"The magic finger technique" a simplified approach for more symmetric results in alar base resection. Alar base surgery is one of the most important and challenging steps of aesthetic rhinoplasty. While an ideally shaped alar base is the goal in a desired nose, nearly all patients have asymmetric nostrils preoperatively. Ethnicity, trauma, cocaine use, or previous rhinoplasties are some factors affecting the width and shape of the nasal base. After the conclusion of all planned rhinoplasty sequences and closure of the mid-columellarincision, we mark the midline inferior to the columella at the nasolabial junction and use acaliper to measure an equal distance from the mid-columellar point to the alar creases on eachside, and mark the medial points of the alar creases. Next we draw on the natural creasesbilaterally extending to 3 o'clock on the right side and 9 o'clock on the left side as the limit ofthe lateral excisions to avoid scarring. We then gently depress the alae and alar-facial grooveswith the index finger and allow the formation of new creases superior to the original alarcreases in order to detect excess skin to remove. After marking, the resection was performed with a no. 15 blade. The excision was closed using 6-0 Prolene sutures. We aimed to describe a simple technique for making asymmetric resections in which theapplication of pressure by a finger reveals excess skin in both nostril sill and nostril flareindependently for each alar base. With these asymmetric excisions from the right and left alar bases, a more symmetric nostrils and nasal base can be achieved. Level of Evidence: Level IV, therapeutic study.

Question: How to make asymmetric resections in alar base surgery?


Estimation of Planar Curves, Surfaces, and Nonplanar Space Curves Defined by Implicit Equations with Applications to Edge and Range Image Segmentation. This paper addresses the problem of parametric representation and estimation of complex planar curves in 2-D, surfaces in 3-D and nonplanar space curves in 3-D. Curves and surfaces can be defined either parametrically or implicitly, and we use the latter representation. A planar curve is the set of zeros of a smooth function of two variables X-Y, a surface is the set of zeros of a smooth function of three variables X-~-Z, and a space curve is the intersection of two surfaces, which are the set of zeros of two linearly independent smooth functions of three variables X-!/-Z. For example, the surface of a complex object in 3D can be represented as a subset of a single implicit surface, with similar results for planar and space curves. We show how this unified representation can be used for object recognition, object position estimation, and segmentation of objects into meaningful subobjects, that is, the detection of "interest regions" that are more complex than high curvature regions and, hence, more useful as features for object recognition. Fitting implicit curves and surfaces to data would be ideally based on minimizing the mean square distance from the data points to the curve or surface. Since the distance from a point to a curve or surface cannot be computed exactly by direct methods, the approximate distance, which is a first-order approximation of the real distance, is introduced, generalizing and unifying previous results. We fit implicit curves and surfaces to data minimizing the approximate mean square distance, which is a nonlinear least squares problem. We show that in certain cases, this problem reduces to the generalized eigenvector fit, which is the minimization of the sum of squares of the values of the functions that define the curves or surfaces under a quadratic constraint function of the data. This

Question: What is the difference between a planar curve and a surface?


Accurate Unlexicalized Parsing. We demonstrate that an unlexicalized PCFG can parse much more accurately than previously shown, by making use of simple, linguistically motivated state splits, which break down false independence assumptions latent in a vanilla treebank grammar. Indeed, its performance of 86.36% (LP/LR F 1) is better than that of earlylexicalizedPCFG models, and surprisingly close to the current state-of-theart. This result has potential uses beyond establishing a strong lower bound on the maximum possible accuracy of unlexicalized models: an unlexicalized PCFG is much more compact, easier to replicate, and easier to interpret than more complex lexical models, and the parsing algorithms are simpler, more widely understood, of lower asymptotic complexity, and easier to optimize. In the early 1990s, as probabilistic methods swept NLP, parsing work revived the investigation of probabilistic context-free grammars ( PCFGs) (Booth and Thomson, 1973; Baker, 1979). However, early results on the utility ofPCFGs for parse disambiguation and language modeling were somewhat disappointing. A conviction arose that lexicalizedPCFGs (where head words annotate phrasal nodes) were the key tool for high performancePCFG parsing. This approach was congruent with the great success of word n-gram models in speech recognition, and drew strength from a broader interest in lexicalized grammars, as well as demonstrations that lexical dependencies were a key tool for resolving ambiguities such asPPattachments (Ford et al., 1982; Hindle and Rooth, 1993). In the following decade, great success in terms of parse disambiguation and even language modeling was achieved by various lexicalized PCFG models (Magerman, 1995; Charniak, 1997; Collins, 1999; Charniak, 2000; Charniak, 2001). However, several results have brought into question how large a role lexicalization plays in such parsers. Johnson (1998) showed that the performance of anunlexicalizedPCFGover the Penn treebank could be i

Question: What is the difference between a lexicalized and an unlexicalized PCFG?


Measuring epistemic curiosity and its diversive and specific components.. A questionnaire constructed to assess epistemic curiosity (EC) and perceptual curiosity (PC) curiosity was administered to 739 undergraduates (546 women, 193 men) ranging in age from 18 to 65. The study participants also responded to the trait anxiety, anger, depression, and curiosity scales of the State-Trait Personality Inventory (STPI; Spielberger et al., 1979) and selected subscales of the Sensation Seeking (SSS; Zuckerman, Kolin, Price, & Zoob, 1964) and Novelty Experiencing (NES; Pearson, 1970) scales. Factor analyses of the curiosity items with oblique rotation identified EC and PC factors with clear simple structure. Subsequent analyses of the EC items provided the basis for developing an EC scale, with Diversive and Specific Curiosity subscales. Moderately high correlations of the EC scale and subscales with other measures of curiosity provided strong evidence of convergent validity. Divergent validity was demonstrated by minimal correlations with trait anxiety and the sensation-seeking measures, and essentially zero correlations with the STPI trait anger and depression scales. Male participants had significantly higher scores on the EC scale and the NES External Cognition subscale (effect sizes of r =.16 and.21, respectively), indicating that they were more interested than female participants in solving problems and discovering how things work. Male participants also scored significantly higher than female participants on the SSS Thrill-and-Adventure and NES External Sensation subscales (r =.14 and.22, respectively), suggesting that they were more likely to engage in sensation-seeking activities.

Question: What is the difference between epistemic curiosity and perceptual curiosity?


Sexuality of male-to-female transsexuals.. Blanchard's (J Nerv Ment Dis 177:616-623, 1989) theory of autogynephilia suggests that male-to-female transsexuals can be categorized into different types based on their sexuality. Little previous research has compared the sexuality of male-to-female transsexuals to biological females. The present study examined 15 aspects of sexuality among a non-clinical sample of 234 transsexuals and 127 biological females, using either an online or a paper questionnaire. The results showed that, overall, transsexuals tended to place more importance on partner's physical attractiveness and reported higher scores on Blanchard's Core Autogynephilia Scale than biological females. In addition, transsexuals classified as autogynephilic scored significantly higher on Attraction to Feminine Males, Core Autogynephilia, Autogynephilic Interpersonal Fantasy, Fetishism, Preference for Younger Partners, Interest in Uncommitted Sex, Importance of Partner Physical Attractiveness, and Attraction to Transgender Fiction than other transsexuals and biological females. In accordance with Blanchard's theory, autogynephilia measures were positively correlated to Sexual Attraction to Females among transsexuals. In contrast to Blanchard's theory, however, those transsexuals classified as autogynephilic scored higher on average on Sexual Attraction to Males than those classified as non-autogynephilic, and no transsexuals classified as autogynephilic reported asexuality.

Question: What is the difference between autogynephilia and autogynephilic transsexuals?


Understanding self-reflection: how people reflect on personal data through visual data exploration. Rapid advancements in consumer technologies enable people to collect a wide range of personal data. With a proper means for people to ask questions and explore their data, longitudinal data feeds from multiple self-tracking tools pose great opportunities to foster deep self-reflection. However, most self-tracking tools lack support for self-reflection beyond providing simple feedback. Our overarching goal is to support self-trackers in reflecting on their data and gaining rich insights through visual data exploration. As a first step toward the goal, we built a web-based application called Visualized Self, and conducted an in-lab think-aloud study (N = 11) to examine how people reflect on their personal data and what types of insights they gain throughout the reflection. We discuss lessons learned from studying with Visualized Self, and suggest directions for designing visual data exploration tools for fostering self-reflection.

Question: How do people reflect on their personal data?


Analog inference circuits for deep learning. Deep Machine Learning (DML) algorithms have proven to be highly successful at challenging, high-dimensional learning problems, but their widespread deployment is limited by their heavy computational requirements and the associated power consumption. Analog computational circuits offer the potential for large improvements in power efficiency, but noise, mismatch, and other effects cause deviations from ideal computations. In this paper we describe circuits useful for DML algorithms, including a tunable-width bump circuit and a configurable distance calculator. We also discuss the impacts of computational errors on learning performance. Finally we will describe a complete deep learning engine implemented using current-mode analog circuits and compare its performance to digital equivalents.

Question: What is a deep learning engine?


RIC: Relaxed Inclusion Caches for mitigating LLC side-channel attacks. Recently, side-channel attacks on Last Level Caches (LLCs) were demonstrated. The attacks require the ability to evict critical data from the cache hierarchy, making future accesses visible. We propose Relaxed Inclusion Caches (RIC), a low-complexity cache design protecting against LLC side channel attacks. RIC relaxes inclusion when it is not needed, preventing the attacker from replacing the victim's data from the local core caches thus protecting critical data from leakage. RIC improves performance (by about 10%) and retains snoop filtering capabilities of inclusive cache hierarchies, while requiring only minimal changes to the cache.

Question: What is a relaxed inclusion cache?


Vector space model adaptation and pseudo relevance feedback for content-based image retrieval. Image retrieval is an important problem for researchers in computer vision and content-based image retrieval (CBIR) fields. Over the last decades, many image retrieval systems were based on image representation as a set of extracted low-level features such as color, texture and shape. Then, systems calculate similarity metrics between features in order to find similar images to a query image. The disadvantage of this approach is that images visually and semantically different may be similar in the low level feature space. So, it is necessary to develop tools to optimize retrieval of information. Integration of vector space models is one solution to improve the performance of image retrieval. In this paper, we present an efficient and effective retrieval framework which includes a vectorization technique combined with a pseudo relevance model. The idea is to transform any similarity matching model (between images) to a vector space model providing a score. A study on several methodologies to obtain the vectorization is presented. Some experiments have been undertaken on Wang, Oxford5k and Inria Holidays datasets to show the performance of our proposed framework.

Question: What is the vectorization technique?


Pulsewidth modulation for electronic power conversion. The efficient and fast control of electric power forms part of the key technologies of modern automated production. It is performed using electronic power converters. The converters transfer energy from a source to a controlled process in a quantized fashion, using semiconductor switches which are turned on and off at fast repetition rates. The algorithms which generate the switching functions – pulsewidth modulation techniques – are manifold. They range from simple averaging schemes to involved methods of real-time optimization. This paper gives an overview.

Question: What is pulsewidth modulation?


Learning to predict by the methods of temporal differences. This article introduces a class of incremental learning procedures specialized for prediction-that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit by means of the difference between predicted and actual outcomes, the new methods assign credit by means of the difference between temporally successive predictions. Although such temporal-difference methods have been used in Samuel's checker player, Holland's bucket brigade, and the author's Adaptive Heuristic Critic, they have remained poorly understood. Here we prove their convergence and optimality for special cases and relate them to supervised-learning methods. For most real-world prediction problems, temporal-difference methods require less memory and less peak computation than conventional methods and they produce more accurate predictions. We argue that most problems to which supervised learning is currently applied are really prediction problems of the sort to which temporal-difference methods can be applied to advantage.

Question: What is the difference between temporal-difference methods and supervised learning?


Pulse width modulation with resonant DC link converters. A technique for realizing pulse-width modulation capability in resonant DC link converters is presented. The technique eliminates the subharmonics present in the conventional discrete pulse modulated converters without requiring additional switching devices. Detailed design-oriented analyses enumerating the tradeoffs involved are presented. Control techniques for simultaneously accomplishing link control and output control are considered. Simulation results verifying the principle of operation are presented.<<ETX>>

Question: What is ETX?


Crowdlet: Optimal worker recruitment for self-organized mobile crowdsourcing. In this paper, we advocate Crowdlet, a novel self-organized mobile crowdsourcing paradigm, in which a mobile task requester can proactively exploit a massive crowd of encountered mobile workers at real-time for quick and high-quality results. We present a comprehensive system model of Crowdlet that defines task, worker arrival and worker ability models. Further, we introduce a service quality concept to indicate the expected service gain that a requester can enjoy when he recruits an encountered worker, by jointly taking into account worker ability, real-timeness and task reward. Based on the models, we formulate an online worker recruitment problem to maximize the expected sum of service quality. We derive an optimal worker recruitment policy through the dynamic programming principle, and show that it exhibits a nice threshold based structure. We conduct extensive performance evaluation based on real traces, and numerical results demonstrate that our policy can achieve superior performance and improve more than 30% performance gain over classic policies. Besides, our Android prototype shows that Crowdlet is cost-efficient, requiring less than 7 seconds and 6 Joule in terms of time and energy cost for policy computation in most cases.

Question: What is a self-organized mobile crowdsourcing paradigm?


A Review of Window Query Processing for Data Streams. In recent years, progress in hardware technology has resulted in the possibility of monitoring many events in real time. The volume of incoming data may be so large, that monitoring all individual data might be intractable. Revisiting any particular record can also be impossible in this environment. Therefore, many database schemes, such as aggregation, join, frequent pattern mining, and indexing, become more challenging in this context. This paper surveys the previous efforts to resolve these issues in processing data streams. The emphasis is on specifying and processing sliding window queries, which are supported in many stream processing engines. We also review the related work on stream query processing, including synopsis structures, plan sharing, operator scheduling, load shedding, and disorder control. Category: Ubiquitous computing

Question: What is the size of the canadian military?


Discriminative Word Alignment with Conditional Random Fields. In this paper we present a novel approach for inducing word alignments from sentence aligned data. We use a Conditional Random Field (CRF), a discriminative model, which is estimated on a small supervised training set. The CRF is conditioned on both the source and target texts, and thus allows for the use of arbitrary and overlapping features over these data. Moreover, the CRF has efficient training and decoding processes which both find globally optimal solutions. We apply this alignment model to both French-English and Romanian-English language pairs. We show how a large number of highly predictive features can be easily incorporated into the CRF, and demonstrate that even with only a few hundred word-aligned training sentences, our model improves over the current state-ofthe-art with alignment error rates of 5.29 and 25.8 for the two tasks respectively.

Question: How to induce word alignments from sentence aligned data?


Incivility, retention and new graduate nurses: an integrated review of the literature.. AIM To evaluate the influence of incivility on the new graduate nurse transition experience.   BACKGROUND Incivility in the work environment is a major source of dissatisfaction and new graduate nurses are especially vulnerable. Incivility contributes to the high levels of turnover associated within the first 2 years of new graduate nurse employment.   EVALUATION An integrated review of the literature was conducted using MEDLINE-EBSCOhost, PsycInfo and CINAHL databases. Relevant articles were reviewed for appropriateness related to inclusion/exclusion criteria and for quality using established criteria. Sixteen studies were included in the final analysis.   KEY ISSUES Themes that emerged included workplace incivility, nurse residency programmes, mentoring through preceptors and empowerment/work environment. Findings indicated that incivility in the workplace was a significant predictor of low job satisfaction in new graduate nurses transitioning into practice.   CONCLUSIONS While graduate nurse transition programmes are associated with improved satisfaction and retention, they appear to address incivility by acculturating new graduate nurses to the experience of incivility. There is little evidence that the culture of incivility has been addressed.   IMPLICATIONS FOR NURSING MANAGEMENT Nurse managers have the responsibility to be aware of the prevalence of incivility, assess for its occurrence, and implement strategies which eliminate workplace incivility and tolerance for uncivil behaviours.

Question: What is the prevalence of incivility in the workplace?


Design of charge pump circuit with consideration of gate-oxide reliability in low-voltage CMOS processes. A new charge pump circuit with consideration of gateoxide reliability is designed with two pumping branches in this paper. The charge transfer switches in the new proposed circuit can be completely turned on and turned off, so its pumping efficiency is higher than that of the traditional designs. Moreover, the maximum gate-source and gate-drain voltages of all devices in the proposed charge pump circuit do not exceed the normal operating power supply voltage (VDD). Two test chips have been implemented in a 0.35m 3.3-V CMOS process to verify the new proposed charge pump circuit. The measured output voltage of the new proposed four-stage charge pump circuit with each pumping capacitor of 2 pF to drive the capacitive output load is around 8.8 V under 3.3-V power supply (VDD = 3 3 V), which is limited by the junction breakdown voltage of the parasitic pn-junction in the given process. The new proposed circuit is suitable for applications in low-voltage CMOS processes because of its high pumping efficiency and no overstress across the gate oxide of devices.

Question: How to design a charge pump circuit with consideration of gate-oxide reliability in low-voltage CMOS processes?


Emerging ethical issues in neuroscience. There is growing public awareness of the ethical issues raised by progress in many areas of neuroscience. This commentary reviews the issues, which are triaged in terms of their novelty and their imminence, with an exploration of the relevant ethical principles in each case.

Question: What are the ethical issues raised by progress in neuroscience?


Cluster Ensembles for High Dimensional Clustering : An Empirical Study. This paper studies cluster ensembles for high dimensional data clustering. We examine three different approaches to constructing cluster ensembles. To address high dimensionality, we focus on ensemble construction methods that build on two popular dimension reduction techniques, random projection and principal component analysis (PCA). We present evidence showing that ensembles generated by random projection perform better than those by PCA and further that this can be attributed to the capability of random projection to produce diverse base clusterings. We also examine four different consensus functions for combining the clusterings of the ensemble. We compare their performance using two types of ensembles, each with different properties. In both cases, we show that a recent consensus function based on bipartite graph partitioning achieves the best performance.

Question: What is the difference between cluster ensembles and clusterings?


EvalVid - A Framework for Video Transmission and Quality Evaluation. With EvalVid we present a complete framework and tool-set for evaluation of the quality of video transmitted over a real or simu lated communication network. Besides measuring QoS parameters of the underlyin g network, like loss rates, delays, and jitter, we support also a subjective vide o quality evaluation of the received video based on the frame-by-frame PSNR calcula tion. The tool-set has a modular construction, making it possible to exchange b oth the network and the codec. We present here its application for MPEG-4 as exam ple. EvalVid is targeted for researchers who want to evaluate their network designs or setups in terms of user perceived video quality. The tool-set is publi cly available [11].

Question: What is the name of the tool-set?


Adaptive Mixtures of Local Experts. We present a new supervised learning procedure for systems composed of many separate networks, each of which learns to handle a subset of the complete set of training cases. The new procedure can be viewed either as a modular version of a multilayer supervised network, or as an associative version of competitive learning. It therefore provides a new link between these two apparently different approaches. We demonstrate that the learning procedure divides up a vowel discrimination task into appropriate subtasks, each of which can be solved by a very simple expert network.

Question: What is a vowel discrimination task?


A Hybrid Framework for News Clustering Based on the DBSCAN-Martingale and LDA. Nowadays there is an important need by journalists and media monitoring companies to cluster news in large amounts of web articles, in order to ensure fast access to their topics or events of interest. Our aim in this work is to identify groups of news articles that share a common topic or event, without a priori knowledge of the number of clusters. The estimation of the correct number of topics is a challenging issue, due to the existence of "noise", i.e. news articles which are irrelevant to all other topics. In this context, we introduce a novel density-based news clustering framework, in which the assignment of news articles to topics is done by the well-established Latent Dirichlet Allocation, but the estimation of the number of clusters is performed by our novel method, called "DBSCAN-Martingale", which allows for extracting noise from the dataset and progressively extracts clusters from an OPTICS reachability plot. We evaluate our framework and the DBSCAN-Martingale on the 20newsgroups-mini dataset and on 220 web news articles, which are references to specific Wikipedia pages. Among twenty methods for news clustering, without knowing the number of clusters k, the framework of DBSCAN-Martingale provides the correct number of clusters and the highest Normalized Mutual Information.

Question: How to cluster news articles?


UniqueID: Decentralized Proof-of-Unique-Human. Bitcoin and Ethereum are novel mechanisms for decentralizing the concept of money and computation. Extending decentralization to the human identity concept, we can think of using blockchain for creating a list of verified human identities with a one-person-one-ID property. UniqueID is a Decentralized Autonomous Organization(DAO) for maintaining human identities such that every physical human entity can have no more that one account. One part of this identity is simply the user's claim on one of his unique, permanent, and measurable characteristics -biometrics. Blockchain has proved its integrity as a platform for storing and performing computations on such claims. The biggest challenge here is to ensure that the user has submitted his own valid biometric data. Human verifiers can check if there is any inconsistency in other users' data, by peer-to-peer checks. For preventing bad behavior and centralization in the verification process, UniqueID benefits from novel governance mechanisms to choose verifiers and punish unjust ones. Also, there are incentives for honest verifiers and users by newly generated tokens. We show how the users' privacy can be preserved by using stateof-the-art cryptographic techniques, and so they can use their identity without any concerns for votings, financial and banking purposes, social media accounts, reputation systems etc.

Question: What is a decentralized autonomous organization?


Why do Larks Perform Better at School than Owls ? The Mediating Effect of Conscientiousness. Article History: Received 26.06.2016 Received in revised form 25.10.2016 Accepted 26.10.2016 Available online 25.11.2016 Circadian preference refers to individuals' preference for morning or evening activities. Its two dimensions (i.e., morningness and eveningness) are related to a number of academic outcomes. While morningness shows positive relations with academic achievement, eveningness shows negative relations. Further, morningness and eveningness show the same correlational pattern with conscientiousness (i.e., positive relations for morningness, negative relations for eveningness), which – in turn – predicts academic achievement. Therefore, the main aim of the present study was to investigate if the relation between circadian preference and academic achievement was mediated by conscientiousness. The sample comprised 422 students attending the 11th grade at a grammar school in Germany. Circadian preference (morningness and eveningness) and conscientiousness were assessed by self-report questionnaires; academic achievement was operationalized by school grades. Using confirmatory analyses and structural equation modelling, the results supported the assumption that conscientiousness mediates the relation between circadian preference and academic achievement. Implications for research into circadian preference and for education are discussed. © 2016 IOJES. All rights reserved

Question: Why do larks perform better at school than owls?


The Meteor metric for automatic evaluation of machine translation. The Meteor Automatic Metric for Machine Translation evaluation, originally developed and released in 2004, was designed with the explicit goal of producing sentence-level scores which correlate well with human judgments of translation quality. Several key design decisions were incorporated into Meteor in support of this goal. In contrast with IBM's Bleu, which uses only precision-based features, Meteor uses and emphasizes recall in addition to precision, a property that has been confirmed by several metrics as being critical for high correlation with human judgments. Meteor also addresses the problem of reference translation variability by utilizing flexible word matching, allowing for morphological variants and synonyms to be taken into account as legitimate correspondences. Furthermore, the feature ingredients within Meteor are parameterized, allowing for the tuning of the metric's free parameters in search of values that result in optimal correlation with human judgments. Optimal parameters can be separately tuned for different types of human judgments and for different languages. We discuss the initial design of the Meteor metric, subsequent improvements, and performance in several independent evaluations in recent years.

Question: What is the meteor metric for automatic evaluation of machine translation?


A whispered Mandarin corpus for speech technology applications. Whispered speech is a natural mode of speech in which voicing is absent – its acoustics differ significantly from normally spoken speech or so-called neutral speech, such that it is challenging to use only neutral speech to build speech processing and automatic recognition systems that can deal effectively with whisper. At the same time, humans can naturally produce and perceive whispered speech without explicit training. Tonal languages such as Mandarin present an interesting dilemma – tone is primarily encoded by pitch tracks which are absent during whispered speech, but humans can still tell tones apart. How humans manage to process whispered speech well without explicit training on it, whereas machine algorithms fail, is presently an unresolved question which could prove fruitful with study. This, however, is hindered by the lack of suitable, systematically collected corpora. We present iWhisper-Mandarin, a 25-hour parallel corpus of neutral and whispered Mandarin, designed to support research in linguistics and speech technology. We demonstrate and verify that earlier techniques applied to whispered speech from non-tonal languages also work with Mandarin, and present some preliminary studies on voice activity detection and whispered Mandarin speech recognition.

Question: What is the difference between neutral and whispered speech?


Backward Stochastic Differential Equations and Applications. A new type of stochastic differential equation, called the backward stochastic differentil equation (BSDE), where the value of the solution is prescribed at the final (rather than the initial) point of the time interval, but the solution is nevertheless required to be at each time a function of the past of the underlying Brownian motion, has been introduced recently, independently by Peng and the author in [16], and by Dufne and Epstein in [7]. This class of equations is a natural nonlinear extension of linear equations that appear both as the equation for the adjoint process in the maximum principle for optimal stochastic control (see [2]), and as a basic model for asset pricing in financial mathematics. It was soon after discovered (see [22], [17]) that those BSDEs provide probabilistic formulas for solutions of certain semilinear partial differential equations (PDEs), which generalize the well-known Feynmann-Kac formula for second order linear PDEs. This provides a new additional tool for analyzing solutions of certain PDEs, for instance reaction-diffusion equations.

Question: What is a semilinear PDE?


Ask the Mutants: Mutating Faulty Programs for Fault Localization. We present MUSE (MUtation-baSEd fault localization technique), a new fault localization technique based on mutation analysis. A key idea of MUSE is to identify a faulty statement by utilizing different characteristics of two groups of mutants-one that mutates a faulty statement and the other that mutates a correct statement. We also propose a new evaluation metric for fault localization techniques based on information theory, called Locality Information Loss (LIL): it can measure the aptitude of a localization technique for automated fault repair systems as well as human debuggers. The empirical evaluation using 14 faulty versions of the five real-world programs shows that MUSE localizes a fault after reviewing 7.4 statements on average, which is about 25 times more precise than the state-of-the-art SBFL technique Op2.

Question: What is the average number of statements that need to be reviewed to localize a fault?


New Canonical Representations by Augmenting OBDDs with Conjunctive Decomposition (Extended Abstract). We identify two families of canonical representations called ROBDD[∧î]C and ROBDD[∧T̂ ,i]T by augmenting ROBDD with two types of conjunctive decompositions. These representations cover the three existing languages ROBDD, ROBDD with as many implied literals as possible (ROBDDL∞), and AND/OR BDD. We introduce a new time efficiency criterion called rapidity which reflects the idea that exponential operations may be preferable if the language can be exponentially more succinct. Then we demonstrate that the expressivity, succinctness and operation rapidity do not decrease from ROBDD[∧T̂ ,i]T to ROBDD[∧î]C , and then to ROBDD[∧ î+1 ]C . We also demonstrate that ROBDD[∧î]C (i > 1) and ROBDD[∧T̂ ,i]T are not less tractable than ROBDD-L∞ and ROBDD, respectively. Finally, we develop a compiler for ROBDD[∧∞̂]C which significantly advances the compiling efficiency of canonical representations.

Question: What is the difference between ROBDD and ROBDDL?


k-Stacks: High-density valet parking for automated vehicles. Automated valet parking not only improves driving comfort, but can have a considerable impact on the urban landscape by reducing the required parking space. We present the first study of parking space optimization for automated valet parking with an in-depth theoretical analysis of the parking lot properties under various aspects, inluding the worst-case extraction time, total shunting distance, and the number of shunting operations (each per car). Most importantly, the proposed model bounds all these values. We verify the theoretical properties of our model in four simulated scenarios, one of which is based on real-world data from a downtown parking garage. We show that very good pick-up times of about 1 min are possible with very little overhead in terms of shunting distance and time, while providing a significantly improved parking density as compared to conventional parking lots.

Question: What is the worst-case extraction time?


Adaptive Neuro-Fuzzy Inference System based speed controller for brushless DC motor. In this paper, a novel controller for brushless DC (BLDC) motor has been presented. The proposed controller is based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and the rigorous analysis through simulation is performed using simulink tool box in MATLAB environment. The performance of the motor with proposed ANFIS controller is analyzed and compared with classical Proportional Integral (PI) controller, Fuzzy Tuned PID controller and Fuzzy Variable Structure controller. The dynamic characteristics of the brushless DC motor is observed and analyzed using the developed MATLAB/simulink model. Control system response parameters such as overshoot, undershoot, rise time, recovery time and steady state error are measured and compared for the above controllers. In order to validate the performance of the proposed controller under realistic working environment, simulation result has been obtained and analyzed for varying load and varying set speed conditions. & 2014 Elsevier B.V. All rights reserved.

Question: What is the difference between adaptive neuro-fuzzy inference system and fuzzy logic?


An inventory for measuring clinical anxiety: psychometric properties.. The development ofa 2 l-item self-report inventory for measuring the severity of anxiety in psychiatric populations is described. The initial item pool of 86 items was drawn from three preexisting scales: the Anxiety Checklist, the Physician's Desk Reference Checklist, and the Situational Anxiety Checklist. A series of analyses was used to reduce the item pool. The resulting Beck Anxiety Inventory (BAI) is a 21-item scale that showed high internal consistency (a = .92) and test-retest reliability over 1 week, r(81) = .75. The BAI discriminated anxious diagnostic groups (panic disorder, generalized anxiety disorder, etc.) from nonanxious diagnostic groups (major depression, dysthymic disorder, etc). In addition, the BAI was moderately correlated with the revised Hamilton Anxiety Rating Scale, r(150) = .51, and was only mildly correlated with the revised Hamilton Depression Rating Scale, r(l 53) = .25.

Question: How do you measure anxiety?


UAV-based crop and weed classification for smart farming. Unmanned aerial vehicles (UAVs) and other robots in smart farming applications offer the potential to monitor farm land on a per-plant basis, which in turn can reduce the amount of herbicides and pesticides that must be applied. A central information for the farmer as well as for autonomous agriculture robots is the knowledge about the type and distribution of the weeds in the field. In this regard, UAVs offer excellent survey capabilities at low cost. In this paper, we address the problem of detecting value crops such as sugar beets as well as typical weeds using a camera installed on a light-weight UAV. We propose a system that performs vegetation detection, plant-tailored feature extraction, and classification to obtain an estimate of the distribution of crops and weeds in the field. We implemented and evaluated our system using UAVs on two farms, one in Germany and one in Switzerland and demonstrate that our approach allows for analyzing the field and classifying individual plants.

Question: What is a UAV?


Performance Analysis and Application of Mobile Blockchain. Mobile security has become more and more important due to the boom of mobile commerce (m-commerce). However, the development of m-commerce is facing many challenges regarding data security problems. Recently, blockchain has been introduced as an effective security solution deployed successfully in many applications in practice, such as, Bitcoin, cloud computing, and Internet-of-Things. In this paper, we introduce a new m-commerce application using blockchain technology, namely, MobiChain, to secure transactions in the m-commerce. Especially, in the MobiChain application, the mining processes can be executed efficiently on mobile devices using our proposed Android core module. Through real experiments, we evaluate the performance of the proposed model and show that blockchain will be an efficient security solution for future m-commerce.

Question: What is the performance of the proposed model?


Photogeometric Scene Flow for High-Detail Dynamic 3D Reconstruction. Photometric stereo (PS) is an established technique for high-detail reconstruction of 3D geometry and appearance. To correct for surface integration errors, PS is often combined with multiview stereo (MVS). With dynamic objects, PS reconstruction also faces the problem of computing optical flow (OF) for image alignment under rapid changes in illumination. Current PS methods typically compute optical flow and MVS as independent stages, each one with its own limitations and errors introduced by early regularization. In contrast, scene flow methods estimate geometry and motion, but lack the fine detail from PS. This paper proposes photogeometric scene flow (PGSF) for high-quality dynamic 3D reconstruction. PGSF performs PS, OF, and MVS simultaneously. It is based on two key observations: (i) while image alignment improves PS, PS allows for surfaces to be relit to improve alignment, (ii) PS provides surface gradients that render the smoothness term in MVS unnecessary, leading to truly data-driven, continuous depth estimates. This synergy is demonstrated in the quality of the resulting RGB appearance, 3D geometry, and 3D motion.

Question: How does the canadian military work?


Genetic Algorithm-Based Motion Estimation Method using Orientations and EMGs for Robot Controls. Demand for interactive wearable devices is rapidly increasing with the development of smart devices. To accurately utilize wearable devices for remote robot controls, limited data should be analyzed and utilized efficiently. For example, the motions by a wearable device, called Myo device, can be estimated by measuring its orientation, and calculating a Bayesian probability based on these orientation data. Given that Myo device can measure various types of data, the accuracy of its motion estimation can be increased by utilizing these additional types of data. This paper proposes a motion estimation method based on weighted Bayesian probability and concurrently measured data, orientations and electromyograms (EMG). The most probable motion among estimated is treated as a final estimated motion. Thus, recognition accuracy can be improved when compared to the traditional methods that employ only a single type of data. In our experiments, seven subjects perform five predefined motions. When orientation is measured by the traditional methods, the sum of the motion estimation errors is 37.3%; likewise, when only EMG data are used, the error in motion estimation by the proposed method was also 37.3%. The proposed combined method has an error of 25%. Therefore, the proposed method reduces motion estimation errors by 12%.

Question: How accurate is the proposed method?


On Throughput Efficiency of Geographic Opportunistic Routing in Multihop Wireless Networks. Geographic opportunistic routing (GOR) is a new routing concept in multihop wireless networks. In stead of picking one node to forward a packet to, GOR forwards a packet to a set of candidate nodes and one node is selected dynamically as the actual forwarder based on the instantaneous wireless channel condition and node position and availability at the time of transmission. GOR takes advantages of the spatial diversity and broadcast nature of wireless communications and is an efficient mechanism to combat the unreliable links. The existing GOR schemes typically involve as many as available next-hop neighbors into the local opportunistic forwarding, and give the nodes closer to the destination higher relay priorities. In this paper, we focus on realizing GOR's potential in maximizing throughput. We start with an insightful analysis of various factors and their impact on the throughput of GOR, and propose a local metric named expected one-hop throughput (EOT) to balance the tradeoff between the benefit (i.e., packet advancement and transmission reliability) and the cost (i.e., medium time delay). We identify an upper bound of EOT and proof its concavity. Based on the EOT, we also propose a local candidate selection and prioritization algorithm. Simulation results validate our analysis and show that the metric EOT leads to both higher one-hop and path throughput than the corresponding pure GOR and geographic routing.

Question: What is the upper bound of expected one-hop throughput?


Mirrored Light Field Video Camera Adapter. This paper proposes the design of a custom mirror-based light field camera adapter that is cheap, simple in construction, and accessible. Mirrors of different shape and orientation reflect the scene into an upwards-facing camera to create an array of virtual cameras with overlapping field of view at specified depths, and deliver video frame rate light fields. We describe the design, construction, decoding and calibration processes of our mirrorbased light field camera adapter in preparation for an open-source release to benefit the robotic vision community. The latest report, computer-aided design models, diagrams and code can be obtained from the following repository: https://bitbucket.org/acrv/mirrorcam.

Question: What is a mirror-based light field camera?


Photo-Realistic Talking-Heads from Image Samples. This paper describes a system for creating a photo-realistic model of the human head that can be animated and lip-synched from phonetic transcripts of text. Combined with a state-of-the-art text-to-speech synthesizer (TTS), it generates video animations of talking heads that closely resemble real people. To obtain a naturally looking head, we choose a "data-driven" approach. We record a talking person and apply image recognition to extract automatically bitmaps of facial parts. These bitmaps are normalized and parameterized before being entered into a database. For synthesis, the TTS provides the audio track, as well as the phonetic transcript from which trajectories in the space of parameterized bitmaps are computed for all facial parts. Sampling these trajectories and retrieving the corresponding bitmaps from the database produces animated facial parts. These facial parts are then projected and blended onto an image of the whole head using its pose information. This talking head model can produce new, never recorded speech of the person who was originally recorded. Talking-head animations of this type are useful as a front-end for agents and avatars in multimedia applications such as virtual operators, virtual announcers, help desks, educational, and expert systems.

Question: How do you create a photo-realistic model of the human head?


No Free Lunch Theorems: Limitations and Perspectives of Metaheuristics. The No Free Lunch (NFL) theorems for search and optimization are reviewed and their implications for the design of metaheurist ics are discussed. The theorems state that any two search or optimization algorith ms are equivalent when their performance is averaged across all possible problems and even over subsets of problems fulfilling certain constraints. The NFL results show that if there is no assumption regarding the relation between visited and unse en arch points, efficient search and optimization is impossible. There is no wel l performing universal metaheuristic, but the heuristics must be tailored to the pr oblem class at hand using prior knowledge. In practice, it is not likely that the pr econditions of the NFL theorems are fulfilled for a problem class and thus differenc es between algorithms exist. Therefore, tailored algorithms can exploit structu re nderlying the optimization problem. Given full knowledge about the problem class, it i in theory possible to construct an optimal algorithm.

Question: What is a metaheuristic?


Adaptive content generation for games. From a simple entertainment activity to a learning tool, it is undeniable that video games are one of the most active and relevant areas in today's society. Since their inception, video games experienced an evolution unmatched by almost any other area and today's video games are complex pieces of software built by expensive multidisciplinary teams. Procedural Content Generation (PCG) offers an alternative to manual design of video game content, optimizing the process of development and thus reducing its cost. However, the content that is generated by traditional PCG techniques is usually very generic and it struggles to offer meaningful game experiences to a diverse player base. Recent years have brought some new PCG techniques that try to solve this problem by dynamically adjusting the generation of content to suit the needs of each individual player. The work presented in this paper focuses on the development of a new PCG methodology that aims to close the gap between game developers and their players. This is achieved by providing the developers with relevant real time player and playing context information and thus creating an easier way for developers to adjust their content generation process in run time to better suit the needs of each player. This was achieved by first designing a methodology that models the player, their context and the game. It was also developed a simple game to showcase the potential of such methodology. The end result was a game that adapts some of its content to different types of players and contexts.

Question: What is the difference between procedural content generation and adaptive content generation?


A DUAL-BAND CIRCULARLY POLARIZED STUB LOADED MICROSTRIP PATCH ANTENNA FOR GPS APPLICATIONS. A single-feed low-profile and easy to fabricate circularly polarized microstrip patch antenna has been developed for GPS applications. For dual frequency operation, four slots are etched near edges of the patch and a crossed slot etched in the center for generating circular polarization. In order to reducing the frequency ratio of two frequency bands of the antenna, the patch is loaded by four short circuit microstrip stubs. The paper reports several simulation results that confirm the desired characteristics of the antenna. Using stub loading, the frequency ratio of two bands of the antenna can be, even, reduced to 1.1.

Question: What is the frequency ratio of two bands of the antenna?


Early Detection and Quantification of Verticillium Wilt in Olive Using Hyperspectral and Thermal Imagery over Large Areas. Automatic methods for an early detection of plant diseases (i.e., visible symptoms at early stages of disease development) using remote sensing are critical for precision crop protection. Verticillium wilt (VW) of olive caused by Verticillium dahliae can be controlled only if detected at early stages of development. Linear discriminant analysis (LDA) and support vector machine (SVM) classification methods were applied to classify V. dahliae severity using remote sensing at large scale. High-resolution thermal and hyperspectral imagery were acquired with a manned platform which flew a 3000-ha commercial olive area. LDA reached an overall accuracy of 59.0% and a κ of 0.487 while SVM obtained a higher overall accuracy, 79.2% with a similar κ, 0.495. However, LDA better classified trees at initial and low severity levels, reaching accuracies of 71.4 and 75.0%, respectively, in comparison with the 14.3% and 40.6% obtained by SVM. Normalized canopy temperature, chlorophyll fluorescence, structural, xanthophyll, chlorophyll, carotenoid and disease indices were found to be the best indicators for early and advanced stage infection by VW. These results demonstrate that the methods developed in other studies at orchard scale are valid for flights in large areas comprising several olive orchards differing in soil and crop management characteristics.

Question: What is the difference between LDA and SVM?


The Mathematics of Statistical Machine Translation: Parameter Estimation. We describe a series o,f five statistical models o,f the translation process and give algorithms,for estimating the parameters o,f these models given a set o,f pairs o,f sentences that are translations o,f one another. We define a concept o,f word-by-word alignment between such pairs o,f sentences. For any given pair of such sentences each o,f our models assigns a probability to each of the possible word-by-word alignments. We give an algorithm for seeking the most probable o,f these alignments. Although the algorithm is suboptimal, the alignment thus obtained accounts well for the word-by-word relationships in the pair o,f sentences. We have a great deal o,f data in French and English from the proceedings o,f the Canadian Parliament. Accordingly, we have restricted our work to these two languages; but we,feel that because our algorithms have minimal linguistic content they would work well on other pairs o,f languages. We also ,feel, again because of the minimal linguistic content o,f our algorithms, that it is reasonable to argue that word-by-word alignments are inherent in any sufficiently large bilingual corpus.

Question: What is the most probable word-by-word alignment between the following two sentences?


Automatic Semantic Face Recognition Nawaf. Recent expansion in surveillance systems has motivated research in soft biometrics that enable the unconstrained recognition of human faces. Comparative soft biometrics show superior recognition performance than categorical soft biometrics and have been the focus of several studies which have highlighted their ability for recognition and retrieval in constrained and unconstrained environments. These studies, however, only addressed face recognition for retrieval using human generated attributes, posing a question about the feasibility of automatically generating comparative labels from facial images. In this paper, we propose an approach for the automatic comparative labelling of facial soft biometrics. Furthermore, we investigate unconstrained human face recognition using these comparative soft biometrics in a human labelled gallery (and vice versa). Using a subset from the LFW dataset, our experiments show the efficacy of the automatic generation of comparative facial labels, highlighting the potential extensibility of the approach to other face recognition scenarios and larger ranges of attributes.

Question: How to automatically generate comparative labels from facial images?


Double-Input Bidirectional DC/DC Converter Using Cell-Voltage Equalizer With Flyback Transformer. In this paper, a double-input bidirectional dc/dc converter that uses a rechargeable battery and an ultracapacitor (UC) is proposed. This converter is connected to a cell-voltage equalizer between the battery and UC. The cell-voltage equalizer enables cell-voltage equalization and energy transfer between the battery and UC. This converter has six operational modes. These modes are investigated by reduced-power-scale circuit experiment. In addition, the circuit operation under the combination of the six modes is verified using a PSIM simulator in a large power scale.

Question: How to design a double-input bidirectional dc/dc converter?


Toward Abstractive Summarization Using Semantic Representations. We present a novel abstractive summarization framework that draws on the recent development of a treebank for the Abstract Meaning Representation (AMR). In this framework, the source text is parsed to a set of AMR graphs, the graphs are transformed into a summary graph, and then text is generated from the summary graph. We focus on the graph-tograph transformation that reduces the source semantic graph into a summary graph, making use of an existing AMR parser and assuming the eventual availability of an AMR-totext generator. The framework is data-driven, trainable, and not specifically designed for a particular domain. Experiments on goldstandard AMR annotations and system parses show promising results. Code is available at: https://github.com/summarization

Question: What is a treebank for the Abstract Meaning Representation?


Real Time Identification and Prevention of Doziness using Face Recognition System. A large number of road accidents in the world occur due to driver's not taking proper breaks while driving long distances. This System is used to detect fatigue behaviour of the driver well and before any accident occurs. In this system driver's facial appearance is extracted via high speed camera which is placed in car in front of driver. This camera will capture the frames at every instant of time. Each of this frame will be processed using image processing algorithm. The mouth and eye region is separated from each of the frames and analyzed for concluding whether the driver is dozy or not. The system draws conclusion if the eyes are found closed for five consecutive frame. If doziness is detected, a signal alert is triggered. In case accidents occurs, message is sent to the desired number.

Question: How to detect doziness?


Eval all, trust a few, do wrong to none: Comparing sentence generation models. In this paper, we study recent neural generative models for text generation related to variational autoencoders. These models employ various techniques to match the posterior and prior distributions, which is important to ensure a high sample quality and a low reconstruction error. In our study, we follow a rigorous evaluation protocol using a large set of previously used and novel automatic metrics and human evaluation of both generated samples and reconstructions. We hope that it will become the new evaluation standard when comparing neural generative models for text.

Question: What is a variational autoencoder?


A wirelessly controlled implantable LED system for deep brain optogenetic stimulation. In recent years optogenetics has rapidly become an essential technique in neuroscience. Its temporal and spatial specificity, combined with efficacy in manipulating neuronal activity, are especially useful in studying the behavior of awake behaving animals. Conventional optogenetics, however, requires the use of lasers and optic fibers, which can place considerable restrictions on behavior. Here we combined a wirelessly controlled interface and small implantable light-emitting diode (LED) that allows flexible and precise placement of light source to illuminate any brain area. We tested this wireless LED system in vivo, in transgenic mice expressing channelrhodopsin-2 in striatonigral neurons expressing D1-like dopamine receptors. In all mice tested, we were able to elicit movements reliably. The frequency of twitches induced by high power stimulation is proportional to the frequency of stimulation. At lower power, contraversive turning was observed. Moreover, the implanted LED remains effective over 50 days after surgery, demonstrating the long-term stability of the light source. Our results show that the wireless LED system can be used to manipulate neural activity chronically in behaving mice without impeding natural movements.

Question: How does the LED system work?


Mapping interference resolution across task domains: A shared control process in left inferior frontal gyrus. Work in functional neuroimaging has mapped interference resolution processing onto left inferior frontal regions for both verbal working memory and a variety of semantic processing tasks. The proximity of the identified regions from these different tasks suggests the existence of a common, domain-general interference resolution mechanism. The current research specifically tests this idea in a within-subject design using fMRI to assess the activation associated with variable selection requirements in a semantic retrieval task (verb generation) and a verbal working memory task with a trial-specific proactive interference manipulation (recent-probes). High interference trials on both tasks were associated with activity in the midventrolateral region of the left inferior frontal gyrus, and the regions activated in each task strongly overlapped. The results indicate that an elemental component of executive control associated with interference resolution during retrieval from working memory and from semantic memory can be mapped to a common portion of the left inferior frontal gyrus.

Question: What is the left inferior frontal gyrus?


Prediction of Indian election using sentiment analysis on Hindi Twitter. Sentiment analysis is considered to be a category of machine learning and natural language processing. It is used to extricate, recognize, or portray opinions from different content structures, including news, audits and articles and categorizes them as positive, neutral and negative. It is difficult to predict election results from tweets in different Indian languages. We used Twitter Archiver tool to get tweets in Hindi language. We performed data (text) mining on 42,235 tweets collected over a period of a month that referenced five national political parties in India, during the campaigning period for general state elections in 2016. We made use of both supervised and unsupervised approaches. We utilized Dictionary Based, Naive Bayes and SVM algorithm to build our classifier and classified the test data as positive, negative and neutral. We identified the sentiment of Twitter users towards each of the considered Indian political parties. The results of the analysis for Naive Bayes was the BJP (Bhartiya Janta Party), for SVM it was the BJP (Bhartiya Janta Party) and for the Dictionary Approach it was the Indian Nathional Congress. SVM predicted a 78.4% chance that the BJP would win more elections in the general election due to the positive sentiment they received in tweets. As it turned out, BJP won 60 out of 126 constituencies in the 2016 general election, far more than any other political party as the next party (the Indian National Congress) only won 26 out of 126 constituencies.

Question: How to predict Indian election using sentiment analysis on Hindi Twitter?


Software development governance: A meta-management perspective. Software development governance is a nascent field of research. Establishing how it is framed early, can significantly affect the progress of contributions. This position paper considers the nature and role of governance in organizations and in the software development domain in particular. In contrast to the dominant functional and structural perspectives, an integrated view of governance is proposed as managing the management of a particular domain (that is, a meta-management perspective). Principles are developed and applied to software development governance and illustrated by a case study.

Question: What is a meta-management perspective?


Learning narrative structure from annotated folktales. Narrative structure is an ubiquitous and intriguing phenomenon. By virtue of structure we recognize the presence of Villainy or Revenge in a story, even if that word is not actually present in the text. Narrative structure is an anvil for forging new artificial intelligence and machine learning techniques, and is a window into abstraction and conceptual learning as well as into culture and its influence on cognition. I advance our understanding of narrative structure by describing Analogical Story Merging (ASM), a new machine learning algorithm that can extract culturally-relevant plot patterns from sets of folktales. I demonstrate that ASM can learn a substantive portion of Vladimir Propp's influential theory of the structure of folktale plots. The challenge was to take descriptions at one semantic level, namely, an event timeline as described in folktales, and abstract to the next higher level: structures such as Villainy, StuggleVictory, and Reward. ASM is based on Bayesian Model Merging, a technique for learning regular grammars. I demonstrate that, despite ASM's large search space, a carefully-tuned prior allows the algorithm to converge, and furthermore it reproduces Propp's categories with a chance-adjusted Rand index of 0.511 to 0.714. Three important categories are identified with F-measures above 0.8. The data are 15 Russian folktales, comprising 18,862 words, a subset of Propp's original tales. This subset was annotated for 18 aspects of meaning by 12 annotators using the Story Workbench, a general text-annotation tool I developed for this work. Each aspect was doubly-annotated and adjudicated at inter-annotator F-measures that cluster around 0.7 to 0.8. It is the largest, most deeply-annotated narrative corpus assembled to date. The work has significance far beyond folktales. First, it points the way toward important applications in many domains, including information retrieval, persuasion and negoti

Question: What is the largest, most deeply-annotated narrative corpus assembled to date?


Induction of fuzzy rules and membership functions from training examples. Most fuzzy controllers and fuzzy expert systems must predefine membership functions and fuzzy inference rules to map numeric data into linguistic variable terms and to make fuzzy reasoning work. In this paper, we propose a general learning method as a framework for automatically deriving membership functions and fuzzy if-then rules from a set of given training examples to rapidly build a prototype fuzzy expert system. Based on the membership functions and the fuzzy rules derived, a corresponding fuzzy inference procedure to process inputs is also developed.

Question: How to automatically derive fuzzy rules and membership functions from training examples?


Analysis of PWM nonlinearity in non-inverting buck-boost power converters. This paper focuses on the non-inverting buck- boost converter operated as either buck or boost converter. It is shown that a pulse-width modulation (PWM) discontinuity around buck/boost mode transition can result in substantial increases in output voltage ripple. The effect of the PWM nonlinearity is studied using periodic steady state analysis to quantify the worst case ripple voltage in terms of design parameters. Furthermore, a bifurcation analysis shows that the PWM discontinuity leads to quasi- periodic route to chaos, which results in erratic operation around the buck/boost mode transition. The increased ripple is a very significant problem when the converter is used as a power supply for an RF power amplifier, as is the case in WCDMA handsets. An approach is proposed to remove the discontinuity which results in reduced output voltage ripple, at the expense of reduced efficiency, as demonstrated on an experimental prototype.

Question: What is the effect of the PWM nonlinearity on the output voltage ripple?


You Are What You Tweet: Analyzing Twitter for Public Health. Analyzing user messages in social media can measure different population characteristics, including public health measures. For example, recent work has correlated Twitter messages with influenza rates in the United States; but this has largely been the extent of mining Twitter for public health. In this work, we consider a broader range of public health applications for Twitter. We apply the recently introduced Ailment Topic Aspect Model to over one and a half million health related tweets and discover mentions of over a dozen ailments, including allergies, obesity and insomnia. We introduce extensions to incorporate prior knowledge into this model and apply it to several tasks: tracking illnesses over times (syndromic surveillance), measuring behavioral risk factors, localizing illnesses by geographic region, and analyzing symptoms and medication usage. We show quantitative correlations with public health data and qualitative evaluations of model output. Our results suggest that Twitter has broad applicability for public health research.

Question: What is the canadian military?


A Meta-plugin for Bespoke Data Management in WordPress. WordPress is a powerful and extensible platform for webbased information publishing and management. While the WordPress core is targeted to the publication of chronologically ordered textual articles typical of blogs, users have developed plugins as well as themes to support the data management requirements of specific domains such as e-commerce or e-learning. However, the creation of such plugins requires development skills and effort. We present a meta-plugin that automatically generates bespoke plugins for data management based on user-defined ER models. We illustrate the approach using an example of creating a WordPress site for managing information about courses.

Question: How to create a bespoke plugin for data management?


Alobar holoprosencephaly, mobile proboscis and trisomy 13 in a fetus with maternal gestational diabetes mellitus: a 2D ultrasound diagnosis and review of the literature. Alobar holoprosencephaly is a rare and severe brain malformation due to early arrest in brain cleavage and rotation. We report a congenital anomalous fetus with alobar holoprosencephaly, prenatally diagnosed by two-dimensional (2D) sonography at the 40 weeks of gestation. The mother was affected by gestational diabetes mellitus and was obese (BMI > 30 kg/m2). 2D Ultrasound depicted the cerebral malformation, cyclopy, proboscis, cardiac defects (atrial septal defect, hypoplastic left heart, anomalous communication between right ventricle and aorta) and extremities defects. The newborn died just after delivery. External examination confirmed a mobile proboscis-like nose on the normal nose position. The fetus had both claw hands. The right and left foots showed to be equine. Autopsy confirmed the ultrasound diagnosis and chromosome analysis revealed trisomy 13 (47,XY,+13). Fetopathologic examination showed cyclopy, proboscis and alobar holoprosencephaly of the fetus, which was consistent with Patau syndrome. The teratogenic effect of diabetes on fetus has been described, but no previous clinical case of a congenital anomalous fetus with trisomy 13 and maternal gestational diabetes has been reported. This case report is the first to describe 2D ultrasound diagnosis of alobar holoprosencephaly and trisomy 13 with maternal gestational diabetes mellitus.

Question: What is the teratogenic effect of diabetes on fetus?


Network traffic anomaly detection using clustering techniques and performance comparison. Real-time network traffic anomaly detection is crucial for the confidentiality, integrity, and security of network information. Machine learning approaches are widely used to distinguish traffic flow outliers based on different anomalies with unique statistical characteristics. K-means clustering and Gaussian Mixture Model (GMM) are effective clustering techniques with many variations and easy to implement. Fuzzy clustering is more flexible than hard clustering and is practical for intrusion detection because of the natural treatment of data using fuzzy clustering. Fuzzy c-means clustering (FCM) is an iteratively optimal algorithm normally based on the least square method to partition data sets, which has high computational overhead. This paper proposes modifications to the objective function and the distance function that reduce the computational complexity of FCM while keeping clustering accurate. A combination of FCM clustering GMM, and feature transformation methods are proposed and a comparison of the related testing results and clustering methods is presented.

Question: What is the difference between clustering and classification?


Ring-LWE Ciphertext Compression and Error Correction: Tools for Lightweight Post-Quantum Cryptography. Some lattice-based public key cryptosystems allow one to transform ciphertext from one lattice or ring representation to another efficiently and without knowledge of public and private keys. In this work we explore this lattice transformation property from cryptographic engineering viewpoint. We apply ciphertext transformation to compress Ring-LWE ciphertexts and to enable efficient decryption on an ultra-lightweight implementation targets such as Internet of Things, Smart Cards, and RFID applications. Significantly, this can be done without modifying the original encryption procedure or its security parameters. Such flexibility is unique to lattice-based cryptography and may find additional, unique real-life applications.  Ciphertext compression can significantly increase the probability of decryption errors. We show that the frequency of such errors can be analyzed, measured and used to derive precise failure bounds for n-bit error correction. We introduce XECC, a fast multi-error correcting code that allows constant time implementation in software.  We use these tools to construct and explore TRUNC8, a concrete Ring-LWE encryption and authentication system. We analyze its implementation, security, and performance. We show that our lattice compression technique reduces ciphertext size by more than 40% at equivalent security level, while also enabling public key cryptography on previously unreachable ultra-lightweight platforms.  The experimental public key encryption and authentication system has been implemented on an 8-bit AVR target, where it easily outperforms elliptic curve and RSA-based proposals at similar security level. Similar results have been obtained with a Cortex M0 implementation. The new decryption code requires only a fraction of the software footprint of previous Ring-LWE implementations with the same encryption parameters, and is well suited fo

Question: What is a lattice-based public key cryptosystem?


Culture as situated cognition : Cultural mindsets , cultural fluency , and meaning making. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable

Question: What is the publisher?


Maglev: A Fast and Reliable Software Network Load Balancer. Maglev is Google's network load balancer. It is a large distributed software system that runs on commodity Linux servers. Unlike traditional hardware network load balancers, it does not require a specialized physical rack deployment, and its capacity can be easily adjusted by adding or removing servers. Network routers distribute packets evenly to the Maglev machines via Equal Cost Multipath (ECMP); each Maglev machine then matches the packets to their corresponding services and spreads them evenly to the service endpoints. To accommodate high and ever-increasing traffic, Maglev is specifically optimized for packet processing performance. A single Maglev machine is able to saturate a 10Gbps link with small packets. Maglev is also equipped with consistent hashing and connection tracking features, to minimize the negative impact of unexpected faults and failures on connection-oriented protocols. Maglev has been serving Google's traffic since 2008. It has sustained the rapid global growth of Google services, and it also provides network load balancing for Google Cloud Platform.

Question: What is a network load balancer?


Tracking football player movement from a single moving camera using particle filters. This paper deals with the problem of tracking football players in a football match using data from a single moving camera. Tracking footballers from a single video source is di cult: not only do the football players occlude each other, but they frequently enter and leave the camera's eld of view, making initialisation and destruction of a player's tracking a di cult task. The system presented here uses particle lters to track players. The multiple state estimates used by a particle lter provide an elegant method for maintaining tracking of players following an occlusion. Automated tracking can be achieved by creating and stopping particle lters depending on the input player data.

Question: How to track football players from a single moving camera using particle filters?


Landsat continuity : Issues and opportunities for land cover monitoring. Initiated in 1972, the Landsat program has provided a continuous record of earth observation for 35 years. The assemblage of Landsat spatial, spectral, and temporal resolutions, over a reasonably sized image extent, results in imagery that can be processed to represent land cover over large areas with an amount of spatial detail that is absolutely unique and indispensable for monitoring, management, and scientific activities. Recent technical problems with the two existing Landsat satellites, and delays in the development and launch of a successor, increase the likelihood that a gap in Landsat continuity may occur. In this communication, we identify the key features of the Landsat program that have resulted in the extensive use of Landsat data for large area land covermapping andmonitoring.We then augment this list of key features by examining the data needs of existing large area land cover monitoring programs. Subsequently, we use this list as a basis for reviewing the current constellation of earth observation satellites to identify potential alternative data sources for large area land cover applications. Notions of a virtual constellation of satellites to meet large area land cover mapping and monitoring needs are also presented. Finally, research priorities that would facilitate the integration of these alternative data sources into existing large area land cover monitoring programs are identified. Continuity of the Landsat program and the measurements provided are critical for scientific, environmental, economic, and social purposes. It is difficult to overstate the importance of Landsat; there are no other systems in orbit, or planned for launch in the short-term, that can duplicate or approach replication, of the measurements and information conferred by Landsat. While technical and political options are being pursued, there is no satellite image data stream poised to enter the Nationa

Question: What is the largest land cover dataset?


Recent approaches to non-intrusive load monitoring techniques in residential settings. The concept of Smart Grids is closely related to energy conservation and load shedding concepts. However, it is difficult to quantify the effectiveness of energy conservation efforts in residential settings without any sort of end-use energy information as feedback. In order to achieve that, load monitoring methods are normally used. In recent years, non-intrusive load monitoring (NILM) approaches are gaining popularity due to their minimal installation requirements and cost effectiveness. For a NILM system to work, only one sensor at the entry point to a home is required. Fluctuations in the aggregate power consumption signals are used to mathematically estimate the composition of operation of appliances. This approach eliminates the requirement of installing plug-meters for every appliance in the house. In this paper, we provide a review of recent research efforts on state-of-the-art NILM algorithms before concluding with a baseline and overall vision for our future research direction.

Question: What is the difference between a smart grid and a smart home?


A 5.58 nW Crystal Oscillator Using Pulsed Driver for Real-Time Clocks. A 5.58 nW real-time clock using a crystal oscillator is presented. In this circuit, the amplifier used in a traditional circuit is replaced with pulsed drivers. The pulse is generated with precise timing using a DLL. With this approach, an extremely low oscillation amplitude of 160 mV results in low-power operation. This low-amplitude oscillation is sustained robustly using additional supply voltages: a lower supply for the final drive stage and a higher supply used for pulses that drive the final drive stage, which ensures low ON-resistance necessary for reliable operation. The different supply levels are generated on-chip by a switched capacitor network (SCN) from a single supply. The circuit has been tested at different supply voltages and temperatures. It shows a minimum power consumption of 5.58 nW and power supply sensitivity of 30.3 ppm/V over supply voltage of 0.94-1.2 V, without degrading the crystal's temperature dependency: between -20 °C and 80 °C. Moreover, its performance as a real-time clock has been verified by measurement of an Allan deviation of 1.16 × 10-8.

Question: What is a crystal oscillator?


A topology for three-stage Solid State Transformer. Solid State Transformer (SST) is a new type of power transformer based on power electronic converters and high frequency transformers. The SST realizes voltage transformation, galvanic isolation, and power quality improvements in a single device. In the literature, a number of topologies have been introduced for the SST. In this work, employing a modular multilevel converter, a new SST topology is introduced which provides not only high-voltage AC (HVAC), low-voltage AC (LVAC) and low-voltage DC (LVDC) ports, but also high-voltage DC (HVDC) port. Besides, the proposed topology is easily scalable to higher voltage levels.

Question: What is a solid state transformer?


Nerve root sedimentation sign: evaluation of a new radiological sign in lumbar spinal stenosis.. STUDY DESIGN Retrospective case-referent study.   OBJECTIVE To assess whether the new sedimentation sign discriminates between nonspecific low back pain (LBP) and symptomatic lumbar spinal stenosis (LSS).   SUMMARY OF BACKGROUND DATA In the diagnosis of LSS, radiologic findings do not always correlate with clinical symptoms, and additional diagnostic signs are needed. In patients without LSS, we observe the sedimentation of lumbar nerve roots to the dorsal part of the dural sac on supine magnetic resonance image scans. In patients with symptomatic and morphologic central LSS, this sedimentation is rarely seen. We named this phenomenon "sedimentation sign" and defined the absence of sedimenting nerve roots as positive sedimentation sign for the diagnosis of LSS.   METHODS This study included 200 patients. Patients in the LSS group (n = 100) showed claudication with or without LBP and leg pain, a cross-sectional area <80 mm, and a walking distance <200 m; patients in the LBP group (n = 100) had LBP, no leg pain, no claudication, a cross-sectional area of the dural sac >120 mm, and a walking distance >1000 m. The frequency of a positive sedimentation sign was compared between the 2 groups, and intraobserver and interobserver reliability were assessed in a random subsample (n = 20).   RESULTS A positive sedimentation sign was identified in 94 patients in the LSS group (94%; 95% confidence interval, 90%-99%) but none in the LBP group (0%; 95% confidence interval, 0%-4%). Reliability was kappa = 1.0 (intraobserver) and kappa = 0.93 (interobserver), respectively. There was no difference in the detection of the sign between segmental levels L1-L5 in the LSS group.   CONCLUSION A positive sedimentation sign exclusively and reliably occurs in patients with LSS, suggesting its usefulness in clinical practice. Future accuracy studies will address its sensitivity and specificity. If

Question: What is the sedimentation sign?


Factors contributing to the facial aging of identical twins.. BACKGROUND The purpose of this study was to identify the environmental factors that contribute to facial aging in identical twins.   METHODS During the Twins Day Festival in Twinsburg, Ohio, 186 pairs of identical twins completed a comprehensive questionnaire, and digital images were obtained. A panel reviewed the images independently and recorded the differences in the perceived twins' ages and their facial features. The perceived age differences were then correlated with multiple factors.   RESULTS Four-point higher body mass index was associated with an older appearance in twins younger than age 40 but resulted in a younger appearance after age 40 (p = 0.0001). Eight-point higher body mass index was associated with an older appearance in twins younger than age 55 but was associated with a younger appearance after age 55 (p = 0.0001). The longer the twins smoked, the older they appeared (p < 0.0001). Increased sun exposure was associated with an older appearance and accelerated with age (p = 0.015), as was a history of outdoor activities and lack of sunscreen use. Twins who used hormone replacement had a younger appearance (p = 0.002). Facial rhytids were more evident in twins with a history of skin cancer (p = 0.05) and in those who smoked (p = 0.005). Dark and patchy skin discoloration was less prevalent in twins with a higher body mass index (p = 0.01) and more common in twins with a history of smoking (p = 0.005) and those with sun exposure (p = 0.005). Hair quantity was better with a higher body mass index (p = 0.01) although worse with a history of skin cancer (p = 0.005) and better with the use of hormones (p = 0.05).   CONCLUSION This study offers strong statistical evidence to support the role of some of the known factors that govern facial aging.

Question: What factors contribute to facial aging in identical twins?


Wasserstein Propagation for Semi-Supervised Learning. Probability distributions and histograms are natural representations for product ratings, traffic measurements, and other data considered in many machine learning applications. Thus, this paper introduces a technique for graph-based semisupervised learning of histograms, derived from the theory of optimal transportation. Our method has several properties making it suitable for this application; in particular, its behavior can be characterized by the moments and shapes of the histograms at the labeled nodes. In addition, it can be used for histograms on non-standard domains like circles, revealing a strategy for manifold-valued semi-supervised learning. We also extend this technique to related problems such as smoothing distributions on graph nodes.

Question: What is the name of the paper?


The Fast Fourier Transform and Its Applications. The advent of the fast Fourier transform method has greatly extended our ability to implement Fourier methods on digital computers. A description of the alogorithm and its programming is given here and followed by a theorem relating its operands, the finite sample sequences, to the continuous functions they often are intended to approximate. An analysis of the error due to discrete sampling over finite ranges is given in terms of aliasing. Procedures for computing Fourier integrals, convolutions and lagged products are outlined. HISTORICAL BACKGROUND T HE FAST Fourier transform algorithm has an interesting history which has been described in [3]. Time does not permit repeating this history here in detail. The essentials are, however, that until the recent publication of fast Fourier transform methods, computer programs were using up hundreds of hours of computer time with procedures requiring something proportional to N2 operations to compute Fourier transforms of N data points. It is not surprising then that the "new" methods requiring a number of operations proportional to N log N received considerable attention and led to revisions in computer programs and in problemn-solving techniques using Fourier methods. It was discovered later that the base 2 form of the fast Fourier transform algorithm had been published many years ago by Runge and Konig [10] and by Stumpff [12], [13]. These are authors whose works are widely read and their papers certainly were used by those computing Fourier series. How then could these important algorithms have gone unnoticed? The answer is that the papers of Runge, K6nig, and Stumpif described primarily how one could use symmetries of the sine-cosine functions to reduce the amount of computation by factors of 4, 8, or even more. Relatively small portions of these papers mentioned the successive doubling algorithm which permitted one to take two Fourier analyses of N-point samples of data

Question: What is the fast fourier transform?


Communicating neuronal ensembles between neuromorphic chips. I describe an interchip communication system that reads out pulse trains from a 64 64 array of neurons on one chip, and transmits them to corresponding locations in a 64 64 array of neurons on a second chip. It uses a random-access, time-multiplexed, asynchronous digital bus to transmit log2N -bit addresses that uniquely identify each of theN neurons in the sending population. A peak transmission rate of 2.5MSpikes/s is achieved by pipelining the operation of the channel. I discuss how the communication channel design is optimized for sporadic stimulus-triggered activity which causes some small subpopulation to re in synchrony, by adopting an arbitered, event-driven architecture. I derive the bandwidth required to transmit this neuronal ensemble, without temporal dispersion, in terms of the number of neurons, the probability that a neuron is part of the ensemble, and the degree of synchrony.

Question: How many neurons are in a neuronal ensemble?


Lifelong Machine Learning November , 2016. Lifelong machine learning (or lifelong learning) is an advanced machine learning paradigm that learns continuously, accumulates the knowledge learned in previous tasks, and uses it to help future learning. In the process, the learner becomes more and more knowledgeable and effective at learning. This learning ability is one of the hallmarks of human intelligence. However, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model. It makes no attempt to retain the learned knowledge and use it in future learning. Although this isolated learning paradigm has been very successful, it requires a large number of training examples, and is only suitable for well-defined and narrow tasks. In comparison, we humans can learn effectively with a few examples because we have accumulated so much knowledge in the past which enables us to learn with little data or effort. Lifelong learning aims to achieve this capability. As statistical machine learning matures, it is time to make a major effort to break the isolated learning tradition and to study lifelong learning to bring machine learning to a new height. Applications such as intelligent assistants, chatbots, and physical robots that interact with humans and systems in real-life environments are also calling for such lifelong learning capabilities. Without the ability to accumulate the learned knowledge and use it to learn more knowledge incrementally, a system will probably never be truly intelligent. This book serves as an introductory text and survey to lifelong learning.

Question: What is the difference between machine learning and lifelong learning?


Automatic Root Cause Analysis for LTE Networks Based on Unsupervised Techniques. The increase in the size and complexity of current cellular networks is complicating their operation and maintenance tasks. While the end-to-end user experience in terms of throughput and latency has been significantly improved, cellular networks have also become more prone to failures. In this context, mobile operators start to concentrate their efforts on creating self-healing networks, i.e., those networks capable of troubleshooting in an automatic way, making the network more reliable and reducing costs. In this paper, an automatic diagnosis system based on unsupervised techniques for Long-Term Evolution (LTE) networks is proposed. In particular, this system is built through an iterative process, using self-organizing maps (SOMs) and Ward's hierarchical method, to guarantee the quality of the solution. Furthermore, to obtain a number of relevant clusters and label them properly from a technical point of view, an approach based on the analysis of the statistical behavior of each cluster is proposed. Moreover, with the aim of increasing the accuracy of the system, a novel adjustment process is presented. It intends to refine the diagnosis solution provided by the traditional SOM according to the so-called silhouette index and the most similar cause on the basis of the minimum Xth percentile of all distances. The effectiveness of the developed diagnosis system is validated using real and simulated LTE data by analyzing its performance and comparing it with reference mechanisms.

Question: What is the size of the canadian military?


Active learning strategies for rating elicitation in collaborative filtering: A system-wide perspective. The accuracy of collaborative-filtering recommender systems largely depends on three factors: the quality of the rating prediction algorithm, and the quantity and quality of available ratings. While research in the field of recommender systems often concentrates on improving prediction algorithms, even the best algorithms will fail if they are fed poor-quality data during training, that is, garbage in, garbage out. Active learning aims to remedy this problem by focusing on obtaining better-quality data that more aptly reflects a user's preferences. However, traditional evaluation of active learning strategies has two major flaws, which have significant negative ramifications on accurately evaluating the system's performance (prediction error, precision, and quantity of elicited ratings). (1) Performance has been evaluated for each user independently (ignoring system-wide improvements). (2) Active learning strategies have been evaluated in isolation from unsolicited user ratings (natural acquisition).  In this article we show that an elicited rating has effects across the system, so a typical user-centric evaluation which ignores any changes of rating prediction of other users also ignores these cumulative effects, which may be more influential on the performance of the system as a whole (system centric). We propose a new evaluation methodology and use it to evaluate some novel and state-of-the-art rating elicitation strategies. We found that the system-wide effectiveness of a rating elicitation strategy depends on the stage of the rating elicitation process, and on the evaluation measures (MAE, NDCG, and Precision). In particular, we show that using some common user-centric strategies may actually degrade the overall performance of a system. Finally, we show that the performance of many common active learning strategies changes significantly when evaluated concur

Question: What is the difference between a user-centric evaluation and a system-centric evaluation?


HyperFlex: An SDN virtualization architecture with flexible hypervisor function allocation. Network Virtualization (NV) and Software-Defined Networking (SDN) are both expected to increase the flexibility and programmability of today's communication networks. Combining both approaches may even be a further step towards increasing the efficiency of network resource utilization. Multiple solutions for virtualizing SDN networks have already been proposed, however, they are either implemented in software or they require special network hardware. We propose HyperFlex, an SDN hypervisor architecture that relies on the decomposition of the hypervisor into functions that are essential for virtualizing SDN networks. The hypervisor functions can be flexibly executed in software or hosted on SDN network elements. Furthermore, existing hypervisor solutions focus on data-plane virtualization mechanisms and neglect the virtualization of the control-plane of SDN networks. HyperFlex provides control-plane virtualization by adding a control-plane isolation function, either in software or on network elements. The isolation function ensures that the resources of the control-plane are shared correctly between each virtual SDN network while it also protects the hypervisor resources from resource exhaustion.

Question: What is the difference between a hypervisor and a virtualization?


Augmenting geospatial data provenance through metadata tracking in geospatial service chaining. In a service-oriented environment, heterogeneous data from distributed data archiving centers and various geo-processing services are chained together dynamically to generate on-demand data products. Creating an executable service chain requires detailed specification of metadata for data sets and service instances. Using metadata tracking, semantics-enabled metadata are generated and propagated through a service chain. This metadata can be employed to validate a service chain, e.g. whether metadata preconditions on the input data of services can be satisfied. This paper explores how this metadata can be further exploited to augment geospatial data provenance, i.e., how a geospatial data product is derived. Provenance information is automatically captured during the metadata tracking process. Semantic Web technologies, including OWL and SPARQL, are used for representation and query of this provenance information. The approach can not only contribute to the automatic recording of geospatial data provenance, but also provide a more informed understanding of provenance information using Semantic Web technologies. & 2009 Elsevier Ltd. All rights reserved.

Question: What is the provenance of a geospatial data product?


A Quadratic-Complexity Observability-Constrained Unscented Kalman Filter for SLAM. This paper addresses two key limitations of the unscented Kalman filter (UKF) when applied to the simultaneous localization and mapping (SLAM) problem: the cubic computational complexity in the number of states and the inconsistency of the state estimates. To address the first issue, we introduce a new sampling strategy for the UKF, which has constant computational complexity. As a result, the overall computational complexity of UKF-based SLAM becomes of the same order as that of the extended Kalman filter (EKF)-based SLAM, i.e., quadratic in the size of the state vector. Furthermore, we investigate the inconsistency issue by analyzing the observability properties of the linear-regression-based model employed by the UKF. Based on this analysis, we propose a new algorithm, termed observability-constrained (OC)-UKF, which ensures the unobservable subspace of the UKF's linear-regression-based system model is of the same dimension as that of the nonlinear SLAM system. This results in substantial improvement in the accuracy and consistency of the state estimates. The superior performance of the OC-UKF over other state-of-the-art SLAM algorithms is validated by both Monte-Carlo simulations and real-world experiments.

Question: What is the size of the state vector?