Query: A Fully Automatic Crossword Generator

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Wordnet Enhanced Automatic Crossword Generation <=SEP=> We report on a system for automatically generating and displaying crosswords from a system manager supplied database of potential clues and corresponding words that index those clues. The system relies on the lexical relations encoded in WordNet to enhance the aesthetics of the resulting crossword by making it easier to automatically identify a grid that may be populated with words and clues that have a thematic focus. The system architecture is provided in overview, as is empirical evaluation.

A Maximum Entropy Model for Part-Of-Speech Tagging <=SEP=> This paper presents a statistical model which trains from a corpus annotated with Part Of Speech tags and assigns them to previously unseen text with state of the art accuracy The model can be classi ed as a Maximum Entropy model and simultaneously uses many contextual features to predict the POS tag Furthermore this paper demonstrates the use of specialized fea tures to model di cult tagging decisions discusses the corpus consistency problems discovered during the implementation of these features and proposes a training strategy that mitigates these problems

TnT -- A Statistical Part-of-Speech Tagger <=SEP=> Trigrams'n'Tags (TnT) is an efficient statistical part-of-speech tagger. Contrary to claims found elsewhere in the literature, we argue that a tagger based on Markov models performs at least as well as other current approaches, including the Maximum Entropy framework. A recent comparison has even shown that TnT performs significantly better for the tested corpora. We describe the basic model of TnT, the techniques used for smoothing and for handling unknown words. Furthermore, we present evaluations on two corpora.

Chunking with Support Vector Machines <=SEP=> We apply Support Vector Machines (SVMs) to identify English base phrases (chunks). SVMs are known to achieve high generalization performance even with input data of high dimensional feature spaces. Furthermore, by the Kernel principle, SVMs can carry out training with smaller computational overhead independent of their dimensionality. We apply weighted voting of 8 SVMsbased systems trained with distinct chunk representations. Experimental results show that our approach achieves higher accuracy than previous approaches.

WebCrow: A Web-Based System for Crossword Solving <=SEP=> Language games represent one of the most fascinating challenges of research in artificial intelligence. In this paper we give an overview of WebCrow, a system that tackles crosswords using the Web as a knowledge base. This appears to be a novel approach with respect to the available literature. It is also the first solver for nonEnglish crosswords and it has been designed to be potentially multilingual. Although WebCrow has been implemented only in a preliminary version, it already displays very interesting results reaching the performance of a human beginner: crosswords that are “easy” for expert humans are solved, within competition time limits, with 80% of correct words and over 90% of correct letters. Introduction Crosswords are probably the most played language puzzles worldwide. Until recently, the research in artificial intelligence focused its interest only on the NP-complete crossword generation problem (Mazlack 1976) (Ginsberg et al. 1990) which can now be solved in a few seconds for reasonable dimensions (Ginsberg 1993). Conversely, problems like solving crosswords from clues have been defined as AI-complete (Littman 2000) and are extremely challenging for machines since there is no closedworld assumption and they require human-level knowledge. Interestingly, AI has nowadays the opportunity to exploit a stable nucleus of technology, such as search engines, information retrieval and machine learning techniques, that can enable computers to enfold with semantics real-life concepts. We will present here a software system, called WebCrow, whose major assumption is to attack crosswords making use of the Web as its primary source of knowledge, being this an extremely rich and self-updating repository of human knowledge1. This paper gives a general overview of the modules and the subproblems present in the project. Copyright c © 2005, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. 1The WebCrow project, with the emphasis on the power coming from searching the Web, was briefly described in the Nature magazine(News-Nature 2004) and mentioned in the AAAI Web site concerning games and puzzles http://www.aaai.org/AITopics/html/crosswd.html To the best of our knowledge the only significant attempt reported in the literature to tackle this problem is the Proverb system, which has reached human-like performances (Keim, Shazeer, & Littman 1999). Unlike Proverb, WebCrow does not possess any knowledge-specific expert module nor a crossword database of great dimensions. WebCrow, instead, in order to stress the generality of its knowledge and language-independence, has in its core a module performing a special form of question answering on the Web that we shall call clue-answering. The second goal of the system, that is filling the crossword grid with the best set of candidate answers, has been tackled as a Probabilistic-CSP. Italian crosswords are structurally similar to Americans, but they differ in two aspects: two-letter words2 are allowed and, more importantly, they contain stronger and more ambiguous references to socio-cultural and political topics. The latter is a phenomenon especially present in newspapers and it introduces an additional degree of complexity in crossword solving, since it requires the possession of a knowledge that is extremely broad, fresh and also robust to volunteer vagueness and ambiguity. Neologisms and compound words are often present. We have collected 685 examples of Italian crosswords. The majority was obtained from two sources: the main Italian crossword magazine La Settimana Enigmistica and the main on-line newspaper’s crossword section La Repubblica. 60 puzzles were randomly chosen to form the experimental test suite. The remaining part constituted a database of clue-answer pairs used as an aid in the answering process. In this paper the challenge of WebCrow is to solve Italian crosswords within a 15 minutes time limit (running the program on a common laptop), as in real competitions. The version of WebCrow that is discussed here is preliminary but it has already given very promising results. WebCrow averaged around 70% words correct and 80% letters correct in the overall test set. On the examples that expert players consider “easy” WebCrow performed with 80% words correct (100% in one case) and over 90% letters correct. Architectural overview WebCrow has been designed as a modular and data-driven system. Two are the main sources that it applies to: Web 2Name initials, acronyms, abbreviations or portions of a word.

Muscle glycogen synthesis after exercise: effect of time of carbohydrate ingestion. <=SEP=> The time of ingestion of a carbohydrate supplement on muscle glycogen storage postexercise was examined. Twelve male cyclists exercised continuously for 70 min on a cycle ergometer at 68% VO2max, interrupted by six 2-min intervals at 88% VO2max, on two separate occasions. A 25% carbohydrate solution (2 g/kg body wt) was ingested immediately postexercise (P-EX) or 2 h postexercise (2P-EX). Muscle biopsies were taken from the vastus lateralis at 0, 2, and 4 h postexercise. Blood samples were obtained from an antecubital vein before and during exercise and at specific times after exercise. Muscle glycogen immediately postexercise was not significantly different for the P-EX and 2P-EX treatments. During the first 2 h postexercise, the rate of muscle glycogen storage was 7.7 mumol.g wet wt-1.h-1 for the P-EX treatment, but only 2.5 mumol.g wet wt-1.h-1 for the 2P-EX treatment. During the second 2 h of recovery, the rate of glycogen storage slowed to 4.3 mumol.g wet wt-1.h-1 during treatment P-EX but increased to 4.1 mumol.g wet wt-1.h-1 during treatment 2P-EX. This rate, however, was still 45% slower (P less than 0.05) than that for the P-EX treatment during the first 2 h of recovery. This slower rate of glycogen storage occurred despite significantly elevated plasma glucose and insulin levels. The results suggest that delaying the ingestion of a carbohydrate supplement post-exercise will result in a reduced rate of muscle glycogen storage.

VERL: An Ontology Framework for Representing and Annotating Video Events <=SEP=> in large multimedia databases and make inferences about the data stream’s content. A human operator can specify gross indices, such as the date or hour, and perhaps the location and the activity (for example, a football game) at the time of capture. Finer-grain annotations require separating the video into meaningful segments such as shots and scenes. Many automated techniques and annotation formats for capturing this type of information exist. However, the descriptions they produce don’t define the data stream content adequately for semantic retrieval and reasoning. Such descriptions must be based on observable or inferrable events in the data streams. We describe a framework for video event representation and annotation that’s based on the definition of an ontology suitable for video content. An ontology consists of a specific vocabulary for describing a certain reality and a set of explicit assumptions regarding the vocabulary’s intended meaning.1 The Video Event Representation Language (VERL)2-4 describes an event ontology, and the Video Event Markup Language (VEML)4 lets us annotate instances of the events described in VERL (see the sidebar “Using VERL and VEML” on p. 78 for an example). Annotating video in VEML consists of describing instances of events and objects, in a previously defined ontology using VERL. Figure 1 illustrates the conceptual elements involved and their relationships: annotations draw on one or several domain-specific ontologies, defined according to VERL concepts and constructs. VEML incorporates extensible event and object type hierarchies rooted in VERL’s event and object concepts. The annotations also draw on VEML for content organization. Marking up data streams in a well-defined language rooted in an ontology enables nonambiguous data sharing among users. Furthermore, annotation data is accessible to automatic machine manipulation for indexing or inferencing. The framework we describe resulted from discussions at a series of workshops sponsored by the US government’s Advanced Research and Development Activity (ARDA). It therefore includes the intellectual contributions of many individuals. Currently, only a small community of researchers is using VERL and VEML; this article aims to publicize the framework to a broader community as its relevance depends on its widespread adoption. An earlier description can be found elsewhere.5

A 12-bit 1.6 GS/s interleaved SAR ADC with dual reference shifting and interpolation achieving 17.8 fJ/conv-step in 65nm CMOS <=SEP=> A 12-bit SAR ADC architecture with dual reference shifting and interpolation technique has been proposed and implemented with 8-way time interleaving in 65nm CMOS. The proposed technique converts 4 bits per SAR conversion cycle with reduced overhead, which is a key to achieve both high speed and resolution while maintaining low power consumption. The measured peak SNDR is 72dB and remains above 65.3dB at 1-GHz input frequency at sample rate of 1.6 GS/s. It achieves a record power efficiency of 17.8fJ/conv-step among the recently published high-speed/resolution ADCs.

It’s All Fun and Games until Someone Annotates: Video Games with a Purpose for Linguistic Annotation <=SEP=> Annotated data is prerequisite for many NLP applications. Acquiring large-scale annotated corpora is a major bottleneck, requiring significant time and resources. Recent work has proposed turning annotation into a game to increase its appeal and lower its cost; however, current games are largely text-based and closely resemble traditional annotation tasks. We propose a new linguistic annotation paradigm that produces annotations from playing graphical video games. The effectiveness of this design is demonstrated using two video games: one to create a mapping from WordNet senses to images, and a second game that performs Word Sense Disambiguation. Both games produce accurate results. The first game yields annotation quality equal to that of experts and a cost reduction of 73% over equivalent crowdsourcing; the second game provides a 16.3% improvement in accuracy over current state-of-the-art sense disambiguation games with WordNet.

Natural Language Processing for Information Retrieval <=SEP=> The paper summarizes the essential properties of document retrieval and reviews both conventional practice and research findings, the latter suggesting that simple statistical techniques can be effective. It then considers the new opportunities and challenges presented by the user’s ability to search full text directly (rather than e.g. titles and abstracts), and suggests appropriate approaches to doing this, with a focus on the potential role of natural language processing. The paper also comments on possible connections with data and knowledge retrieval, and concludes by emphasizing the importance of rigorous performance testing.

Anticipating the Future by Constructing Human Activities using Object Affordances <=SEP=> An important aspect of human perception is anticipation and anticipating which activities will a human do next (and how to do them) in useful for many applications, for example, anticipation enables an assistive robot to plan ahead for reactive responses in the human environments. In this work, we present a constructive approach for generating various possible future human activities by reasoning about the rich spatial-temporal relations through object affordances. We represent each possible future using an anticipatory temporal conditional random field (ATCRF) where we sample the nodes and edges corresponding to future object trajectories and human poses from a generative model. We then represent the distribution over the potential futures using a set of constructed ATCRF particles. In extensive evaluation on CAD-120 human activity RGB-D dataset, for new subjects (not seen in the training set), we obtain an activity anticipation accuracy (defined as whether one of top three predictions actually happened) of 75.4%, 69.2% and 58.1% for an anticipation time of 1, 3 and 10 seconds respectively. 1

Constraint-Based Approaches for Balancing Bike Sharing Systems <=SEP=> In order to meet the users’ demand, bike sharing systems must be regularly rebalanced. The problem of balancing bike sharing systems (BBSS) is concerned with designing optimal tours and operating instructions for relocating bikes among stations to maximally comply with the expected future bike demands. In this paper, we tackle the BBSS by means of Constraint Programming: first, we introduce two novel constraint models for the BBSS including a smart branching strategy that focusses on the most promising routes. Second, in order to speed-up the search process, we incorporate both models in a Large Neighborhood Search (LNS) approach that is adapted to the respective CP model. Third, we perform an extensive computational evaluation on instances based on real-world data, where we see that the LNS approach outperforms the Branch & Bound approach and is competitive with other existing approaches.

Total Anomalous Pulmonary Venous Connection: From Embryology to a Prenatal Ultrasound Diagnostic Update <=SEP=> .08.002 C and the Chinese Taipei Society o mons.org/licenses/by-nc-nd/4.0/ also as a part of complex heart diseases or genetic syndromes. Among cyanotic heart diseases, total anomalous pulmonary venous connection (TAPVC) is the only condition involving a venous system malformation [1] and is easily misdiagnosed. The key characteristic of this uncommon congenital heart disease (CHD) is that all four pulmonary veins (PVs) fail to form a direct connection to the left atrium (LA); instead, they drain into the right heart through different routes of systemic venous return. The incidence of this condition is approximately 7e9 per 100,000 live births [2,3], and it accounts for 0.7e1.5% of all CHDs [4]. Patients with CHD who were born from 2000 to 2006 in Taiwan also exhibited a similar prevalence of TAPVC (0.11/ 1000) [5].

Informative path planning for an autonomous underwater vehicle <=SEP=> We present a path planning method for autonomous underwater vehicles in order to maximize mutual information. We adapt a method previously used for surface vehicles, and extend it to deal with the unique characteristics of underwater vehicles. We show how to generate near-optimal paths while ensuring that the vehicle stays out of high-traffic areas during predesignated time intervals. In our objective function we explicitly account for the fact that underwater vehicles typically take measurements while moving, and that they do not have the ability to communicate until they resurface. We present field results from ocean trials on planning paths for a specific AUV, an underwater glider.

Memristive devices for computing: Beyond CMOS and beyond von Neumann <=SEP=> Traditional CMOS technology and its continuous down-scaling have been the driving force to improve performance of existing computer architectures. Today, however, both technology and computer architectures are facing challenges that make them incapable of delivering the growing computing performance requirement at pre-defined constraints. This forces the exploration of both novel architectures and technologies; not only to maintain the economic profit of technology scaling, but also to enable the computing architecture solutions for big-data and data-intensive applications. This paper discusses the emerging memristive device as a complement (or an alternative) to CMOS devices and shows how such devices enable novel computing paradigms that will solve the challenges of today's architectures for certain applications. The paper covers not only the potential of memristor devices in enabling novel memory technologies, logic design styles, and arithmetic operations, but also their potential in enabling in-memory computing and neuromorphic computing.

An Approach for Factory-Wide Control Utilizing Virtual Metrology <=SEP=> In the semiconductor manufacturing industry, market demands and technology trends drive manufacturers towards increases in wafer size and decreases in device size. Application of factory-wide advanced process control (APC) techniques down to the wafer-to-wafer (W2W) level control capability is becoming the only choice to cope with the demanding situation. However, one of the main limitations in undertaking W2W control is the nonavailability of timely metrology data at the wafer level. Recently virtual metrology (VM) techniques have been proposed to provide timely wafer level metrology data; they have the potential to be used in realizing W2W control. In this paper, the VM approach to W2W control on factory level is described. VM for an individual process is realized by utilizing the preprocess metrology and the process data from the underlying tools that is generally collected in real time for fault detection purposes. The VM implementation for factory-wide run-to-run control brings unique opportunities and issues to the forefront such as dealing with expected lower quality of VM data, coordination between VM modules of cascading processes for better prediction quality, flexibility of the factory-wide controller to accommodate lower quality VM data, dynamic adjustments to the target values of individual processes by the factory-wide controller when using VM data, and dealing with metrology delays at the factory level. Near and long-term solutions are presented to address these issues in order to allow VM to be used today and become an integral part of the factory-wide APC solution for W2W control.

High Confidence Policy Improvement <=SEP=> We present a batch reinforcement learning (RL) algorithm that provides probabilistic guarantees about the quality of each policy that it proposes, and which has no hyper-parameters that require expert tuning. The user may select any performance lower-bound, ρ−, and confidence level, δ, and our algorithm will ensure that the probability that it returns a policy with performance below ρ− is at most δ. We then propose an incremental algorithm that executes our policy improvement algorithm repeatedly to generate multiple policy improvements. We show the viability of our approach with a simple gridworld and the standard mountain car problem, as well as with a digital marketing application that uses real world data.

UltraGesture: Fine-Grained Gesture Sensing and Recognition <=SEP=> With the rising of AR/VR technology and miniaturization of mobile devices, gesture is becoming an increasingly popular means of interacting with smart devices. Some pioneer ultrasound based human gesture recognition systems have been proposed. They mostly rely on low resolution Doppler Effect, and hence focus on whole hand motion and cannot deal with minor finger motions. In this paper, we present UltraGesture, a Channel Impulse Response (CIR) based ultrasonic finger motion perception and recognition system. CIR measurements can provide with 7 mm resolution, rendering it sufficient for minor finger motion recognition. UltraGesture encapsulates CIR measurements into an image, and builds a Convolutional Neural Network model to classify these images into different categories, which corresponding to distinct gestures. Our system runs on commercial speakers and microphones that already exist on most mobile devices without hardware modification. Our results show that UltraGesture achieves an average accuracy of greater than 97% for 12 gestures including finger click and rotation.

A classification of transaction processing systems <=SEP=> The problems in building a transaction processing system are discussed, and it is shown that the difficulties are a function of specific attributes of the underlying database system. A model of a transaction processing system is presented, and five system dimensions important in classifying transaction processing systems-the process, machine, heterogeneity, data, and site components-are introduced. The specific problems posed by various combinations of system characteristics are analyzed. The evolution of transaction processing systems are described in terms of the framework.<<ETX>>

Characterizing 3D Floating Gate NAND Flash <=SEP=> In this paper, we characterize a state-of-the-art 3D floating gate NAND flash memory through comprehensive experiments on an FPGA platform. Then, we present distinct observations on performance and reliability, such as operation latencies and various error patterns. We believe that through our work, novel 3D NAND flash-oriented designs can be developed to achieve better performance and reliability.

Web Mining Research: A Survey <=SEP=> With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. The Web mining research is at the cross road of research from several research communities, such as database, information retrieval, and within AI, especially the sub-areas of machine learning and natural language processing. However, there is a lot of confusions when comparing research efforts from different point of views. In this paper, we survey the research in the area of Web mining, point out some confusions regarded the usage of the term Web mining and suggest three Web mining categories. Then we situate some of the research with respect to these three categories. We also explore the connection between the Web mining categories and the related agent paradigm. For the survey, we focus on representation issues, on the process, on the learning algorithm, and on the application of the recent works as the criteria. We conclude the paper with some research issues.

SpaceTwist: Managing the Trade-Offs Among Location Privacy, Query Performance, and Query Accuracy in Mobile Services <=SEP=> In a mobile service scenario, users query a server for nearby points of interest but they may not want to disclose their locations to the service. Intuitively, location privacy may be obtained at the cost of query performance and query accuracy. The challenge addressed is how to obtain the best possible performance, subjected to given requirements for location privacy and query accuracy. Existing privacy solutions that use spatial cloaking employ complex server query processing techniques and entail the transmission of large quantities of intermediate result. Solutions that use transformation-based matching generally fall short in offering practical query accuracy guarantees. Our proposed framework, called SpaceTwist, rectifies these shortcomings for k nearest neighbor (kNN) queries. Starting with a location different from the user's actual location, nearest neighbors are retrieved incrementally until the query is answered correctly by the mobile terminal. This approach is flexible, needs no trusted middleware, and requires only well-known incremental NN query processing on the server. The framework also includes a server-side granular search technique that exploits relaxed query accuracy guarantees for obtaining better performance. The paper reports on empirical studies that elicit key properties of SpaceTwist and suggest that the framework offers very good performance and high privacy, at low communication cost.

Nonlinear controller design for a magnetic levitation device <=SEP=> Various applications of micro-robotic technology suggest the use of new actuator systems which allow motions to be realized with micrometer accuracy. Conventional actuation techniques such as hydraulic or pneumatic systems are no longer capable of fulfilling the demands of hi-tech micro-scale areas such as miniaturized biomedical devices and MEMS production equipment. These applications pose significantly different problems from actuation on a large scale. In particular, large scale manipulation systems typically deal with sizable friction, whereas micro manipulation systems must minimize friction to achieve submicron precision and avoid generation of static electric fields. Recently, the magnetic levitation technique has been shown to be a feasible actuation method for microscale applications. In this paper, a magnetic levitation device is recalled from the authors’ previous work and a control approach is presented to achieve precise motion control of a magnetically levitated object with sub-micron positioning accuracy. The stability of the controller is discussed through the Lyapunov method. Experiments are conducted and showed that the proposed control technique is capable of performing a positioning operation with rms accuracy of 16 lm over a travel range of 30 mm. The nonlinear control strategy proposed in this paper showed a significant improvement in comparison with the conventional control strategies for large gap magnetic levitation systems.

Motor control exercise for chronic non-specific low-back pain. <=SEP=> BACKGROUND
Non-specific low back pain (LBP) is a common condition. It is reported to be a major health and socioeconomic problem associated with work absenteeism, disability and high costs for patients and society. Exercise is a modestly effective treatment for chronic LBP. However, current evidence suggests that no single form of exercise is superior to another. Among the most commonly used exercise interventions is motor control exercise (MCE). MCE intervention focuses on the activation of the deep trunk muscles and targets the restoration of control and co-ordination of these muscles, progressing to more complex and functional tasks integrating the activation of deep and global trunk muscles. While there are previous systematic reviews of the effectiveness of MCE, recently published trials justify an updated systematic review.


OBJECTIVES
To evaluate the effectiveness of MCE in patients with chronic non-specific LBP.


SEARCH METHODS
We conducted electronic searches in CENTRAL, MEDLINE, EMBASE, five other databases and two trials registers from their inception up to April 2015. We also performed citation tracking and searched the reference lists of reviews and eligible trials.


SELECTION CRITERIA
We included randomised controlled trials (RCTs) that examined the effectiveness of MCE in patients with chronic non-specific LBP. We included trials comparing MCE with no treatment, another treatment or that added MCE as a supplement to other interventions. Primary outcomes were pain intensity and disability. We considered function, quality of life, return to work or recurrence as secondary outcomes. All outcomes must have been measured with a valid and reliable instrument.


DATA COLLECTION AND ANALYSIS
Two independent review authors screened the search results, assessed risk of bias and extracted the data. A third independent review author resolved any disagreement. We assessed risk of bias using the Cochrane Back and Neck (CBN) Review Group expanded 12-item criteria (Furlan 2009). We extracted mean scores, standard deviations and sample sizes from the included trials, and if this information was not provided we calculated or estimated them using methods recommended in the Cochrane Handbook. We also contacted the authors of the trials for any missing or unclear information. We considered the following time points: short-term (less than three months after randomisation); intermediate (at least three months but less than 12 months after randomisation); and long-term (12 months or more after randomisation) follow-up. We assessed heterogeneity by visual inspection of the forest plots, and by calculating the Chi(2) test and the I(2) statistic. We combined results in a meta-analysis expressed as mean difference (MD) and 95% confidence interval (CI). We assessed the overall quality of the evidence using the GRADE approach.


MAIN RESULTS
We included 29 trials (n = 2431) in this review. The study sample sizes ranged from 20 to 323 participants. We considered a total of 76.6% of the included trials to have a low risk of bias, representing 86% of all participants. There is low to high quality evidence that MCE is not clinically more effective than other exercises for all follow-up periods and outcomes tested. When compared with minimal intervention, there is low to moderate quality evidence that MCE is effective for improving pain at short, intermediate and long-term follow-up with medium effect sizes (long-term, MD -12.97; 95% CI -18.51 to -7.42). There was also a clinically important difference for the outcomes function and global impression of recovery compared with minimal intervention. There is moderate to high quality evidence that there is no clinically important difference between MCE and manual therapy for all follow-up periods and outcomes tested. Finally, there is very low to low quality evidence that MCE is clinically more effective than exercise and electrophysical agents (EPA) for pain, disability, global impression of recovery and quality of life with medium to large effect sizes (pain at short term, MD -30.18; 95% CI -35.32 to -25.05). Minor or no adverse events were reported in the included trials.


AUTHORS' CONCLUSIONS
There is very low to moderate quality evidence that MCE has a clinically important effect compared with a minimal intervention for chronic low back pain. There is very low to low quality evidence that MCE has a clinically important effect compared with exercise plus EPA. There is moderate to high quality evidence that MCE provides similar outcomes to manual therapies and low to moderate quality evidence that it provides similar outcomes to other forms of exercises. Given the evidence that MCE is not superior to other forms of exercise, the choice of exercise for chronic LBP should probably depend on patient or therapist preferences, therapist training, costs and safety.

Treatment planning of the edentulous maxilla <=SEP=> The predictability of successful osseointegrated implant rehabilitation of the edentulous jaw as described by Branemark et al., introduced a new era of management for the edentulous predicament. Implant rehabilitation of the edentulous maxilla remains one of the most complex restorative challenges because of the number of variables that affect both the aesthetic and functional aspect of the prosthesis. Among the prosthesis designs used to treat the edentulous maxilla are fixed or removable implant-supported restorations. Since the aesthetic requirements and preoperative situation of each patient varies, considerable time must be spent on accurate diagnosis to ensure patient desires are satisfied and predictable outcomes are achieved. The purpose of this article is to compare the treatment options and prosthesis designs for the edentulous maxilla. Emphasis will be placed on diagnosis and treatment planning. Criteria will be given to guide the practitioner in deciding whether a fixed or removable restoration should be placed. This objective will be accomplished through the review of cases with regard to varying design considerations and factors that influence the decision-making process.

An Efficient Diagnosis System for Parkinson's Disease Using Kernel-Based Extreme Learning Machine with Subtractive Clustering Features Weighting Approach <=SEP=> A novel hybrid method named SCFW-KELM, which integrates effective subtractive clustering features weighting and a fast classifier kernel-based extreme learning machine (KELM), has been introduced for the diagnosis of PD. In the proposed method, SCFW is used as a data preprocessing tool, which aims at decreasing the variance in features of the PD dataset, in order to further improve the diagnostic accuracy of the KELM classifier. The impact of the type of kernel functions on the performance of KELM has been investigated in detail. The efficiency and effectiveness of the proposed method have been rigorously evaluated against the PD dataset in terms of classification accuracy, sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC), f-measure, and kappa statistics value. Experimental results have demonstrated that the proposed SCFW-KELM significantly outperforms SVM-based, KNN-based, and ELM-based approaches and other methods in the literature and achieved highest classification results reported so far via 10-fold cross validation scheme, with the classification accuracy of 99.49%, the sensitivity of 100%, the specificity of 99.39%, AUC of 99.69%, the f-measure value of 0.9964, and kappa value of 0.9867. Promisingly, the proposed method might serve as a new candidate of powerful methods for the diagnosis of PD with excellent performance.

Speaker-listener neural coupling underlies successful communication. <=SEP=> Verbal communication is a joint activity; however, speech production and comprehension have primarily been analyzed as independent processes within the boundaries of individual brains. Here, we applied fMRI to record brain activity from both speakers and listeners during natural verbal communication. We used the speaker's spatiotemporal brain activity to model listeners' brain activity and found that the speaker's activity is spatially and temporally coupled with the listener's activity. This coupling vanishes when participants fail to communicate. Moreover, though on average the listener's brain activity mirrors the speaker's activity with a delay, we also find areas that exhibit predictive anticipatory responses. We connected the extent of neural coupling to a quantitative measure of story comprehension and find that the greater the anticipatory speaker-listener coupling, the greater the understanding. We argue that the observed alignment of production- and comprehension-based processes serves as a mechanism by which brains convey information.

Practical Signal-Dependent Noise Parameter Estimation From a Single Noisy Image <=SEP=> The additive white Gaussian noise is widely assumed in many image processing algorithms. However, in the real world, the noise from actual cameras is better modeled as signal-dependent noise (SDN). In this paper, we focus on the SDN model and propose an algorithm to automatically estimate its parameters from a single noisy image. The proposed algorithm identifies the noise level function of signal-dependent noise assuming the generalized signal-dependent noise model and is also applicable to the Poisson-Gaussian noise model. The accuracy is achieved by improved estimation of local mean and local noise variance from the selected low-rank patches. We evaluate the proposed algorithm with both synthetic and real noisy images. Experiments demonstrate that the proposed estimation algorithm outperforms the state-of-the-art methods.

Human-Assisted Graph Search: It's Okay to Ask Questions <=SEP=> We consider the problem of human-assisted graph search: given a directed acyclic graph with some (unknown) target node(s), we consider the problem of finding the target node(s) by asking an omniscient human questions of the form “Is there a target node that is reachable from the current node?”. This general problem has applications in many domains that can utilize human intelligence, including curation of hierarchies, debugging workflows, image segmentation and categorization, interactive search and filter synthesis. To our knowledge, this work provides the first formal algorithmic study of the optimization of human computation for this problem. We study various dimensions of the problem space, providing algorithms and complexity results. We also compare the performance of our algorithm against other algorithms, for the problem of webpage categorization on a real taxonomy. Our framework and algorithms can be used in the design of an optimizer for crowdsourcing platforms such as Mechanical Turk.

Joint detection and decoding for MIMO systems with polar codes <=SEP=> As well known, the near-optimal K-best detection is popular in multiple-input and multiple-output (MIMO) systems. In this paper, we first propose the joint approaches of K-best detection and polar decoding. For joint detection and decoding (JDD) approach, both hard and soft decisions are considered. The simplified successive cancellation (SSC) decoding is exploited for hard decision, and the successive cancellation list (SCL) decoding is used as soft decision. The system setup for JDD is als o introduced, in which the modulation points across several channels are considered together. Simulation results have demonstrated the performance advantage of the JDD algorithms over the separated ones. For 1/2-rate polar codes, JDD schemes show 50% complexity reduction compared to the separated ones. Furthermore, by employing SSC hard decoding, the JDD algorithm is promising for high-throughput and low-complexity application s.