From Polynesian healers to health food stores: changing perspectives of Morinda citrifolia (Rubiaceae).. Morinda citrifolia L (noni) is one of the most important traditional Polynesian medicinal plants. Remedies from isolated Polynesian cultures, such as that of Rotuma, illustrate traditional indications that focus upon leaves, roots, bark, and green fruit, primarily for topical ailments. Anecdotally collected Hawaiian remedies that employ noni fruit illustrate changing usage patterns with shifts in recent times to preparation of juice made of ripe or decaying fruit. Ralph M. Heinicke promoted a wide range of claims about noni, and these seem to have fueled much of the current commercial interest in the plant. Recent studies of the proliferation of commercial products have shown that noni product manufacturers are promoting a range of therapeutic claims. These claims are based upon traditional Polynesian uses, Heinicke's ideas, and fragments of recent scientific studies including the activity of noni in the treatment of cancer. A review is provided of recent studies of potential anticancer activity of noni fruit. While noni's anticancer potential is still being explored, it continues to be widely used by Polynesians and non-Polynesians alike for both traditional and newly hypothesized indications.

Question: What is the traditional use of noni?


Twitter games: how successful spammers pick targets. Online social networks, such as Twitter, have soared in popularity and in turn have become attractive targets of spam. In fact, spammers have evolved their strategies to stay ahead of Twitter's anti-spam measures in this short period of time. In this paper, we investigate the strategies Twitter spammers employ to reach relevant target audiences. Due to their targeted approaches to send spam, we see evidence of a large number of the spam accounts forming relationships with other Twitter users, thereby becoming deeply embedded in the social network.  We analyze nearly 20 million tweets from about 7 million Twitter accounts over a period of five days. We identify a set of 14,230 spam accounts that manage to live longer than the other 73% of other spam accounts in our data set. We characterize their behavior, types of tweets they use, and how they target their audience. We find that though spam campaigns changed little from a recent work by Thomas et al., spammer strategies evolved much in the same short time span, causing us to sometimes find contradictory spammer behavior from what was noted in Thomas et al.'s work. Specifically, we identify four major strategies used by 2/3rd of the spammers in our data. The most popular of these was one where spammers targeted their own followers. The availability of various kinds of services that help garner followers only increases the popularity of this strategy. The evolution in spammer strategies we observed in our work suggests that studies like ours should be undertaken frequently to keep up with spammer evolution.

Question: How successful spammers pick targets.


Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic Oracles. Parsing accuracy using efficient greedy transition systems has improved dramatically in recent years thanks to neural networks. Despite striking results in dependency parsing, however, neural models have not surpassed stateof-the-art approaches in constituency parsing. To remedy this, we introduce a new shiftreduce system whose stack contains merely sentence spans, represented by a bare minimum of LSTM features. We also design the first provably optimal dynamic oracle for constituency parsing, which runs in amortized O(1) time, compared to O(n) oracles for standard dependency parsing. Training with this oracle, we achieve the best F1 scores on both English and French of any parser that does not use reranking or external data.

Question: What is the best constituency parser?


The Measurement of Dielectric Properties of Liquids at Microwave Frequencies Using Open-ended Coaxial Probes. Significant progress has been made in recent years in the development of microwave tomographic imaging systems for medical applications. In order to design an appropriate microwave imaging system for industrial applications, and to interpret the images produced, the materials under imaging need to be characterised. In this paper, we describe the use of open-ended coaxial probes for the measurement of dielectric properties of liquids at frequencies between 400MHz and 20GHz. The results obtained using the Misra-Blackham model for a number of liquids including water of different salinity are compared with those published in the literature showing a good agreement. For saline water, in particular, the frequency of the minimum loss depends on the salinity. It may change from 1.5GHz for the inclusion of 0.2% NaCl to 7GHz for the inclusion of 3.5% NaCl. The real part of the permittivity may also change by approximately 50% from 400MHz to 20GHz.

Question: What is the minimum loss of a liquid at 20GHz?


A novel LLC resonant controller with best-in-class transient performance and low standby power consumption. This paper introduces a novel LLC resonant controller with better light load efficiency and best-in-class transient performance. A new control algorithm — hybrid hysteretic control, is proposed which combines the benefit of charge control and frequency control. It maintains the good transient performance of charge control, but avoids the related stability issues by adding slope compensation. The slope compensation also helps to sense the resonant capacitor voltage by only using lossless capacitor divider. Burst mode operation is developed to improve the light load efficiency. By turning on the power devices for only a short period during light load, it helps to reduce the equivalent switching frequency and achieve high efficiency. The effectiveness of the proposed controller has been validated in a 12 V/120 W half-bridge LLC converter, as well as a commercial power supply unit.

Question: What is the novel LLC resonant controller?


Heuristics for High-Utility Local Process Model Mining. Local Process Models (LPMs) describe structured fragments of process behavior occurring in the context of less structured business processes. In contrast to traditional support-based LPM discovery, which aims to generate a collection of process models that describe highly frequent behavior, High-Utility Local Process Model (HU-LPM) discovery aims to generate a collection of process models that provide useful business insights by specifying a utility function. Mining LPMs is a computationally expensive task, because of the large search space of LPMs. In supportbased LPM mining, the search space is constrained by making use of the property that support is anti-monotonic. We show that in general, we cannot assume a provided utility function to be anti-monotonic, therefore, the search space of HU-LPMs cannot be reduced without loss. We propose four heuristic methods to speed up the mining of HU-LPMs while still being able to discover useful HU-LPMs. We demonstrate their applicability on three real-life data sets.

Question: What is the utility of a process model?


Ontological Constructs to Create Money Laundering Schemes. There is an increasing tendency in the money laundering sector to utilize electronic commerce and web services. Misuse of web services and electronic money transfers occurs at many points in complex trading schemes. We provide ontological components that can be combined to construct some of these money laundering schemes. These constructs can be helpful for investigators, in order to decompose suspected financial schemes and recognize

Question: What is ontological?


Tutorial on Machine Learning for Spectrum Sharing in Wireless Networks. As spectrum utilization efficiency is the major bottleneck in current wireless networking, many stakeholders discuss that spectrum should be shared rather than being exclusively allocated. Shared spectrum access raises many challenges which, if not properly addressed, degrades the performance level of the co-existing networks. Coexistence scenarios may involve two or more networks: same or different types; operated by the same or different operators. The complex interactions among the coexisting networks can be addressed by the machine learning tools which by their nature embrace uncertainty and can model the complex interactions. In this tutorial, we start with the basics of coexistence of wireless networks in the unlicensed bands. Then, we focus on WiFi and LTE-U coexistence. After providing a brief overview of machine learning topics such as supervised learning, unsupervised learning, reinforcement learning, we overview five particular examples which exploit learning schemes to enable efficient spectrum sharing entailing a generic cognitive radio setting as well as LTE and WiFi coexistence scenarios. We conclude with a list of challenges and research directions. I. SPECTRUM SHARING IN WIRELESS NETWORKS Spectrum sharing is the situation where at least two users or technologies are authorized to use the same portion of the radio spectrum on a non-exclusive manner1. We overview the current state of spectrum sharing and provide a taxonomy of spectrum sharing scenarios. We can list the main challenges in providing peaceful coexistence as follows: (i) scarcity of the resources, (ii) heterogeneity of the coexisting networks, (iii) power asymmetry, and (iv) lack of coordination, communication, and cooperation among the coexisting networks. II. COEXISTENCE IN THE UNLICENSED BANDS: THE CASE OF WIFI AND LTE-U The success of IEEE 802.11 networks in the unlicensed bands, i.e., 2.4 GHz and 5 GHz, has prove

Question: What is the difference between spectrum sharing and spectrum sharing?


OPEM: A Static-Dynamic Approach for Machine-Learning-Based Malware Detection. Malware is any computer software potentially harmful to both computers and networks. The amount of malware is growing every year and poses a serious global security threat. Signature-based detection is the most extended method in commercial antivirus software, however, it consistently fails to detect new malware. Supervised machine learning has been adopted to solve this issue. There are two types of features that supervised malware detectors use: (i) static features and (ii) dynamic features. Static features are extracted without executing the sample whereas dynamic ones requires an execution. Both approaches have their advantages and disadvantages. In this paper, we propose for the first time, OPEM, an hybrid unknown malware detector which combines the frequency of occurrence of operational codes (statically obtained) with the information of the execution trace of an executable (dynamically obtained). We show that this hybrid approach enhances the performance of both approaches when run separately.

Question: What is the difference between static and dynamic features?


Actions and Attributes from Wholes and Parts. We investigate the importance of parts for the tasks of action and attribute classification. We develop a part-based approach by leveraging convolutional network features inspired by recent advances in computer vision. Our part detectors are a deep version of poselets and capture parts of the human body under a distinct set of poses. For the tasks of action and attribute classification, we train holistic convolutional neural networks and show that adding parts leads to top-performing results for both tasks. We observe that for deeper networks parts are less significant. In addition, we demonstrate the effectiveness of our approach when we replace an oracle person detector, as is the default in the current evaluation protocol for both tasks, with a state-of-the-art person detection system.

Question: What is the importance of parts for the tasks of action and attribute classification?


Subcutaneous and visceral adipose tissue: their relation to the metabolic syndrome.. Methods for assessment, e.g., anthropometric indicators and imaging techniques, of several phenotypes of human obesity, with special reference to abdominal fat content, have been evaluated. The correlation of fat distribution with age, gender, total body fat, energy balance, adipose tissue lipoprotein lipase and lipolytic activity, adipose tissue receptors, and genetic characteristics are discussed. Several secreted or expressed factors in the adipocyte are evaluated in the context of fat tissue localization. The body fat distribution and the metabolic profile in nonobese and obese individuals is discussed relative to lipolysis, antilypolysis and lipogenesis, insulin sensitivity, and glucose, lipid, and protein metabolism. Finally, the endocrine regulation of abdominal visceral fat in comparison with the adipose tissue localized in other areas is presented.

Question: What is the difference between subcutaneous and visceral adipose tissue?


Pixel-Oriented Visualization of Change in Social Networks. We propose a new approach to visualize social networks. Most common network visualizations rely on graph drawing. While without doubt useful, graphs suffer from limitations like cluttering and important patterns may not be realized especially when networks change over time. Our approach adapts pixel-oriented visualization techniques to social networks as an addition to traditional graph visualizations. The visualization is exemplified using social networks based on corporate wikis.

Question: How to visualize social networks?


Ear Biometrics Based on Geometrical Feature Extraction. Biometrics identification methods proved to be very efficient, more natural and easy for users than traditional methods of human identification. In fact, only biometrics methods truly identify humans, not keys and cards they posses or passwords they should remember. The future of biometrics will surely lead to systems based on image analysis as the data acquisition is very simple and requires only cameras, scanners or sensors. More importantly such methods could be passive, which means that the user does not have to take active part in the whole process or, in fact, would not even know that the process of identification takes place. There are many possible data sources for human identification systems, but the physiological biometrics seem to have many advantages over methods based on human behaviour. The most interesting human anatomical parts for such passive, physiological biometrics systems based on images acquired from cameras are face and ear. Both of those methods contain large volume of unique features that allow to distinctively identify many users and will be surely implemented into efficient biometrics systems for many applications. The article introduces to ear biometrics and presents its advantages over face biometrics in passive human identification systems. Then the geometrical method of feature extraction from human ear images in order to perform human identification is presented.

Question: What is the difference between face and ear biometrics?


A survey of frequent subgraph mining algorithms. Graph mining is an important research area within the domain of data mining. The field of study concentrates on the identification of frequent subgraphs within graph data sets. The research goals are directed at: (i) effective mechanisms for generating candidate subgraphs (without generating duplicates) and (ii) how best to process the generated candidate subgraphs so as to identify the desired frequent subgraphs in a way that is computationally efficient and procedurally effective. This paper presents a survey of current research in the field of frequent subgraph mining, and proposed solutions to address the main research issues.

Question: What is a survey of frequent subgraph mining algorithms?


No free lunch theorems for optimization. A framework is developed to explore the connection between e ective optimization algorithms and the problems they are solving A number of no free lunch NFL theorems are presented that establish that for any algorithm any elevated performance over one class of problems is exactly paid for in performance over another class These theorems result in a geometric interpretation of what it means for an algorithm to be well suited to an optimization problem Applications of the NFL theorems to information theoretic aspects of optimization and benchmark measures of performance are also presented Other issues addressed are time varying optimization problems and a priori head to head minimax distinctions between optimization algorithms distinctions that can obtain despite the NFL theorems enforcing of a type of uniformity over all algorithms

Question: What is a no free lunch theorem?


An Efficient Algorithm for Fractal Analysis of Textures. In this paper we propose a new and efficient texture feature extraction method: the Segmentation-based Fractal Texture Analysis, or SFTA. The extraction algorithm consists in decomposing the input image into a set of binary images from which the fractal dimensions of the resulting regions are computed in order to describe segmented texture patterns. The decomposition of the input image is achieved by the Two-Threshold Binary Decomposition (TTBD) algorithm, which we also propose in this work. We evaluated SFTA for the tasks of content-based image retrieval (CBIR) and image classification, comparing its performance to that of other widely employed feature extraction methods such as Haralick and Gabor filter banks. SFTA achieved higher precision and accuracy for CBIR and image classification. Additionally, SFTA was at least 3.7 times faster than Gabor and 1.6 times faster than Haralick with respect to feature extraction time.

Question: What is the fastest texture feature extraction algorithm?


Design and implementation of full bridge bidirectional isolated DC-DC converter for high power applications. This paper proposes the design and implementation of a high power full bridge bidirectional isolated DC-DC converter (BIDC) which comprises of two symmetrical voltage source converters and a high frequency transformer. In the proposed BIDC, a well-known PI controller based single phase-shift (SPS) modulation technique is used in order to achieve high power transfer. Besides, different phase shift methods such as extended phase-shift (EPS) and double phase-shift (DPS) are compared with SPS. Both simulation and experimental results are caried out to verify PI controller based simple phase-shift controlled BIDC prototype that is designed for 300-V 2.4-kW and operating at 20 kHz.

Question: What is the difference between a full bridge and a half bridge?


A method for business process reengineering based on enterprise ontology. Business Process Reengineering increases enterprise's chance to survive in competition among organizations , but failure rate among reengineering efforts is high, so new methods that decrease failure, are needed, in this paper a business process reengineering method is presented that uses Enterprise Ontology for modelling the current system and its goal is to improve analysing current system and decreasing failure rate of BPR, and cost and time of performing processes, In this method instead of just modelling processes, processes with their : interactions and relations, environment, staffs and customers will be modelled in enterprise ontology. Also in choosing processes for reengineering step, after choosing them, processes which, according to the enterprise ontology, has the most connection with the chosen ones, will also be chosen to reengineer, finally this method is implemented on a company and As-Is and To-Be processes are simulated and compared by ARIS tools, Report and Simulation Experiment

Question: What is enterprise ontology?


Microfibrillated cellulose and new nanocomposite materials: a review - DTU Orbit (15/10/2018). (15/10/2018)  Microfibrillated cellulose and new nanocomposite materials: a review Due to their abundance, high strength and stiffness, low weight and biodegradability, nano-scale cellulose fiber materials (e.g., microfibrillated cellulose and bacterial cellulose) serve as promising candidates for bio-nanocomposite production. Such new high-value materials are the subject of continuing research and are commercially interesting in terms of new products from the pulp and paper industry and the agricultural sector. Cellulose nanofibers can be extracted from various plant sources and, although the mechanical separation of plant fibers into smaller elementary constituents has typically required high energy input, chemical and/or enzymatic fiber pre-treatments have been developed to overcome this problem. A challenge associated with using nanocellulose in composites is the lack of compatibility with hydrophobic polymers and various chemical modification methods have been explored in order to address this hurdle. This review summarizes progress in nanocellulose preparation with a particular focus on microfibrillated cellulose and also discusses recent developments in bio-nanocomposite fabrication based on nanocellulose.

Question: What is microfibrillated cellulose?


Application of data mining methods for link prediction in social networks. Using social networking services is becoming more popular day by day. Social network analysis views relationships in terms of nodes (people) and edges (links or connections—the relationship between the people). The websites of the social networks such as Facebook currently are among the most popular internet services just after giant portals such as Yahoo, MSN and search engines such as Google. One of the main problems in analyzing these networks is the prediction of relationships between people in the network. It is hard to find one method that can identify relation between people in the social network. The purpose of this paper is to forecast the friendship relation between individuals among a social network, especially the likelihood of a relation between an existing member with a new member. For this purpose, we used a few hypotheses to make the graph of relationships between members of social network, and we used the method of logistic regression to complete the graph. Test data from Flickr website are used to evaluate the proposed method. The results show that the method has achieved 99 % accuracy in prediction of friendship relationships.

Question: What is the purpose of this paper?


Gimli: open source and high-performance biomedical name recognition. Automatic recognition of biomedical names is an essential task in biomedical information extraction, presenting several complex and unsolved challenges. In recent years, various solutions have been implemented to tackle this problem. However, limitations regarding system characteristics, customization and usability still hinder their wider application outside text mining research. We present Gimli, an open-source, state-of-the-art tool for automatic recognition of biomedical names. Gimli includes an extended set of implemented and user-selectable features, such as orthographic, morphological, linguistic-based, conjunctions and dictionary-based. A simple and fast method to combine different trained models is also provided. Gimli achieves an F-measure of 87.17% on GENETAG and 72.23% on JNLPBA corpus, significantly outperforming existing open-source solutions. Gimli is an off-the-shelf, ready to use tool for named-entity recognition, providing trained and optimized models for recognition of biomedical entities from scientific text. It can be used as a command line tool, offering full functionality, including training of new models and customization of the feature set and model parameters through a configuration file. Advanced users can integrate Gimli in their text mining workflows through the provided library, and extend or adapt its functionalities. Based on the underlying system characteristics and functionality, both for final users and developers, and on the reported performance results, we believe that Gimli is a state-of-the-art solution for biomedical NER, contributing to faster and better research in the field. Gimli is freely available at http://bioinformatics.ua.pt/gimli .

Question: What is the name of the open source and high-performance biomedical name recognition tool?


A compact printed wide-slot UWB antenna with band-notched characteristics. In this paper, we present an offset microstrip-fed ultrawideband antenna with band notched characteristics. The antenna structure consists of rectangular radiating patch and ground plane with rectangular shaped slot, which increases impedance bandwidth upto 123.52%(2.6–11GHz). A new modified U slot is etched in the radiating patch to create band-notched properties in the WiMAX (3.3–3.7GHz) and C-band satellite communication (3.7–4.15GHz). Furthermore, parametric studies have been conducted using EM simulation software CADFEKO suite(7.0). A prototype of antenna is fabricated on 1.6mm thick FR-4 substrate with dielectric constant of 4.4 and loss tangent of 0.02. The proposed antenna exhibits directional and omnidirectional radiation patterns along E and H-plane with stable efficiency over the frequency band from 2.6GHz to 11GHz with VSWR less than 2, except 3.3–4.15GHz notched frequency band. The proposed antenna shows good time domain analysis.

Question: What is the difference between a compact printed UWB antenna and a wide-slot UWB antenna?


A Surface-Growing Approach to Multi-View Stereo Reconstruction. We present a new approach to reconstruct the shape of a 3D object or scene from a set of calibrated images. The central idea of our method is to combine the topological flexibility of a point-based geometry representation with the robust reconstruction properties of scene-aligned planar primitives. This can be achieved by approximating the shape with a set of surface elements (surfels) in the form of planar disks which are independently fitted such that their footprint in the input images matches. Instead of using an artificial energy functional to promote the smoothness of the recovered surface during fitting, we use the smoothness assumption only to initialize planar primitives and to check the feasibility of the fitting result. After an initial disk has been found, the recovered region is iteratively expanded by growing further disks in tangent direction. The expansion stops when a disk rotates by more than a given threshold during the fitting step. A global sampling strategy guarantees that eventually the whole surface is covered. Our technique does not depend on a shape prior or silhouette information for the initialization and it can automatically and simultaneously recover the geometry, topology, and visibility information which makes it superior to other state-of-the-art techniques. We demonstrate with several high-quality reconstruction examples that our algorithm performs highly robustly and is tolerant to a wide range of image capture modalities.

Question: What is a surface-growing approach to multi-view stereo reconstruction?


Bethe-ADMM for Tree Decomposition based Parallel MAP Inference. We consider the problem of maximum a posteriori (MAP) inference in discrete graphical models. We present a parallel MAP inference algorithm called Bethe-ADMM based on two ideas: tree-decomposition of the graph and the alternating direction method of multipliers (ADMM). However, unlike the standard ADMM, we use an inexact ADMM augmented with a Bethe-divergence based proximal function, which makes each subproblem in ADMM easy to solve in parallel using the sum-product algorithm. We rigorously prove global convergence of Bethe-ADMM. The proposed algorithm is extensively evaluated on both synthetic and real datasets to illustrate its effectiveness. Further, the parallel Bethe-ADMM is shown to scale almost linearly with increasing number of cores.

Question: What is the difference between the standard ADMM and the proposed algorithm?


A magnetostatic 2-axis MEMS scanner with I-section rib-reinforcement and slender permanent magnet patterns. This study demonstrates the 2-axis epitaxial silicon scanner driven by the coil-less magnetostatic force using electroplated permanent magnet (CoNiMnP) film. The present approach has four merits: (1) the process employs the cheap silicon wafer with epitaxial layer; and the electrochemical etching stop technique is used to precisely control the thickness of scanner; (2) the I-section rib-reinforced structure is implemented to provide high stiffness of the mirror plate; (3) the magnetostatic driving force on scanner is increased by electroplated permanent magnet film with slender patterns; (4) the size of packaged scanner is reduced since the assembled permanent magnets are not required.

Question: What is the size of the scanner?


Multilabel Text Classification for Automated Tag Suggestion. The increased popularity of tagging during the last few years can be mainly attributed to its embracing by most of the recently thriving user-centric content publishing and management Web 2.0 applications. However, tagging systems have some limitations that have led researchers to develop methods that assist users in the tagging process, by automatically suggesting an appropriate set of tags. We have tried to model the automated tag suggestion problem as a multilabel text classification task in order to participate in the ECML/PKDD 2008 Discovery Challenge.

Question: What is the multilabel text classification?


A Short-Term Rainfall Prediction Model Using Multi-task Convolutional Neural Networks. Precipitation prediction, such as short-term rainfall prediction, is a very important problem in the field of meteorological service. In practice, most of recent studies focus on leveraging radar data or satellite images to make predictions. However, there is another scenario where a set of weather features are collected by various sensors at multiple observation sites. The observations of a site are sometimes incomplete but provide important clues for weather prediction at nearby sites, which are not fully exploited in existing work yet. To solve this problem, we propose a multi-task convolutional neural network model to automatically extract features from the time series measured at observation sites and leverage the correlation between the multiple sites for weather prediction via multi-tasking. To the best of our knowledge, this is the first attempt to use multi-task learning and deep learning techniques to predict short-term rainfall amount based on multi-site features. Specifically, we formulate the learning task as an end-to-end multi-site neural network model which allows to leverage the learned knowledge from one site to other correlated sites, and model the correlations between different sites. Extensive experiments show that the learned site correlations are insightful and the proposed model significantly outperforms a broad set of baseline models including the European Centre for Medium-range Weather Forecasts system (ECMWF).

Question: What is the first large-scale Canadian peacekeeping mission?


Procedural Modeling of Interconnected Structures. The complexity and detail of geometric scenes that are used in today's computer animated films and interactive games have reached a level where the manual creation by traditional 3D modeling tools has become infeasible. This is why procedural modeling concepts have been developed which generate highly complex 3D models by automatically executing a set of formal construction rules. Well-known examples are variants of L-systems which describe the bottom-up growth process of plants and shape grammars which define architectural buildings by decomposing blocks in a top-down fashion. However, none of these approaches allows for the easy generation of interconnected structures such as bridges or roller coasters where a functional interaction between rigid and deformable parts of an object is needed. Our approach mainly relies on the top-down decomposition principle of shape grammars to create an arbitrarily complex but well structured layout. During this process, potential attaching points are collected in containers which represent the set of candidates to establish interconnections. Our grammar then uses either abstract connection patterns or geometric queries to determine elements in those containers that are to be connected. The two different types of connections that our system supports are rigid object chains and deformable beams. The former type is constructed by inverse kinematics, the latter by spline interpolation. We demonstrate the descriptive power of our grammar by example models of bridges, roller coasters, and wall-mounted catenaries.

Question: Procedural Modeling of Interconnected Structures


Evoked-potential correlates of stimulus uncertainty.. The average evoked-potential waveforms to sound and light stimuli recorded from scalp in awake human subjects show differences as a function of the subject's degree of uncertainty with respect to the sensory modality of the stimulus to be presented. Differences are also found in the evoked potential as a function of whether or not the sensorymodality of the stimulus was anticipated correctly. The major waveform alteration is in the amplitude of a positive-going component which reaches peak amplitude at about 300 milliseconds.

Question: What is the average evoked-potential waveform to sound and light stimuli recorded from scalp in awake human subjects?


Light at the End of the Tunnel: High-Speed LiDAR-Based Train Localization in Challenging Underground Environments. In this paper, we present an infrastructure-free mapping and localization framework for rail vehicles using only a lidar sensor. Our method is designed to handle the pathological environment found in modern underground tunnels: narrow, parallel, and relatively smooth concrete walls with very little infrastructure to break up the empty spaces in the tunnel. By using an RQE-based, point-cloud alignment approach, we are able to implement a sliding-window algorithm, used for both mapping and localization. We demonstrate the proposed method with datasets gathered on a subway train travelling at high speeds (up to 70 km/h) in an underground tunnel for a total of 20 km across 6 runs. Our method is capable of mapping the tunnel with less than 0.6% error over the total length of the generated map. It is capable of continuously localizing, relative to the generated map, to within 10 cm in stations and at crossovers, and 1.8 m in pathological sections of tunnel. This method improves rail-based localization in a tunnel, which can be used to increase capacity on existing railways and for automated trains.

Question: How does this method improve rail-based localization in a tunnel?


Sieve: Cryptographically Enforced Access Control for User Data in Untrusted Clouds. Modern web services rob users of low-level control over cloud storage; a user's single logical data set is scattered across multiple storage silos whose access controls are set by the web services, not users. The result is that users lack the ultimate authority to determine how their data is shared with other web services. In this thesis, we introduce Sieve, a new architecture for selectively exposing user data to third party web services in a provably secure manner. Sieve starts with a user-centric storage model: each user uploads encrypted data to a single cloud store, and by default, only the user knows the decryption keys. Given this storage model, Sieve defines an infrastructure to support rich, legacy web applications. Using attribute-based encryption, Sieve allows users to define intuitive, understandable access policies that are cryptographically enforceable. Using key homomorphism, Sieve can re-encrypt user data on storage providers in situ, revoking decryption keys from web services without revealing new ones to the storage provider. Using secret sharing and two-factor authentication, Sieve protects against the loss of user devices like smartphones and laptops. The result is that users can enjoy rich, legacy web applications, while benefitting from cryptographically strong controls over what data the services can access. Thesis Supervisor: Nickolai Zeldovich Title: Associate Professor Thesis Supervisor: James Mickens Title: Associate Professor

Question: What is the difference between a cryptographically enforced access control and a cryptographically enforced access control for user data in untrusted clouds?


Space syntax and spatial cognition: or why the axial line?. Space syntax research has found that spatial configuration alone explains a substantial proportion of the variance between aggregate human movement rates in different locations in both urban and building interior space. Although it seems possible to explain how people move on the basis of these analyses, the question of why they move this way has always seemed problematic because the analysis contains no explicit representations of either motivations or individual cognition. One possible explanation for the method's predictive power is that some aspects of cognition are implicit in space syntax analysis. This article reviews the contribution made by syntax research to the understanding of environmental cognition. It proposes that cognitive space, defined as that space which supports our understanding of configurations more extensive than our current visual field, is not a metric space, but topological. A hypothetical process for deriving a nonmetric space from the metric visibility graph involving exploratory movement is developed. The resulting space is shown to closely resemble the axial graph. One of the favored assumptions of those dealing with human action is that the "prime mover" is individual motivation or goal-directed behavior. The evidence from space syntax research, however, is that one can get quite a long way in predicting movement behavior without invoking goals and motivations and in fact, without explicitly assuming anything about individuals or their cognitive capacity. This empirical success is sometimes taken to suggest that individuals and their motivations are not considered important or of interest in space syntax research. Here I argue the reverse. I propose that clues to the nature of individual motivation and cognition may be implicit in space 30 ENVIRONMENT AND BEHAVIOR, Vol. 35 No. 1, January 2003 30-65 DOI: 10.1177/0013916502238864 © 2003 Sage Publications © 2003 SAGE Publicatio

Question: What is the axial graph?


Thermal management consumption and its effect on remaining range estimation of electric vehicles. This paper investigates the significance of thermal management energy consumption on the range of electric vehicles. It is necessary to combine consumption models of powertrain components such as the electric machine, power electronics and high voltage battery that take into account trip-based information containing a predicted speed profile and elevation data with a thermal management system that has the ability to incorporate predicted thermal losses of the powertrain. The thermal management system refers to the sum of all components within the refrigeration cycle of the vehicle as well as the associated control strategy. Based on the results of a real world drive study in an electric vehicle, it is demonstrated, that the range uncertainties introduced by the consumption of the thermal management are significant and should be considered when developing predictive range estimation algorithms. These uncertainties are further investigated by an energy flow based thermal system model. In order to minimize range uncertainties, a system architecture for the prediction of thermal management consumption is proposed.

Question: What is the significance of thermal management consumption on the range of electric vehicles?


Head Nod Detection from a Full 3D Model. As a non-verbal communication mean, head gestures play an important role in face-to-face conversation and recognizing them is therefore of high value for social behavior analysis or Human Robotic Interactions (HRI) modelling. Among the various gestures, head nod is the most common one and can convey agreement or emphasis. In this paper, we propose a novel nod detection approach based on a full 3D face centered rotation model. Compared to previous approaches, we make two contributions. Firstly, the head rotation dynamic is computed within the head coordinate instead of the camera coordinate, leading to pose invariant gesture dynamics. Secondly, besides the rotation parameters, a feature related to the head rotation axis is proposed so that nod-like false positives due to body movements could be eliminated. The experiments on two-party and four-party conversations demonstrate the validity of the approach.

Question: How to detect head nod?


Path Planning of Unmanned Aerial Vehicles using B-Splines and Particle Swarm Optimization. Military operations are turning to more complex and advanced automation technologies for minimum risk and maximum efficiency.A critical piece to this strategy is unmanned aerial vehicles. Unmanned aerial vehicles require the intelligence to safely maneuver along a path to an intended target and avoiding obstacles such as other aircrafts or enemy threats. This paper presents a unique three-dimensional path planning problem formulation and solution approach using particle swarm optimization. The problem formulation was designed with three objectives: 1) minimize risk owing to enemy threats, 2) minimize fuel consumption incurred by deviating from the original path, and 3) fly over defined reconnaissance targets. The initial design point is defined as the original path of the unmanned aerial vehicles. Using particle swarm optimization, alternate paths are generated using B-spline curves, optimized based on the three defined objectives. The resulting paths can be optimized with a preference toward maximum safety, minimum fuel consumption, or target reconnaissance. This method has been implemented in a virtual environment where the generated alternate paths can be visualized interactively to better facilitate the decision-making process. The problem formulation and solution implementation is described along with the results from several simulated scenarios demonstrating the effectiveness of the method.

Question: How to plan a path for an unmanned aerial vehicle?


Lessons Learned from AlphaGo. The game of Go is known to be one of the most complicated board games. Competing in Go against a professional human player has been a long-standing challenge for AI. In this paper we shed light on the AlphaGo program that could beat a Go world champion, which was previously considered non-achievable for the state of the art AI.

Question: What is the state of the art in AI?


Using phrases and document metadata to improve topic modeling of clinical reports. Probabilistic topic models provide an unsupervised method for analyzing unstructured text, which have the potential to be integrated into clinical automatic summarization systems. Clinical documents are accompanied by metadata in a patient's medical history and frequently contains multiword concepts that can be valuable for accurately interpreting the included text. While existing methods have attempted to address these problems individually, we present a unified model for free-text clinical documents that integrates contextual patient- and document-level data, and discovers multi-word concepts. In the proposed model, phrases are represented by chained n-grams and a Dirichlet hyper-parameter is weighted by both document-level and patient-level context. This method and three other Latent Dirichlet allocation models were fit to a large collection of clinical reports. Examples of resulting topics demonstrate the results of the new model and the quality of the representations are evaluated using empirical log likelihood. The proposed model was able to create informative prior probabilities based on patient and document information, and captured phrases that represented various clinical concepts. The representation using the proposed model had a significantly higher empirical log likelihood than the compared methods. Integrating document metadata and capturing phrases in clinical text greatly improves the topic representation of clinical documents. The resulting clinically informative topics may effectively serve as the basis for an automatic summarization system for clinical reports.

Question: How can we improve topic modeling of clinical reports?


Soft-Switched CCM Boost Converters With High Voltage Gain for High-Power Applications. This paper proposes a new soft-switched continuous-conduction-mode (CCM) boost converter suitable for high-power applications such as power factor correction, hybrid electric vehicles, and fuel cell power conversion systems. The proposed converter achieves zero-voltage-switched (ZVS) turn-on of active switches in CCM and zero-current-switched turn-off of diodes leading to negligible reverse-recovery loss. The components' voltage ratings and energy volumes of passive components of the proposed converter are greatly reduced compared to the conventional zero-voltage-transition converter. Voltage conversion ratio is almost doubled compared to the conventional boost converter. Extension of the proposed concept to realize multiphase dc-dc converters is discussed. Experimental results from a 1.5-kW prototype are provided to validate the proposed concept.

Question: What is the voltage conversion ratio of the proposed converter?


Rigid-Body Dynamics with Friction and Impact. Rigid-body dynamics with unilateral contact is a good approximation for a wide range of everyday phenomena, from the operation of car brakes to walking to rock slides. It is also of vital importance for simulating robots, virtual reality, and realistic animation. However, correctly modeling rigid-body dynamics with friction is difficult due to a number of discontinuities in the behavior of rigid bodies and the discontinuities inherent in the Coulomb friction law. This is particularly crucial for handling situations with large coefficients of friction, which can result in paradoxical results known at least since Painlevé [C. R. Acad. Sci. Paris, 121 (1895), pp. 112–115]. This single example has been a counterexample and cause of controversy ever since, and only recently have there been rigorous mathematical results that show the existence of solutions to his example. The new mathematical developments in rigid-body dynamics have come from several sources: "sweeping processes" and the measure differential inclusions of Moreau in the 1970s and 1980s, the variational inequality approaches of Duvaut and J.-L. Lions in the 1970s, and the use of complementarity problems to formulate frictional contact problems by Lötstedt in the early 1980s. However, it wasn't until much more recently that these tools were finally able to produce rigorous results about rigid-body dynamics with Coulomb friction and impulses.

Question: What is a rigid-body dynamics with friction?


Lest We Remember: Cold Boot Attacks on Encryption Keys. Contrary to widespread assumption, dynamic RAM (DRAM), the main memory in most modern computers, retains its contents for several seconds after power is lost, even at room temperature and even if removed from a motherboard. Although DRAM becomes less reliable when it is not refreshed, it is not immediately erased, and its contents persist sufficiently for malicious (or forensic) acquisition of usable full-system memory images. We show that this phenomenon limits the ability of an operating system to protect cryptographic key material from an attacker with physical access to a machine. It poses a particular threat to laptop users who rely on disk encryption: we demonstrate that it could be used to compromise several popular disk encryption products without the need for any special devices or materials. We experimentally characterize the extent and predictability of memory retention and report that remanence times can be increased dramatically with simple cooling techniques. We offer new algorithms for finding cryptographic keys in memory images and for correcting errors caused by bit decay. Though we discuss several strategies for mitigating these risks, we know of no simple remedy that would eliminate them.

Question: How can I protect my keys from cold boot attacks?


Crime Data Analysis and Prediction of Perpetrator Identity Using Machine Learning Approach. Crime is one of the most predominant and alarming aspects in our society and its prevention is a vital task. Crime analysis is a systematic way of detecting and investigating patterns and trends in crime. The aim of this model is to increase the efficiency of crime investigation systems. This model detects crime patterns from inferences collected from the crime scene and predicts the description of the perpetrator who is likely suspected to commit the crime. This work has two major aspects: Crime Analysis and Prediction of perpetrator identity. The Crime Analysis phase identifies the number of unsolved crimes, and analyses the influence of various factors like year, month, and weapon on the unsolved crimes. The prediction phase estimates the description of the perpetrators like, their age, sex and relationship with the victim. These predictions are done based on the evidences collected from the crime scene. The system predicts the description of the perpetrator using algorithms like, Multilinear Regression, K- Neighbors Classifier and Neural Networks. It was trained and tested using San Francisco Homicide dataset (1981–2014) and implemented using python.

Question: What is the aim of this model?


A Model of a Localized Cross-Border E-Commerce. By the explosive growth of B2B e-commerce transactions in international supply chains and the rapid increase of business documents in Iran's cross-border trading, effective management of trade processes over borders is vital in B2B e-commerce systems. Structure of the localized model in this paper is based on three major layers of a B2B e-commerce infrastructure, which are messaging layer, business process layer and content layer. For each of these layers proper standards and solutions are chosen due to Iran's e-commerce requirements. As it is needed to move smoothly towards electronic documents in Iran, UNedocs standard is suggested to support the contents of both paper and electronic documents. The verification of the suggested model is done by presenting a four phase scenario through case study method. The localized model in this paper tries to make a strategic view of business documents exchange in trade processes, and getting closer to the key target of regional single windows establishment in global trade e-supply chains.

Question: What is the structure of a localized cross-border e-commerce?


SybilInfer: Detecting Sybil Nodes using Social Networks. SybilInfer is an algorithm for labelling nodes in a social network as honest users or Sybils controlled by an adversary. At the heart of SybilInfer lies a probabilistic model of honest social networks, and an inference engine that returns potential regions of dishonest nodes. The Bayesian inference approach to Sybil detection comes with the advantage label has an assigned probability, indicating its degree of certainty. We prove through analytical results as well as experiments on simulated and real-world network topologies that, given standard constraints on the adversary, SybilInfer is secure, in that it successfully distinguishes between honest and dishonest nodes and is not susceptible to manipulation by the adversary. Furthermore, our results show that SybilInfer outperforms state of the art algorithms, both in being more widely applicable, as well as providing vastly more accurate results.

Question: What is a Sybil node?


Combining Language and Vision with a Multimodal Skip-gram Model. We extend the SKIP-GRAM model of Mikolov et al. (2013a) by taking visual information into account. Like SKIP-GRAM, our multimodal models (MMSKIP-GRAM) build vector-based word representations by learning to predict linguistic contexts in text corpora. However, for a restricted set of words, the models are also exposed to visual representations of the objects they denote (extracted from natural images), and must predict linguistic and visual features jointly. The MMSKIP-GRAM models achieve good performance on a variety of semantic benchmarks. Moreover, since they propagate visual information to all words, we use them to improve image labeling and retrieval in the zero-shot setup, where the test concepts are never seen during model training. Finally, the MMSKIP-GRAM models discover intriguing visual properties of abstract words, paving the way to realistic implementations of embodied theories of meaning.

Question: What is the zero-shot setup?


A game-theoretic method of fair resource allocation for cloud computing services. As cloud-based services become more numerous and dynamic, resource provisioning becomes more and more challenging. A QoS constrained resource allocation problem is considered in this paper, in which service demanders intend to solve sophisticated parallel computing problem by requesting the usage of resources across a cloud-based network, and a cost of each computational service depends on the amount of computation. Game theory is used to solve the problem of resource allocation. A practical approximated solution with the following two steps is proposed. First, each participant solves its optimal problem independently, without consideration of the multiplexing of resource assignments. A Binary Integer Programming method is proposed to solve the independent optimization. Second, an evolutionary mechanism is designed, which changes multiplexed strategies of the initial optimal solutions of different participants with minimizing their efficiency losses. The algorithms in the evolutionary mechanism take both optimization and fairness into account. It is demonstrated that Nash equilibrium always exists if the resource allocation game has feasible solutions.

Question: What is a game-theoretic method of fair resource allocation for cloud computing services?


Enhancing Neural Disfluency Detection with Hand-Crafted Features. In this paper, we apply a bidirectional Long Short-Term Memory with a Conditional Random Field to the task of disfluency detection. Long-range dependencies is one of the core problems for disfluency detection. Our model handles long-range dependencies by both using the Long Short-Term Memory and hand-crafted discrete features. Experiments show that utilizing the hand-crafted discrete features significantly improves the model's performance by achieving the state-of-the-art score of 87.1% on the Switchboard corpus.

Question: How to detect disfluency in a sentence?


Multi-type attributes driven multi-camera person re-identification. One of the major challenges in person Re-Identification (ReID) is the inconsistent visual appearance of a person. Current works on visual feature and distance metric learning have achieved significant achievements, but still suffer from the limited robustness to pose variations, viewpoint changes, etc., and the high computational complexity. This makes person ReID among multiple cameras still challenging. This work is motivated to learn mid-level human attributes which are robust to visual appearance variations and could be used as efficient features for person matching. We propose a weakly supervised multi-type attribute learning framework which considers the contextual cues among attributes and progressively boosts the accuracy of attributes only using a limited number of labeled data. Specifically, this framework involves a threestage training. A deep Convolutional Neural Network (dCNN) is first trained on an independent dataset labeled with attributes. Then it is fine-tuned on another dataset only labeled with person IDs using our defined triplet loss. Finally, the updated dCNN predicts attribute labels for the target dataset, which is combined with the independent dataset for the final round of fine-tuning. The predicted attributes, namely deep attributes exhibit promising generalization ability across different datasets. By directly using the deep attributes with simple Cosine distance, we have obtained competitive accuracy on four person ReID datasets. Experiments also show that a simple distance metric learning modular further boosts our method, making it outperform many recent ∗Corresponding author Email address: slzhang.jdl@pku.edu.cn (Shiliang Zhang) Preprint submitted to Pattern Recognition September 11, 2017

Question: What is the difference between a person and a person?


New circularly polarized slot radiator for substrate integrated waveguide (SIW) planar array. A circularly polarized slot radiator in millimetre band is presented. Low reflection and good polarization performance is obtained in comparison with state of art slot elements. A high gain array prototype has been implemented in substrate integrated waveguides (SIW) technology, in order to check the new element performance. A cosecant amplitude coverage in the elevation plane, as well as a 6° tilted monopulse pattern in the azimuth plane are obtained. A gain peak value of 28.6 dBi and 79% of efficiency at 36.7 GHz have been obtained in the specified operation band (36.7 – 37 GHz) for a manufactured antenna prototype. Likewise, the top measured axial ratio is 1.95 dB at 36.85 GHz.

Question: What is a slot radiator?


Maxillary length at 11-14 weeks of gestation in fetuses with trisomy 21.. OBJECTIVE To determine the value of measuring maxillary length at 11-14 weeks of gestation in screening for trisomy 21.   METHODS In 970 fetuses ultrasound examination was carried out for measurement of crown-rump length (CRL), nuchal translucency and maxillary length, and to determine if the nasal bone was present or absent, immediately before chorionic villus sampling for karyotyping at 11-14 weeks of gestation. In 60 cases the maxillary length was measured twice by the same operator to calculate the intraobserver variation in measurements.   RESULTS The median gestation was 12 (range, 11-14) weeks. The maxilla was successfully examined in all cases. The mean difference between paired measurements of maxillary length was -0.012 mm and the 95% limits of agreement were -0.42 (95% CI, -0.47 to -0.37) to 0.40 (95% CI, 0.35 to 0.44) mm. The fetal karyotype was normal in 839 pregnancies and abnormal in 131, including 88 cases of trisomy 21. In the chromosomally normal group the maxillary length increased significantly with CRL from a mean of 4.8 mm at a CRL of 45 mm to 8.3 mm at a CRL of 84 mm. In the trisomy 21 fetuses the maxillary length was significantly shorter than normal by 0.7 mm and in the trisomy 21 fetuses with absent nasal bone the maxilla was shorter than in those with present nasal bone by 0.5 mm. In fetuses with other chromosomal defects there were no significant differences from normal in the maxillary length.   CONCLUSION At 11-14 weeks of gestation, maxillary length in trisomy 21 fetuses is significantly shorter than in normal fetuses.

Question: What is the maxillary length in fetuses with trisomy 21?


Mechanical thrombectomy in acute ischemic stroke: Consensus statement by ESO-Karolinska Stroke Update 2014/2015, supported by ESO, ESMINT, ESNR and EAN.. The original version of this consensus statement on mechanical thrombectomy was approved at the European Stroke Organisation (ESO)-Karolinska Stroke Update conference in Stockholm, 16-18 November 2014. The statement has later, during 2015, been updated with new clinical trials data in accordance with a decision made at the conference. Revisions have been made at a face-to-face meeting during the ESO Winter School in Berne in February, through email exchanges and the final version has then been approved by each society. The recommendations are identical to the original version with evidence level upgraded by 20 February 2015 and confirmed by 15 May 2015. The purpose of the ESO-Karolinska Stroke Update meetings is to provide updates on recent stroke therapy research and to discuss how the results may be implemented into clinical routine. Selected topics are discussed at consensus sessions, for which a consensus statement is prepared and discussed by the participants at the meeting. The statements are advisory to the ESO guidelines committee. This consensus statement includes recommendations on mechanical thrombectomy after acute stroke. The statement is supported by ESO, European Society of Minimally Invasive Neurological Therapy (ESMINT), European Society of Neuroradiology (ESNR), and European Academy of Neurology (EAN).

Question: What is the purpose of the ESO-Karolinska Stroke Update meetings?


Real-time 3D Walking Pattern Generation for a Biped Robot with Telescopic Legs. Meltran V, a new biped robot with telescopic legs, is introduced. For 3D walking control of the robot we analyze the dynamics of a three-dimensional inverted pendulum in which motion is constrained to move along an arbitrarily de ned plane. From this analysis we obtain simple linear dynamics, the ThreeDimensional Linear Inverted Pendulum Mode (3DLIPM). Using a real-time control method based on 3DLIPM, the Meltran V robot successfully demonstrated 3D dynamic walking without the use of any prepared trajectories.

Question: How do you control a biped robot?


Dwarf germplasm: the key to giant Cannabis hempseed and cannabinoid crops. After a century of banishment, both euphoric ("marijuana") and non-euphoric ("industrial hemp") classes of Cannabis sativa are attracting billions of dollars of investment as new legitimate crops. Most domesticated C. sativa is very tall, a phenotype that is desirable only for hemp fibre obtained from the stems. However, because the principal demands today are for chemicals from the inflorescence and oilseeds from the infructescence, an architecture maximizing reproductive tissues while minimizing stems is appropriate. Such a design was the basis of the greatest short-term increases in crop productivity in the history of agriculture: the creation of short-stature ("semi-dwarf"), high-harvest-index grain cultivars, especially by ideotype breeding, as demonstrated during the "Green Revolution." This paradigm has considerable promise for C. sativa. The most critical dwarfing character for breeding such productivity into C. sativa is contraction of internodes. This reduces stem tissues (essentially a waste product except for fibre hemp) and results in compact inflorescences (which, on an area basis, maximize cannabinoid chemicals) and infructescences (which maximize oilseed production), as well as contributing to ease of harvesting and efficiency of production on an area basis. Four sources of germplasm useful for breeding semi-dwarf biotypes deserve special attention: (1) Naturally short northern Eurasian wild plants (often photoperiodically day-neutral, unlike like most biotypes) adapted to the stress of very short seasons by maximizing relative development of reproductive tissues. (2) Short, high-harvest-index, oilseed plants selected in northern regions of Eurasia. (3) "Indica type" marijuana, an ancient semi-dwarf cultigen tracing to the Afghanistan-Pakistan area. (4) Semi-dwarf strains of marijuana bred illegally in recent decades to avoid detection when grown clandestinely indoors for the

Question: What is the difference between a semi-dwarf and a dwarf?


Liability and Computer Security: Nine Principles. The conventional wisdom is that security priorities should be set by risk analysis. However, reality is subtly different: many computer security systems are at least as much about shedding liability as about minimising risk. Banks use computer security mechanisms to transfer liability to their customers; companies use them to transfer liability to their insurers, or (via the public prosecutor) to the taxpayer; and they are also used to shift the blame to other departments ("we did everything that GCHQ/the internal auditors told us to"). We derive nine principles which might help designers avoid the most common pitfalls.

Question: What are the nine principles of liability and computer security?


Single-Fed Low-Profile High-Gain Circularly Polarized Slotted Cavity Antenna Using a High-Order Mode. In this letter, a single-fed low-profile high-gain circularly polarized slotted cavity antenna using a high-order cavity mode, i.e., TE440 mode, is proposed. The proposed antenna has a simple structure that consists of a radiating linearly polarized slotted cavity antenna and a linear-to-circular polarization converter. An antenna prototype operating at WLAN 5.8-GHz band fabricated with low-cost standard printed circuit board (PCB) process is exemplified to validate the proposed concept. Measured results compared to their simulated counterparts are presented, and a good agreement between simulation and measurement is obtained.

Question: What is a single-fed low-profile high-gain circularly polarized slotted cavity antenna using a high-order cavity mode?


Modeling of a Vertical Axis Wind Turbine with Permanent Magnet Synchronous Generator for Nigeria. The performances of a vertical axis wind turbine (VAWT) with permanent magnet synchronous generator (PMSG) under low and unsteady wind speed is investigated and Modelled in this paper. The ever increasing demand for electrical energy has led to the continuous search for the most readily available means of providing electricity. Wind turbine is an alternative source of electricity. To implement a wind turbine, careful and extensive investigation is required since different environments or geographical locations call for different wind speed. Nigeria is a typical example of locations with low and unsteady wind speed. The wind turbine system consists of three main parts; the wind speed, the turbine and the generator. A three phase PMSG is chosen in this work due to its higher efficiency and less need for maintenance when compared to other types of generators. It does not require rotor windings which imply a reduction of weight and cost. These elements and entire idea of this work have been modelled and simulated using MATLAB/ SIMULINK. Results obtained showed good system performance. The methodology used in this work, if fully implemented, will not only reduce the cost of design but will also reduce the cost of maintenance, making it economical and affordable.

Question: What is the wind turbine system?


An Improved DC-Link Voltage Fast Control Scheme for a PWM Rectifier-Inverter System. This paper presents an improved dc-link voltage fast control strategy based on energy balance for a pulse width modulation (PWM) rectifier-inverter system to reduce the fluctuation of dc-link voltage. A conclusion is drawn that the energy in dc-link capacitor cannot be kept constant in dynamic process when the system operates in rectifier mode, even in ideal case. Meanwhile, the minimum dc-link voltage deviation is analyzed. Accordingly, a predictive dc-link voltage control scheme based on energy balance is proposed, while the grid current is regulated by deadbeat predictive control. A prediction method for output power is also introduced. Furthermore, a small-signal model with the control delay is adopted to analyze the stability and robustness of the control strategy. The simulation and experimental results demonstrate both good dynamic and steady-state performances of the rectifier-inverter system with the proposed control scheme.

Question: What is the minimum dc-link voltage deviation?


Bidirectional Dynamics for Protein Secondary Structure Prediction. Connectionist models for learning in sequential domains are typically dynamical systems that use hidden states to store contextual information. In principle, these models can adapt to variable time lags and perform complex sequential mappings. In spite of several successful applications (mostly based on hidden Markov models), the general class of sequence learning problems is still far from being satisfactorily solved. In particular, learning sequential translations is generally a hard task and current models seem to exhibit a number of limitations. One of these limitations, at least for some application domains, is the causality assumption. A dynamical system is said to be causal if the output at (discrete) time t does not depend on future inputs. Causality is easy to justify in dynamics that attempt to model the behavior of many physical systems. Clearly, in these cases the response at time t cannot depend on stimulae that the system has not yet received as input. As it turns out, non-causal dynamics over infinite time horizons cannot be realized by any physical or computational device. For certain categories of finite sequences, however, information from both the past and the future can be very useful for analysis and predictions at time t. This is the case, for example, of DNA and protein sequences where the structure and function of a region in the sequence may strongly depend on events located both upstream and downstream of the region, sometimes at considerable distances. Another good example is provided by the off-line translation of a language into another one. Even in the so-called "simultaneous" translation, it is well known that interpreters are constantly forced to introduce small delays in order to acquire "future" information within a sentence to resolve semantic ambiguities and preserve syntactic correctness. Non-causal dynamics are sometimes used in other disciplines (for example, Ka

Question: What is the difference between a dynamical system and a non-causal system?


Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos. There has recently been a great deal of interest in the development of algorithms that objectively measure the integrity of video signals. Since video signals are being delivered to human end users in an increasingly wide array of applications and products, it is important that automatic methods of video quality assessment (VQA) be available that can assist in controlling the quality of video being delivered to this critical audience. Naturally, the quality of motion representation in videos plays an important role in the perception of video quality, yet existing VQA algorithms make little direct use of motion information, thus limiting their effectiveness. We seek to ameliorate this by developing a general, spatio-spectrally localized multiscale framework for evaluating dynamic video fidelity that integrates both spatial and temporal (and spatio-temporal) aspects of distortion assessment. Video quality is evaluated not only in space and time, but also in space-time, by evaluating motion quality along computed motion trajectories. Using this framework, we develop a full reference VQA algorithm for which we coin the term the MOtion-based Video Integrity Evaluation index, or MOVIE index. It is found that the MOVIE index delivers VQA scores that correlate quite closely with human subjective judgment, using the Video Quality Expert Group (VQEG) FRTV Phase 1 database as a test bed. Indeed, the MOVIE index is found to be quite competitive with, and even outperform, algorithms developed and submitted to the VQEG FRTV Phase 1 study, as well as more recent VQA algorithms tested on this database.

Question: What is the canadian military?


Through-Wall Opportunistic Sensing System Utilizing a Low-Cost Flat-Panel Array. A UWB through-wall imaging system is proposed based on a planar low profile aperture array operating from 0.9 GHz to 2.3 GHz. The goal is to provide a lightweight, fixed array to serve as an alternative to synthetic aperture radars (SAR) that require continuous array movement while collecting data. The proposed system consists of 12 dual-linear printed elements arranged within a triangular lattice, each forming a "flower" shape and backed by a ground plane. The array delivers half-space radiation with wideband performance, necessary for imaging applications. UWB capability is realized by suppressing grating lobes via the introduction of virtual phase centers interwoven within the actual array feeds. The proposed system is demonstrated for through-wall imaging via a non-coherent process. Distinctively, several coherent images are forged from various fixed aperture locations (referred to as "snapshot" locations) and appropriately combined to create a composite scene image. In addition to providing a unique wideband imaging capability (as an alternative to SAR), the system is portable and inexpensive for collecting/storing scattering data. The array design and data collection system is described, and several through-wall images are presented to demonstrate functionality.

Question: What is a through-wall imaging system?


A task description language for robot control. Robot systems must achieve high level goals while remaining reactive to contingencies and new opportunities. This typically requires robot systems to coordinate concurrent activities, monitor the environment, and deal with exceptions. We have developed a new language to support such task-level control. The language, TDL, is an extension of C++ that provides syntactic support for task decomposition, synchronization, execution monitoring, and exception handling. A compiler transforms TDL into pure C++ code that utilizes a platform-independent task management library. This paper introduces TDL, describes the task treerepresentation that underlies the language, and presents some aspects of its implementation and use in an autonomous mobile robot.

Question: What is a task description language?


Force-mode control of rotary series elastic actuators in a lower extremity exoskeleton using model-inverse time delay control (MiTDC). For physical human-robot interaction (pHRI), it has been an important issue to control the output force of actuators. The aim of this study was to apply a new control strategy, named model-inverse time delay control (MiTDC), to series elastic actuators (SEA) in a lower extremity exoskeleton, even in the presence of uncertainties from pHRI. The law for time delay control (TDC) is derived and implementation issues are discussed, including the design of the state observer and the selection of the nominal value of the control distribution coefficient. Additionally, a new concept, a new reference position using the inverse of model dynamics, is introduced to realize satisfactory tracking performance without delay. Experimental results showed that the suggested controller achieved satisfactory performance without accurate information on system parameters, requiring only a nominal value of the control distribution coefficient.

Question: How to control a series elastic actuator?


A Novel Single-Stage High-Power-Factor AC-to-DC LED Driving Circuit With Leakage Inductance Energy Recycling. This paper proposes a novel single-stage ac-to-dc light-emitting-diode (LED) driver circuit. A buck-boost power factor (PF) corrector is integrated with a flyback converter. A recycling path is built to recover the inductive leakage energy. In this way, the presented circuit can provide not only high PF and low total harmonic distortion but also high conversion efficiency and low switching voltage spikes. Above 0.95 PF and 90% efficiency are obtained from an 8-W LED lamp driver prototype.

Question: What is the purpose of this circuit?


Sensors for 3D Imaging: Metric Evaluation and Calibration of a CCD/CMOS Time-of-Flight Camera. 3D imaging with Time-of-Flight (ToF) cameras is a promising recent technique which allows 3D point clouds to be acquired at video frame rates. However, the distance measurements of these devices are often affected by some systematic errors which decrease the quality of the acquired data. In order to evaluate these errors, some experimental tests on a CCD/CMOS ToF camera sensor, the SwissRanger (SR)-4000 camera, were performed and reported in this paper. In particular, two main aspects are treated: the calibration of the distance measurements of the SR-4000 camera, which deals with evaluation of the camera warm up time period, the distance measurement error evaluation and a study of the influence on distance measurements of the camera orientation with respect to the observed object; the second aspect concerns the photogrammetric calibration of the amplitude images delivered by the camera using a purpose-built multi-resolution field made of high contrast targets.

Question: How to calibrate a ToF camera?


The Anatomic Basis of Midfacial Aging. Facial aging is a multifactorial, three-dimensional (3D) process with anatomic, biochemical, and genetic correlates. Many exogenous and endogenous factors can signifi cantly impact the perceived age of an individual. Solar exposure [ 1– 3 ] , cigarette smoking [ 1, 2, 4, 5 ] , medications [ 1 ] , alcohol use [ 1 ] , body mass index [ 2 ] , and endocrinologic status [ 1, 6, 7 ] have all been implicated as factors that accelerate cutaneous and subcutaneous aging. These factors act in concert to create a variegated spectrum of facial morphologic aging changes, and thus, Mme. Chanel was partially correct in her statement from the last century. Most of the aging changes that occur in the midface, however, occur predictably in the majority of individuals. Stigmata of midfacial aging typically appear by the middle of the fourth decade. Degenerative changes occur in nearly every anatomic component of the midface and include cranial bone remodeling, tissue descent secondary to gravity, fat atrophy, and deterioration in the condition and appearance of the skin. The lower eyelids and adjacent tissues are often the initial areas of patient concern. This chapter reviews the morphologic changes that occur in the aging midface and discusses the pathogenesis of midfacial aging based upon its anatomic components. An integrated theory of facial aging will be presented. A. E. Wulc , MD, FACS ( ) Associate Clinical Professor of Ophthalmology , University of Pennsylvania

Question: What is the pathogenesis of midfacial aging?


On-road driver eye movement tracking using head-mounted devices. It is now evident from anecdotal evidence and preliminary research that distractions can hinder the task of operating a vehicle, and consequently reduce driver safety. However with increasing wireless connectivity and the portability of office devices, the vehicle of the future is visualized as an extension of the static work place - i.e. an office-on-the-move, with a phone, a fax machine and a computer all within the reach of the vehicle operator. For this research a Head mounted Eye-tracking Device (HED), is used for tracking the eye movements of a driver navigating a test route in an automobible while completing various driving tasks. Issues arising from data collection of eye movements during the completion of various driving tasks as well as the analysis of this data are discussed. Methods for collecting video and scan-path data, as well as difficulties and limitations are also reported.

Question: What is the difference between a head-mounted eye-tracking device and a head-mounted camera?


Algorithmic Di erentiation in Python with AlgoPy. Many programs for scienti c computing in Python are based on NumPy and therefore make heavy use of numerical linear algebra (NLA) functions, vectorized operations, slicing and broadcasting. AlgoPy provides the means to compute derivatives of arbitrary order and Taylor approximations of such programs. The approach is based on a combination of univariate Taylor polynomial arithmetic and matrix calculus in the (combined) forward/reverse mode of Algorithmic Di erentiation (AD). In contrast to existing AD tools, vectorized operations and NLA functions are not considered to be a sequence of scalar elementary functions. Instead, dedicated algorithms for the matrix product, matrix inverse and the Cholesky, QR, and symmetric eigenvalue decomposition are implemented in AlgoPy. We discuss the reasons for this alternative approach and explain the underlying idea. Examples illustrate how AlgoPy can be used from a user's point of view.

Question: How to compute the derivative of a function?


Modeling and Tracking the Driving Environment With a Particle-Based Occupancy Grid. Modeling and tracking the driving environment is a complex problem due to the heterogeneous nature of the real world. In many situations, modeling the obstacles and the driving surfaces can be achieved by the use of geometrical objects, and tracking becomes the problem of estimating the parameters of these objects. In the more complex cases, the scene can be modeled and tracked as an occupancy grid. This paper presents a novel occupancy grid tracking solution based on particles for tracking the dynamic driving environment. The particles will have a dual nature-they will denote hypotheses, as in the particle filtering algorithms, but they will also be the building blocks of our modeled world. The particles have position and speed, and they can migrate in the grid from cell to cell, depending on their motion model and motion parameters, but they will be also created and destroyed using a weighting-resampling mechanism that is specific to particle filtering algorithms. The tracking algorithm will be centered on particles, instead of cells. An obstacle grid derived from processing a stereovision-generated elevation map is used as measurement information, and the measurement model takes into account the uncertainties of the stereo reconstruction. The resulting system is a flexible real-time tracking solution for dynamic unstructured driving environments.

Question: How to track the driving environment?


A 0.1-1.8 GHz, 100 W GaN HEMT Power Amplifier Module. We have demonstrated a 0.1-1.8 GHz, 100 W GaN HEMT power amplifier module with 11 dB gain, 94-142 W CW output power and 40.6-74.0 % drain efficiency over the band. The power amplifier module uses broadband low loss coaxial baluns to combine four 30 W broadband lossy matched GaN HEMT PAs on a 2 x 2 inch compact PCB. The individual PAs are fully matched to 50 Ohms and obtains 23.6-30.9 W with 44.5-63.7 % drain efficiency over the band. The packaged amplifiers contain a GaN on SiC device operating at 50 V drain voltage with a GaAs integrated passive matching circuitry. These amplifiers are targeted for use in multi-band multi-standard communication systems and for instrumentation applications.

Question: What is a GaN HEMT?


Prevalence of chronic pain with neuropathic characteristics in the general population. We conducted a large nationwide postal survey to estimate the prevalence of chronic pain with or without neuropathic characteristics in the French general population. A questionnaire aimed at identifying chronic pain (defined as daily pain for at least 3 months), evaluating its intensity, duration and body locations, was sent to a representative sample of 30,155 subjects. The DN4 questionnaire was used to identify neuropathic characteristics. Of the questionnaires, 24,497 (81.2%) were returned and 23,712 (96.8%) could be assessed. Seven thousand five hundred and twenty-two respondents reported chronic pain (prevalence=31.7%; [95%CI: 31.1-32.3]) and 4709 said the pain intensity was moderate to severe (prevalence=19.9%; [95%CI: 19.5-20.4]). Neuropathic characteristics were reported by 1631 respondents with chronic pain (prevalence=6.9%; [95%CI: 6.6-7.2]), which was moderate to severe in 1209 (prevalence=5.1% [95%CI: 4.8-5.4]). A higher prevalence of chronic pain with neuropathic characteristics was associated with middle age (50-64 years), manual professions and those living in rural areas. It was more frequently located in the lower limbs and its intensity and duration were higher in comparison with chronic pain without neuropathic characteristics. This large national population-based study indicates that a significant proportion of chronic pain patients report neuropathic characteristics. We identified distinctive socio-demographic profile and clinical features indicating that chronic pain with neuropathic characteristics is a specific health problem.

Question: What is the prevalence of chronic pain with neuropathic characteristics in the french general population?


Automatic knowledge extraction from OCR documents using hierarchical document analysis. Industries can improve their business efficiency by analyzing and extracting relevant knowledge from large numbers of documents. Knowledge extraction manually from large volume of documents is labor intensive, unscalable and challenging. Consequently there have been a number of attempts to develop intelligent systems to automatically extract relevant knowledge from OCR documents. Moreover, the automatic system can improve the capability of search engine by providing application-specific domain knowledge. However, extracting the efficient information from OCR documents is challenging due to highly unstructured format [1, 11, 18, 26]. In this paper, we propose an efficient framework for a knowledge extraction system that takes keywords based queries and automatically extracts their most relevant knowledge from OCR documents by using text mining techniques. The framework can provide relevance ranking of knowledge to a given query. We tested the proposed framework on corpus of documents at GE Power where document consists of more than hundred pages in PDF.

Question: What is the most efficient framework for a knowledge extraction system?


Brain Activity during Episodic Retrieval of Autobiographical and Laboratory Events: An fMRI Study using a Novel Photo Paradigm. Functional neuroimaging studies of episodic memory retrieval generally measure brain activity while participants remember items encountered in the laboratory (controlled laboratory condition) or events from their own life (open autobiographical condition). Differences in activation between these conditions may reflect differences in retrieval processes, memory remoteness, emotional content, retrieval success, self-referential processing, visual/spatial memory, and recollection. To clarify the nature of these differences, a functional MRI study was conducted using a novel photo paradigm, which allows greater control over the autobiographical condition, including a measure of retrieval accuracy. Undergraduate students took photos in specified campus locations (controlled autobiographical condition), viewed in the laboratory similar photos taken by other participants (controlled laboratory condition), and were then scanned while recognizing the two kinds of photos. Both conditions activated a common episodic memory network that included medial temporal and prefrontal regions. Compared with the controlled laboratory condition, the controlled autobiographical condition elicited greater activity in regions associated with self-referential processing (medial prefrontal cortex), visual/ spatial memory (visual and parahippocampal regions), and recollection (hippocampus). The photo paradigm provides a way of investigating the functional neuroanatomy of real-life episodic memory under rigorous experimental control.

Question: What is the difference between the controlled autobiographical condition and the controlled laboratory condition?


Relationship between color and emotion : a study of college students. Ninety-eight college students were asked to indicate their emotional responses to five principle hues (i.e., red, yellow, green, blue, purple), five intermediate hues (i.e., yellow-red, greenyellow, blue-green, purple-blue, and red-purple), and three achromatic colors (white, gray, and black) and the reasons for their choices. The color stimuli were referenced from the Munsell Color System. The results revealed that the principle hues comprised the highest number of positive emotional responses, followed by the intermediate hues and the achromatic colors. The color green evoked mainly positive emotions such as relaxation and comfort because it reminded most of the respondents of nature. The color green-yellow had the lowest number of positive responses because it was associated with vomit and elicited the feelings of sickness and disgust. For the achromatic colors, white attained a large number of positive responses, followed by the colors black and gray. The reasons for the color-emotion associations are discussed and future research areas are suggested.

Question: What is the relationship between color and emotion?


Rules for an Ontology-based Approach to Adaptation. Adaptation addresses the complexity and overload caused by an increasing amount of available resources by the identification and presentation of relevant resources. Drawbacks of existing approaches to this end include the limited degree of customization, difficulties in the acquisition of model information and the lack of control and transparency of the system's adaptive behavior. This paper proposes an approach which addresses these drawbacks to perform meaningful and effective adaptation by the application of semantic Web technologies. This comprises the use of an ontology and adaptation rules for knowledge representation and inference engines for reasoning. The focus of this paper lies in the presentation of adaptation rules

Question: What is an ontology?


A smooth-walled spline-profile horn as an alternative to the corrugated horn for wide band millimeter-wave applications. At millimeter-wave frequencies, corrugated horns can be difficult and expensive to manufacture. As an alternative we present here the results of a theoretical and measurement study of a smooth-walled spline-profile horn for specific application in the 80-120 GHz band. While about 50% longer than its corrugated counterpart, the smooth-walled horn is shown to give improved performance across the band as well as being much easier to manufacture.

Question: What is a smooth-walled spline-profile horn?


Introduction of the automated assessment of homework assignments in a university-level programming course. Modern teaching paradigms promote active student participation, encouraging teachers to adapt the teaching process to involve more practical work. In the introductory programming course at the Faculty of Computer and Information Science, University of Ljubljana, Slovenia, homework assignments contribute approximately one half to the total grade, requiring a significant investment of time and human resources in the assessment process. This problem was alleviated by the automated assessment of homework assignments. In this paper, we introduce an automated assessment system for programming assignments that includes dynamic testing of student programs, plagiarism detection, and a proper presentation of the results. We share our experience and compare the introduced system with the manual assessment approach used before.

Question: What is the difference between the manual and automated assessment of homework assignments?


ULA-OP: an advanced open platform for ultrasound research. The experimental test of novel ultrasound (US) investigation methods can be made difficult by the lack of flexibility of commercial US machines. In the best options, these only provide beamformed radiofrequency or demodulated echo-signals for acquisition by an external PC. More flexibility is achieved in high-level research platforms, but these are typically characterized by high cost and large size. This paper presents a powerful but portable US system, specifically developed for research purposes. The system design has been based on high-level commercial integrated circuits to obtain the maximum flexibility and wide data access with minimum of electronics. Preliminary applications involving nonstandard imaging transmit/receive strategies and simultaneous B-mode and multigate spectral Doppler mode are discussed.

Question: What is a high-level commercial integrated circuit?


Selective Sampling with Redundant Views. Selective sampling, a form of active learning, reduces the cost of labeling training data by asking only for the labels of the most informative unlabeled examples. We introduce a novel approach to selective sampling which we call co-testing. Cotesting can be applied to problems with redundant views (i.e., problems with multiple disjoint sets of attributes that can be used for learning). We analyze the most general algorithm in the co-testing family, naive co-testing, which can be used with virtually any type of learner. Naive co-testing simply selects at random an example on which the existing views disagree. We applied our algorithm to a variety of domains, including three real-world problems: wrapper induction, Web page classi cation, and discourse trees parsing. The empirical results show that besides reducing the number of labeled examples, naive co-testing may also boost the classi cation accuracy.

Question: What is the most general algorithm in the co-testing family?


Modeling and control of a three-port DC-DC converter for PV-battery systems. This paper presents modeling and decoupled control design for an isolated three-port DC-DC converter (TPC), which interfaces a photovoltaic (PV) panel, a rechargeable battery, and an isolated load. A small signal model is derived from the state-space equations of the TPC. Based on the model, two decoupled controllers are designed to control the power flows of the two sources and the load voltage independently. Experimental studies are performed to verify that the two controllers are capable of regulating the power flows of the PV panel and the battery while maintaining a constant output voltage for the TPC.

Question: How to model a three-port DC-DC converter?


Multi-vehicle detection with identity awareness using cascade Adaboost and Adaptive Kalman filter for driver assistant system. Vision-based vehicle detection is an important issue for advanced driver assistance systems. In this paper, we presented an improved multi-vehicle detection and tracking method using cascade Adaboost and Adaptive Kalman filter(AKF) with target identity awareness. A cascade Adaboost classifier using Haar-like features was built for vehicle detection, followed by a more comprehensive verification process which could refine the vehicle hypothesis in terms of both location and dimension. In vehicle tracking, each vehicle was tracked with independent identity by an Adaptive Kalman filter in collaboration with a data association approach. The AKF adaptively adjusted the measurement and process noise covariance through on-line stochastic modelling to compensate the dynamics changes. The data association correctly assigned different detections with tracks using global nearest neighbour(GNN) algorithm while considering the local validation. During tracking, a temporal context based track management was proposed to decide whether to initiate, maintain or terminate the tracks of different objects, thus suppressing the sparse false alarms and compensating the temporary detection failures. Finally, the proposed method was tested on various challenging real roads, and the experimental results showed that the vehicle detection performance was greatly improved with higher accuracy and robustness.

Question: How to detect vehicles?


Lane detection using spline model. In this paper, a Catmull±Rom spline-based lane model which describes the perspective e€ect of parallel lines has been proposed for generic lane boundary. Since Catmull±Rom spline can form arbitrary shapes by di€erent sets of control points, it can describe a wider range of lane structures compared with other lane models, i.e. straight and parabolic models. Moreover, the lane detection problem has been formulated here as the problem of determining the set of control points of lane model. The proposed algorithm ®rst detects the vanishing point (line) by using a Hough-like technique and then solves the lane detection problem by suggesting a maximum likelihood approach. Also, we have employed a multi-resolution strategy for rapidly achieving an accurate solution. This coarse-to-®ne matching o€ers us an acceptable solution at an a€ordable computational cost, and thus speeds up the process of lane detection. As a result, the proposed method is robust to noise, shadows, and illumination variations in the captured road images, and is also applicable to both the marked and the unmarked roads. Ó 2000 Elsevier Science B.V. All rights reserved.

Question: What is the vanishing point?


Head Pose Estimation for Driver Assistance Systems: A Robust Algorithm and Experimental Evaluation. Recognizing driver awareness is an important prerequisite for the design of advanced automotive safety systems. Since visual attention is constrained to a driver's field of view, knowing where a driver is looking provides useful cues about his activity and awareness of the environment. This work presents an identity-and lighting-invariant system to estimate a driver's head pose. The system is fully autonomous and operates online in daytime and nighttime driving conditions, using a monocular video camera sensitive to visible and near-infrared light. We investigate the limitations of alternative systems when operated in a moving vehicle and compare our approach, which integrates Localized Gradient Orientation histograms with support vector machines for regression. We estimate the orientation of the driver's head in two degrees-of-freedom and evaluate the accuracy of our method in a vehicular testbed equipped with a cinematic motion capture system.

Question: How to estimate the head pose of a driver?


A Four-Switch Single-Stage Single-Phase Buck–Boost Inverter. This paper proposes a single-phase, single-stage buck–boost inverter for photovoltaic (PV) systems. The presented topology has one common terminal in input and output ports which eliminates common mode leakage current problem in the grid-connected PV applications. Although it uses four switches, its operation is bimodal and only two switches receive high-frequency pulse width modulation signals in each mode. Its principle of operation is described in detail with the help of equivalent circuits. Its dynamic model is presented, based on which a bimodal controller is designed. Experimental results, in stand-alone and grid-connected mode, obtained with a 300-W laboratory prototype are presented to validate its performance.

Question: What is a single-stage single-phase buck–boost inverter?