Query: Deep Recurrent Models with Fast-Forward Connections for Neural Machine Translation

Relevant passages:
N-gram Counts and Language Models from the Common Crawl <=SEP=> We contribute 5-gram counts and language models trained on the Common Crawl corpus, a collection over 9 billion web pages. This release improves upon the Google n-gram counts in two key ways: the inclusion of low-count entries and deduplication to reduce boilerplate. By preserving singletons, we were able to use Kneser-Ney smoothing to build large language models. This paper describes how the corpus was processed with emphasis on the problems that arise in working with data at this scale. Our unpruned Kneser-Ney English 5-gram language model, built on 975 billion deduplicated tokens, contains over 500 billion unique n-grams. We show gains of 0.5–1.4 BLEU by using large language models to translate into various languages.

Improving neural networks by preventing co-adaptation of feature detectors <=SEP=> When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data. This overfitting is greatly reduced by randomly omitting half of the feature detectors on each training case. This prevents complex co-adaptations in which a feature detector is only helpful in the context of several other specific feature detectors. Instead, each neuron learns to detect a feature that is generally helpful for producing the correct answer given the combinatorially large variety of internal contexts in which it must operate. Random “dropout” gives big improvements on many benchmark tasks and sets new records for speech and object recognition. A feedforward, artificial neural network uses layers of non-linear hidden units between its inputs and its outputs. By adapting the weights on the incoming connections of these hidden units it learns feature detectors that enable it to predict the correct output when given an input vector [15]. If the relationship between the input and the correct output is complicated and the network has enough hidden units to model it accurately, there will typically be many different settings of the weights that can model the training set almost perfectly, especially if there is only a limited amount of labeled training data. Each of these weight vectors will make different predictions on held-out test data and almost all of them will do worse on the test data than on the training data because the feature detectors have been tuned to work well together on the training data but not on the test data. Overfitting can be reduced by using “dropout” to prevent complex co-adaptations on the training data. On each presentation of each training case, each hidden unit is randomly omitted from the network with a probability of 0.5, so a hidden unit cannot rely on other hidden units being present. Another way to view the dropout procedure is as a very efficient way of performing model averaging with neural networks. A good way to reduce the error on the test set is to average the predictions produced by a very large number of different networks. The standard way to do this is to train many separate networks and then to apply each of these networks to the test data, but this is computationally expensive during both training and testing. Random dropout makes it possible to train a huge number of different networks in a reasonable time. There is almost certainly a different network for each presentation of each training case but all of these networks share the same weights for the hidden units that are present. We use the standard, stochastic gradient descent procedure for training the dropout neural networks on mini-batches of training cases, but we modify the penalty term that is normally used to prevent the weights from growing too large. Instead of penalizing the squared length (L2 norm) of the whole weight vector, we set an upper bound on the L2 norm of the incoming weight vector for each individual hidden unit. If a weight-update violates this constraint, we renormalize the weights of the hidden unit by division. Using a constraint rather than a penalty prevents weights from growing very large no matter how large the proposed weight-update is. This makes it possible to start with a

Statistical Phrase-Based Translation <=SEP=> We propose a new phrase-based translation model and decoding algorithm that enables us to evaluate and compare several, previously proposed phrase-based translation models. Within our framework, we carry out a large number of experiments to understand better and explain why phrase-based models outperform word-based models. Our empirical results, which hold for all examined language pairs, suggest that the highest levels of performance can be obtained through relatively simple means: heuristic learning of phrase translations from word-based alignments and lexical weighting of phrase translations. Surprisingly, learning phrases longer than three words and learning phrases from high-accuracy wordlevel alignment models does not have a strong impact on performance. Learning only syntactically motivated phrases degrades the performance of our systems.

Empirical Study on Deep Learning Models for QA <=SEP=> In this paper we explore deep learning models with memory component or attention mechanism for question answering task. We combine and compare three models, Neural Machine Translation [1], Neural Turing Machine [5], and Memory Networks [15] for a simulated QA data set [14]. This paper is the first one that uses Neural Machine Translation and Neural Turing Machines for solving QA tasks. Our results suggest that the combination of attention and memory have potential to solve certain QA problem.

Depth-Gated LSTM <=SEP=> In this short note, we present an extension of long short-term memory (LSTM) neural networks to using a depth gate to connect memory cells of adjacent layers. Doing so introduces a linear dependence between lower and upper layer recurrent units. Importantly, the linear dependence is gated through a gating function, which we call depth gate. This gate is a function of the lower layer memory cell, the input to and the past memory cell of this layer. We conducted experiments and verified that this new architecture of LSTMs was able to improve machine translation and language modeling performances.

A review on fingerprint orientation estimation <=SEP=> Fingerprint orientation plays important roles in fingerprint enhancement, fingerprint classification, and fingerprint recognition. This paper critically reviews the primary advances on fingerprint orientation estimation. Advantages and limitations of existing methods have been addressed. Issues on future development have been discussed. Copyright © 2010 John Wiley & Sons, Ltd.

Clustering Spatial Data in the Presence of Obstacles: a Density-Based Approach <=SEP=> Clustering spatial data is a well-known problem that has been extensively studied. Grouping similar data in large 2-dimensional spaces to find hidden patterns or meaningful sub-groups has many applications such as satellite imagery, geographic information systems, medical image analysis, marketing, computer visions, etc. Although many methods have been proposed in the literature, very few have considered physical obstacles that may have significant consequences on the effectiveness of the clustering. Taking into account these constraints during the clustering process is costly and the modeling of the constraints is paramount for good performance. In this paper, we investigate the problem of clustering in the presence of constraints such as physical obstacles and introduce a new approach to model these constraints using polygons. We also propose a strategy to prune the search space and reduce the number of polygons to test during clustering. We devise a density-based clustering algorithm, DBCluC, which takes advantage of our constraint modeling to efficiently cluster data objects while considering all physical constraints. The algorithm can detect clusters of arbitrary shape and is insensitive to noise, the input order, and the difficulty of constraints. Its average running complexity is O(NlogN) where N is the number of

The selection of fusion levels in thoracic idiopathic scoliosis. <=SEP=> From the material and data reviewed in our study of 405 patients, it appears that postoperative correction of the thoracic spine approximately equals the correction noted on preoperative side-bending roentgenograms. Selective thoracic fusion can be safely performed on a Type-II curve of less than 80 degrees, but care must be taken to use the vertebra that is neutral and stable so that the lower level of the fusion is centered over the sacrum. The lumbar curve spontaneously corrects to balance the thoracic curve when selective thoracic fusion is performed and the lower level of fusion is properly selected. In Type-III, IV, and V thoracic curves the lower level of fusion should be centered over the sacrum to achieve a balanced, stable spine.

An effect analysis of industry 4.0 to higher education <=SEP=> From history to the present day, especially in the field of technology is provided a wide range of developments. The most important of these developments has achieved the realization of the industrial revolution, has led to play a leading role in the production is done by human beings. The effects on production of industry 4.0 emerging with this mind is very important. The qualified employees should be trained to make the necessary preparations for the company in this period, it is experiencing the fourth industrial revolution. Therefore, the impacts on higher education of industry 4.0 are examined in this paper. The importance in education of industry 4.0 has revealed with statistical data presented in this study.

Image quality measures and their performance <=SEP=> A number of quality measures are evaluated for gray scale image compression. They are all bivariate, exploiting the differences between corresponding pixels in the original and degraded images. It is shown that although some numerical measures correlate well with the observers’ response for a given compression technique, they are not reliable for an evaluation across different techniques. A graphical measure called Hosaka plots, however, can be used to appropriately specify not only the amount, but also the type of degradation in reconstructed images.

CacheAudit: A Tool for the Static Analysis of Cache Side Channels <=SEP=> We present CacheAudit, a versatile framework for the automatic, static analysis of cache side channels. CacheAudit takes as input a program binary and a cache configuration and derives formal, quantitative security guarantees for a comprehensive set of side-channel adversaries, namely, those based on observing cache states, traces of hits and misses, and execution times. Our technical contributions include novel abstractions to efficiently compute precise overapproximations of the possible side-channel observations for each of these adversaries. These approximations then yield upper bounds on the amount of information that is revealed.
 In case studies, we apply CacheAudit to binary executables of algorithms for sorting and encryption, including the AES implementation from the PolarSSL library, and the reference implementations of the finalists of the eSTREAM stream cipher competition. The results we obtain exhibit the influence of cache size, line size, associativity, replacement policy, and coding style on the security of the executables and include the first formal proofs of security for implementations with countermeasures such as preloading and data-independent memory access patterns.

A correlation game for unsupervised learning yields computational interpretations of Hebbian excitation, anti-Hebbian inhibition, and synapse elimination <=SEP=> Much has been learned about plasticity of biological synapses from empirical studies. Hebbian plasticity is driven by correlated activity of presynaptic and postsynaptic neurons. Synapses that converge onto the same neuron often behave as if they compete for a fixed resource; some survive the competition while others are eliminated. To provide computational interpretations of these aspects of synaptic plasticity, we formulate unsupervised learning as a zero-sum game between Hebbian excitation and anti-Hebbian inhibition in a neural network model. The game formalizes the intuition that Hebbian excitation tries to maximize correlations of neurons with their inputs, while anti-Hebbian inhibition tries to decorrelate neurons from each other. We further include a model of synaptic competition, which enables a neuron to eliminate all connections except those from its most strongly correlated inputs. Through empirical studies, we show that this facilitates the learning of sensory features that resemble parts of objects. Much has been learned about plasticity of biological synapses from empirical studies. One line of research has explored Hebbian plasticity, which is triggered by correlated presynaptic and postsynaptic activity. Many kinds of excitatory synapses are strengthened by correlated activity, and are said to be Hebbian [Bi and Poo, 2001]. Strengthening of inhibitory synapses by correlated activity has also been observed [Gaiarsa et al., 2002]. The phenomenon is sometimes said to be anti-Hebbian, because strengthening an inhibitory synapse is like making a negative number more negative. Another line of research has shown that synapses converging onto the same neuron often behave as if they compete for shares of a fixed “pie”; some survive the competition while others are eliminated [Lichtman and Colman, 2000]. According to this viewpoint, Hebbian plasticity does not increase or decrease the pie of synaptic resources; it only allocates resources across convergent synapses [Miller, 1996] Theoretical neuroscientists have proposed a number of computational functions for Hebbian excitation, anti-Hebbian inhibition, and synaptic competition/elimination. Hebbian excitation has long been invoked as a mechanism for learning features from sensory input [Von der Malsburg, 1973]. Lateral inhibition has been used to sparsen neural activity, thereby facilitating Hebbian feature learning [Von der Malsburg, 1973, Fukushima, 1980, Rumelhart and Zipser, 1985, Kohonen, 1990]. Endowing the lateral inhibition with anti-Hebbian plasticity can give more robust control over sparseness of activity. Földiák [1990] demonstrated this with numerical experiments, but did not provide an interpretation in terms of an optimization principle. Less relevant here are antiHebbian models without reference to sparse activity in linear [Foldiak, 1989, Rubner and Tavan, 1989, Rubner and Schulten, 1990] and nonlinear [Carlson, 1990, Girolami and Fyfe, 1997] networks. Also less relevant is the application of anti-Hebbian plasticity to feedforward rather than lateral connections [Hyvärinen and Oja, 1998]. 1 ar X iv :1 70 4. 00 64 6v 1 [ cs .N E ] 3 A pr 2 01 7 Leen [1991] performed a stability analysis for a linear network with Hebbian feedforward connections and anti-Hebbian lateral connections. Plumbley [1993] derived a linear network with antiHebbian lateral inhibition (but no plasticity of feedforward connections) from the principle of information maximization with a power constraint. Pehlevan et al. [2015] showed that a linear network with Hebbian feedforward connections and anti-Hebbian lateral inhibition can be interpreted as online gradient optimization of a “similarity matching” cost function. Pehlevan and Chklovskii [2014] and Hu et al. [2014] went on to extend the similarity matching principle to derive nonlinear neural networks for unsupervised learning. Synaptic competition and elimination have been studied in models of cortical development, and have been shown to play an important role in the emergence of feature selectivity [Miller, 1996]. The subject of the present work is a mathematical formalism that provides computational interpretations of Hebbian excitation, anti-Hebbian inhibition, and synaptic competition/elimination in nonlinear neural networks. We start by formulating unsupervised learning as the maximization of output-input correlations subject to upper bound constraints on output-output correlations. We motivate our formulation by describing its relation to previous theoretical frameworks for unsupervised learning, such as maximization [Linsker, 1988, Atick and Redlich, 1990, Plumbley, 1993, Bell and Sejnowski, 1995] or minimization of mutual information [Hyvärinen and Oja, 2000], (2) projection onto a subspace that maximizes a moment-based statistic such as variance [Oja, 1982, Linsker, 1988] or kurtosis [Huber, 1985], and the (3) similarity matching principle [Pehlevan et al., 2015]. To solve our constrained maximization problem, we introduce Lagrange multipliers. This Lagrangian dual formulation of unsupervised learning can in turn be solved by a nonlinear neural network with Hebbian excitation and anti-Hebbian inhibition. The network is very similar to the original model of Földiák [1990], differing mainly by its use of rectification rather than sigmoidal nonlinearity. (The latter can also be handled by our formalism, as shown in Appendix B.) Lagrange multipliers were also used to study anti-Hebbian plasticity by Plumbley [1993], but only for linear networks. Effectively, excitation and inhibition behave like players in a game, and the inhibitory connections can be interpreted as Lagrange multipliers. The game is zero-sum, in that excitation tries to maximize a payoff function and inhibition tries to minimize exactly the same payoff function. Roughly speaking, however, one could say that excitation aims to maximize the correlation of each output neuron with its inputs, while inhibition aims to decorrelate the output neurons from each other. Our term “correlation game” is derived from this intuitive picture. Within our mathematical formalism, we also consider a dynamics of synaptic competition and elimination that is drawn from models of cortical development [Miller and MacKay, 1994]. Competition between the excitatory synapses convergent on a single neuron is capable of driving the strengths of some synapses to zero. In numerical experiments with the MNIST dataset, we show that synapse elimination has the computational function of facilitating the learning of features that resemble “parts” of objects. Theoretical analysis shows that the surviving synapses converging onto an output neuron come from its most strongly correlated inputs; synapses from weakly correlated inputs are eliminated. Our correlation game is closely related to the similarity matching principle of Pehlevan and Chklovskii [2014] and Pehlevan et al. [2015]. The major novelty is the introduction of decorrelation as a constraint for the optimization. Paralleling our work, Pehlevan et al. [2017] have shown that the similarity matching principle leads to a game theoretic formulation through Hubbard-Stratonovich duality. Again our novelty is the use of decorrelation as a constraint, which leads to our correlation game through Lagrangian duality. Our model of synaptic competition and elimination was borrowed with slight modification from the literature on modeling neural development [Miller and MacKay, 1994]. It can be viewed as a more biologically plausible alternative to previous unsupervised learning algorithms that sparsen features. For example, Hoyer [2004] is similar to ours because it can be interpreted as independently sparsening each set of convergent synapses, rather than applying a global L1 regularizer to all synapses.

Deep learning with support vector data description <=SEP=> One of the most critical problems for machine learning methods is overfitting. The overfitting problem is a phenomenon in which the accuracy of the model on unseen data is poor whereas the training accuracy is nearly perfect. This problem is particularly severe in complex models that have a large set of parameters. In this paper, we propose a deep learning neural network model that adopts the support vector data description (SVDD). The SVDD is a variant of the support vector machine, which has high generalization performance by acquiring a maximal margin in one-class classification problems. The proposed model strives to obtain the representational power of deep learning. Generalization performance is maintained using the SVDD. The experimental results showed that the proposed model can learn multiclass data without severe overfitting problems.

Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control <=SEP=> A broad range of neural and behavioral data suggests that the brain contains multiple systems for behavioral choice, including one associated with prefrontal cortex and another with dorsolateral striatum. However, such a surfeit of control raises an additional choice problem: how to arbitrate between the systems when they disagree. Here, we consider dual-action choice systems from a normative perspective, using the computational theory of reinforcement learning. We identify a key trade-off pitting computational simplicity against the flexible and statistically efficient use of experience. The trade-off is realized in a competition between the dorsolateral striatal and prefrontal systems. We suggest a Bayesian principle of arbitration between them according to uncertainty, so each controller is deployed when it should be most accurate. This provides a unifying account of a wealth of experimental evidence about the factors favoring dominance by either system.

Stakeholders in Explainable AI <=SEP=> There is general consensus that it is important for artificial intelligence (AI) and machine learning systems to be explainable and/or interpretable. However, there is no general consensus over what is meant by ‘explainable’ and ‘interpretable’. In this paper, we argue that this lack of consensus is due to there being several distinct stakeholder communities. We note that, while the concerns of the individual communities are broadly compatible, they are not identical, which gives rise to different intents and requirements for explainability/interpretability. We use the software engineering distinction between validation and verification, and the epistemological distinctions between knowns/unknowns, to tease apart the concerns of the stakeholder communities and highlight the areas where their foci overlap or diverge. It is not the purpose of the authors of this paper to ‘take sides’ — we count ourselves as members, to varying degrees, of multiple communities — but rather to help disambiguate what stakeholders mean when they ask ‘Why?’ of an AI.

Loughborough University Institutional Repository Product attachment and replacement : implications for sustainable design <=SEP=> This research investigated a number of main areas of attachment in order to determine how consumer-product relationships are formed and to identify whether these feelings influence replacement decisions. Primary research comprised of interviews with consumers to discuss the topic area in relation to three possessions selected for their attachment qualities. The research highlighted how attachment is determined by multiple themes, many of which are circumstantial to consumers’ experiences and therefore difficult for designers to control. Findings showed that memories were the most prominent theme of participants’ attachment, closely followed by pleasure and usability. Enjoyment and pleasure were found to be the primary reason for attachment to newly purchased items, whereas nostalgia was highest for older possessions. Appearance and reliability were found to have considerable influence on participants’ attitudes towards replacement.

PID: the Pathway Interaction Database <=SEP=> The Pathway Interaction Database (PID, http://pid.nci.nih.gov) is a freely available collection of curated and peer-reviewed pathways composed of human molecular signaling and regulatory events and key cellular processes. Created in a collaboration between the US National Cancer Institute and Nature Publishing Group, the database serves as a research tool for the cancer research community and others interested in cellular pathways, such as neuroscientists, developmental biologists and immunologists. PID offers a range of search features to facilitate pathway exploration. Users can browse the predefined set of pathways or create interaction network maps centered on a single molecule or cellular process of interest. In addition, the batch query tool allows users to upload long list(s) of molecules, such as those derived from microarray experiments, and either overlay these molecules onto predefined pathways or visualize the complete molecular connectivity map. Users can also download molecule lists, citation lists and complete database content in extensible markup language (XML) and Biological Pathways Exchange (BioPAX) Level 2 format. The database is updated with new pathway content every month and supplemented by specially commissioned articles on the practical uses of other relevant online tools.

Global Inference for Sentence Compression: An Integer Linear Programming Approach <=SEP=> Sentence compression holds promise for many applications ranging from summarization to subtitle generation. Our work views sentence compression as an optimization problem and uses integer linear programming (ILP) to infer globally optimal compressions in the presence of linguistically motivated constraints. We show how previous formulations of sentence compression can be recast as ILPs and extend these models with novel global constraints. Experimental results on written and spoken texts demonstrate improvements over state-of-the-art models.

Toward tense as a clinical marker of specific language impairment in English-speaking children. <=SEP=> A critical clinical issue is the identification of a clinical marker, a linguistic form or principle that can be shown to be characteristic of children with Specific Language Impairment (SLI). In this paper we evaluate, as candidate clinical markers, a set of morphemes that mark Tense. In English, this includes -s third person singular, -ed regular past, BE, and DO. According to the Extended Optional Infinitive Account (EOI) of Rice, Wexler, and Cleave (1995), this set of morphemes is likely to appear optionally in the grammars of children with SLI at a rate lower than the optionality evident in younger controls. Three groups of preschool children participated: 37 children with SLI, and two control groups, one of 40 MLU-equivalent children and another of 45 age-equivalent children. Three kinds of evidence support the conclusion that a set of morphemes that marks Tense can be considered a clinical marker: (a) low levels of accuracy for the target morphemes for the SLI group relative to either of the two control groups; (b) affectedness for the set of morphemes defined by the linguistic function of Tense, but not for morphemes unrelated to Tense; and (c) a bimodal distribution for Tense-marking morphemes relative to age peers, in which the typical children are at essentially adult levels of the grammar, whereas children in the SLI group were at low (i.e., non-adultlike) levels of performance. The clinical symptoms are evident in omissions of surface forms. Errors of subject-verb agreement and syntactic misuses are rare, showing that, as predicted, children in an EOI stage who are likely to mark Tense optionally at the same time know a great deal about the grammatical properties of finiteness and agreement in the adult grammar. The findings are discussed in terms of alternative accounts of the grammatical limitations of children with SLI and implications for clinical identification.

A Privacy-Aware Authentication Scheme for Distributed Mobile Cloud Computing Services <=SEP=> In modern societies, the number of mobile users has dramatically risen in recent years. In this paper, an efficient authentication scheme for distributed mobile cloud computing services is proposed. The proposed scheme provides security and convenience for mobile users to access multiple mobile cloud computing services from multiple service providers using only a single private key. The security strength of the proposed scheme is based on bilinear pairing cryptosystem and dynamic nonce generation. In addition, the scheme supports mutual authentication, key exchange, user anonymity, and user untraceability. From system implementation point of view, verification tables are not required for the trusted smart card generator (SCG) service and cloud computing service providers when adopting the proposed scheme. In consequence, this scheme reduces the usage of memory spaces on these corresponding service providers. In one mobile user authentication session, only the targeted cloud service provider needs to interact with the service requestor (user). The trusted SCG serves as the secure key distributor for distributed cloud service providers and mobile clients. In the proposed scheme, the trusted SCG service is not involved in individual user authentication process. With this design, our scheme reduces authentication processing time required by communication and computation between cloud service providers and traditional trusted third party service. Formal security proof and performance analyses are conducted to show that the scheme is both secure and efficient.

A Real Options Approach for the Valuation of Highway Concessions <=SEP=> T theory of real options offers an approach for the valuation of investments in real assets, based on the methodology developed for financial options. This approach is especially appropriate in the context of strategic decision making under conditions of uncertainty. This is the case for the valuation of highway concessions, where real options arise from certain clauses of the contracts, for example, a minimum traffic guarantee. The possible exercise of these kinds of rights means an added value for the project that cannot be easily captured using traditional procedures for investment valuation. In this paper, we develop a methodology to value these clauses of highway concessions and, for that purpose, we consider the traffic on the highway as the underlying asset in an option contract, taking into account that, when a nonfinancial asset is used, some adjustments have to be made to the options approach. This methodology is applied to the case of an already operating highway concession, using real data, and the authors obtain an estimate of the value of a minimum traffic guarantee, which depends on several parameters that are analyzed. The authors conclude that this methodology is an appropriate tool for the valuation of highway concessions that have operational flexibility.

Pedestrian detection in low resolution videos <=SEP=> Pedestrian detection in low resolution videos can be challenging. In outdoor surveillance scenarios, the size of pedestrians in the images is often very small (around 20 pixels tall). The most common and successful approaches for single frame pedestrian detection use gradient-based features and a support vector machine classifier. We propose an extension of these ideas, and develop a new algorithm that extracts gradient features from a spatiotemporal volume, consisting of a short sequence of images (about one second in duration). The additional information provided by the motion of the person compensates for the loss of resolution. On standard datasets (PETS2001, VIRAT) we show a significant improvement in performance over single-frame detection.

Computer-generated pen-and-ink illustration <=SEP=> This paper describes the principles of traditional pen-and-ink illustration, and shows how a great number of them can be implemented as part of an automated rendering system. It introduces “stroke textures,” which can be used for achieving both texture and tone with line drawing. Stroke textures also allow resolution-dependent rendering, in which the choice of strokes used in an illustration is appropriately tied to the resolution of the target medium. We demonstrate these techniques using complex architectural models, including Frank Lloyd Wright's “Robie House.”

No need to war-drive: unsupervised indoor localization <=SEP=> We propose UnLoc, an unsupervised indoor localization scheme that bypasses the need for war-driving. Our key observation is that certain locations in an indoor environment present identifiable signatures on one or more sensing dimensions. An elevator, for instance, imposes a distinct pattern on a smartphone's accelerometer; a corridor-corner may overhear a unique set of WiFi access points; a specific spot may experience an unusual magnetic fluctuation. We hypothesize that these kind of signatures naturally exist in the environment, and can be envisioned as internal landmarks of a building. Mobile devices that "sense" these landmarks can recalibrate their locations, while dead-reckoning schemes can track them between landmarks. Results from 3 different indoor settings, including a shopping mall, demonstrate median location errors of 1:69m. War-driving is not necessary, neither are floorplans the system simultaneously computes the locations of users and landmarks, in a manner that they converge reasonably quickly. We believe this is an unconventional approach to indoor localization, holding promise for real-world deployment.

jModelTest 2: more models, new heuristics and parallel computing <=SEP=> jModelTest 2: more models, new heuristics and parallel computing Diego Darriba, Guillermo L. Taboada, Ramón Doallo and David Posada Supplementary Table 1. New features in jModelTest 2 Supplementary Table 2. Model selection accuracy Supplementary Table 3. Mean square errors for model averaged estimates Supplementary Note 1. Hill-climbing hierarchical clustering algorithm Supplementary Note 2. Heuristic filtering Supplementary Note 3. Simulations from prior distributions Supplementary Note 4. Speed-up benchmark on real and simulated datasets

Vegan diets: practical advice for athletes and exercisers <=SEP=> With the growth of social media as a platform to share information, veganism is becoming more visible, and could be becoming more accepted in sports and in the health and fitness industry. However, to date, there appears to be a lack of literature that discusses how to manage vegan diets for athletic purposes. This article attempted to review literature in order to provide recommendations for how to construct a vegan diet for athletes and exercisers. While little data could be found in the sports nutrition literature specifically, it was revealed elsewhere that veganism creates challenges that need to be accounted for when designing a nutritious diet. This included the sufficiency of energy and protein; the adequacy of vitamin B12, iron, zinc, calcium, iodine and vitamin D; and the lack of the long-chain n-3 fatty acids EPA and DHA in most plant-based sources. However, via the strategic management of food and appropriate supplementation, it is the contention of this article that a nutritive vegan diet can be designed to achieve the dietary needs of most athletes satisfactorily. Further, it was suggested here that creatine and β-alanine supplementation might be of particular use to vegan athletes, owing to vegetarian diets promoting lower muscle creatine and lower muscle carnosine levels in consumers. Empirical research is needed to examine the effects of vegan diets in athletic populations however, especially if this movement grows in popularity, to ensure that the health and performance of athletic vegans is optimised in accordance with developments in sports nutrition knowledge.

An approach to automotive ECG measurement validation using a car-integrated test framework <=SEP=> Development and integration of physiological sensors into automotive applications is gaining importance. Assistance systems which possess knowledge about the driver's cognitive state could increase road safety. In this paper we present a flexible framework that enables the development, evaluation and verification of sensors and algorithms for automotive applications using physiological signals under realistic driving conditions. We have integrated a custom capacitive ECG measurement system into a test car and validated its performance in real world driving tests. During first test runs, the capacitive system achieved a sensitivity of up to 95.5% and a precision rate of up to 92.6%. Our system also records synchronized vehicle dynamics. We discuss the road test measurements which suggest that the driving situation highly impacts the quality of ECG signal. Therefore, information on driving dynamics could be used to improve the precision rate of future capacitive ECG measurement.

Distributed Multi-Robot Localization <=SEP=> This paper presents a new approach to the cooperative localization problem, namely distributed multi-robot localization. A group of M robots is viewed as a single system composed of robots that carry, in general, di erent sensors and have di erent positioning capabilities. A single Kalman lter is formulated to estimate the position and orientation of all the members of the group. This centralized schema is capable of fusing information provided by the sensors distributed on the individual robots while accommodating independencies and interdependencies among the collected data. In order to allow for distributed processing, the equations of the centralized Kalman lter are treated so that this lter can be decomposed into M modi ed Kalman lters each running on a separate robot. The distributed localization algorithm is applied to a group of 3 robots and the improvement in localization accuracy is presented.

Spatial delineation of soil erosion vulnerability in the Lake Tana Basin , Ethiopia <=SEP=> The main objective of this study was to identify the most vulnerable areas to soil erosion in the Lake Tana Basin, Blue Nile, Ethiopia using the Soil and Water Assessment Tool (SWAT), a physically based distributed hydrological model, and a Geographic Information System based decision support system that uses multi-criteria evaluation (MCE). The SWAT model was used to estimate the sediment yield within each sub-basin and identify the most sediment contributing areas in the basin. Using the MCE analysis, an attempt was made to combine a set of factors (land use, soil, slope and river layers) to make a decision according to the stated objective. On the basis of simulated SWAT, sediment yields greater than 30 tons/ha for each of the sub-basin area, 18Ð4% of the watershed was determined to be high erosion potential area. The MCE results indicated that 12–30Ð5% of the watershed is high erosion potential area. Both approaches show comparable watershed area with high soil erosion susceptibility. The output of this research can aid policy and decision makers in determining the soil erosion ‘hot spots’ and the relevant soil and water conservation measures. Copyright  2009 John Wiley & Sons, Ltd.

The development of the SGI-16: a shortened sensory gating deficit and distractibility questionnaire for adults with ADHD. <=SEP=> The Sensory Gating Inventory (SGI) is a questionnaire composed of 36 items designed to investigate abnormal perception related to the inability to control sensitivity to sensory stimuli frequently reported in adult with ADHD. This questionnaire can be considered too lengthy to be taken by people with ADHD, and a shortened version is needed. One hundred and sixty-three adults with ADHD responded to the SGI-36. An item reduction process took into account both the results of statistical analyses and the expertise of a steering committee. Construct validity, reliability, and external validity were tested for a short version (16 items). The structure of the SGI-16 was confirmed by principal components factor analysis. Cronbach's alpha coefficients ranged from 0.78 to 0.89. The SGI-16 dimension scores were highly correlated with their respective SGI-36 dimension scores. The SGI-16 seems to be both appropriate and useful for use in clinical practice to investigate perceptual abnormalities in adults with ADHD.