A DC-90 GHz 4-Vpp differential linear driver in a 0.13 μm SiGe:C BiCMOS technology for optical modulators. In this paper, a linear driver for optical modulators in a 0.13 μm SiGe:C BiCMOS technology with fT/fmax of 300/500 GHz is presented. The driver is implemented following a distributed amplifier topology in a differential manner. In a 50-Ω environment, the circuit delivers a maximum differential output amplitude of 4 Vpp, featuring a small-signal gain of 13 dB and 3-dB bandwidth of 90 GHz. Time-domain measurements using OOK (up to 56 Gb/s) and PAM-4 (at 30Gbaud) are performed, demonstrating the maximum output swing and linearity of the driver. The output power to power dissipation ratio is 3.6%. To the best knowledge of the authors, this is the first time a linear driver for optical modulators demonstrates such bandwidth.

Question: What is the maximum output swing of the driver?


Trend Analysis in Social Networking using Opinion Mining A Survey. The popularity of social media in recent years has increased a lot and created new opportunities to study the interactions of different groups of people. Because of this it comes to possible to study the user"s opinion. Two popular topics in the study of social networks are community detection and finding trends. Trend Analysis is an ongoing field of research in Data mining field, May referred as an "Opinion Mining" by some researchers. Trend Analysis is the computational treatment of opinions, sentiments and subjectivity of text. The related field to Trend Analysis contains finding emotions in a statement which attracted researchers recently are discussed. The main target of this survey is to give nearly full image of Trend Analysis along with the community detection and their related fields with brief details. The main contributions of this paper include the overall analysis to bridge the concept between trend analyses with community detection. To find out the anomaly detection in a community with using the concept of trend analysis is a difficult task.

Question: What is the difference between community detection and trend analysis?


The effects of students' motivation, cognitive load and learning anxiety in gamification software engineering education: a structural equation modeling study. Past research has proven the significant effects of game-based learning on learning motivation and academic performance, and described the key factors in game-based design. Nonetheless, research on the correlations among learning motivation, cognitive load, learning anxiety and academic performance in gamified learning environments has been minimal. This study, therefore, aims to develop a Gamification Software Engineering Education Learning System (GSEELS) and evaluate the effects of gamification, learning motivation, cognitive load and learning anxiety on academic performance. By applying Structural Equation Modeling (SEM) to the empirical research, the questionnaire contains: 1. a Gamification Learning Scale; 2. a Learning Motivation Scale; 3. a Cognitive Load Scale; 4. a Learning Anxiety Scale; and 5. an Academic Performance Scale. A total of 107 undergraduates in two classes participated in this study. The Structural Equation Modeling (SEM) analysis includes the path directions and relationship between descriptive statistics, measurement model, structural model evaluation and five variables. The research results support all nine hypotheses, and the research findings also show the effects of cognitive load on learning anxiety, with strong learning motivation resulting from a low learning anxiety. As a result, it is further proven in this study that a well-designed GSEELS would affect student learning motivation and academic performance. Finally, the relationship model between gamification learning, learning motivation, cognitive load, learning anxiety and academic performance is elucidated, and four suggestions are proffered for instructors of software engineering education courses and for further research, so as to assist instructors in the application of favorable gamification teaching strategies.

Question: What is the relationship between gamification, learning motivation, cognitive load, learning anxiety and academic performance?


ANALYTIC PROBLEMS FOR ELLIPTIC CURVES. We consider some problems of analytic number theory for elliptic curves which can be considered as analogues of classical questions around the distribution of primes in arithmetic progressions to large moduli, and to the question of twin primes. This leads to some local results on the distribution of the group structures of elliptic curves defined over a prime finite field, exhibiting an interesting dichotomy for the occurence of the possible groups.

Question: What is the distribution of the group structures of elliptic curves defined over a prime finite field?


Intracranial EEG and human brain mapping. This review is an attempt to highlight the value of human intracranial recordings (intracranial electro-encephalography, iEEG) for human brain mapping, based on their technical characteristics and based on the corpus of results they have already yielded. The advantages and limitations of iEEG recordings are introduced in detail, with an estimation of their spatial and temporal resolution for both monopolar and bipolar recordings. The contribution of iEEG studies to the general field of human brain mapping is discussed through a review of the effects observed in the iEEG while patients perform cognitive tasks. Those effects range from the generation of well-localized evoked potentials to the formation of large-scale interactions between distributed brain structures, via long-range synchrony in particular. A framework is introduced to organize those iEEG studies according to the level of complexity of the spatio-temporal patterns of neural activity found to correlate with cognition. This review emphasizes the value of iEEG for the study of large-scale interactions, and describes in detail the few studies that have already addressed this point.

Question: What is the value of intracranial EEG recordings?


The challenges of building mobile underwater wireless networks for aquatic applications. The large-scale mobile underwater wireless sensor network (UWSN) is a novel networking paradigm to explore aqueous environments. However, the characteristics of mobile UWSNs, such as low communication bandwidth, large propagation delay, floating node mobility, and high error probability, are significantly different from ground-based wireless sensor networks. The novel networking paradigm poses interdisciplinary challenges that will require new technological solutions. In particular, in this article we adopt a top-down approach to explore the research challenges in mobile UWSN design. Along the layered protocol stack, we proceed roughly from the top application layer to the bottom physical layer. At each layer, a set of new design intricacies is studied. The conclusion is that building scalable mobile UWSNs is a challenge that must be answered by interdisciplinary efforts of acoustic communications, signal processing, and mobile acoustic network protocol design.

Question: What is the difference between a top-down and a bottom-up approach?


3D printing with polymers: Challenges among expanding options and opportunities.. OBJECTIVES Additive manufacturing, which is more colloquially referred to as 3D printing, is quickly approaching mainstream adoption as a highly flexible processing technique that can be applied to plastic, metal, ceramic, concrete and other building materials. However, taking advantage of the tremendous versatility associated with in situ photopolymerization as well as the ability to select from a variety of preformed processible polymers, 3D printing predominantly targets the production of polymeric parts and models. The goal of this review is to connect the various additive manufacturing techniques with the monomeric and polymeric materials they use while highlighting emerging material-based developments.   METHODS Modern additive manufacturing technology was introduced approximately three decades ago but this review compiles recent peer-reviewed literature reports to demonstrate the evolution underway with respect to the various building techniques that differ significantly in approach as well as the new variations in polymer-based materials being employed.   RESULTS Recent growth of 3D printing has been dramatic and the ability of the various platform technologies to expand from rapid production prototypic models to the greater volume of readily customizable production of working parts is critical for continued high growth rates. This transition to working part production is highly dependent on adapting materials that deliver not only the requisite design accuracy but also the physical and mechanical properties necessary for the application.   SIGNIFICANCE With the weighty distinction of being called the next industrial revolution, 3D printing technologies is already altering many industrial and academic operations including changing models for future healthcare delivery in medicine and dentistry.

Question: What is the next industrial revolution?


Heat, vibration, dust, salt spray, weather - taking wireless positioning to the extreme. There are lots of local positioning systems available for indoor applications, partly even for outdoor use. Many of them rely on a rather clean environment which does not interfere in their measurements too much. Industrial environments, in- or outdoors, often do not meet these requirements. The positioning applications here are surrounded by dirt, dust and vibrations. The number of local positioning systems being able to perform properly here at all times is limited to such not dependent on optical or sonic measurements. Local secondary radar systems proved to be a reliable source of distance and position measurements, even in harsh environments. This paper examines a few use cases and depicts the performance of local positioning radar systems there.

Question: What is the performance of local positioning radar systems in harsh environments?


Central Retinal Artery Occlusion and Partial Ophthalmoplegia Caused by Hyaluronic Acid Injection Filler Cosmetic. Central retinal artery occlusion is a rare ocular emergency condition that can lead to a total blindness. Hereby we report a 30-year-old woman with a sudden visual loss of the left eye after being injected by hyaluronic acid filler at the dorsum nasal area for cosmetic purpose. Visual acuity was light perception while anterior segment and intraocular pressure were within normal limit. Partial ophthalmoplegia include restriction to the nasal gaze were found. Funduscopic examination revealed total retinal edema with a cherry red spot at the fovea. Ocular massage was performed as initial management followed by humor aqueous paracentesis. All procedures were done within 90 minutes. The patients discharge with the visual improvement to hand motion and the Partial ophthalmophlegia has been improved after being evaluated less than a month.

Question: What is the cherry red spot?


Key2Vec: Automatic Ranked Keyphrase Extraction from Scientific Articles using Phrase Embeddings. Keyphrase extraction is a fundamental task in natural language processing that facilitates mapping of documents to a set of representative phrases. In this paper, we present an unsupervised technique (Key2Vec) that leverages phrase embeddings for ranking keyphrases extracted from scientific articles. Specifically, we propose an effective way of processing text documents for training multi-word phrase embeddings that are used for thematic representation of scientific articles and ranking of keyphrases extracted from them using theme-weighted PageRank. Evaluations are performed on benchmark datasets producing state-of-the-art results.

Question: What is the keyphrase extraction technique?


A Consistent Metric for Performance Evaluation of Multi-Object Filters. The concept of a miss-distance, or error, between a reference quantity and its estimated/controlled value, plays a fundamental role in any filtering/control problem. Yet there is no satisfactory notion of a miss-distance in the well-established field of multi-object filtering. In this paper, we outline the inconsistencies of existing metrics in the context of multi-object miss-distances for performance evaluation. We then propose a new mathematically and intuitively consistent metric that addresses the drawbacks of current multi-object performance evaluation metrics.

Question: What is a consistent metric for performance evaluation of multi-object filters?


Multi-view Clustering via Multi-manifold Regularized Nonnegative Matrix Factorization. Multi-view clustering integrates complementary information from multiple views to gain better clustering performance rather than relying on a single view. NMF based multi-view clustering algorithms have shown their competitiveness among different multi-view clustering algorithms. However, NMF fails to preserve the locally geometrical structure of the data space. In this paper, we propose a multi-manifold regularized nonnegative matrix factorization framework (MMNMF) which can preserve the locally geometrical structure of the manifolds for multi-view clustering. MMNMF regards that the intrinsic manifold of the dataset is embedded in a convex hull of all the views' manifolds, and incorporates such an intrinsic manifold and an intrinsic (consistent) coefficient matrix with a multi-manifold regularizer to preserve the locally geometrical structure of the multi-view data space. We use linear combination to construct the intrinsic manifold, and propose two strategies to find the intrinsic coefficient matrix, which lead to two instances of the framework. Experimental results show that the proposed algorithms outperform existing NMF based algorithms for multi-view clustering.

Question: What is the difference between NMF and MNNMF?


Non-local sparse models for image restoration. We propose in this paper to unify two different approaches to image restoration: On the one hand, learning a basis set (dictionary) adapted to sparse signal descriptions has proven to be very effective in image reconstruction and classification tasks. On the other hand, explicitly exploiting the self-similarities of natural images has led to the successful non-local means approach to image restoration. We propose simultaneous sparse coding as a framework for combining these two approaches in a natural manner. This is achieved by jointly decomposing groups of similar signals on subsets of the learned dictionary. Experimental results in image denoising and demosaicking tasks with synthetic and real noise show that the proposed method outperforms the state of the art, making it possible to effectively restore raw images from digital cameras at a reasonable speed and memory cost.

Question: What is the state of the art in image restoration?


Usability Heuristics for Transactional Web Sites. One of the most recognized methods to evaluate usability in software applications is the heuristic evaluation. In this inspection method, Nielsen's heuristics, are the most widely used evaluation instrument. However, there is evidence in the literature which establishes that these heuristics are not appropriate when they are used to measure the level of usability of new emerging categories of software applications. This paper presents a case study where this concept is verified for Transactional Web Sites. Therefore, given the present limitations, a new set of usability heuristics was proposed following a structured and systematic methodology. Finally, fifteen new usability heuristics were obtained as final product of this research. A validation phase allowed to contrast this new proposal with the Nielsen's principles under a real context, where a heuristic evaluation was performed to a Transactional Web Site. The results established that the new set of usability heuristics, which is presented in this study, provides more accurate and promising results than the current proposal of Nielsen.

Question: What is a transactional web site?


Description of the YaCy Distributed Web Search Engine. Distributed web search engines have been proposed to mitigate the privacy issues that arise in centralized search systems. These issues include censorship, disclosure of sensitive queries to the search server and to any third parties with whom the search service operator might share data, as well as the lack of transparency of proprietary search algorithms. YaCy is a deployed distributed search engine that aims to provide censorship resistance and privacy to its users. Its user base has been steadily increasing and it is currently being used by several hundreds of people every day. Unfortunately, there exists no document that thoroughly describes how YaCy exactly works. We therefore investigated the source code of YaCy and summarize in this document our findings and explanation on YaCy. We confirmed with the YaCy community that our description on YaCy is accurate.

Question: What is YaCy?


Learning Across Scales - Multiscale Methods for Convolution Neural Networks. In this work, we establish the relation between optimal control and training deep Convolution Neural Networks (CNNs). We show that the forward propagation in CNNs can be interpreted as a time-dependent nonlinear differential equation and learning can be seen as controlling the parameters of the differential equation such that the network approximates the data-label relation for given training data. Using this continuous interpretation, we derive two new methods to scale CNNs with respect to two different dimensions. The first class of multiscale methods connects low-resolution and high-resolution data using prolongation and restriction of CNN parameters inspired by algebraic multigrid techniques. We demonstrate that our method enables classifying highresolution images using CNNs trained with low-resolution images and vice versa and warm-starting the learning process. The second class of multiscale methods connects shallow and deep networks and leads to new training strategies that gradually increase the depths of the CNN while re-using parameters for initializations.

Question: How do we scale CNNs?


GramFuzz: Fuzzing testing of web browsers based on grammar analysis and structural mutation. Fuzz testing is an automated black-box testing technique providing random data as input to a software system in the hope to find vulnerability. In order to be effective, the fuzzed input must be common enough to pass elementary consistency checks. Web Browser accepts JavaScript, CSS files as well as the html as input, which must be considered in fuzzing testing, while traditional fuzzing technology generates test cases using simple mutation strategies, ignoring the grammar and code structure. In this article, vulnerability patterns are summarized and a new fuzzing testing method are proposed based on grammar analysis of input data and mutation of code structure. Combining methods of generation and mutation, test cases will be more effective in the fuzzing testing of web browsers. Applied on the Mozilla and IE web browsers, it discovered a total of 36 new severe vulnerabilities(and thus became one of the top security bug bounty collectors within this period).

Question: What is gramfuzz?


Autonomic nervous system activity in emotion: A review. Autonomic nervous system (ANS) activity is viewed as a major component of the emotion response in many recent theories of emotion. Positions on the degree of specificity of ANS activation in emotion, however, greatly diverge, ranging from undifferentiated arousal, over acknowledgment of strong response idiosyncrasies, to highly specific predictions of autonomic response patterns for certain emotions. A review of 134 publications that report experimental investigations of emotional effects on peripheral physiological responding in healthy individuals suggests considerable ANS response specificity in emotion when considering subtypes of distinct emotions. The importance of sound terminology of investigated affective states as well as of choice of physiological measures in assessing ANS reactivity is discussed.

Question: What is the autonomic nervous system?


Abstraction Learning. ion Learning Fei Deng fei.deng@rutgers.edu Jinsheng Ren rjs17@mails.tsinghua.edu.cn Feng Chen chenfeng@mail.tsinghua.edu.cn

Question: What is the name of the author of the document?


GeoDa: An Introduction to Spatial Data Analysis. This article presents an overview of GeoDa, a free software program intended to serve as a user-friendly and graphical introduction to spatial analysis for nongeographic information systems (GIS) specialists. It includes functionality ranging from simple mapping to exploratory data analysis, the visualization of global and local spatial autocorrelation, and spatial regression. A key feature of GeoDa is an interactive environment that combines maps with statistical graphics, using the technology of dynamically linked windows. A brief review of the software design is given, as well as some illustrative examples that highlight distinctive features of the program in applications dealing with public health, economic development, real estate analysis, and criminology.

Question: What is GeoDa?


Efficacy and safety of Meriva®, a curcumin-phosphatidylcholine complex, during extended administration in osteoarthritis patients.. In a previous three-month study of Meriva, a proprietary curcumin-phosphatidylcholine phytosome complex, decreased joint pain and improvement in joint function were observed in 50 osteoarthritis (OA) patients. Since OA is a chronic condition requiring prolonged treatment, the long-term efficacy and safety of Meriva were investigated in a longer (eight months) study involving 100 OA patients. The clinical end points (Western Ontario and McMaster Universities [WOMAC] score, Karnofsky Performance Scale Index, and treadmill walking performance) were complemented by the evaluation of a series of inflammatory markers (interleukin [IL]-1beta, IL-6, soluble CD40 ligand [sCD40L], soluble vascular cell adhesion molecule (sVCAM)-1, and erythrocyte sedimentation rate [ESR]). This represents the most ambitious attempt, to date, to evaluate the clinical efficacy and safety of curcumin as an anti-inflammatory agent. Significant improvements of both the clinical and biochemical end points were observed for Meriva compared to the control group. This, coupled with an excellent tolerability, suggests that Meriva is worth considering for the long-term complementary management of osteoarthritis.

Question: What is the efficacy and safety of Meriva?


A Technique for Measuring Data Persistence Using the Ext4 File System Journal. In this paper, we propose a method of measuring data persistence using the Ext4 journal. Digital Forensic tools and techniques are commonly used to extract data from media. A great deal of research has been dedicated to the recovery of deleted data, however, there is a lack of information on quantifying the chance that an investigator will be successful in this endeavor. To that end, we suggest the file system journal be used as a source to gather empirical evidence of data persistence, which can later be used to formulate the probability of recovering deleted data under various conditions. Knowing this probability can help investigators decide where to best invest their resources. We have implemented a proof of concept system that interrogates the Ext4 file system journal and logs relevant data. We then detail how this information can be used to track the reuse of data blocks from the examination of file system metadata structures. This preliminary design contributes a novel method of tracking deleted data persistence that can be used to generate the information necessary to formulate probability models regarding the full and/or partial recovery of deleted data.

Question: How can we measure data persistence?


Dual-Band Textile MIMO Antenna Based on Substrate-Integrated Waveguide (SIW) Technology. A dual-band textile antenna for multiple-input-multiple-output (MIMO) applications, based on substrate-integrated waveguide (SIW) technology, is designed. The fundamental SIW cavity mode is designed to resonate at 2.4 GHz. Meanwhile, the second and third modes are modified and combined by careful placement of a via within the cavity to enable wideband coverage in the 5-GHz WLAN band. The simple antenna topology can be fabricated fully using textiles in a planar form, ensuring reliability and comfort. Numerical and experimental results indicate satisfactory antenna performance when worn on body in terms of impedance bandwidth, radiation efficiency, and specific absorption ratio (SAR). In order to validate its potential for MIMO applications, two elements of the proposed SIW antenna are arranged in six configurations to study the performance in terms of mutual coupling and envelope correlation. It is observed that the placement of the shorted edges of the two elements adjacent to each other produces the lowest mutual coupling and consequently the best envelope correlation.

Question: What is the dual-band textile antenna based on substrate-integrated waveguide (SIW) technology?


Ally Friendly Jamming: How to Jam Your Enemy and Maintain Your Own Wireless Connectivity at the Same Time. This paper presents a novel mechanism, called Ally Friendly Jamming, which aims at providing an intelligent jamming capability that can disable unauthorized (enemy) wireless communication but at the same time still allow authorized wireless devices to communicate, even if all these devices operate at the same frequency. The basic idea is to jam the wireless channel continuously but properly control the jamming signals with secret keys, so that the jamming signals are unpredictable interference to unauthorized devices, but are recoverable by authorized ones equipped with the secret keys. To achieve the ally friendly jamming capability, we develop new techniques to generate ally jamming signals, to identify and synchronize with multiple ally jammers. This paper also reports the analysis, implementation, and experimental evaluation of ally friendly jamming on a software defined radio platform. Both the analytical and experimental results indicate that the proposed techniques can effectively disable enemy wireless communication and at the same time maintain wireless communication between authorized devices.

Question: How to jam your enemy and maintain your own wireless connectivity at the same time.


Concept-Based Knowledge Discovery in Texts Extracted from the Web. This paper presents an approach for knowledge discovery in texts extracted from the Web. Instead of analyzing words or attribute values, the approach is based on concepts, which are extracted from texts to be used as characteristics in the mining process. Statistical techniques are applied on concepts in order to find interesting patterns in concept distributions or associations. In this way, users can perform discovery in a high level, since concepts describe real world events, objects, thoughts, etc. For identifying concepts in texts, a categorization algorithm is used associated to a previous classification task for concept definitions. Two experiments are presented: one for political analysis and other for competitive intelligence. At the end, the approach is discussed, examining its problems and advantages in the Web context.

Question: What is a concept-based knowledge discovery in texts extracted from the web?


An Operating System for the Home. Network devices for the home such as remotely controllable locks, lights, thermostats, cameras, and motion sensors are now readily available and inexpensive. In theory, this enables scenarios like remotely monitoring cameras from a smartphone or customizing climate control based on occupancy patterns. However, in practice today, such smarthome scenarios are limited to expert hobbyists and the rich because of the high overhead of managing and extending current technology. We present HomeOS, a platform that bridges this gap by presenting users and developers with a PC-like abstraction for technology in the home. It presents network devices as peripherals with abstract interfaces, enables cross-device tasks via applications written against these interfaces, and gives users a management interface designed for the home environment. HomeOS already has tens of applications and supports a wide range of devices. It has been running in 12 real homes for 4–8 months, and 42 students have built new applications and added support for additional devices independent of our efforts.

Question: What is the name of the operating system?


Focused Crawling: A New Approach to Topic-Specific Web Resource Discovery. The rapid growth of the World-Wide Web poses unprecedented scaling challenges for general-purpose crawlers and search engines. In this paper we describe a new hypertext resource discovery system called a Focused Crawler. The goal of a focused crawler is to selectively seek out pages that are relevant to a pre-defined set of topics. The topics are specified not using keywords, but using exemplary documents. Rather than collecting and indexing all accessible Web documents to be able to answer all possible ad-hoc queries, a focused crawler analyzes its crawl boundary to find the links that are likely to be most relevant for the crawl, and avoids irrelevant regions of the Web. This leads to significant savings in hardware and network resources, and helps keep the crawl more up-to-date. To achieve such goal-directed crawling, we designed two hypertext mining programs that guide our crawler: a classifier that evaluates the relevance of a hypertext document with respect to the focus topics, and a distiller that identifies hypertext nodes that are great access points to many relevant pages within a few links. We report on extensive focused-crawling experiments using several topics at different levels of specificity. Focused crawling acquires relevant pages steadily while standard crawling quickly loses its way, even though they are started from the same root set. Focused crawling is robust against large perturbations in the starting set of URLs. It discovers largely overlapping sets of resources in spite of these perturbations. It is also capable of exploring out and discovering valuable resources that are dozens of links away from the start set, while carefully pruning the millions of pages that may lie within this same radius. Our anecdotes suggest that focused crawling is very effective for building high-quality collections of Web documents on specific topics, using modest desktop hardware.  1999

Question: What is a focused crawler?


Decoding as Continuous Optimization in Neural Machine Translation. In this work, we propose a novel decoding approach for neural machine translation (NMT) based on continuous optimisation. The resulting optimisation problem can then be tackled using a whole range of continuous optimisation algorithms which have been developed and used in the literature mainly for training. Our approach is general and can be applied to other sequence-to-sequence neural models as well. We make use of this powerful decoding approach to intersect an underlying NMT with a language model, to intersect left-to-right and right-to-left NMT models, and to decode with soft constraints involving coverage and fertility of the source sentence words. The experimental results show the promise of the proposed framework.

Question: What is the difference between a neural machine translation and a language model?


Big Data Analytics with Spark. What's more, Big Data Analytics with Spark provides an introduction to other big data technologies that are commonly used along with Spark, such as HDFS, Avro, Parquet, Ka a, Cassandra, HBase, Mesos, and so on. It also provides an introduction to machine learning and graph concepts. So the book is self-suffi cient; all the technologies that you need to know to use Spark are covered. The only thing that you are expected to have is some programming knowledge in any language.

Question: What is the difference between big data analytics and big data?


An improved algorithm for computing logarithms over GF(p) and its cryptographic significance (Corresp.).

Question: What is the significance of the algorithm?


Interference-Aware Channel Assignment in Multi-Radio Wireless Mesh Networks. The capacity problem in wireless mesh networks can be alleviated by equipping the mesh routers with multiple radios tuned to non-overlapping channels. However, channel assignment presents a challenge because co-located wireless networks are likely to be tuned to the same channels. The resulting increase in interference can adversely affect performance. This paper presents an interference-aware channel assignment algorithm and protocol for multi-radio wireless mesh networks that address this interference problem. The proposed solution intelligently assigns channels to radios to minimize interference within the mesh network and between the mesh network and co-located wireless networks. It utilizes a novel interference estimation technique implemented at each mesh router. An extension to the conflict graph model, the multi-radio conflict graph, is used to model the interference between the routers. We demonstrate our solution's practicality through the evaluation of a prototype implementation in a IEEE 802.11 testbed. We also report on an extensive evaluation via simulations. In a sample multi-radio scenario, our solution yields performance gains in excess of 40% compared to a static assignment of channels.

Question: How to solve the capacity problem in wireless mesh networks?


Software-Defined "Hardware" Infrastructures: A Survey on Enabling Technologies and Open Research Directions. This paper provides an overview of software-defined "hardware" infrastructures (SDHI). SDHI builds upon the concept of hardware (HW) resource disaggregation. HW resource disaggregation breaks today's physical server-oriented model where the use of a physical resource (e.g., processor or memory) is constrained to a physical server's chassis. SDHI extends the definition of of software-defined infrastructures (SDI) and brings greater modularity, flexibility, and extensibility to cloud infrastructures, thus allowing cloud operators to employ resources more efficiently and allowing applications not to be bounded by the physical infrastructure's layout. This paper aims to be an initial introduction to SDHI and its associated technological advancements. This paper starts with an overview of the cloud domain and puts into perspective some of the most prominent efforts in the area. Then, it presents a set of differentiating use-cases that SDHI enables. Next, we state the fundamentals behind SDI and SDHI, and elaborate why SDHI is of great interest today. Moreover, it provides an overview of the functional architecture of a cloud built on SDHI, exploring how the impact of this transformation goes far beyond the cloud infrastructure level in its impact on platforms, execution environments, and applications. Finally, an in-depth assessment is made of the technologies behind SDHI, the impact of these technologies, and the associated challenges and potential future directions of SDHI.

Question: What is a software-defined infrastructure?


Brain activation and sexual arousal in healthy, heterosexual males.. Despite the brain's central role in sexual function, little is known about relationships between brain activation and sexual response. In this study, we employed functional MRI (fMRI) to examine relationships between brain activation and sexual arousal in a group of young, healthy, heterosexual males. Each subject was exposed to two sequences of video material consisting of explicitly erotic (E), relaxing (R) and sports (S) segments in an unpredictable order. Data on penile turgidity was collected using a custom-built pneumatic pressure cuff. Both traditional block analyses using contrasts between sexually arousing and non-arousing video clips and a regression using penile turgidity as the covariate of interest were performed. In both types of analyses, contrast images were computed for each subject and these images were subsequently used in a random effects analysis. Strong activations specifically associated with penile turgidity were observed in the right subinsular region including the claustrum, left caudate and putamen, right middle occipital/ middle temporal gyri, bilateral cingulate gyrus and right sensorimotor and pre-motor regions. Smaller, but significant activation was observed in the right hypothalamus. Few significant activations were found in the block analyses. Implications of the findings are discussed. Our study demonstrates the feasibility of examining brain activation/sexual response relationships in an fMRI environment and reveals a number of brain structures whose activation is time-locked to sexual arousal.

Question: What is the difference between a block and a regression analysis?


Fuzzy sets, advanced fuzzy sets and hybrids. Fuzzy Sets were proposed several years ago with various extensions in later years. Each extension has advantages over the fuzzy sets. Rough sets are used to handle incomplete information. Interval-valued fuzzy sets deal with uncertainty and vagueness. Intuitionistic fuzzy sets contain a sub-interval hesitation degree that lies between membership and non-membership degrees. Soft sets overcome the problem of insufficiency of parameterization. Advanced fuzzy sets have myriad number of advantages due to their applications in realistic examples. This paper briefly explains the abstract of fuzzy sets, advanced fuzzy sets and their hybrids. Advanced fuzzy sets include rough sets, intuitionistic fuzzy sets, interval-valued fuzzy sets and soft sets.

Question: What are fuzzy sets?


Mining Cluster-Based Temporal Mobile Sequential Patterns in Location-Based Service Environments. Researches on Location-Based Service (LBS) have been emerging in recent years due to a wide range of potential applications. One of the active topics is the mining and prediction of mobile movements and associated transactions. Most of existing studies focus on discovering mobile patterns from the whole logs. However, this kind of patterns may not be precise enough for predictions since the differentiated mobile behaviors among users and temporal periods are not considered. In this paper, we propose a novel algorithm, namely, Cluster-based Temporal Mobile Sequential Pattern Mine (CTMSP-Mine), to discover the Cluster-based Temporal Mobile Sequential Patterns (CTMSPs). Moreover, a prediction strategy is proposed to predict the subsequent mobile behaviors. In CTMSP-Mine, user clusters are constructed by a novel algorithm named Cluster-Object-based Smart Cluster Affinity Search Technique (CO-Smart-CAST) and similarities between users are evaluated by the proposed measure, Location-Based Service Alignment (LBS-Alignment). Meanwhile, a time segmentation approach is presented to find segmenting time intervals where similar mobile characteristics exist. To our best knowledge, this is the first work on mining and prediction of mobile behaviors with considerations of user relations and temporal property simultaneously. Through experimental evaluation under various simulated conditions, the proposed methods are shown to deliver excellent performance.

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


Design and Implementation of a Network Security Model for Cooperative Network. In this paper a design and implementation of a network security model was presented, using routers and firewall. Also this paper was conducted the network security weakness in router and firewall network devices, type of threats and responses to those threats, and the method to prevent the attacks and hackers to access the network. Also this paper provides a checklist to use in evaluating whether a network is adhering to best practices in network security and data confidentiality. The main aim of this research is to protect the network from vulnerabilities, threats, attacks, configuration weaknesses and security policy weaknesses. KeywordsNetwork security, network management, threats, security policy Received December 17, 2008; Accepted March 2 2009

Question: What is the main aim of this research?


Convolutional Neural Networks for Automatic State-Time Feature Extraction in Reinforcement Learning Applied to Residential Load Control. Direct load control of a heterogeneous cluster of residential demand flexibility sources is a high-dimensional control problem with partial observability. This paper proposes a novel approach that uses a convolutional neural network (CNN) to extract hidden state-time features to mitigate the curse of partial observability. More specific, a CNN is used as a function approximator to estimate the state-action value function or <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-function in the supervised learning step of fitted <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-iteration. The approach is evaluated in a qualitative simulation, comprising a cluster of thermostatically controlled loads that only share their air temperature, while their envelope temperature remains hidden. The simulation results show that the presented approach is able to capture the underlying hidden features and able to successfully reduce the electricity cost the cluster.

Question: What is the curse of partial observability?


Age Prediction in Blogs: A Study of Style, Content, and Online Behavior in Pre- and Post-Social Media Generations. Research Assistant May 2008 present Currently working on a project to influence power and rifts in unstructured text project in classifying the age of authors as well as identifying opinion and persuasion in blogs (funded by IARPA SCIL) Previously worked on a project maintaining a pipeline for Question Answering System and creating system tools using Natural Language Processing techniques (funded by DARPA GALE). Computer administrator of the NLP group.

Question: What is the age of the author of this document?


Deep convolutional neural network based large-scale oil palm tree detection for high-resolution remote sensing images. This paper proposed a deep convolutional neural network (DCNN) based framework for large-scale oil palm tree detection using high-resolution remote sensing images in Malaysia. Different from the previous palm tree or tree crown detection studies, the palm trees in our study area are very crowded and their crowns often overlap. Moreover, there are various land cover types in our study area, e.g. impervious, bare land, and other vegetation, etc. The main steps of our proposed method include large-scale and multi-class sample collection, AlexNet-based DCNN training and optimization, sliding window-based label prediction, and post-processing. Compared with the manually interpreted ground truth, our proposed method achieves detection accuracies of 92%–97% in our study area, which are greatly higher than the accuracies obtained from another two detection methods used in this paper.

Question: What is the main steps of the proposed method?


A face(book) in the crowd: social Searching vs. social browsing. Large numbers of college students have become avid Facebook users in a short period of time. In this paper, we explore whether these students are using Facebook to find new people in their offline communities or to learn more about people they initially meet offline. Our data suggest that users are largely employing Facebook to learn more about people they meet offline, and are less likely to use the site to initiate new connections.

Question: What is the difference between social searching and social browsing?


Learning from Labeled and Unlabeled Data with LabelPropagationXiaojin. We investigate the use of unlabeled data to help labeled data in cl ssification. We propose a simple iterative algorithm, label pro pagation, to propagate labels through the dataset along high density are as d fined by unlabeled data. We analyze the algorithm, show its solution , and its connection to several other algorithms. We also show how to lear n p ameters by minimum spanning tree heuristic and entropy minimiz ation, and the algorithm's ability to perform feature selection. Expe riment results are promising.

Question: How to learn parameters of a model by unlabeled data?


The Taste for Privacy: An Analysis of College Student Privacy Settings in an Online Social Network. The rapid growth of contemporary social network sites (SNSs) has coincided with an increasing concern over personal privacy. College students and adolescents routinely provide personal information on profiles that can be viewed by large numbers of unknown people and potentially used in harmful ways. SNSs like Facebook and MySpace allow users to control the privacy level of their profile, thus limiting access to this information. In this paper, we take the preference for privacy itself as our unit of analysis, and analyze the factors that are predictive of a student having a private versus public profile. Drawing upon a new social network dataset based on Facebook, we argue that privacy behavior is an upshot of both social influences and personal incentives. Students are more likely to have a private profile if their friends and roommates have them; women are more likely to have private profiles than are men; and having a private profile is associated with a higher level of online activity. Finally, students who have private versus public profiles are characterized by a unique set of cultural preferences—of which the ''taste for privacy'' may be only a small but integral part.

Question: What is the taste for privacy?


Bootstrapping coreference resolution using word associations. In this paper, we present an unsupervised framework that bootstraps a complete coreference resolution (CoRe) system from word associations mined from a large unlabeled corpus. We show that word associations are useful for CoRe – e.g., the strong association between Obama and President is an indicator of likely coreference. Association information has so far not been used in CoRe because it is sparse and difficult to learn from small labeled corpora. Since unlabeled text is readily available, our unsupervised approach addresses the sparseness problem. In a self-training framework, we train a decision tree on a corpus that is automatically labeled using word associations. We show that this unsupervised system has better CoRe performance than other learning approaches that do not use manually labeled data.

Question: How to bootstrap a coreference resolution system from word associations?


Effect of Manipulated Amplitude and Frequency of Human Voice on Dominance and Persuasiveness in Audio Conferences. We propose to artificially manipulate participants' vocal cues, amplitude and frequency, in real time to adjust their dominance and persuasiveness during audio conferences. We implemented a prototype system and conducted two experiments. The first experiment investigated the effect of vocal cue manipulation on the perception of dominance. The results showed that participants perceived higher dominance while listening to a voice with a high amplitude and low frequency. The second experiment investigated the effect of vocal cue manipulation on persuasiveness. The results indicated that a person with a low amplitude and low frequency voice had greater persuasiveness in conversations with biased dominance, while there was no statistically significant difference in persuasiveness in conversations with unbiased dominance.

Question: How to increase the persuasiveness of a person?


Data-Driven Intelligent Transportation Systems: A Survey. For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sources and can be processed into various forms for different stakeholders. The availability of a large amount of data can potentially lead to a revolution in ITS development, changing an ITS from a conventional technology-driven system into a more powerful multifunctional data-driven intelligent transportation system (D2ITS) : a system that is vision, multisource, and learning algorithm driven to optimize its performance. Furthermore, D2ITS is trending to become a privacy-aware people-centric more intelligent system. In this paper, we provide a survey on the development of D2ITS, discussing the functionality of its key components and some deployment issues associated with D2ITS Future research directions for the development of D2ITS is also presented.

Question: What is the difference between a conventional ITS and a D2ITS?


Lung Nodule Detection Using Convolutional Neural Networks. We present a novel procedure to apply deep learning techniques to medical image classification. With increasing popularity of computed tomography (CT) lung screening, fully manual diagnosis of lung cancer puts a burden on the radiologists who need to spend hours reading through CT scanned images to identify Region of Interests (ROIs) to schedule follow-ups. Accurate computer-aided diagnosis of lung cancer can effectively reduce their workload and help training new radiologists. However, lung cancer detection is challenging because of the varying size, location, shape, and density of nodules. Many studies have approached this problem using image-processing techniques with the intention of developing an optimal set of features. Convolutional neural network has demonstrated to learn discriminative visual features automatically and has beat many state-of-art algorithms in image-processing tasks, such as pattern recognition, object detection, segmentation, etc. In this report, we evaluate the feasibility of implementing deep learning algorithms for lung cancer diagnosis with the Lung Image Database Consortium (LIDC) database. We compare the performance of two 2.5D convolutional neural network models, two 3D convolutional neural network models, and one 3D convolutional neural networks with a 3D spatial transformer network module on the task of detecting lung nodules. The performance of our best model is comparable with the state-of-art results in the lung nodule detection task.

Question: What is the best way to detect lung nodules?


Investment and Financing Constraints : Evidence from the Funding of Corporate Pension Plans. I exploit sharply nonlinear funding rules for defined benefit pension plans in order to identify the dependence of corporate investment on internal financial resources in a large sample. Capital expenditures decline with mandatory contributions to defined benefit pension plans, even when controlling for correlations between the pension funding status itself and the firm's unobserved investment opportunities. The effect is particularly evident among firms that face financing constraints based on observable variables such as credit ratings. Investment also displays strong negative correlations with the part of mandatory contributions resulting solely from unexpected asset market movements. * Graduate School of Business, University of Chicago. I thank James Poterba, Stew Myers, Jonathan Gruber, Dirk Jenter, Heitor Almeida, Daniel Bergstresser, Mihir Desai, Michael Greenstone, Robin Greenwood, David Scharfstein, Antoinette Schoar, Jeremy Stein, and Amir Sufi for helpful comments and discussions. I would also like to thank Rob Stambaugh (the editor) and the referees. This work benefited greatly from the thoughts of economics seminar participants at MIT, Harvard, Princeton, and the Kennedy School of Government, and of finance seminar participants at the University of Chicago, the University of Pennsylvania (Wharton), Harvard, Stanford, Columbia, NYU (Stern), Dartmouth (Tuck), Michigan (Ross), Boston College (Carroll), Northwestern (Kellogg), Duke (Fuqua), and the 2004 Western Finance Association meetings in Vancouver. I am grateful to the Center for Retirement Research at Boston College and the National Bureau of Economic Research for financial support.

Question: What is the funding of corporate pension plans?


Innovation Engines: Automated Creativity and Improved Stochastic Optimization via Deep Learning. The Achilles Heel of stochastic optimization algorithms is getting trapped on local optima. Novelty Search avoids this problem by encouraging a search in all interesting directions. That occurs by replacing a performance objective with a reward for novel behaviors, as defined by a human-crafted, and often simple, behavioral distance function. While Novelty Search is a major conceptual breakthrough and outperforms traditional stochastic optimization on certain problems, it is not clear how to apply it to challenging, high-dimensional problems where specifying a useful behavioral distance function is difficult. For example, in the space of images, how do you encourage novelty to produce hawks and heroes instead of endless pixel static? Here we propose a new algorithm, the Innovation Engine, that builds on Novelty Search by replacing the human-crafted behavioral distance with a Deep Neural Network (DNN) that can recognize interesting differences between phenotypes. The key insight is that DNNs can recognize similarities and differences between phenotypes at an abstract level, wherein novelty means interesting novelty. For example, a novelty pressure in image space does not explore in the low-level pixel space, but instead creates a pressure to create new types of images (e.g. churches, mosques, obelisks, etc.). Here we describe the long-term vision for the Innovation Engine algorithm, which involves many technical challenges that remain to be solved. We then implement a simplified version of the algorithm that enables us to explore some of the algorithm's key motivations. Our initial results, in the domain of images, suggest that Innovation Engines could ultimately automate the production of endless streams of interesting solutions in any domain: e.g. producing intelligent software, robot controllers, optimized physical components, and art.

Question: What is the long-term vision for the innovation engine?


A survey on techniques to handle face recognition challenges: occlusion, single sample per subject and expression. Face recognition is receiving a significant attention due to the need of facing important challenges when developing real applications under unconstrained environments. The three most important challenges are facial occlusion, the problem of dealing with a single sample per subject (SSPS) and facial expression. This paper describes and analyzes various strategies that have been developed recently for overcoming these three major challenges that seriously affect the performance of real face recognition systems. This survey is organized in three parts. In the first part, approaches to tackle the challenge of facial occlusion are classified, illustrated and compared. The second part briefly describes the SSPS problem and the associated solutions. In the third part, facial expression challenge is illustrated. In addition, pros and cons of each technique are stated. Finally, several improvements for future research are suggested, providing a useful perspective for addressing new research in face recognition.

Question: What is the difference between facial occlusion and facial expression?


Stationary common spatial patterns for brain-computer interfacing.. Classifying motion intentions in brain-computer interfacing (BCI) is a demanding task as the recorded EEG signal is not only noisy and has limited spatial resolution but it is also intrinsically non-stationary. The non-stationarities in the signal may come from many different sources, for instance, electrode artefacts, muscular activity or changes of task involvement, and often deteriorate classification performance. This is mainly because features extracted by standard methods like common spatial patterns (CSP) are not invariant to variations of the signal properties, thus should also change over time. Although many extensions of CSP were proposed to, for example, reduce the sensitivity to noise or incorporate information from other subjects, none of them tackles the non-stationarity problem directly. In this paper, we propose a method which regularizes CSP towards stationary subspaces (sCSP) and show that this increases classification accuracy, especially for subjects who are hardly able to control a BCI. We compare our method with the state-of-the-art approaches on different datasets, show competitive results and analyse the reasons for the improvement.

Question: How to classify motion intentions in brain-computer interfacing?


THE INFORMATION CAPACITY OF THE HUMAN MOTOR SYSTEM IN CONTROLLING THE AMPLITUDE OF MOVEMENT. Information theory has recently been employed to specify more precisely than has hitherto been possible man's capacity in certain sensory, perceptual, and perceptual-motor functions (5, 10, 13, 15, 17, 18). The experiments reported in the present paper extend the theory to the human motor system. The applicability of only the basic concepts, amount of information, noise, channel capacity, and rate of information transmission, will be examined at this time. General familiarity with these concepts as formulated by recent writers (4,11, 20, 22) is assumed. Strictly speaking, we cannot study man's motor system at the behavioral level in isolation from its associated sensory mechanisms. We can only analyze the behavior of the entire receptor-neural-effector system. How-

Question: What is the information capacity of the human motor system?


A novel wideband waveguide-to-microstrip transition with waveguide stepped impedance transformer. A novel wideband waveguide-to-microstrip transition is presented in this paper. A printed fan-shaped probe is used, while a waveguide stepped impedance transformer is utilized to broaden the bandwidth further. With an optimized design, a relative bandwidth of 27.8% for -20dB return loss with a center frequency of 15GHz is achieved. The simulated insertion loss in this bandwidth is less than 0.29dB. A back-to-back transition is fabricated and measured. The measured result is similar to that of simulation.

Question: What is a waveguide stepped impedance transformer?


Text Detection and Recognition in Imagery: A Survey. This paper analyzes, compares, and contrasts technical challenges, methods, and the performance of text detection and recognition research in color imagery. It summarizes the fundamental problems and enumerates factors that should be considered when addressing these problems. Existing techniques are categorized as either stepwise or integrated and sub-problems are highlighted including text localization, verification, segmentation and recognition. Special issues associated with the enhancement of degraded text and the processing of video text, multi-oriented, perspectively distorted and multilingual text are also addressed. The categories and sub-categories of text are illustrated, benchmark datasets are enumerated, and the performance of the most representative approaches is compared. This review provides a fundamental comparison and analysis of the remaining problems in the field.

Question: What is the difference between stepwise and integrated text detection and recognition?


Ischemic oculomotor nerve palsy and skin necrosis caused by vascular embolization after hyaluronic acid filler injection: a case report.. Hyaluronic acid filler injection is widely used for soft tissue augmentation. However, there can be disastrous complications by direct vascular embolization. We present a case of ischemic oculomotor nerve palsy and skin necrosis after hyaluronic acid filler injection on glabellar.blepharoptosis, exotropia and diplopia developed suddenly after the injection, and skin necrosis gradually occurred. Symptoms and signs of oculomotor nerve palsy continuously improved with steroid therapy. Skin defects healed with minimal scars through intensive wound care.Percutaneous filler injection of periorbital areas should be performed carefully by experienced surgeons, and the possibility of embolization should be considered promptly if symptoms develop.

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


vTPM: Virtualizing the Trusted Platform Module. We present the design and implementation of a system that enables trusted computing for an unlimited number of virtual machines on a single hardware platform. To this end, we virtualized the Trusted Platform Module (TPM). As a result, the TPM's secure storage and cryptographic functions are available to operating systems and applications running in virtual machines. Our new facility supports higher-level services for establishing trust in virtualized environments, for example remote attestation of software integrity. We implemented the full TPM specification in software and added functions to create and destroy virtual TPM instances. We integrated our software TPM into a hypervisor environment to make TPM functions available to virtual machines. Our virtual TPM supports suspend and resume operations, as well as migration of a virtual TPM instance with its respective virtual machine across platforms. We present four designs for certificate chains to link the virtual TPM to a hardware TPM, with security vs. efficiency trade-offs based on threat models. Finally, we demonstrate a working system by layering an existing integrity measurement application on top of our virtual TPM facility.

Question: What is a virtual TPM?


Swara Histogram Based Structural Analysis And Identification Of Indian Classical Ragas. This work is an attempt towards robust automated analysis of Indian classical ragas through machine learning and signal processing tools and techniques. Indian classical music has a definite heirarchical structure where macro level concepts like thaats and raga are defined in terms of micro entities like swaras and shrutis. Swaras or notes in Indian music are defined only in terms of their relation to one another (akin to the movable do-re-mi-fa system), and an inference must be made from patterns of sounds, rather than their absolute frequency structure. We have developed methods to perform scale-independent raga identification using a random forest classifier on swara histograms and achieved state-of-the-art results for the same. The approach is robust as it directly works on partly noisy raga recordings from Youtube videos without knowledge of the scale used, whereas previous work in this direction often use audios generated in a controlled environment with the desired scale. The current work demonstrates the approach for 8 ragas namely Darbari, Khamaj, Malhar, Sohini, Bahar, Basant, Bhairavi and Yaman and we have achieved an average identification accuracy of 94.28% through the framework.

Question: What is the average identification accuracy of the framework?


Towards SVC-based Adaptive Streaming in information centric networks. HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for video streaming services. In HAS, each video is segmented and stored in different qualities. The client can dynamically select the most appropriate quality level to download, allowing it to adapt to varying network conditions. As the Internet was not designed to deliver such applications, optimal support for multimedia delivery is still missing. Information Centric Networking (ICN) is a recently proposed disruptive architecture that could solve this issue, where the focus is given to the content rather than to end-to-end connectivity. Due to the bandwidth unpredictability typical of ICN, standard AVC-based HAS performs quality selection sub-optimally, thus leading to a poor Quality of Experience (QoE). In this article, we propose to overcome this inefficiency by using Scalable Video Coding (SVC) instead. We individuate the main advantages of SVC-based HAS over ICN and outline, both theoretically and via simulation, the research challenges to be addressed to optimize the delivered QoE.

Question: What is the difference between HAS and ICN?


Compact Proofs of Retrievability. In a proof-of-retrievability system, a data storage center must prove to a verifier that he is actually storing all of a client's data. The central challenge is to build systems that are both efficient and provably secure—that is, it should be possible to extract the client's data from any prover that passes a verification check. In this paper, we give the first proof-of-retrievability schemes with full proofs of security against arbitrary adversaries in the strongest model, that of Juels and Kaliski. Our first scheme, built from BLS signatures and secure in the random oracle model, features a proof-of-retrievability protocol in which the client's query and server's response are both extremely short. This scheme allows public verifiability: anyone can act as a verifier, not just the file owner. Our second scheme, which builds on pseudorandom functions (PRFs) and is secure in the standard model, allows only private verification. It features a proof-of-retrievability protocol with an even shorter server's response than our first scheme, but the client's query is long. Both schemes rely on homomorphic properties to aggregate a proof into one small authenticator value.

Question: What is a proof-of-retrievability scheme?


Automated camera dysfunctions detection. Surveillance systems depend greatly on the robustness and availability of the video streams. The cameras must deliver reliable streams from an angle corresponding to the correct viewpoint. In other words, the field of view and video quality must remain unchanged after the initial installation of a surveillance camera. The paper proposes an approach to detect changes (such as displacement, out of focus, obstruction) automatically in a difficult environment with illumination variations and dynamic background and foreground objects.

Question: How to detect changes in a video stream?


Global cancer statistics.. The global burden of cancer continues to increase largely because of the aging and growth of the world population alongside an increasing adoption of cancer-causing behaviors, particularly smoking, in economically developing countries. Based on the GLOBOCAN 2008 estimates, about 12.7 million cancer cases and 7.6 million cancer deaths are estimated to have occurred in 2008; of these, 56% of the cases and 64% of the deaths occurred in the economically developing world. Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death among females, accounting for 23% of the total cancer cases and 14% of the cancer deaths. Lung cancer is the leading cancer site in males, comprising 17% of the total new cancer cases and 23% of the total cancer deaths. Breast cancer is now also the leading cause of cancer death among females in economically developing countries, a shift from the previous decade during which the most common cause of cancer death was cervical cancer. Further, the mortality burden for lung cancer among females in developing countries is as high as the burden for cervical cancer, with each accounting for 11% of the total female cancer deaths. Although overall cancer incidence rates in the developing world are half those seen in the developed world in both sexes, the overall cancer mortality rates are generally similar. Cancer survival tends to be poorer in developing countries, most likely because of a combination of a late stage at diagnosis and limited access to timely and standard treatment. A substantial proportion of the worldwide burden of cancer could be prevented through the application of existing cancer control knowledge and by implementing programs for tobacco control, vaccination (for liver and cervical cancers), and early detection and treatment, as well as public health campaigns promoting physical activity and a healthier dietary intake. Clinicians, public health professionals, and policy ma

Question: What is the global burden of cancer?


Winter 2003 COMPETENCE , CULPABILITY , AND PUNISHMENT : IMPLICATIONS OF A TKINS FOR EXECUTING AND SENTENCING ADOLESCENTS. The Supreme Court has explored the issues of culpability, proportionality, and deserved punishment most fully in the context of capital punishment. In death penalty decisions addressing developmental impairments and culpability, the Court has considered the cases of defendants with mental retardation and older adolescents, and has created an anomalous inconsistency by reaching opposite conclusions about the deserved punishment for each group of defendants. Recently, in Atkins v. Virginia, the Court relied on both empirical and normative justifications to categorically prohibit states from executing defendants with mental retardation. Atkins reasoned that mentally retarded offenders lacked the reasoning, judgment, and impulse control necessary to equate their culpability with that of other death-eligible criminal defendants. This Article contends that the same psychological and developmental * Centennial Professor of Law, University of Minnesota, B.A., University of Pennsylvania, 1966; J.D., University of Minnesota Law School, 1969; Ph.D. (Sociology), Harvard University, 1973. 1 would like to thank Steven Drizin, Richard Frase, Wayne Logan, Monte Norgaard, and Victor Streib for critical and helpful comments on an earlier draft of this article. The questions and comments of faculty and student participants at the University of Houston Law School Symposium, January, 2004, helped to sharpen the analysis. I received outstanding research assistance from Brittany Stephens, Class of 2005. 1 Feld: Competence, Culpability, and Punishment: Implications of Atkins f Published by Scholarly Commons at Hofstra Law, 2003

Question: What is the difference between the Supreme Court and the Supreme Court of Canada?


State-of-the-art autogenous ear reconstruction in cases of microtia.. Ear reconstruction is considered to be a challenging form of surgery. In cases of microtia, surgeons must reconstruct complex missing contours, which necessitates the use of a support and skin remnants to cover this support. Although the use of synthetic material has been proposed in order to avoid harvesting and carving cartilage, the best long-term choice for reconstructing an ear is autologous rib cartilage. This procedure requires good understanding of the 3-dimensional architecture of the ear and learning the step-by-step construction of a harmonious framework (which with practice will become the most straightforward part of the procedure). Surgery, usually performed at the age of 9 or 10 years, is planned in 2 stages. In the first stage, the framework is placed under a skin pocket. Six months later, the sulcus is created using an additional cartilage graft for projection and a skin-grafted galeal fascial flap. In order to shorten the learning curve, a detailed carving process is described here, as well as a tool to enable training before surgery. Remnants of the microtic ear can have many different shapes; therefore, a comprehensive approach to skin management is proposed, providing a simple surgical classification for all types of microtia. Furthermore, some refinements of the cartilage framework and the construction of the retroauricular sulcus have improved results. Whenever possible, successful reconstruction of a microtic ear with autologous rib cartilage, as opposed to synthetic materials, is by far the best option.

Question: How to reconstruct a microtic ear?


STAIR Captions: Constructing a Large-Scale Japanese Image Caption Dataset. In recent years, automatic generation of image descriptions (captions), that is, image captioning, has attracted a great deal of attention. In this paper, we particularly consider generating Japanese captions for images. Since most available caption datasets have been constructed for English language, there are few datasets for Japanese. To tackle this problem, we construct a large-scale Japanese image caption dataset based on images from MS-COCO, which is called STAIR Captions. STAIR Captions consists of 820,310 Japanese captions for 164,062 images. In the experiment, we show that a neural network trained using STAIR Captions can generate more natural and better Japanese captions, compared to those generated using English-Japanese machine translation after generating English captions.

Question: How large is the dataset?


Networking over Next-generation Satellite Systems Committee in Charge: Networking over Next-generation Satellite Systems Abstract Networking over Next-generation Satellite Systems Contents. Thanks to both the rapid deployment of the Internet and advances in satellite technology, the market for broadband satellite services is poised for substantial growth in the coming decade. Current communications satellite systems have generally been designed to provide either voice or data transaction (low data rate) services through small terminals, or trunking (high data rate, or broadband) services through large terminals. However, technological advances are enabling new systems that combine broadband data rates with small terminals, thereby providing more affordable " last-mile " network access to home and small business users worldwide. In particular, two types of broadband satellite systems are under development: high-power satellites deployed at traditional geostationary (GEO) orbits, and large constellations of satellites deployed at much lower (LEO) orbits. In this thesis, we explore research problems that have arisen from this shift in satellite network architectures. When using GEO satellites to provide Internet access service, the performance of the Internet's Transmission Control Protocol (TCP) is degraded by the high latency and high degree of bandwidth asymmetry present in such systems. We therefore undertook a comprehensive study of TCP performance in the context of broadband satellite systems used for network access. We first studied whether TCP's congestion avoidance algorithm can be adjusted to provide better fairness when satellite connections are forced to share bottleneck links with other (shorter delay) connections. Our data suggests that adjustments of the policy used in that algorithm may yield substantial fairness benefits without compromizing utilization. We next demonstrated how minor variations in TCP implementations can have drastic performance impli

Question: What is the difference between a high-power satellite and a low-power satellite?


Clinical application of the Hybrid Assistive Limb (HAL) for gait training—a systematic review. OBJECTIVE The aim of this study was to review the literature on clinical applications of the Hybrid Assistive Limb system for gait training.   METHODS A systematic literature search was conducted using Web of Science, PubMed, CINAHL and clinicaltrials.gov and additional search was made using reference lists in identified reports. Abstracts were screened, relevant articles were reviewed and subject to quality assessment.   RESULTS Out of 37 studies, 7 studies fulfilled inclusion criteria. Six studies were single group studies and 1 was an explorative randomized controlled trial. In total, these studies involved 140 participants of whom 118 completed the interventions and 107 used HAL for gait training. Five studies concerned gait training after stroke, 1 after spinal cord injury (SCI) and 1 study after stroke, SCI or other diseases affecting walking ability. Minor and transient side effects occurred but no serious adverse events were reported in the studies. Beneficial effects on gait function variables and independence in walking were observed.   CONCLUSIONS The accumulated findings demonstrate that the HAL system is feasible when used for gait training of patients with lower extremity paresis in a professional setting. Beneficial effects on gait function and independence in walking were observed but data do not allow conclusions. Further controlled studies are recommended.

Question: What is the clinical application of the Hybrid Assistive Limb?


Smoothing LUT classifiers for robust face detection. Look-up table (LUT) classifiers are often used to construct concise classifiers for rapid object detection due to their favorable convergent ability. However, their poor generalization ability imposes restrictions on their applications. A novel improvement to LUT classifiers is proposed in this paper where the new confident of each partition is recalculated by smoothing the old ones within its neighbor partitions. The new confidents are more generalizable than the old ones because each of the new predicts is supported by more training samples and the high frequency components in the old predict sequences are suppressed greatly through smoothing operation. Both weight sum smoothing method and confident smoothing method are introduced here, which all bring negligible extra computation cost in training time and no extra cost in test time. Experimental results in the domain of upright frontal face detection using smoothed LUT classifiers with identical smoothing width and smoothing factor for all partitions based on Haar-like rectangle features show that smoothed LUT classifiers generalize much better and also converge more or less worse than unsmooth LUT classifiers. Specifically, smoothed LUT classifiers can delicately balance between generalization ability and convergent ability through carefully set smoothing parameters.

Question: What is a look-up table classifier?


Multi-View Unsupervised Feature Selection with Adaptive Similarity and View Weight. With the advent of multi-view data, multi-view learning has become an important research direction in both machine learning and data mining. Considering the difficulty of obtaining labeled data in many real applications, we focus on the multi-view unsupervised feature selection problem. Traditional approaches all characterize the similarity by fixed and pre-defined graph Laplacian in each view separately and ignore the underlying common structures across different views. In this paper, we propose an algorithm named Multi-view Unsupervised Feature Selection with Adaptive Similarity and View Weight (ASVW) to overcome the above mentioned problems. Specifically, by leveraging the learning mechanism to characterize the common structures adaptively, we formulate the objective function by a common graph Laplacian across different views, together with the sparse <inline-formula><tex-math notation="LaTeX">$\ell _{2,p}$</tex-math> <alternatives><inline-graphic xlink:href="hou-ieq1-2681670.gif"/></alternatives></inline-formula>-norm constraint designed for feature selection. We develop an efficient algorithm to address the non-smooth minimization problem and prove that the algorithm will converge. To validate the effectiveness of ASVW, comparisons are made with some benchmark methods on real-world datasets. We also evaluate our method in the real sports action recognition task. The experimental results demonstrate the effectiveness of our proposed algorithm.

Question: Multi-view unsupervised feature selection with adaptive similarity and view weight


Bridging the Gap between Business Strategy and Software Development. In software-intensive organizations, an organizational management system will not guarantee organizational success unless the business strategy can be translated into a set of operational software goals. The Goal Question Metric (GQM) approach has proven itself useful in a variety of industrial settings to support quantitative software project management. However, it does not address linking software measurement goals to higher-level goals of the organization in which the software is being developed. This linkage is important, as it helps to justify software measurement efforts and allows measurement data to contribute to higher-level decisions. In this paper, we propose a GQMStrategies® measurement approach that builds on the GQM approach to plan and implement software measurement. GQMStrategies provides mechanisms for explicitly linking software measurement goals to higher-level goals for the software organization, and further to goals and strategies at the level of the entire business. An example application of the proposed method is illustrated in the context of an example measurement initiative.

Question: How can we bridge the gap between business strategy and software development?


Continuous and fine-grained breathing volume monitoring from afar using wireless signals. In this work, we propose for the first time an autonomous system, called WiSpiro, that continuously monitors a person's breathing volume with high resolution during sleep from afar. WiSpiro relies on a phase-motion demodulation algorithm that reconstructs minute chest and abdominal movements by analyzing the subtle phase changes that the movements cause to the continuous wave signal sent by a 2.4 GHz directional radio. These movements are mapped to breathing volume, where the mapping relationship is obtained via a short training process. To cope with body movement, the system tracks the large-scale movements and posture changes of the person, and moves its transmitting antenna accordingly to a proper location in order to maintain its beam to specific areas on the frontal part of the person's body. It also incorporates interpolation mechanisms to account for possible inaccuracy of our posture detection technique and the minor movement of the person's body. We have built WiSpiro prototype, and demonstrated through a user study that it can accurately and continuously monitor user's breathing volume with a median accuracy from 90% to 95.4% (or 0.0581 to 0.111 of error) to even in the presence of body movement. The monitoring granularity and accuracy are sufficiently high to be useful for diagnosis by clinical doctor.

Question: How accurate is the breathing volume monitoring?


Revised and abbreviated forms of the Genderism and Transphobia Scale: tools for assessing anti-trans prejudice.. Many studies of anti-trans prejudice have measured such attitudes using the Genderism and Transphobia Scale (GTS; Hill & Willoughby, 2005). The GTS is unique in assessing negative attitudes and propensity for violence toward trans people. The present research addressed previously observed limitations in the psychometric properties of data produced by the GTS, including inconsistencies in factor structure and subscale scoring across studies. Results across the present 2 studies (Ns = 314 and 250) yielded 2 refined versions of the GTS: the 22-item GTS-Revised (GTS-R) and a more abbreviated 13-item GTS-R-Short Form (GTS-R-SF), each of which produced stable 2-factor structures corresponding with the intended negative attitudes and propensity for violence dimensions of the GTS. The 2 versions differ in that the GTS-R-SF Genderism/Transphobia subscale focuses on more severe expressions of prejudicial attitudes, whereas the longer GTS-R Genderism/Transphobia subscale assesses subtle expressions of prejudice as well. The Gender-Bashing subscale is identical across the 2 versions. Thus, researchers and practitioners may choose the GTS-R or GTS-R-SF depending on the breadth of prejudicial attitudes they wish to assess. Reliability estimates for GTS-R and GTS-R-SF scale and subscale item responses were acceptable and stable across the 2 studies, and validity evidence was garnered in Study 2. These findings can inform use of the GTS-R and GTS-R-SF in research and practice settings, where psychometric precision and efficiency are both critical.

Question: What is the difference between the Genderism and Transphobia Scale and the Genderism and Transphobia Scale Revised?


Automatic Seed Classification by Shape and Color Features using Machine Vision Technology. : In this paper the proposed system uses content based image retrieval (CBIR) technique for identification of seed e.g. wheat, rice, gram etc. on the basis of their features. CBIR is a technique to identify or recognize the image on the basis of features present in image. Basically features are classified in to four categories 1.color 2.Shape 3. texture 4. size .In this system we are extracting color, shape feature extraction. After that classifying images in to categories using neural network according to the weights and image displayed from the category for which neural network shows maximum weight. category1 belongs to wheat and category2 belongs to gram. Experiment was conducted on 200 images of wheat and gram by using Euclidean distance(ED) and artificial neural network techniques. From 200 images 150 are used for training purpose and 50 images are used for testing purpose. The precision rate of the system by using ED is 84.4 percent By using Artificial neural network precision rate is 95 percent.

Question: How to classify images of wheat and gram?


NEUROSCIENTIFIC INVE STIGATIONS OF MUSICAL RHYTHM. Music occurs in every human society, unfolds over time, and enables synchronized movements. The neural mechanisms underlying the perception, cognition, and production of musical rhythm have been investigated using a variety of methods. FMRI studies in particular have shown that the motor system is crucially involved in rhythm and beat perception. Studies using other methods demonstrate that oscillatory neural activity entrains to regularities in musical rhythm, and that motor system excitability is modulated by listening to musical rhythm. This review paper describes some of the recent neuroscientific findings regarding musical rhythm, and especially the perception of a regular beat. INTRODUCTION The temporal structure of music enables synchronized movement, such as tapping one's foot, clapping, or dancing to the 'beat' of musical rhythms. Such movement is precisely timed to align with the periodic, salient beats in the music, and with the movements of other individuals. Given this relationship between musical rhythm and movement, it is perhaps unsurprising that the brain's motor system is heavily involved in the neural processing of auditory rhythms. However, it is a relatively recent discovery that the motor system is involved even in the absence of movement – subtle differences in the temporal structure or context of an auditory rhythm can elicit robust differences in motor system activity. These discoveries are the topic of this review paper, with a focus on findings from functional magnetic resonance imaging (fMRI). FMRI measures the change in oxygenated blood levels following neural activity [see 1, 2]. This 'blood-oxygen-level dependent' (or BOLD) signal is considered to be an indirect measure of brain activity, and therefore increases in BOLD are termed 'activations' in this review. Findings from patient studies, as well as electroencephalography (EEG), magnetoencephalography (MEG), and transcranial magnetic

Question: What is the difference between fMRI and EEG?


Network Slicing in 5G Mobile Communication Architecture, Profit Modeling, and Challenges. Efficient flexibility and higher system scalability call for enhanced network performance, better energy consumption, lower infrastructure cost, and effective resource utilization. To accomplish this, an architectural optimization and reconstruction of existing cellular network is required. Network slicing is considered to be one of the key enablers and an architectural answer of communication system of 2020 and beyond. Traditional mobile operators provide all types of services to various kinds of customers through a single network, however, with the deployment of network slicing operators are now able to divide entire network into different slices each with its own configuration and specific Quality of Service (QoS) requirements. In a slice-based network, each slice will be considered as a separate logical network. In this way, the infrastructure utilization and resource allocation will be much more energy and cost efficient in comparison to traditional network. In this paper, we provided a comprehensive discussion on concept and system architecture of network slicing with particular focus on its business aspect and profit modeling. We throughly discussed two different dimensions of profit modeling, so called Own-Slice Implementation and Resource Leasing for Outsourced Slices. We further addressed open research directions and existing challenges with the purpose of motivating new advances and adding realistic solutions to this emerging technology.

Question: What is the difference between network slicing and network virtualization?


Information network or social network?: the structure of the twitter follow graph. In this paper, we provide a characterization of the topological features of the Twitter follow graph, analyzing properties such as degree distributions, connected components, shortest path lengths, clustering coefficients, and degree assortativity. For each of these properties, we compare and contrast with available data from other social networks. These analyses provide a set of authoritative statistics that the community can reference. In addition, we use these data to investigate an often-posed question: Is Twitter a social network or an information network? The "follow" relationship in Twitter is primarily about information consumption, yet many follows are built on social ties. Not surprisingly, we find that the Twitter follow graph exhibits structural characteristics of both an information network and a social network. Going beyond descriptive characterizations, we hypothesize that from an individual user's perspective, Twitter starts off more like an information network, but evolves to behave more like a social network. We provide preliminary evidence that may serve as a formal model of how a hybrid network like Twitter evolves.

Question: What is the structure of the twitter follow graph?


A compact broadband hybrid ridged SIW and GCPW coupler. In this paper, a novel hybrid ridged substrate integrated waveguide (RSIW) and ground coplanar waveguide (GCPW) structure coupler is presented. In this coupler, the RSIW and GCPW are arranged back to back to realize high-integration. Such hybrid coupler combines the broadband advantage of RSIW and the flexibility of GCPW. According to the simulated results, it can operate at 28 GHz and achieve a relative bandwidth of 17%.

Question: What is a hybrid coupler?


Google's PageRank and beyond - the science of search engine rankings. Why doesn't your home page appear on the first page of search results, even when you query your own name? How do other web pages always appear at the top? What creates these powerful rankings? And how? The first book...

Question: How do search engines work?


Automated Trajectory Planner of Industrial Robot for Pick-and-Place Task. Industrial  robots,  due  to  their  great  speed, precision  and  cost‐effectiveness  in  repetitive  tasks,  now tend  to be used  in place of human workers  in automated manufacturing  systems.  In  particular,  they  perform  the pick‐and‐place  operation,  a  non‐value‐added  activity which  at  the  same  time  cannot  be  eliminated.  Hence, minimum time is an important consideration for economic reasons  in  the  trajectory  planning  system  of  the manipulator.  The  trajectory  should  also  be  smooth  to handle  parts  precisely  in  applications  such  as semiconductor manufacturing, processing and handling of chemicals and medicines, and fluid and aerosol deposition. In this paper, an automated trajectory planner is proposed to determine  a  smooth, minimum‐time  and  collision‐free trajectory  for  the  pick‐and‐place  operations  of  a  6‐DOF robotic  manipulator  in  the  presence  of  an  obstacle. Subsequently,  it  also  proposes  an  algorithm  for  the  jerk‐ bounded  Synchronized  Trigonometric  S‐curve  Trajectory (STST)  and  the  'forbidden‐sphere'  technique  to  avoid  the obstacle.  The  proposed  planner  is  demonstrated  with suitable examples and comparisons. The experiments show that  the  proposed  planner  is  capable  of  providing  a smoother trajectory than the cubic spline based trajectory.

Question: What is a pick-and-place operation?


Dual Switches DC/DC Converter With Three-Winding-Coupled Inductor and Charge Pump. In order to obtain a high step-up voltage gain, high-efficiency converter, this paper proposed a dual switches dc/dc converter with three-winding-coupled inductor and charge pump. The proposed converter composed of dual switches structure, three-winding-coupled inductor, and charge pump. This combination facilitates realization of high step-up voltage gain with a low voltage/current stress on the power switches. Meanwhile, the voltage across the diodes is low and the diode reverse-recovery problem is alleviated by the leakage inductance of the three-winding-coupled inductor. Taking all these into consideration, the efficiency can be high. This paper illustrated the operation principle of the proposed converter; discussed the effect of leakage inductance on voltage gain; the conditions of zero current shutting off of the diodes are illustrated; the voltage and current stress of the power devices are shown; a comparison between the performance of the proposed converter and previous high step-up converters was conducted. Finally, a prototype rated at 500 W has been established, and the experimental results verify the correctness of the analysis.

Question: What is the purpose of the paper?


Trends in ship electric propulsion. Since the 1980s there has been an explosion in the number and variety of electric propulsion ships being built around the world with everything from cruise liners to amphibious assault ships adopting electric propulsion. This technology revolution has occurred, largely un-noticed outside the marine industry. This paper briefly describes what has transpired in recent decades, why the resurgence is occurring and what the future may hold for electrically propelled ships.

Question: What is the future of electric propulsion?


Robotic excavation in construction automation. C onstruction is of prime economic significance to many industry sectors. Intense competition, shortages of skilled labor, and technological advances are forcing rapid change in the construction industry, thus motivating construction automation [1]. Earthmoving machines, such as bulldozers, wheel loaders, excavators, scrapers, and graders, are common in construction. Excavationbased operations are used in general earthmoving, digging, and sheet-piling for displacing large amounts of material. On a smaller scale, operations such as trenching and footing formation require precisely controlled excavation. Although the fully automated construction site is still a dream of some civil engineers, research developments have shown the promise of robotics and automation in construction [2]. Despite the apparent economic importance of excavation in construction, there have been few implementations of autonomous or teleoperated excavators. A number of researchers have investigated the feasibility of automating excavation. Many of these studies have addressed the possible use of autonomous excavators during unmanned phases of establishing manned Lunar or Martian research stations [3], [4]. Much of the work on terrestrial excavation has focused on teleoperation, rather than on the system requirements for autonomous operation. Although there have been a number of valuable theoretical and experimental contributions to the field of autonomous, robotic or teleoperated excavation [5]-[8], autonomous operation of a full-scale excavator has not been commercially demonstrated. Many of the experimental studies reported in the literature involve using conventional industrial robots fitted with buckets to excavate in a bed of loose sand. While there are parallels between "classical" robotics and robotic excavation, there are also some pronounced differences. In particular, an excavator is not fixed relative to the work piece; it plastically def

Question: What is the difference between a robot and a excavator?


Multitarget miss distance via optimal assignment. The concept of miss distance-Euclidean, Mahalanobis, etc.-is a fundamental, far-reaching, and taken-for-granted element of the engineering theory and practice of single-target systems. In this paper we introduce a comprehensive L/sub p/-type theory of distance metrics for multitarget (and, more generally, multiobject) systems. We show that this theory extends, and provides a rigorous theoretical basis for, an intuitively appealing optimal-assignment approach proposed by Drummond for evaluating the performance of multitarget tracking algorithms. We describe tractable computational approaches for computing such metrics based on standard optimal assignment or convex optimization techniques. We describe the potentially far-reaching implications of these metrics for applications such as performance evaluation and sensor management. In the former case, we demonstrate the application of multitarget miss-distance metrics as measures of effectiveness (MoEs) for multitarget tracking algorithms.

Question: What is the miss distance?


The Anatomy of a Large-Scale Hypertextual Web Search Engine. In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at http://google.stanford.edu/ To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale web search engine -the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.

Question: What is the anatomy of a large-scale hypertextual web search engine?