Query: Algorithmic Nuggets in Content Delivery

Relevant passages:
Consistent Hashing and Random Trees: Distributed Caching Protocols for Relieving Hot Spots on the World Wide Web <=SEP=> We describe a family of caching protocols for distrib-uted networks that can be used to decrease or eliminate the occurrence of hot spots in the network. Our protocols are particularly designed for use with very large networks such as the Internet, where delays caused by hot spots can be severe, and where it is not feasible for every server to have complete information about the current state of the entire network. The protocols are easy to implement using existing network protocols such as TCP/fF’,and require very little overhead. The protocols work with local control, make efficient use of existing resources, and scale gracefully as the network grows. Our caching protocols are based on a special kind of hashing that we call consistent hashing. Roughly speaking, a consistent hash function is one which changes nr.inimaflyas the range of the function changes. Through the development of good consistent hash functions, we are able to develop caching protocols which do not require users to have a current or even consistent view of the network. We believe that consistent hash functions may eventually prove to be useful in other applications such as distributed name servers and/or quorum systems.

In Search of an Understandable Consensus Algorithm <=SEP=> Raft is a consensus algorithm for managing a replicated log. It produces a result equivalent to (multi-)Paxos, and it is as efficient as Paxos, but its structure is different from Paxos; this makes Raft more understandable than Paxos and also provides a better foundation for building practical systems. In order to enhance understandability, Raft separates the key elements of consensus, such as leader election, log replication, and safety, and it enforces a stronger degree of coherency to reduce the number of states that must be considered. Results from a user study demonstrate that Raft is easier for students to learn than Paxos. Raft also includes a new mechanism for changing the cluster membership, which uses overlapping majorities to guarantee safety.

Paxos Made Simple <=SEP=> The Paxos algorithm, when presented in plain English, is very simple.

Space/Time Trade-offs in Hash Coding with Allowable Errors <=SEP=> In this paper trade-offs among certain computational factors in hash coding are analyzed. The paradigm problem considered is that of testing a series of messages one-by-one for membership in a given set of messages. Two new hash-coding methods are examined and compared with a particular conventional hash-coding method. The computational factors considered are the size of the hash area (space), the time required to identify a message as a nonmember of the given set (reject time), and an allowable error frequency.
The new methods are intended to reduce the amount of space required to contain the hash-coded information from that associated with conventional methods. The reduction in space is accomplished by exploiting the possibility that a small fraction of errors of commission may be tolerable in some applications, in particular, applications in which a large amount of data is involved and a core resident hash area is consequently not feasible using conventional methods.
In such applications, it is envisaged that overall performance could be improved by using a smaller core resident hash area in conjunction with the new methods and, when necessary, by using some secondary and perhaps time-consuming test to “catch” the small fraction of errors associated with the new methods. An example is discussed which illustrates possible areas of application for the new methods.
Analysis of the paradigm problem demonstrates that allowing a small number of test messages to be falsely identified as members of the given set will permit a much smaller hash area to be used without increasing reject time.

Web Caching with Consistent Hashing <=SEP=> A key performance measure for the World Wide Web is the speed with which content is served to users. As traffic on the Web increases, users are faced with increasing delays and failures in data delivery. Web caching is one of the key strategies that has been explored to improve performance. An important issue in many caching systems is how to decide what is cached where at any given time. Solutions have included multicast queries and directory schemes. In this paper, we offer a new Web caching strategy based on consistent hashing. Consistent hashing provides an alternative to multicast and directory schemes, and has several other advantages in load balancing and fault tolerance. Its performance was analyzed theoretically in previous work; in this paper we describe the implementation of a consistent-hashing-based system and experiments that support our thesis that it can provide performance improvements.  1999 Published by Elsevier Science B.V. All rights reserved.

An Empirical Investigation into Programming Language Syntax <=SEP=> Recent studies in the literature have shown that syntax remains a significant barrier to novice computer science students in the field. While this syntax barrier is known to exist, whether and how it varies across programming languages has not been carefully investigated. For this article, we conducted four empirical studies on programming language syntax as part of a larger analysis into the, so called, programming language wars. We first present two surveys conducted with students on the intuitiveness of syntax, which we used to garner formative clues on what words and symbols might be easy for novices to understand. We followed up with two studies on the accuracy rates of novices using a total of six programming languages: Ruby, Java, Perl, Python, Randomo, and Quorum. Randomo was designed by randomly choosing some keywords from the ASCII table (a metaphorical placebo). To our surprise, we found that languages using a more traditional C-style syntax (both Perl and Java) did not afford accuracy rates significantly higher than a language with randomly generated keywords, but that languages which deviate (Quorum, Python, and Ruby) did. These results, including the specifics of syntax that are particularly problematic for novices, may help teachers of introductory programming courses in choosing appropriate first languages and in helping students to overcome the challenges they face with syntax.

Scale out for large minibatch SGD: Residual network training on ImageNet-1K with improved accuracy and reduced time to train <=SEP=> For the past 5 years, the ILSVRC competition and the ImageNet dataset have attracted a lot of interest from the Computer Vision community, allowing for state-of-the-art accuracy to grow tremendously. This should be credited to the use of deep artificial neural network designs. As these became more complex, the storage, bandwidth, and compute requirements increased. This means that with a non-distributed approach, even when using the most high-density server available, the training process may take weeks, making it prohibitive. Furthermore, as datasets grow, the representation learning potential of deep networks grows as well by using more complex models. This synchronicity triggers a sharp increase in the computational requirements and motivates us to explore the scaling behaviour on petaflop scale supercomputers. In this paper we will describe the challenges and novel solutions needed in order to train ResNet50 in this large scale environment. We demonstrate above 90% scaling efficiency and a training time of 28 minutes using up to 104K x86 cores. This is supported by software tools from Intel’s ecosystem. Moreover, we show that with regular 90 120 epoch train runs we can achieve a top-1 accuracy as high as 77% for the unmodified ResNet-50 topology. We also introduce the novel Collapsed Ensemble (CE) technique that allows us to obtain a 77.5% top-1 accuracy, similar to that of a ResNet-152, while training a unmodified ResNet-50 topology for the same fixed training budget. All ResNet-50 models as well as the scripts needed to replicate them will be posted shortly. Keywords—deep learning, scaling, convergence, large minibatch, ensembles.

Ensemble learning for free with evolutionary algorithms? <=SEP=> Evolutionary Learning proceeds by evolving a population of classifiers, from which it generally returns (with some notable exceptions) the single best-of-run classifier as final result. In the meanwhile, Ensemble Learning, one of the most efficient approaches in supervised Machine Learning for the last decade, proceeds by building a population of diverse classifiers. Ensemble Learning with Evolutionary Computation thus receives increasing attention. The Evolutionary Ensemble Learning (EEL) approach presented in this paper features two contributions. First, a new fitness function, inspired by co-evolution and enforcing the classifier diversity, is presented. Further, a new selection criterion based on the classification margin is proposed. This criterion is used to extract the classifier ensemble from the final population only (Off-EEL) or incrementally along evolution (On-EEL). Experiments on a set of benchmark problems show that Off-EEL outperforms single-hypothesis evolutionary learning and state-of-art Boosting and generates smaller classifier ensembles.

Person-Independent 3D Gaze Estimation Using Face Frontalization <=SEP=> Person-independent and pose-invariant estimation of eye-gaze is important for situation analysis and for automated video annotation. We propose a fast cascade regression based method that first estimates the location of a dense set of markers and their visibility, then reconstructs face shape by fitting a part-based 3D model. Next, the reconstructed 3D shape is used to estimate a canonical view of the eyes for 3D gaze estimation. The model operates in a feature space that naturally encodes local ordinal properties of pixel intensities leading to photometric invariant estimation of gaze. To evaluate the algorithm in comparison with alternative approaches, three publicly-available databases were used, Boston University Head Tracking, Multi-View Gaze and CAVE Gaze datasets. Precision for head pose and gaze averaged 4 degrees or less for pitch, yaw, and roll. The algorithm outperformed alternative methods in both datasets.

End-to-End Radio Traffic Sequence Recognition with Deep Recurrent Neural Networks <=SEP=> We investigate sequence machine learning techniques on raw radio signal time-series data. By applying deep recurrent neural networks we learn to discriminate between several application layer traffic types on top of a constant envelope modulation without using an expert demodulation algorithm. We show that complex protocol sequences can be learned and used for both classification and generation tasks using this approach. Keywords—Machine Learning, Software Radio, Protocol Recognition, Recurrent Neural Networks, LSTM, Protocol Learning, Traffic Classification, Cognitive Radio, Deep Learning

Homography estimation using one ellipse correspondence and minimal additional information <=SEP=> In sport scenarios like football or basketball, we often deal with central views where only the central circle and some additional primitives like the central line and the central point or a touch line are visible. In this paper we first characterize, from a mathematical point of view, the set of homographies that project a given ellipse into the unit circle, next, using some extra minimal additional information like the knowledge of the position in the image of the central line and central point or a touch line we show a method to fully determine the plane homography. We present some experiments in sport scenarios to show the ability of the proposed method to properly recover the plane homography.

Connectionist models of recognition memory: constraints imposed by learning and forgetting functions. <=SEP=> Multilayer connectionist models of memory based on the encoder model using the backpropagation learning rule are evaluated. The models are applied to standard recognition memory procedures in which items are studied sequentially and then tested for retention. Sequential learning in these models leads to 2 major problems. First, well-learned information is forgotten rapidly as new information is learned. Second, discrimination between studied items and new items either decreases or is nonmonotonic as a function of learning. To address these problems, manipulations of the network within the multilayer model and several variants of the multilayer model were examined, including a model with prelearned memory and a context model, but none solved the problems. The problems discussed provide limitations on connectionist models applied to human memory and in tasks where information to be learned is not all available during learning.

A self-boost charge pump topology for a gate drive high-side power supply <=SEP=> A self-boost charge pump topology is presented for a floating high-side gate drive power supply that features high voltage and current capabilities for use in integrated power electronic modules (IPEMs). The transformerless topology uses a small capacitor to transfer energy to the high-side switch from a single power supply referred to the negative rail. Unlike conventional bootstrap power supplies, no switching of the main phase-leg switches is required to provide power continuously to the high-side gate drive, even if the high-side switch is permanently on. Additional advantages include low parts-count and simple control requirements. A piecewise linear model of the self-boost charge pump is derived and the circuit's operating characteristics are analyzed. Simulation and experimental results are provided to verify the desired operation of the new charge pump circuit. Guidelines are provided to assist with circuit component selection in new applications.

The long-term sequelae of child and adolescent abuse: a longitudinal community study. <=SEP=> The purpose of the present study was to examine the relationship between childhood and adolescent physical and sexual abuse before the age of 18 and psychosocial functioning in mid-adolescence (age 15) and early adulthood (age 21) in a representative community sample of young adults. Subjects were 375 participants in an ongoing 17-years longitudinal study. At age 21, nearly 11% reported physical or sexual abuse before age 18. Psychiatric disorders based on DSM-III-R criteria were assessed utilizing the NIMH Diagnostic Interview Schedule, Revised Version (DIS-III-R). Approximately 80% of the abused young adults met DSM-III-R criteria for at least one psychiatric disorder at age 21. Compared to their nonabused counterparts, abused subjects demonstrated significant impairments in functioning both at ages 15 and at 21, including more depressive symptomatology, anxiety, psychiatric disorders, emotional-behavioral problems, suicidal ideation, and suicide attempts. While abused individuals were functioning significantly more poorly overall at ages 15 and 21 than their nonabused peers, gender differences and distinct patterns of impaired functioning emerged. These deficits underscore the need for early intervention and prevention strategies to forestall or minimize the serious consequences of child abuse.

A radio resource management framework for TVWS exploitation under an auction-based approach <=SEP=> This paper elaborates on the design, implementation and performance evaluation of a prototype Radio Resource Management (RRM) framework for TV white spaces (TVWS) exploitation, under an auction-based approach. The proposed RRM framework is applied in a centralised Cognitive Radio (CR) network architecture, where exploitation of the available TVWS by Secondary Systems is orchestrated via a Spectrum Broker. Efficient RRM framework performance, as a matter of maximum-possible resources utilization and benefit of Spectrum Broker, is achieved by proposing and evaluating an auction-based algorithm. This auction-based algorithm considers both frequency and time domain during TVWS allocation process which was defined as an optimization problem, where maximum payoff of Spectrum Broker is the optimization goal. Experimental tests that were carried-out under controlled conditions environment, verified the validity of the proposed framework, besides identifying fields for further research.

High power and high efficiency GaN-HEMT for microwave communication applications <=SEP=> Microwaves have been widely used for the modern communication systems, which have advantages in high bit rate transmission and the easiness of compact circuit and antenna design. Gallium Nitride (GaN), featured with high breakdown and high saturation velocity, is one of the promising material for high power and high frequency devices, and a kW-class output power has already been achieved [1]. We have developed the high power and high efficiency GaN HEMTs [2–5], targeting the amplifier for the base transceiver station (BTS). This presentation summarizes our recent works, focusing on the developments for the efficiency boosting and the robustness in high power RF operation.

Coloring-based coalescing for graph coloring register allocation <=SEP=> Graph coloring register allocation tries to minimize the total cost of spilled live ranges of variables. Live-range splitting and coalescing are often performed before the coloring to further reduce the total cost. Coalescing of split live ranges, called sub-ranges, can decrease the total cost by lowering the interference degrees of their common interference neighbors. However, it can also increase the total cost because the coalesced sub-ranges can become uncolorable. In this paper, we propose coloring-based coalescing, which first performs trial coloring and next coalesces all copyrelated sub-ranges that were assigned the same color. The coalesced graph is then colored again with the graph coloring register allocation. The rationale is that coalescing of differently colored sub-ranges could result in spilling because there are some interference neighbors that prevent them from being assigned the same color. Experiments on Java programs show that the combination of live-range splitting and coloring-based coalescing reduces the static spill cost by more than 6% on average, comparing to the baseline coloring without splitting. In contrast, well-known iterated and optimistic coalescing algorithms, when combined with splitting, increase the cost by more than 20%. Coloring-based coalescing improves the execution time by up to 15% and 3% on average, while the existing algorithms improve by up to 12% and 1% on average.

Mathematics Learning through Computational Thinking Activities: A Systematic Literature Review <=SEP=> Computational Thinking represents a terminology that embraces the complex set of reasoning processes that are held for problem stating and solving through a computational tool. The ability of systematizing problems and solve them by these means is currently being considered a skill to be developed by all students, together with Language, Mathematics and Sciences. Considering that Computer Science has many of its roots on Mathematics, it is reasonable to ponder if and how Mathematics learning can be influenced by offering activities related to Computational Thinking to students. In this sense, this article presents a Systematic Literature Review on reported evidences of Mathematics learning in activities aimed at developing Computational Thinking skills. Forty-two articles which presented didactic activities together with an experimental design to evaluate learning outcomes published from 2006 to 2017 were analyzed. The majority of identified activities used a software tool or hardware device for their development. In these papers, a wide variety of mathematical topics has been being developed, with some emphasis on Planar Geometry and Algebra. Conversion of models and solutions between different semiotic representations is a high level cognitive skill that is most frequently associated to educational outcomes. This review indicated that more recent articles present a higher level of rigor in methodological procedures to assess learning effects. However, joint analysis of evidences from more than one data source is still not frequently used as a validation procedure.

Partially overlapping neural networks for real and imagined hand movements. <=SEP=> Neuroimagery findings have shown similar cerebral networks associated with imagination and execution of a movement. On the other hand, neuropsychological studies of parietal-lesioned patients suggest that these networks may be at least partly distinct. In the present study, normal subjects were asked to either imagine or execute auditory-cued hand movements. Compared with rest, imagination and execution showed overlapping networks, including bilateral premotor and parietal areas, basal ganglia and cerebellum. However, direct comparison between the two experimental conditions showed that specific cortico-subcortical areas were more engaged in mental simulation, including bilateral premotor, prefrontal, supplementary motor and left posterior parietal areas, and the caudate nuclei. These results suggest that a specific neuronal substrate is involved in the processing of hand motor representations.

Disconnected aging: Cerebral white matter integrity and age-related differences in cognition <=SEP=> Cognition arises as a result of coordinated processing among distributed brain regions and disruptions to communication within these neural networks can result in cognitive dysfunction. Cortical disconnection may thus contribute to the declines in some aspects of cognitive functioning observed in healthy aging. Diffusion tensor imaging (DTI) is ideally suited for the study of cortical disconnection as it provides indices of structural integrity within interconnected neural networks. The current review summarizes results of previous DTI aging research with the aim of identifying consistent patterns of age-related differences in white matter integrity, and of relationships between measures of white matter integrity and behavioral performance as a function of adult age. We outline a number of future directions that will broaden our current understanding of these brain-behavior relationships in aging. Specifically, future research should aim to (1) investigate multiple models of age-brain-behavior relationships; (2) determine the tract-specificity versus global effect of aging on white matter integrity; (3) assess the relative contribution of normal variation in white matter integrity versus white matter lesions to age-related differences in cognition; (4) improve the definition of specific aspects of cognitive functioning related to age-related differences in white matter integrity using information processing tasks; and (5) combine multiple imaging modalities (e.g., resting-state and task-related functional magnetic resonance imaging; fMRI) with DTI to clarify the role of cerebral white matter integrity in cognitive aging.

Evolving computational intelligence systems <=SEP=> A new paradigm of the evolving computational intelligence systems (ECIS) is introduced in a generic framework of the knowledge and data integration (KDI). This generalization of the recent advances in the development of evolving fuzzy and neuro-fuzzy models and the more analytical angle of consideration through the prism of knowledge evolution as opposed to the usually used datacentred approach marks the novelty of the present paper. ECIS constitutes a suitable paradigm for adaptive modeling of continuous dynamic processes and tracing the evolution of knowledge. The elements of evolution, such as inheritance and structure development are related to the knowledge and data pattern dynamics and are considered in the context of an individual system/model. Another novelty of this paper consists of the comparison at a conceptual level between the concept of models and knowledge captured by these models evolution and the well known paradigm of evolutionary computation. Although ECIS differs from the concept of evolutionary (genetic) computing, both paradigms heavily borrow from the same source – nature and human evolution. As the origin of knowledge, humans are the best model of an evolving intelligent system. Instead of considering the evolution of population of spices or genes as the evolutionary computation algorithms does the ECIS concentrate on the evolution of a single intelligent system. The aim is to develop the intelligence/knowledge of this system through an evolution using inheritance and modification, upgrade and reduction. This approach is also suitable for the integration of new data and existing models into new models that can be incrementally adapted to future incoming data. This powerful new concept has been recently introduced by the authors in a series of parallel works and is still under intensive development. It forms the conceptual basis for the development of the truly intelligent systems. Another specific of this paper includes bringing together the two working examples of ECIS, namely ECOS and EFS. The ideas are supported by illustrative examples (a synthetic non-linear function for the ECOS case and a benchmark problem of house price modelling from UCI repository for the case of EFS).

CCVis : Visual Analytics of Student Online Learning Behaviors Using Course Clickstream Data <=SEP=> As more and more college classrooms utilize online platforms to facilitate teaching and learning activities, analyzing student online behaviors becomes increasingly important for instructors to effectively monitor and manage student progress and performance. In this paper, we present CCVis, a visual analytics tool for analyzing the course clickstream data and exploring student online learning behaviors. Targeting a large college introductory course with over two thousand student enrollments, our goal is to investigate student behavior patterns and discover the possible relationships between student clickstream behaviors and their course performance. We employ higher-order network and structural identity classification to enable visual analytics of behavior patterns from the massive clickstream data. CCVis includes four coordinated views (the behavior pattern, behavior breakdown, clickstream comparative, and grade distribution views) for user interaction and exploration. We demonstrate the effectiveness of CCVis through case studies along with an ad-hoc expert evaluation. Finally, we discuss the limitation and extension of this work.

Modeling the global freight transportation system: A multi-level modeling perspective <=SEP=> The interconnectedness of different actors in the global freight transportation industry has rendered such a system as a large complex system where different sub-systems are interrelated. On such a system, policy-related- exploratory analyses which have predictive capacity are difficult to perform. Although there are many global simulation models for various large complex systems, there is unfortunately very little research aimed to develop a global freight transportation model. In this paper, we present a multi-level framework to develop an integrated model of the global freight transportation system. We employ a system view to incorporate different relevant sub-systems and categorize them in different levels. The fourstep model of freight transport is used as the basic foundation of the framework proposed. In addition to that, we also present the computational framework which adheres to the high level modeling framework to provide a conceptualization of the discrete-event simulation model which will be developed.

A broadband slotted ridge waveguide antenna array <=SEP=> A longitudinally-slotted ridge waveguide antenna array with a compact transverse dimension is presented. To broaden the bandwidth of the array, it is separated into two subarrays fed by a novel compact convex waveguide divider. A 16-element uniform linear array at X-band was fabricated and measured to verify the validity of the design. The measured bandwidth of S11les-15 dB is 14.9% and the measured cross- polarization level is less than -36 dB over the entire bandwidth. This array can be combined with the edge-slotted waveguide array to build a two-dimensional dual-polarization antenna array for the synthetic aperture radar (SAR) application

Segmenting cardiac MRI tagging lines using Gabor filter banks <=SEP=> t—This paper describes a new method for the segmentation and extraction of cardiac MRI s. Our method is based on the novel use of a 2D bank. By convolving the tagged input image with ilters, the tagging lines are automatically enhanced ted out. We design the Gabor filter bank based on age’s spatial and frequency characteristics. The is a combination of each filter’s response in the bank. We demonstrate that compared to bandpass ds such as HARP, this method results in robust and mentation of the tagging lines.

Stepfather Involvement and Stepfather-Child Relationship Quality: Race and Parental Marital Status as Moderators. <=SEP=> Stepparent-child relationship quality is linked to stepfamily stability and children's well-being. Yet, the literature offers an incomplete understanding of factors that promote high-quality stepparent-child relationships, especially among socio-demographically diverse stepfamilies. In this study, we explore the association between stepfather involvement and stepfather-child relationship quality among a racially diverse and predominately low-income sample of stepfamilies with preadolescent children. Using a subsample of 467 mother-stepfather families from year 9 of the Fragile Families and Child Wellbeing Study, results indicate that stepfather involvement is positively associated with stepfather-child relationship quality. This association is statistically indistinguishable across racial groups, although the association is stronger among children in cohabiting stepfamilies compared to children in married stepfamilies.

Conceptual and Database Modelling of Graph Databases <=SEP=> Comparing graph databases with traditional, e.g., relational databases, some important database features are often missing there. Particularly, a graph database schema including integrity constraints is not explicitly defined, also a conceptual modelling is not used at all. It is hard to check a consistency of the graph database, because almost no integrity constraints are defined. In the paper, we discuss these issues and present current possibilities and challenges in graph database modelling. Also a conceptual level of a graph database design is considered. We propose a sufficient conceptual model and show its relationship to a graph database model. We focus also on integrity constraints modelling functional dependencies between entity types, which reminds modelling functional dependencies known from relational databases and extend them to conditional functional dependencies.

Accurate reconstruction of image stimuli from human fMRI based on the decoding model with capsule network architecture <=SEP=> In neuroscience, all kinds of computation models were designed to answer the open question of how sensory stimuli are encoded by neurons and conversely, how sensory stimuli can be decoded from neuronal activities. Especially, functional Magnetic Resonance Imaging (fMRI) studies have made many great achievements with the rapid development of the deep network computation. However, comparing with the goal of decoding orientation, position and object category from activities in visual cortex, accurate reconstruction of image stimuli from human fMRI is a still challenging work. In this paper, the capsule network (CapsNet) architecture based visual reconstruction (CNAVR) method is developed to reconstruct image stimuli. The capsule means containing a group of neurons to perform the better organization of feature structure and representation, inspired by the structure of cortical mini column including several hundred neurons in primates. The high-level capsule features in the CapsNet includes diverse features of image stimuli such as semantic class, orientation, location and so on. We used these features to bridge between human fMRI and image stimuli. We firstly employed the CapsNet to train the nonlinear mapping from image stimuli to high-level capsule features, and from highlevel capsule features to image stimuli again in an end-to-end manner. After estimating the serviceability of each voxel by encoding performance to accomplish the selecting of voxels, we secondly trained the nonlinear mapping from dimension-decreasing fMRI data to high-level capsule features. Finally, we can predict the high-level capsule features with fMRI data, and reconstruct image stimuli with the CapsNet. We evaluated the proposed CNAVR method on the dataset of handwritten digital images, and exceeded about 10% than the accuracy of all existing state-of-the-art methods on the structural similarity index (SSIM).

Gut microbiota functions: metabolism of nutrients and other food components <=SEP=> The diverse microbial community that inhabits the human gut has an extensive metabolic repertoire that is distinct from, but complements the activity of mammalian enzymes in the liver and gut mucosa and includes functions essential for host digestion. As such, the gut microbiota is a key factor in shaping the biochemical profile of the diet and, therefore, its impact on host health and disease. The important role that the gut microbiota appears to play in human metabolism and health has stimulated research into the identification of specific microorganisms involved in different processes, and the elucidation of metabolic pathways, particularly those associated with metabolism of dietary components and some host-generated substances. In the first part of the review, we discuss the main gut microorganisms, particularly bacteria, and microbial pathways associated with the metabolism of dietary carbohydrates (to short chain fatty acids and gases), proteins, plant polyphenols, bile acids, and vitamins. The second part of the review focuses on the methodologies, existing and novel, that can be employed to explore gut microbial pathways of metabolism. These include mathematical models, omics techniques, isolated microbes, and enzyme assays.

The CN2 Induction Algorithm <=SEP=> Systems for inducing concept descriptions from examples are valuable tools for assisting in the task of knowledge acquisition for expert systems. This paper presents a description and empirical evaluation of a new induction system, CN2, designed for the efficient induction of simple, comprehensible production rules in domains where problems of poor description language and/or noise may be present. Implementations of the CN2, ID3, and AQ algorithms are compared on three medical classification tasks.