Query: Leveraging Context-Free Grammar for Efficient Inverted Index Compression

Relevant passages:
Efficient set intersection for inverted indexing <=SEP=> Conjunctive Boolean queries are a key component of modern information retrieval systems, especially when Web-scale repositories are being searched. A conjunctive query q is equivalent to a |q|-way intersection over ordered sets of integers, where each set represents the documents containing one of the terms, and each integer in each set is an ordinal document identifier. As is the case with many computing applications, there is tension between the way in which the data is represented, and the ways in which it is to be manipulated. In particular, the sets representing index data for typical document collections are highly compressible, but are processed using random access techniques, meaning that methods for carrying out set intersections must be alert to issues to do with access patterns and data representation. Our purpose in this article is to explore these trade-offs, by investigating intersection techniques that make use of both uncompressed “integer” representations, as well as compressed arrangements. We also propose a simple hybrid method that provides both compact storage, and also faster intersection computations for conjunctive querying than is possible even with uncompressed representations.

Inverted index compression and query processing with optimized document ordering <=SEP=> Web search engines use highly optimized compression schemes to decrease inverted index size and improve query throughput, and many index compression techniques have been studied in the literature. One approach taken by several recent studies first performs a renumbering of the document IDs in the collection that groups similar documents together, and then applies standard compression techniques. It is known that this can significantly improve index compression compared to a random document ordering. We study index compression and query processing techniques for such reordered indexes. Previous work has focused on determining the best possible ordering of documents. In contrast, we assume that such an ordering is already given, and focus on how to optimize compression methods and query processing for this case. We perform an extensive study of compression techniques for document IDs and present new optimizations of existing techniques which can achieve significant improvement in both compression and decompression performances. We also propose and evaluate techniques for compressing frequency values for this case. Finally, we study the effect of this approach on query processing performance. Our experiments show very significant improvements in index size and query processing speed on the TREC GOV2 collection of 25.2 million web pages.

Super-Scalar RAM-CPU Cache Compression <=SEP=> High-performance data-intensive query processing tasks like OLAP, data mining or scientific data analysis can be severely I/O bound, even when high-end RAID storage systems are used. Compression can alleviate this bottleneck only if encoding and decoding speeds significantly exceed RAID I/O bandwidth. For this purpose, we propose three new versatile compression schemes (PDICT, PFOR, and PFOR-DELTA) that are specifically designed to extract maximum IPC from modern CPUs. We compare these algorithms with compression techniques used in (commercial) database and information retrieval systems. Our experiments on the MonetDB/X100 database system, using both DSM and PAX disk storage, show that these techniques strongly accelerate TPC-H performance to the point that the I/O bottleneck is eliminated.

Inverted files for text search engines <=SEP=> The technology underlying text search engines has advanced dramatically in the past decade. The development of a family of new index representations has led to a wide range of innovations in index storage, index construction, and query evaluation. While some of these developments have been consolidated in textbooks, many specific techniques are not widely known or the textbook descriptions are out of date. In this tutorial, we introduce the key techniques in the area, describing both a core implementation and how the core can be enhanced through a range of extensions. We conclude with a comprehensive bibliography of text indexing literature.

Compressed Inverted Indexes for In-Memory Search Engines <=SEP=> We present the algorithmic core of a full text data base that allows fast Boolean queries, phrase queries, and document reporting using less space than the input text. The system uses a carefully choreographed combination of classical data compression techniques and inverted index based search data structures. It outperforms suffix array based techniques for all the above operations for real world (natural language) texts.

Retinal blood vessel segmentation using matched filter and Laplacian of Gaussian <=SEP=> Automated blood vessel segmentation of retinal images offers huge potential benefits for medical diagnosis of different ocular diseases. In this paper, 2D Matched Filters (MF) are applied to fundus retinal images to detect vessels which are enhanced by Contrast Limited Adaptive Histogram Equalization (CLAHE) method. Due to the Gaussian nature of blood vessel profile, the MF with Gaussian kernel often misclassifies non-vascular structures (e.g., step, ramp or other transients) as vessels. To avoid such false detection, this paper introduces Laplacian of Gaussian (LoG) filters in the vessel segmentation process. The inherent zero-crossing property of LoG filter is used in the algorithm, along with the MF, in order to extract vessels reliably from the retinal images. The proposed method is validated against three publicly available databases, STARE, DRIVE and HRF. Simulation results show that the proposed method is able to segment vessels accurately from the three database images with an average accuracy that is competitive to the existing methodologies.

Deep Learning for the Classification of Lung Nodules <=SEP=> Deep learning, as a promising new area of machine learning, has attracted a rapidly increasing attention in the field of medical imaging. Compared to the conventional machine learning methods, deep learning requires no hand-tuned feature extractor, and has shown a superior performance in many visual object recognition applications. In this study, we develop a deep convolutional neural network (CNN) and apply it to thoracic CT images for the classification of lung nodules. We present the CNN architecture and classification accuracy for the original images of lung nodules. In order to understand the features of lung nodules, we further construct new datasets, based on the combination of artificial geometric nodules and some transformations of the original images, as well as a stochastic nodule shape model. It is found that simplistic geometric nodules cannot capture the important features of lung nodules.

FUNCTIONAL AND LONGITUDINAL DATA ANALYSIS: PERSPECTIVES ON SMOOTHING <=SEP=> The perspectives and methods of functional data analysis and longitudinal data analysis are contrasted and compared. Topics include kernel methods and random effects models for smoothing, basis function methods, and examination of the relation of covariates to curve shapes. Some directions in which methodology might advance are identified.

Ethical issues raised by the treatment of gender-variant prepubescent children. <=SEP=> Transgender issues and transgender rights have become increasingly a matter of media attention and public policy debates. Reflecting changes in psychiatric perspectives, the diagnosis of "trans-sexualism" first appeared in the International Statistical Classification of Diseases and Related Health Problems in 1975 and shortly thereafter, in 1980, in the Diagnostic and Statistical Manual of Mental Disorders. Since that time, international standards of care have been developed, and today those standards are followed by clinicians across diverse cultures. In many instances, treatment of older adolescents and adults is covered by national health care systems and, in some cases, by private health insurance. Most recently, the Medicare ban on coverage for gender reassignment surgery was lifted in 2014. In contrast to the relative lack of controversy about treating adolescents and adults, there is no expert clinical consensus regarding the treatment of prepubescent children who meet diagnostic criteria for what was referred to in both DSM-IV-TR and ICD-10 as gender identity disorder in children and now in DSM-5 as gender dysphoria. One reason for the differing attitudes has to do with the pervasive nature of gender dysphoria in older adolescents and adults: it rarely desists, and so the treatment of choice is gender or sex reassignment. On the subject of treating children, however, as the World Professional Association for Transgender Health notes in their latest Standards of Care, gender dysphoria in childhood does not inevitably continue into adulthood, and only 6 to 23 percent of boys and 12 to 27 percent of girls treated in gender clinics showed persistence of their gender dysphoria into adulthood. Further, most of the boys' gender dysphoria desisted, and in adulthood, they identified as gay rather than as transgender. In an effort to clarify best treatment practices for transgender individuals, a recent American Psychiatric Association Task Force on the Treatment of Gender Identity outlined three differing approaches to treating prepubescent gender dysphoric children.

Revisiting The American Voter on Twitter <=SEP=> The American Voter - a seminal work in political science - uncovered the multifaceted nature of voting behavior which has been corroborated in electoral research for decades since. In this paper, we leverage The American Voter as an analysis framework in the realm of computational political science, employing the factors of party, personality, and policy to structure the analysis of public discourse on online social media during the 2016 U.S. presidential primaries. Our analysis of 50 million tweets reveals the continuing importance of these three factors; our understanding is also enriched by the application of sentiment analysis techniques. The overwhelmingly negative sentiment of conversations surrounding 10 major presidential candidates reveals more "crosstalk" from Democratic leaning users towards Republican candidates, and less vice-versa. We uncover the lack of moderation as the most discussed personality dimension during this campaign season, as the political field becomes more extreme - Clinton and Rubio are perceived as moderate, while Trump, Sanders, and Cruz are not. While the most discussed issues are foreign policy and immigration, Republicans tweet more about abortion than Democrats who tweet more about gay rights than Republicans. Finally, we illustrate the importance of multifaceted political discourse analysis by applying regression to quantify the impact of party, personality, and policy on national polls.

Event Correlation: Language and Semantics <=SEP=> 300.twolf 3000 102 2945 102 2945 Hardware CPU: Intel(R) Xeon(R) CPU 5150 @ 2.66GHz CPU MHz: 2660 FPU: Integrated CPU(s) enabled: 4 cores, 2 chips, 2 cores/chip CPU(s) orderable: 1,2 chip(s) Parallel: No Primary Cache: 32KB(I) + 32KB(D) on chip, per core Secondary Cache: 4096KB(I+D) on chip, per chip, shared L3 Cache: N/A Other Cache: N/A Memory: 4 x 1024MB ECC FB-DIMM DDR2-667MHz Disk Subsystem: 1 x 250GB SATA HDD Other Hardware: Software Operating System: Red Hat Enterprise Linux 4 Update 3 EM64T Compiler: Intel C++ Compiler 9.1.042 for EM64T File System: ext3 System State: Runlevel 3

There is no 16-Clue Sudoku: Solving the Sudoku Minimum Number of Clues Problem <=SEP=> We apply our new hitting set enumeration algorithm to solve the sudoku minimum number of clues problem, which is the following question: What is the smallest number of clues (givens) that a sudoku puzzle may have? It was conjectured that the answer is 17. We have performed an exhaustive search for a 16-clue sudoku puzzle, and we did not find one, thereby proving that the answer is indeed 17. This article describes our method and the actual search. The hitting set problem is computationally hard; it is one of Karp’s twenty-one classic NP-complete problems. We have designed a new algorithm that allows us to efficiently enumerate hitting sets of a suitable size. Hitting set problems have applications in many areas of science, such as bioinformatics and software testing. ∗School of Mathematical Sciences, University College Dublin, Ireland. E-mail: gary.mcguire@ucd.ie

On spatial smoothing for direction-of-arrival estimation of coherent signals <=SEP=> 

Collaborative detection of cyberbullying behavior in Twitter data <=SEP=> As the size of Twitter© data is increasing, so are undesirable behaviors of its users. One of such undesirable behavior is cyberbullying, which may even lead to catastrophic consequences. Hence, it is critical to efficiently detect cyberbullying behavior by analyzing tweets, if possible in realtime. Prevalent approaches to identify cyberbullying are mainly stand-alone and thus, are time-consuming. This research improves detection task using the principles of collaborative computing. Different collaborative paradigms are suggested and discussed in this paper. Preliminary results indicate an improvement in time and accuracy of the detection mechanism over the stand-alone paradigm.

Orthogonal and Idempotent Transformations for Learning Deep Neural Networks <=SEP=> Identity transformations, used as skip-connections in residual networks, directly connect convolutional layers close to the input and those close to the output in deep neural networks, improving information flow and thus easing the training. In this paper, we introduce two alternative linear transforms, orthogonal transformation and idempotent transformation. According to the definition and property of orthogonal and idempotent matrices, the product of multiple orthogonal (same idempotent) matrices, used to form linear transformations, is equal to a single orthogonal (idempotent) matrix, resulting in that information flow is improved and the training is eased. One interesting point is that the success essentially stems from feature reuse and gradient reuse in forward and backward propagation for maintaining the information during flow and eliminating the gradient vanishing problem because of the express way through skip-connections. We empirically demonstrate the effectiveness of the proposed two transformations: similar performance in single-branch networks and even superior in multi-branch networks in comparison to identity transformations.

[Translation and validation for Brazil of the body image scale for adolescents--Offer Self-Image Questionnaire (OSIQ)]. <=SEP=> OBJECTIVE
To evaluate the semantic and measure equivalence of the body image sub-scale of the Offer Self Image Questionnaire (OSIQ).


METHODS
Participants were 386 teenagers, 10 to 18 years old, both sexes, enrolled in a private school (junior and high school age). Translation, back-translation, technique revision and evaluation were conducted. The Portuguese instrument was evaluated for internal consistency, discriminate and concurrent validity.


RESULTS
Internal consistency showed values from 0.43 to 0.54 and was able to discriminate all groups studied--the whole population, boys and girls, and boys in early adolescence, by nutritional status (p<0.001; p<0.009; p=0.030; p=0.043, respectively). Concurrent analyses showed significant correlation with anthropometric measures only for girls (r=-0.16 and p=0.021; r=-0.19 and p=0.007), early adolescence (r=-0.23 and p=0.008; r=-0.26 and p=0.003) and intermediate adolescence (r=-0.29 and p=0.010) and the retest confirmed reliability by the coefficient of interclass correlation. Although the instrument has proven its ability to discriminate between the groups studied by nutritional state, other results were less satisfactory. More studies are necessary for full transcultural adaptation, including the application of other comparative scales.


CONCLUSION
The body image sub-scale of the OSIQ was translated, but the results are not promising and require more studies.

Country-wide rainfall maps from cellular communication networks. <=SEP=> Accurate and timely surface precipitation measurements are crucial for water resources management, agriculture, weather prediction, climate research, as well as ground validation of satellite-based precipitation estimates. However, the majority of the land surface of the earth lacks such data, and in many parts of the world the density of surface precipitation gauging networks is even rapidly declining. This development can potentially be counteracted by using received signal level data from the enormous number of microwave links used worldwide in commercial cellular communication networks. Along such links, radio signals propagate from a transmitting antenna at one base station to a receiving antenna at another base station. Rain-induced attenuation and, subsequently, path-averaged rainfall intensity can be retrieved from the signal's attenuation between transmitter and receiver. Here, we show how one such a network can be used to retrieve the space-time dynamics of rainfall for an entire country (The Netherlands, ∼35,500 km(2)), based on an unprecedented number of links (∼2,400) and a rainfall retrieval algorithm that can be applied in real time. This demonstrates the potential of such networks for real-time rainfall monitoring, in particular in those parts of the world where networks of dedicated ground-based rainfall sensors are often virtually absent.

Smooth assembled mappings for large-scale real walking <=SEP=> Virtual reality applications prefer real walking to provide highly immersive presence than other locomotive methods. Mapping-based techniques are very effective for supporting real walking in small physical workspaces while exploring large virtual scenes. However, the existing methods for computing real walking maps suffer from poor quality due to distortion. In this paper, we present a novel divide-and-conquer method, called Smooth Assembly Mapping (SAM), to compute real walking maps with low isometric distortion for large-scale virtual scenes. First, the input virtual scene is decomposed into a set of smaller local patches. Then, a group of local patches is mapped together into a real workspace by minimizing a low isometric distortion energy with smoothness constraints between the adjacent patches. All local patches are mapped and assembled one by one to obtain a complete map. Finally, a global optimization is adopted to further reduce the distortion throughout the entire map. Our method easily handles teleportation technique by computing maps of individual regions and assembling them with teleporter conformity constraints. A large number of experiments, including formative user studies and comparisons, have shown that our method succeeds in generating high-quality real walking maps from large-scale virtual scenes to small real workspaces and is demonstrably superior to state-of-the-art methods.

Large-Scale Hierarchical Text Classification with Recursively Regularized Deep Graph-CNN <=SEP=> Text classification to a hierarchical taxonomy of topics is a common and practical problem. Traditional approaches simply use bag-of-words and have achieved good results. However, when there are a lot of labels with different topical granularities, bagof-words representation may not be enough. Deep learning models have been proven to be effective to automatically learn different levels of representations for image data. It is interesting to study what is the best way to represent texts. In this paper, we propose a graph-CNN based deep learning model to first convert texts to graph-of-words, and then use graph convolution operations to convolve the word graph. Graph-ofwords representation of texts has the advantage of capturing non-consecutive and long-distance semantics. CNN models have the advantage of learning different level of semantics. To further leverage the hierarchy of labels, we regularize the deep architecture with the dependency among labels. Our results on both RCV1 and NYTimes datasets show that we can significantly improve large-scale hierarchical text classification over traditional hierarchical text classification and existing deep models.

Calibrated recommendations <=SEP=> When a user has watched, say, 70 romance movies and 30 action movies, then it is reasonable to expect the personalized list of recommended movies to be comprised of about 70% romance and 30% action movies as well. This important property is known as calibration, and recently received renewed attention in the context of fairness in machine learning. In the recommended list of items, calibration ensures that the various (past) areas of interest of a user are reflected with their corresponding proportions. Calibration is especially important in light of the fact that recommender systems optimized toward accuracy (e.g., ranking metrics) in the usual offline-setting can easily lead to recommendations where the lesser interests of a user get crowded out by the user's main interests-which we show empirically as well as in thought-experiments. This can be prevented by calibrated recommendations. To this end, we outline metrics for quantifying the degree of calibration, as well as a simple yet effective re-ranking algorithm for post-processing the output of recommender systems.

Improved Bat Algorithm (IBA) on Continuous Optimization Problems <=SEP=> —Heuristic optimization techniques have became very popular techniques and have widespread usage areas. Since they do not include mathematical terms, heuristic methods have been carried out on many fields by researchers. Main purpose of these techniques is to achieve good performance on problem of interest. One of these techniques is Bat Algorithm (BA). BA is an optimization algorithm based on echolocation characteristic of bats and developed by mimics of bats' foraging behavior. In this study, exploration and exploitation mechanisms of BA are improved by three modifications. Performance of proposed and standard version of algorithm is compared on ten basic benchmark test problems. Results indicate that proposed version is better than standard version in terms of solution quality.

Classifying sanctions and designing a conceptual sanctioning process model for socio-technical systems <=SEP=> We understand a socio-technical system (STS) as a cyber-physical system in which two or more autonomous parties interact via or about technical elements, including the parties’ resources and actions. As information technology begins to pervade every corner of human life, STSs are becoming ever more common, and the challenge of governing STSs is becoming increasingly important. We advocate a normative basis for governance, wherein norms represent the standards of correct behaviour that each party in an STS expects from others. A major benefit of focussing on norms is that they provide a socially realistic view of interaction among autonomous parties that abstracts low-level implementation details. Overlaid on norms is the notion of a sanction as a negative or positive reaction to potentially any violation of or compliance with an expectation. Although norms have been well studied as regards governance for STSs, sanctions have not. Our understanding and usage of norms is inadequate for the purposes of governance unless we incorporate a comprehensive representation of sanctions. We address the aforementioned gap by proposing (i) a sanction typology that reflects the relevant features of sanctions, and (ii) a conceptual sanctioning process model providing a functional structure for sanctioning in STS. We demonstrate our contributions via a motivating scenario from the domain of renewable energy trading.

Classification of learning outcomes : evidence from the computer games literature <=SEP=> Following up on an earlier issue of The Curriculum Journal (Vol. 16, No. 1), this article focuses on learning outcomes in the context of video games. Learning outcomes are viewed from two theoretical frameworks: Kirkpatrick’s levels of evaluation and the CRESST model of learning. These are used to analyse the outcomes claimed in journal articles that report empirical work, indicating the usefulness of the frameworks, and the necessity to consider the role of affective learning. The article ends with some comments on the relationship of instructional design to effective games and learning outcomes.

Keyword extraction based on tf/idf for Chinese news document <=SEP=> Keyword extraction is an important research topic of information retrieval. This paper gave the specification of keywords in Chinese news documents based on analyzing linguistic characteristics of news documents and then proposed a new keyword extraction method based on tf/idf with multi-strategies. The approach selected candidate keywords of uni-, bi-and tri- grams, and then defines the features according to their morphological characters and context information. Moreover, the paper proposed several strategies to amend the incomplete words gotten from the word segmentation and found unknown potential keywords in news documents. Experimental results show that our proposed method can significantly outperform the baseline method. We also applied it to retrospective event detection. Experimental results show that the accuracy and efficiency of news retrospective event detection can be significantly improved.

Food-Bridging: A New Network Construction to Unveil the Principles of Cooking <=SEP=> In this manuscript, we propose, analyze, and discuss a possible new principle behind traditional cuisine: the Food-bridging hypothesis and its comparison with the food-pairing hypothesis using the same dataset and graphical models employed in the food-pairing study by Ahn et al. (2011). The Food-bridging hypothesis assumes that if two ingredients do not share a strong molecular or empirical affinity, they may become affine through a chain of pairwise affinities. That is, in a graphical model as employed by Ahn et al., a chain represents a path that joints the two ingredients, the shortest path represents the strongest pairwise chain of affinities between the two ingredients. Food-pairing and Food-bridging are different hypotheses that may describe possible mechanisms behind the recipes of traditional cuisines. Food-pairing intensifies flavor by mixing ingredients in a recipe with similar chemical compounds, and food-bridging smoothes contrast between ingredients. Both food-pairing and food-bridging are observed in traditional cuisines, as shown in this work. We observed four classes of cuisines according to food-pairing and food-bridging: (1) East Asian cuisines, at one extreme, tend to avoid food-pairing as well as food-bridging; and (4) Latin American cuisines, at the other extreme, follow both principles. For the two middle classes: (2) Southeastern Asian cuisines, avoid foodpairing and follow food-bridging; and (3) Western cuisines, follow food-pairing and avoid food-bridging.

Drug Treatment of Sleep Disorders <=SEP=> Sleep is a complex behavior that cyclically alternates with wakefulness and is fundamental for both mental and physical wellness. During normal sleep the body acquires a specific posture; any active behavior is absent and mental activity typically oscillates between a state of “loss of consciousness” and the experience of dreaming. Sleep is, by itself, a cyclical process characterized by the alternation of two phases, non-REM (NREM) sleep and “rapid eye movement” (REM) sleep, during which the typical synchronized electroencephalographic (EEG) pattern of NREM sleep is substituted by a “paradoxical” EEG desynchronization. NREM sleep is a state of minimal energy expenditure and motor activity, during which cardiorespiratory and thermoregulatory variables are driven by the autonomic nervous system at a lower and more stable level compared to wakefulness. During REM sleep, beyond the occurrence of REMs, posture control is lost and dreams become more vivid. Furthermore, autonomic activity is highly unstable leading to centrally driven surges in heart rate and blood pressure, breathing becomes irregular, and thermoregulation is suspended or depressed, suggesting a derangement of the integrative regulation of physiological functions. Overall sleep quality is one of the most important parameters considered for the subjective assessment of quality of life and general health status. However, although sleep function(s) is intuitively associated with some kind of rest that seems to be mostly required by the central nervous system, and many theories on sleep function have been proposed, a full comprehension of sleep function has not yet been achieved and is probably not imminent. C. Berteotti • M. Cerri • M. Luppi • A. Silvani • R. Amici (*) Department of Biomedical and Neuromotor Sciences—Physiology, Alma Mater Studiorum-University of Bologna, Piazza P.ta S. Donato, 2, 40126 Bologna, Italy e-mail: chiara.berteotti@unibo.it; matteo.cerri@unibo.it; marco.luppi@unibo.it; alessandro.silvani3@unibo.it; roberto.amici@unibo.it; http://www.unibo.it/Faculty/default. aspx?UPN1⁄4roberto.amici%40unibo.it © Springer International Publishing Switzerland 2015 A. Guglietta (ed.), Drug Treatment of Sleep Disorders, Milestones in Drug Therapy, DOI 10.1007/978-3-319-11514-6_1 3 1 Neurophysiology and Neurobiology of Sleep

Inductance and Resistance Calculations in Three-Dimensional Packaging Using Cylindrical Conduction-Mode Basis Functions <=SEP=> For the successful electrical design of system-in-package, this paper proposes an efficient method for extracting wideband resistance and inductance from a large number of 3-D interconnections. The proposed method uses the modal equivalent network from the electric field integral equation with cylindrical conduction-mode basis function, which reduces the matrix size for large 3-D interconnection problems. Additional enhancement schemes proposed further reduce the cost for computing the partial inductances. Therefore, the method discussed in this paper can be used to construct accurate models of a large number of 3-D interconnection structures such as more than 100 bonding wires used for stacking chips.

Aligning English Strings with Abstract Meaning Representation Graphs <=SEP=> We align pairs of English sentences and corresponding Abstract Meaning Representations (AMR), at the token level. Such alignments will be useful for downstream extraction of semantic interpretation and generation rules. Our method involves linearizing AMR structures and performing symmetrized EM training. We obtain 86.5% and 83.1% alignment F score on development and test sets.

An Impedance and Multi-Wavelength Near-Infrared Spectroscopy IC for Non-Invasive Blood Glucose Estimation <=SEP=> A multi-modal spectroscopy IC combining impedance spectroscopy (IMPS) and multi-wavelength near-infrared spectroscopy (mNIRS) is proposed for high precision non-invasive glucose level estimation. A combination of IMPS and mNIRS can compensate for the glucose estimation error to improve its accuracy. The IMPS circuit measures dielectric characteristics of the tissue using the RLC resonant frequency and the resonant impedance to estimate the glucose level. To accurately find resonant frequency, a 2-step frequency sweep sinusoidal oscillator (FSSO) is proposed: 1) 8-level coarse frequency switching (fSTEP = 9.4 kHz) in 10-76 kHz, and 2) fine analog frequency sweep in the range of 18.9 kHz. During the frequency sweep, the adaptive gain control loop stabilizes the output voltage swing (400 mVp-p). To improve accuracy of mNIRS, three wavelengths, 850 nm, 950 nm, and 1,300 nm, are used. For highly accurate glucose estimation, the measurement data of the IMPS and mNIRS are combined by an artificial neural network (ANN) in external DSP. The proposed ANN method reduces the mean absolute relative difference to 8.3% from 15% of IMPS, and 15-20% of mNIRS in 80-180 mg/dL blood glucose level. The proposed multi-modal spectroscopy IC occupies 12.5 mm 2 in a 0.18 μm 1P6M CMOS technology and dissipates a peak power of 38 mW with the maximum radiant emitting power of 12.1 mW.

Goalie Actions Attackers and Defenders Goalie Role Allocation Layer Action Selection Layer Vector Field Action Shoot Action Action Selection Layer Vector Field Action Shoot Action Action Selection Layer Vector Field Action Shoot Action Action Selection Layer Vector Field Action <=SEP=> The robot soccer system is being used as a test bed to develop the next generation of field robots. In the multiagent system, action selection is important for the cooperation and coordination among agents. There are many techniques in choosing a proper action of the agent. As the environment is dynamic, reinforcement learning is more suitable than supervised learning. Reinforcement learning is based on trial and error method through experience. Because of this, reinforcement learning is applied to many practical problems. However, straightforward application of reinforcement learning algorithm may not successfully scale up to more complex multi-agent learning problems. To solve this problem, modular Q-learning is employed for multi-agent learning in reinforcement methods. A modified uni-vector field is used for robot navigation. This paper discusses the Y2K2 NaroSot(Nano-Robot World Cup Soccer Tournament) robot soccer system.