions of the Third International Students’ Olympiad in Cryptography NSUCRYPTO’2016 are presented. We consider mathematical problems related to the construction of algebraic immune vectorial Boolean functions and big Fermat numbers, problems about secrete sharing schemes and pseudorandom binary sequences, biometric cryptosystems and the blockchain technology, etc. Two open problems in mathematical cryptography are also discussed and a solution for one of them proposed by a participant during the Olympiad is described. It was the first time in the Olympiad history.
Robust object recognition is a crucial ingredient of many, if not all, real-world robotics applications. This paper leverages recent progress on Convolutional Neural Networks (CNNs) and proposes a novel RGB-D architecture for object recognition. Our architecture is composed of two separate CNN processing streams - one for each modality - which are consecutively combined with a late fusion network. We focus on learning with imperfect sensor data, a typical problem in real-world robotics tasks. For accurate learning, we introduce a multi-stage training methodology and two crucial ingredients for handling depth data with CNNs. The first, an effective encoding of depth information for CNNs that enables learning without the need for large depth datasets. The second, a data augmentation scheme for robust learning with depth images by corrupting them with realistic noise patterns. We present state-of-the-art results on the RGB-D object dataset [15] and show recognition in challenging RGB-D real-world noisy settings.
This paper presents novel super compact microwave power dividers and balun (balanced-to-unbalanced) circuits. The proposed devices are based on multilayer ring resonators (MRR) structure. These new microwave devices are highly compact and flexible in design that can operate within various desirable bandwidths from narrow-band to ultrawideband (UWB), hence performing as bandpass filters simultaneously along with their own functions. It is also possible to have arbitrary power divisions. By this technique, a balun can be simply converted to a power diver and vice versa. Sample circuits are designed and the scattering characteristics are provided using electromagnetic simulation software. The dimensions of the devices are 2.3 mm χ 2.3 mm χ 1.5 mm.
Content Memory (Learning Ability) As Comprehension 82 Vocabulary Cs .30 ( ) .23 .31 ( ) .31 .31 .35 ( ) .29 .48 .35 .38 ( ) .30 .40 .47 .58 .48 ( ) As judged against these latter values, comprehension (.48) and vocabulary (.47), but not memory (.31), show some specific validity. This transmutability of the validation matrix argues for the comparisons within the heteromethod block as the most generally relevant validation data, and illustrates the potential interchangeability of trait and method components. Some of the correlations in Chi's (1937) prodigious study of halo effect in ratings are appropriate to a multitrait-multimethod matrix in which each rater might be regarded as representing a different method. While the published report does not make these available in detail because it employs averaged values, it is apparent from a comparison of his Tables IV and VIII that the ratings generally failed to meet the requirement that ratings of the same trait by different raters should correlate higher than ratings of different traits by the same rater. Validity is shown to the extent that of the correlations in the heteromethod block, those in the validity diagonal are higher than the average heteromethod-heterotrait values. A conspicuously unsuccessful multitrait-multimethod matrix is provided by Campbell (1953, 1956) for rating of the leadership behavior of officers by themselves and by their subordinates. Only one of 11 variables (Recognition Behavior) met the requirement of providing a validity diagonal value higher than any of the heterotrait-heteromethod values, that validity being .29. For none of the variables were the validities higher than heterotrait-monomethod values. A study of attitudes toward authority and nonauthority figures by Burwen and Campbell (1957) contains a complex multitrait-multimethod matrix, one symmetrical excerpt from which is shown in Table 6. Method variance was strong for most of the procedures in this study. Where validity was found, it was primarily at the level of validity diagonal values higher than heterotrait-heteromethod values. As illustrated in Table 6, attitude toward father showed this kind of validity, as did attitude toward peers to a lesser degree. Attitude toward boss showed no validity. There was no evidence of a generalized attitude toward authority which would include father and boss, although such values as the VALIDATION BY THE MULTITRAIT-MULTIMETHOD MATRIX
Vehicle-to-anything (V2X) communications refer to information exchange between a vehicle and various elements of the intelligent transportation system (ITS), including other vehicles, pedestrians, Internet gateways, and transport infrastructure (such as traffic lights and signs). The technology has a great potential of enabling a variety of novel applications for road safety, passenger infotainment, car manufacturer services, and vehicle traffic optimization. Today, V2X communications is based on one of two main technologies: dedicated short-range communications (DSRC) and cellular networks. However, in the near future, it is not expected that a single technology can support such a variety of expected V2X applications for a large number of vehicles. Hence, interworking between DSRC and cellular network technologies for efficient V2X communications is proposed. This paper surveys potential DSRC and cellular interworking solutions for efficient V2X communications. First, we highlight the limitations of each technology in supporting V2X applications. Then, we review potential DSRC-cellular hybrid architectures, together with the main interworking challenges resulting from vehicle mobility, such as vertical handover and network selection issues. In addition, we provide an overview of the global DSRC standards, the existing V2X research and development platforms, and the V2X products already adopted and deployed in vehicles by car manufactures, as an attempt to align academic research with automotive industrial activities. Finally, we suggest some open research issues for future V2X communications based on the interworking of DSRC and cellular network technologies.
In this paper, a novel multi-linear polarization reconfigurable antenna with shorting posts, which can achieve four linear polarizations (0°, 45°, 90°, 135°), has been proposed. By switching the diodes between two groups of shorting posts, four linear polarizations can be realized. The dimensions of the proposed antenna are about 0.56λ× 0.56λ× 0.07λ at 2.4 GHz. The measured results agree well with the simulated ones.
Latent variable models can be enriched with a multi-dimensional structure to consider the many latent factors in a text corpus, such as topic, author perspective and sentiment. We introduce factorial LDA, a multi-dimensional model in which a document is influenced by K different factors, and each word token depends on a K-dimensional vector of latent variables. Our model incorporates structured word priors and learns a sparse product of factors. Experiments on research abstracts show that our model can learn latent factors such as research topic, scientific discipline, and focus (methods vs. applications). Our modeling improvements reduce test perplexity and improve human interpretability of the discovered factors.
This paper describes a hybrid model and a model predictive control (MPC) strategy for solving a traction control problem. The problem is tackled in a systematic way from modeling to control synthesis and implementation. The model is described first in the Hybrid Systems Description Language to obtain a mixed-logical dynamical (MLD) hybrid model of the open-loop system. For the resulting MLD model, we design a receding horizon finite-time optimal controller. The resulting optimal controller is converted to its equivalent piecewise affine form by employing multiparametric programming techniques, and finally experimentally tested on a car prototype. Experiments show that good and robust performance is achieved in a limited development time by avoiding the design of ad hoc supervisory and logical constructs usually required by controllers developed according to standard techniques.
Robot navigation in the environment with obstacles is still a challenging problem. In this paper, the navigation problems with wheeled mobile robots (WMRs) are reviewed, the navigation mechanism of WMRs is analyzed in detail, the methods of solving the sub problems such as mapping, localization and path planning which all both related to robot navigation are summarized and the advantages and disadvantages of the existing methods are expounded. Especially in the agricultural field, the precise navigation of robots in the complex agricultural environment is the prerequisite for the completion of various tasks. This paper is aimed at the special complexity of the agricultural environment, prospected the application of the solution to the navigation problem of WMRs in agricultural engineering, put forward the research direction to solve the problems of precise navigation in agricultural environments.
Brain-computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using the electroencephalographic activity or other electrophysiological measures of the brain function. An essential factor in the successful operation of BCI systems is the methods used to process the brain signals. In the BCI literature, however, there is no comprehensive review of the signal processing techniques used. This work presents the first such comprehensive survey of all BCI designs using electrical signal recordings published prior to January 2006. Detailed results from this survey are presented and discussed. The following key research questions are addressed: (1) what are the key signal processing components of a BCI, (2) what signal processing algorithms have been used in BCIs and (3) which signal processing techniques have received more attention?
This paper proposes a quasi-elliptic filter with slot coupling and nonadjacent cross coupling based on the substrate integrated waveguide (SIW) cavity. The slots etched on the top metal plane of SIW cavity are used to produce electrical coupling, and the cross coupling is realized by the microstrip transmission line above the SIW cavity. The coupling strength is mainly controlled by the width and height of the slot. The length of the open-ended microstrip line controls the sign of the cross coupling. The cross coupling with different signs are used in the filter to produce a pair of transmission zeros (TZs) at both sides of the passband. In order to prove the validity, a fourth-order SIW quasi-elliptic filter with TZsat both sides of the passband is fabricated in a two-layer printed circuit board. The measured insertion loss at a center frequency of 3.7 GHz is 1.1 dB. The return loss within the passband is below -18 dB with a fractional bandwidth of 16%. The measured results are in good agreement with the simulated results.
An improved version of a single-ended primary inductor converter (SEPIC) is presented. The converter consists of a conventional SEPIC converter plus an additional high-frequency transformer and diode to maintain a freewheeling mode of the DC inductor currents during the switch on state. The voltage conversion ratio characteristics and semiconductor device voltage and current stresses are characterized. The main advantages of this converter are the continuous output current, smaller output voltage ripple, and lower semiconductors current stress compared with the conventional SEPIC converter. The design and simulation of the concept is verified by an experiment with a 48-V input and 12-V/3.75-A output converter.
This paper presents a novel transition between a microstrip line and a substrate integrated waveguide (SIW) in a multilayer substrate design environment. In order to achieve a low-loss broadband response, the transition, consisting of a tapered or multisectional ridged SIW and a tapered microstrip line, is modeled and designed by simultaneously considering both impedance matching and field matching. Characteristic impedance and guided wavelength calculated by using closed-form expressions based on a transverse resonant method are used to develop our design procedure. Effective broad bandwidth is obtained in two examples developed in this study, which are validated with simulated and measured results. This transition provides a simple way to design substrate integrated circuits with buried microstrip circuits in the multilayer substrate in which any ratio of impedance transform can be anticipated.
Usual transitions between planar circuit and rectangular waveguide make use of 3-D complex mounting structures. Such an integration requires costly high precision mechanical alignment, In this paper, a new planar platform is developed in which a coplanar waveguide (CPW) and a rectangular waveguide are fully integrated on the same substrate, and they are interconnected via a simple transition. They can be built with a standard PCB process. Our experiments at 28 GHz show that an effective bandwidth of 7% at 15 dB return loss can easily be achieved. The CPW-to-waveguide transition allows for a complete integration of waveguide components on substrate with active components such as MMIC.
A new generation of high-frequency integrated circuits is presented, which is called substrate integrated circuits (SICs). Current state-of-the-art of circuit design and implementation platforms based on this new concept are reviewed and discussed in detail. Different possibilities and numerous advantages of the SICs are shown for microwave, millimeter-wave and optoelectronics applications. Practical examples are illustrated with theoretical and experimental results for substrate integrated waveguide (SIW), substrate integrated slab waveguide (SISW) and substrate integrated nonradiating dielectric (SINRD) guide circuits. Future research and development trends are also discussed with reference to low-cost innovative design of millimeter-wave and optoelectronic integrated circuits.
A fully integrated step-down switched-capacitor dc–dc converter ring with 123 phases has been designed that could achieve fast dynamic voltage scaling for the microprocessor of wearable devices. The symmetrical multiphase converter ring surrounds its load in the square and supplies power to the on-chip power grid that is easily accessible at any point of the chip edges. The frequency of the <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {DD}}$ </tex-math></inline-formula>-controlled oscillator is adjusted through its supply voltage <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {DD}}$ </tex-math></inline-formula>, which allows the unity-gain frequency to be designed higher than the switching frequency. The converter ring has been fabricated in a low-leakage 65-nm CMOS process. This converter achieves a response time of 3 ns, a reference tracking speed of 2.5 V/<inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula>, and a minimum output ripple of 2.2 mV. The peak efficiency is 80% at the power density of 66.6 mW/mm<sup>2</sup>, and the maximum power density is 180 mW/mm<sup>2</sup>.
Along with rapid advancement of power semiconductors, voltage multipliers have introduced new series of pulsed power generators. In this paper, based on conventional voltage multiplier and by using power electronics switches a new topology in high voltage pulsed power application is proposed. This topology is a modular circuit that can generate a high output voltage from a relatively low input voltage with fast rise time and adjustable frequency, pulse width, and voltage levels using a series connection of switched capacitor cells. A comparative analysis is carried out to show the advantages of proposed topology. Experimental and simulation results are presented to confirm the analysis.
CONTEXT
Treatment of osteoporosis with an anabolic agent, teriparatide [human PTH 1-34 (TPTD)], is effective in reducing incident fractures, but patient resistance to daily sc injections has limited its use. A novel transdermal patch, providing a rapid, pulse delivery of TPTD, may provide a desirable alternative.


OBJECTIVE
The aim of the study was to determine the safety and efficacy of a novel transdermal TPTD patch compared to placebo patch and sc TPTD 20-microg injection in postmenopausal women with osteoporosis.


DESIGN
Our study consisted of 6-month, randomized, placebo-controlled, positive control, multidose daily administration.


PATIENTS
We enrolled 165 postmenopausal women (mean age, 64 yr) with osteoporosis.


INTERVENTIONS
A TPTD patch with a 20-, 30-, or 40-microg dose or a placebo patch was self-administered daily for 30-min wear time, or 20 microg of TPTD was injected daily.


OUTCOMES
The primary efficacy measure was mean percentage change in lumbar spine bone mineral density (BMD) from baseline at 6 months.


RESULTS
TPTD delivered by transdermal patch significantly increased lumbar spine BMD vs. placebo patch in a dose-dependent manner at 6 months (P < 0.001). TPTD 40-microg patch increased total hip BMD compared to both placebo patch and TPTD injection (P < 0.05). Bone turnover markers (procollagen type I N-terminal propeptide and C-terminal cross-linked telopeptide of type I collagen) increased from baseline in a dose-dependent manner in all treatment groups and were all significantly different from placebo patch (P < 0.001). All treatments were well tolerated, and no prolonged hypercalcemia was observed.


CONCLUSION
Transdermal patch delivery of TPTD in postmenopausal women with osteoporosis for 6 months is safe and effective in increasing lumbar spine and total hip BMD.
Design equations for the standard Quasi Yagi-Uda antenna built on 60 mil FR4 are presented to be used on fixed wireless telecommunication systems, Wi-Fi, WiMAX and LTE. In this work, the design equations are systematically proposed for the band between 1 GHz and 3 GHz, where relative bandwidth is better than 39%, the cross-polarization is lower than - 15dB for every angle and frequency, the cross-polarization is lower than -25dB in the main lobe and a front to back ratio better than 12dB in all the bandwidth. Three antennas where designed, built and measured with the proposed equations at 1.5GHz, 2GHz and 3 GHz, showing good agreement with the simulation results.
Methods that use latent representations of data, such as matrix and tensor factorization or deep neural methods, are becoming increasingly popular for applications such as knowledge base population and recommendation systems. These approaches have been shown to be very robust and scalable but, in contrast to more symbolic approaches, lack interpretability. This makes debugging such models difficult, and might result in users not trusting the predictions of such systems. To overcome this issue we propose to extract an interpretable proxy model from a predictive latent variable model. We use a socalled pedagogical method, where we query our predictive model to obtain observations needed for learning a descriptive model. We describe two families of (presumably more) descriptive models, simple logic rules and Bayesian networks, and show how members of these families provide descriptive representations of matrix factorization models. Preliminary experiments on knowledge extraction from text indicate that even though Bayesian networks may be more faithful to a matrix factorization model than the logic rules, the latter are possibly more useful for interpretation and debugging.