Query: Convex Color Image Segmentation with Optimal Transport Distances

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Fast Computation of Wasserstein Barycenters <=SEP=> We present new algorithms to compute the mean of a set of empirical probability measures under the optimal transport metric. This mean, known as the Wasserstein barycenter, is the measure that minimizes the sum of its Wasserstein distances to each element in that set. We propose two original algorithms to compute Wasserstein barycenters that build upon the subgradient method. A direct implementation of these algorithms is, however, too costly because it would require the repeated resolution of large primal and dual optimal transport problems to compute subgradients. Extending the work of Cuturi (2013), we propose to smooth the Wasserstein distance used in the definition of Wasserstein barycenters with an entropic regularizer and recover in doing so a strictly convex objective whose gradients can be computed for a considerably cheaper computational cost using matrix scaling algorithms. We use these algorithms to visualize a large family of images and to solve a constrained clustering problem.

Proximal Splitting Methods in Signal Processing ∗ <=SEP=> The proximity operator of a convex function is a natural exte nsion of the notion of a projection operator onto a convex set. This tool, which plays a central role in the analysis and the numerical solution of convex opt imization problems, has recently been introduced in the arena of inverse problems an d, especially, in signal processing, where it has become increasingly important. In this paper, we review the basic properties of proximity operators which are relevant to signal processing and present optimization methods based on these operators. The e proximal splitting methods are shown to capture and extend several well-known a lgorithms in a unifying framework. Applications of proximal methods in signal r ecovery and synthesis are discussed.

Super-Samples from Kernel Herding <=SEP=> We extend the herding algorithm to continuous spaces by using the kernel trick. The resulting “kernel herding” algorithm is an infinite memory deterministic process that learns to approximate a PDF with a collection of samples. We show that kernel herding decreases the error of expectations of functions in the Hilbert space at a rateO(1/T )which is much faster than the usual O(1/ √ T ) for iid random samples. We illustrate kernel herding by approximating Bayesian predictive distributions.

Clustering with Bregman Divergences <=SEP=> A wide variety of distortion functions, such as squared Eucl idean distance, Mahalanobis distance, Itakura-Saito distance and relative entropy, have been use d for clustering. In this paper, we propose and analyze parametric hard and soft clustering algori thms based on a large class of distortion functions known as Bregman divergences. The proposed algor ithms unify centroid-based parametric clustering approaches, such as classical kmeans, the Linde-Buzo-Gray (LBG) algorithm and information-theoretic clustering, which arise by special hoices of the Bregman divergence. The algorithms maintain the simplicity and scalability of the c lassicalkmeans algorithm, while generalizing the method to a large class of clustering loss functi o s. This is achieved by first posing the hard clustering problem in terms of minimizing the loss i n Bregman information, a quantity motivated by rate distortion theory, and then deriving an it erative algorithm that monotonically decreases this loss. In addition, we show that there is a biject ion between regular exponential families and a large class of Bregman divergences, that we call regula r Bregman divergences. This result enables the development of an alternative interpretation o f a efficient EM scheme for learning mixtures of exponential family distributions, and leads to a si mple soft clustering algorithm for regular Bregman divergences. Finally, we discuss the connection be tween rate distortion theory and Bregman clustering and present an information theoretic analys is of Bregman clustering algorithms in terms of a trade-off between compression and loss in Bregman information.

Fast Image Recovery Using Variable Splitting and Constrained Optimization <=SEP=> We propose a new fast algorithm for solving one of the standard formulations of image restoration and reconstruction which consists of an unconstrained optimization problem where the objective includes an l2 data-fidelity term and a nonsmooth regularizer. This formulation allows both wavelet-based (with orthogonal or frame-based representations) regularization or total-variation regularization. Our approach is based on a variable splitting to obtain an equivalent constrained optimization formulation, which is then addressed with an augmented Lagrangian method. The proposed algorithm is an instance of the so-called alternating direction method of multipliers, for which convergence has been proved. Experiments on a set of image restoration and reconstruction benchmark problems show that the proposed algorithm is faster than the current state of the art methods.

Needs, affect, and interactive products - Facets of user experience <=SEP=> Subsumed under the umbrella of User Experience (UX), practitioners and academics of Human–Computer Interaction look for ways to broaden their understanding of what constitutes ‘‘pleasurable experiences” with technology. The present study considered the fulfilment of universal psychological needs, such as competence, relatedness, popularity, stimulation, meaning, security, or autonomy, to be the major source of positive experience with interactive technologies. To explore this, we collected over 500 positive experiences with interactive products (e.g., mobile phones, computers). As expected, we found a clear relationship between need fulfilment and positive affect, with stimulation, relatedness, competence and popularity being especially salient needs. Experiences could be further categorized by the primary need they fulfil, with apparent qualitative differences among some of the categories in terms of the emotions involved. Need fulfilment was clearly linked to hedonic quality perceptions, but not as strongly to pragmatic quality (i.e., perceived usability), which supports the notion of hedonic quality as ‘‘motivator” and pragmatic quality as ‘‘hygiene factor.” Whether hedonic quality ratings reflected need fulfilment depended on the belief that the product was responsible for the experience (i.e., attribution). 2010 Elsevier B.V. All rights reserved.

MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets <=SEP=> A recent technical breakthrough in the domain of machine learning is the discovery and the multiple applications of Generative Adversarial Networks (GANs). Those generative models are computationally demanding, as a GAN is composed of two deep neural networks, and because it trains on large datasets. A GAN is generally trained on a single server. In this paper, we address the problem of distributing GANs so that they are able to train over datasets that are spread on multiple workers. MD-GAN is exposed as the first solution for this problem: we propose a novel learning procedure for GANs so that they fit this distributed setup. We then compare the performance of MD-GAN to an adapted version of Federated Learning to GANs, using the MNIST and CIFAR10 datasets. MD-GAN exhibits a reduction by a factor of two of the learning complexity on each worker node, while providing better performances than federated learning on both datasets. We finally discuss the practical implications of distributing GANs.

An integrated 122GHz differential frequency doubler with 37GHz bandwidth in 130 nm SiGe BiCMOS technology <=SEP=> This paper describes an integrated frequency multiplier, implemented as a Gilbert cell based frequency doubler in a 130 nm SiGe BiCMOS technology. The circuit demonstrates a 3 dB bandwidth of 97–134GHz with peak output power of 1 dBm for 1 dBm input power. The fundamental suppression, measured at the single-ended output, is better than 21 dBc while the frequency doubler consumes 69mW from a 3.3V supply. The doubler is preceded by a differential amplifier functioning as an active balun to generate a differential signal for the Gilbert cell.

Survey on Fraud Detection Techniques Using Data Mining <=SEP=> Now a days, fraud is a million dollar business and every year it is increasing more and more. In a crime survey in 2009, tells that close to 30% of the company’s worldwide reported falling victim to fraud in the past year. Fraud involves individual or in a form of groups who intentionally act secretly to rob another of something of worth, for their own profit. Fraud is as against the humanity and it is unlimited variety of different forms. So in this paper, we introduce different methods and techniques to tackle the fraud and how to prevent the fraud into the organization, businesses, and hospital and insurance companies. Fraud prevention is a continuing battle. Many website owners must continually fight against many kinds of crime ranging from stealing identity theft and credit cards and many other types of fraud related to that. Internet Fraud is also very popular, where fraudster can steal the credit card number and buy thing from the different websites.

Disease recognition in Mango crop using modified rotational kernel transform features <=SEP=> Soft computing in the field of agriculture science is being employed with computer vision techniques in order to detect the diseases in crops to increase the overall yield. A Modified Rotation Kernel Transformation(MRKT) based directional feature extraction scheme is presents to resolve the issues occurring due to shape, color or other deceptive features during plant disease recognition. The MRKT based scheme is used to calculate the directional features and histograms for plant parts like leaf and fruit of Mango crop. These histograms and the directional feature set with use of artificial neural network lead to a better recognition technique of Anthracnose disease which occurs in form of black spots on fruits and leaves of mango plant. The results obtained using proposed MRKT directional feature set have shows the better results with accuracy up to 98%.

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

Deep Clustering with Convolutional Autoencoders <=SEP=> Deep clustering utilizes deep neural networks to learn feature representation that is suitable for clustering tasks. Though demonstrating promising performance in various applications, we observe that existing deep clustering algorithms either do not well take advantage of convolutional neural networks or do not considerably preserve the local structure of data generating distribution in the learned feature space. To address this issue, we propose a deep convolutional embedded clustering algorithm in this paper. Specifically, we develop a convolutional autoencoders structure to learn embedded features in an end-to-end way. Then, a clustering oriented loss is directly built on embedded features to jointly perform feature refinement and cluster assignment. To avoid feature space being distorted by the clustering loss, we keep the decoder remained which can preserve local structure of data in feature space. In sum, we simultaneously minimize the reconstruction loss of convolutional autoencoders and the clustering loss. The resultant optimization problem can be effectively solved by mini-batch stochastic gradient descent and back-propagation. Experiments on benchmark datasets empirically validate the power of convolutional autoencoders for feature learning and the effectiveness of local structure preservation.

Oblivious Dynamic Searchable Encryption on Distributed Cloud Systems <=SEP=> Dynamic Searchable Symmetric Encryption (DSSE) allows search/update operations over encrypted data via an encrypted index. However, DSSE has been shown to be vulnerable to statistical inference attacks, which can extract a significant amount of information from access patterns on encrypted index and files. While generic Oblivious Random Access Machine (ORAM) can hide access patterns, it has been shown to be extremely costly to be directly used in DSSE setting. By exploiting the distributed cloud infrastructure, we develop a series of Oblivious Distributed DSSE schemes called ODSE, which enable oblivious access on the encrypted index with a high security and improved efficiency over the use of generic ORAM. Specifically, ODSE schemes are 3×-57× faster than applying the state-of-the-art generic ORAMs on encrypted dictionary index in real network settings. One of the proposed ODSE schemes offers desirable security guarantees such as informationtheoretic security with robustness against malicious servers. These properties are achieved by exploiting some of the unique characteristics of searchable encryption and encrypted index, which permits us to harness the computation and communication efficiency of multi-server PIR and Write-Only ORAM simultaneously. We fully implemented ODSE and have conducted extensive experiments to assess the performance of our proposed schemes in a real cloud environment.

Automatic categorization of figures in scientific documents <=SEP=> Figures are very important non-textual information contained in scientific documents. Current digital libraries do not provide users tools to retrieve documents based on the information available within the figures. We propose an architecture for retrieving documents by integrating figures and other information. The initial step in enabling integrated document search is to categorize figures into a set of pre-defined types. We propose several categories of figures based on their functionalities in scholarly articles. We have developed a machine-learning-based approach for automatic categorization of figures. Both global features, such as texture, and part features, such as lines, are utilized in the architecture for discriminating among figure categories. The proposed approach has been evaluated on a testbed document set collected from the CiteSeer scientific literature digital library. Experimental evaluation has demonstrated that our algorithms can produce acceptable results for realworld use. Our tools will be integrated into a scientific document digital library.

Persistence Diagrams with Linear Machine Learning Models <=SEP=> Persistence diagrams have been widely recognized as a compact descriptor for characterizing multiscale topological features in data. When many datasets are available, statistical features embedded in those persistence diagrams can be extracted by applying machine learnings. In particular, the ability for explicitly analyzing the inverse in the original data space from those statistical features of persistence diagrams is significantly important for practical applications. In this paper, we propose a unified method for the inverse analysis by combining linear machine learning models with persistence images. The method is applied to point clouds and cubical sets, showing the ability of the statistical inverse analysis and its advantages. This work is partially supported by JSPS KAKENHI Grant Number JP 16K17638, JST CREST Mathematics15656429, JST “Materials research by Information Integration” Initiative (MI2I) project of the Support Program for Starting Up Innovation Hub, Structural Materials for Innovation Strategic Innovation Promotion Program D72, and New Energy and Industrial Technology Development Organization (NEDO). Ippei Obayashi Advanced Institute for Materials Research (WPI-AIMR), Tohoku University. 2-1-1 Katahira, Aoba-ku, Sendai, 980-8577 Japan Tel.: +81-22-217-6320 Fax: +81-22-217-5129 E-mail: ippei.obayashi.d8@tohoku.ac.jp Y. Hiraoka Advanced Institute for Materials Research (WPI-AIMR), Tohoku University. Center for Materials research by Information Integration (CMI2), Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS). E-mail: hiraoka@tohoku.ac.jp

The impact of modified Hatha yoga on chronic low back pain: a pilot study. <=SEP=> PURPOSE
The purpose of this randomized pilot study was to evaluate a possible design for a 6-week modified hatha yoga protocol to study the effects on participants with chronic low back pain.


PARTICIPANTS
Twenty-two participants (M = 4; F = 17), between the ages of 30 and 65, with chronic low back pain (CLBP) were randomized to either an immediate yoga based intervention, or to a control group with no treatment during the observation period but received later yoga training.


METHODS
A specific CLBP yoga protocol designed and modified for this population by a certified yoga instructor was administered for one hour, twice a week for 6 weeks. Primary functional outcome measures included the forward reach (FR) and sit and reach (SR) tests. All participants completed Oswestry Disability Index (ODI) and Beck Depression Inventory (BDI) questionnaires. Guiding questions were used for qualitative data analysis to ascertain how yoga participants perceived the instructor, group dynamics, and the impact of yoga on their life.


ANALYSIS
To account for drop outs, the data were divided into better or not categories, and analyzed using chi-square to examine differences between the groups. Qualitative data were analyzed through frequency of positive responses.


RESULTS
Potentially important trends in the functional measurement scores showed improved balance and flexibility and decreased disability and depression for the yoga group but this pilot was not powered to reach statistical significance. Significant limitations included a high dropout rate in the control group and large baseline differences in the secondary measures. In addition, analysis of the qualitative data revealed the following frequency of responses (1) group intervention motivated the participants and (2) yoga fostered relaxation and new awareness/learning.


CONCLUSION
A modified yoga-based intervention may benefit individuals with CLB, but a larger study is necessary to provide definitive evidence. Also, the impact on depression and disability could be considered as important outcomes for further study. Additional functional outcome measures should be explored. This pilot study supports the need for more research investigating the effect of yoga for this population.

GestureGAN for Hand Gesture-to-Gesture Translation in the Wild <=SEP=> Hand gesture-to-gesture translation in the wild is a challenging task since hand gestures can have arbitrary poses, sizes, locations and self-occlusions. Therefore, this task requires a high-level understanding of the mapping between the input source gesture and the output target gesture. To tackle this problem, we propose a novel hand Gesture Generative Adversarial Network (GestureGAN). GestureGAN consists of a single generator G and a discriminator D, which takes as input a conditional hand image and a target hand skeleton image. GestureGAN utilizes the hand skeleton information explicitly, and learns the gesture-to-gesture mapping through two novel losses, the color loss and the cycle-consistency loss. The proposed color loss handles the issue of "channel pollution" while back-propagating the gradients. In addition, we present the Frechet ResNet Distance (FRD) to evaluate the quality of generated images. Extensive experiments on two widely used benchmark datasets demonstrate that the proposed GestureGAN achieves state-of-the-art performance on the unconstrained hand gesture-to-gesture translation task. Meanwhile, the generated images are in high-quality and are photo-realistic, allowing them to be used as data augmentation to improve the performance of a hand gesture classifier. Our model and code are available at https://github.com/Ha0Tang/GestureGAN.

A review of optical Braille recognition <=SEP=> The process to take a Braille document image and convert its content into its equivalent natural language characters, is called Optical Braille Recognition (OBR). It involves two main consecutive steps: Braille cell recognition and Braille cell transcription. Braille cell recognition contains few steps including: Image acquisition, Image De-skewing, Image pre-processing, dot recognition, cell recognition and segmentation. Image transcription aims to convert the segmented Braille cell, into its equivalent natural language characters. In this survey we aim to study the earlier works done by other researchers on both Braille cell recognition and transcription.

Towards Zero Unknown Word in Neural Machine Translation <=SEP=> Neural Machine translation has shown promising results in recent years. In order to control the computational complexity, NMT has to employ a small vocabulary, and massive rare words outside the vocabulary are all replaced with a single unk symbol. Besides the inability to translate rare words, this kind of simple approach leads to much increased ambiguity of the sentences since meaningless unks break the structure of sentences, and thus hurts the translation and reordering of the in-vocabulary words. To tackle this problem, we propose a novel substitution-translation-restoration method. In substitution step, the rare words in a testing sentence are replaced with similar in-vocabulary words based on a similarity model learnt from monolingual data. In translation and restoration steps, the sentence will be translated with a model trained on new bilingual data with rare words replaced, and finally the translations of the replaced words will be substituted by that of original ones. Experiments on Chinese-to-English translation demonstrate that our proposed method can achieve more than 4 BLEU points over the attention-based NMT. When compared to the recently proposed method handling rare words in NMT, our method can also obtain an improvement by nearly 3 BLEU points.

Memory traces in dynamical systems. <=SEP=> To perform nontrivial, real-time computations on a sensory input stream, biological systems must retain a short-term memory trace of their recent inputs. It has been proposed that generic high-dimensional dynamical systems could retain a memory trace for past inputs in their current state. This raises important questions about the fundamental limits of such memory traces and the properties required of dynamical systems to achieve these limits. We address these issues by applying Fisher information theory to dynamical systems driven by time-dependent signals corrupted by noise. We introduce the Fisher Memory Curve (FMC) as a measure of the signal-to-noise ratio (SNR) embedded in the dynamical state relative to the input SNR. The integrated FMC indicates the total memory capacity. We apply this theory to linear neuronal networks and show that the capacity of networks with normal connectivity matrices is exactly 1 and that of any network of N neurons is, at most, N. A nonnormal network achieving this bound is subject to stringent design constraints: It must have a hidden feedforward architecture that superlinearly amplifies its input for a time of order N, and the input connectivity must optimally match this architecture. The memory capacity of networks subject to saturating nonlinearities is further limited, and cannot exceed square root N. This limit can be realized by feedforward structures with divergent fan out that distributes the signal across neurons, thereby avoiding saturation. We illustrate the generality of the theory by showing that memory in fluid systems can be sustained by transient nonnormal amplification due to convective instability or the onset of turbulence.

Webcam-Based Eye Movement Analysis Using CNN <=SEP=> Due to its low price, webcam has become one of the most promising sensors with the rapid development of computer vision. However, the accuracies of eye tracking and eye movement analysis are largely limited by the quality of the webcam videos. To solve this issue, a novel eye movement analysis model is proposed based on five eye feature points rather than a single point (such as the iris center). First, a single convolutional neural network (CNN) is trained for eye feature point detection, and five eye feature points are detected for obtaining more useful eye movement information. Subsequently, six types of original time-varying eye movement signals can be constructed by feature points of each frame, which can reduce the dependency of the iris center in low quality videos. Finally, behaviors-CNN can be trained by the time-varying eye movement signals for recognizing different eye movement patterns, which is capable of avoiding the influence of errors from the basic eye movement type detection and artificial eye movement feature construction. To validate the performance, a webcam-based visual activity data set was constructed, which contained almost 0.5 million frames collected from 38 subjects. The experimental results on this database have demonstrated that the proposed model can obtain promising results for natural and convenient eye movement-based applications.

Experimental study of desalination using direct contact membrane distillation : a new approach to flux enhancement <=SEP=> New membrane distillation configurations and a new membrane module were investigated to improve water desalination. The performances of three hydrophobic microporous membranes were evaluated under vacuum enhanced direct contact membrane distillation (DCMD) with a turbulent flow regime and with a feed water temperature of only 40 ◦C. The new configurations provide reduced temperature polarization effects due to better mixing and increased mass transport of water due to higher permeability through the membrane and due to a total pressure gradient across the membrane. Comparison with previously reported results in the literature reveals that mass transport of water vapors is substantially improved with the new approach. The performance of the new configuration was investigated with both NaCl and synthetic sea salt feed solutions. Salt rejection was greater than 99.9% in almost all cases. Salt concentrations in the feed stream had only a minor effect on water flux. The economic aspects of the enhanced DCMD process are briefly discussed and comparisons are made with the reverse osmosis (RO) process for desalination. © 2003 Elsevier B.V. All rights reserved.

An efficient active ripple filter for use in DC-DC conversion <=SEP=> When low ripple is required from a switched mode dc-dc converter, dissipative active filters offer an alternative to passive LC and coupled inductor filters. Analytical and experimental results are presented for a simple active ripple filter. The filter employs a pair of current transformers as sensors for feedforward and feedback control and two metal-oxide-semiconductor field-effect transistors (MOSFETs) as cancellation current drivers. Measurements demonstrate good ripple attenuation up to 5 MHz, more than 70 dB being obtained at 100 kHz, the switching frequency of a test converter. The overall efficiency was measured as 95%, with room for further improvement. The filter is suitable for input and output smoothing in dc-dc converters for aerospace and other critical applications.

Revealing the problems with 802.11 medium access control protocol in multi-hop wireless ad hoc networks <=SEP=> The IEEE 802.11 medium access control (MAC) protocol is a standard for wireless LANs, it is also widely used in almost all test beds and simulations for the research in wireless mobile multi-hop ad hoc networks. However, this protocol was not designed for multi-hop networks. Although it can support some ad hoc network architecture, it is not intended to support the wireless mobile ad hoc network, in which multi-hop connectivity is one of the most prominent features. In this paper, we focus on the following question: can IEEE 802.11 MAC protocol function well in multi-hop networks? By presenting several serious problems encountered in transmission control protocol (TCP) connections in an IEEE 802.11 based multi-hop network, we show that the current TCP protocol does not work well above the current 802.11 MAC layer. The relevant problems include the TCP instability problem found in this kind of network, the a b Purchase Export Previous article Next article Check if you have access through your login credentials or your institution.

Electricity Generation Using an Air-Cathode Single Chamber Microbial Fuel Cell in the Presence and Absence of a Proton Exchange Membrane <=SEP=> Microbial fuel cells (MFCs) are typically designed as a two-chamber system with the bacteria in the anode chamber separated from the cathode chamber by a polymeric proton exchange membrane (PEM). Most MFCs use aqueous cathodes where water is bubbled with air to provide dissolved oxygen to electrode. To increase energy output and reduce the cost of MFCs, we examined power generation in an air-cathode MFC containing carbon electrodes in the presence and absence of a polymeric proton exchange membrane (PEM). Bacteria present in domestic wastewater were used as the biocatalyst, and glucose and wastewater were tested as substrates. Power density was found to be much greater than typically reported for aqueous-cathode MFCs, reaching a maximum of 262 ( 10 mW/m2 (6.6 ( 0.3 mW/L; liquid volume) using glucose. Removing the PEM increased the maximum power density to 494 ( 21 mW/m2 (12.5 ( 0.5 mW/L). Coulombic efficiency was 40-55% with the PEM and 9-12% with the PEM removed, indicating substantial oxygen diffusion into the anode chamber in the absence of the PEM. Power output increased with glucose concentration according to saturation-type kinetics, with a half saturation constant of 79 mg/L with the PEM-MFC and 103 mg/L in the MFC without a PEM (1000 Ω resistor). Similar results on the effect of the PEM on power density were found using wastewater, where 28 ( 3 mW/m2 (0.7 ( 0.1 mW/L) (28% Coulombic efficiency) was produced with the PEM, and 146 ( 8 mW/m2 (3.7 ( 0.2 mW/L) (20% Coulombic efficiency) was produced when the PEM was removed. The increase in power output when a PEM was removed was attributed to a higher cathode potential as shown by an increase in the open circuit potential. An analysis based on available anode surface area and maximum bacterial growth rates suggests that mediatorless MFCs may have an upper order-of-magnitude limit in power density of 103 mW/m2. A cost-effective approach to achieving power densities in this range will likely require systems that do not contain a polymeric PEM in the MFC and systems based on direct oxygen transfer to a carbon cathode. Introduction Bacteria can be used to catalyze the conversion of organic matter into electricity (1-7). Fuel cells that use bacteria are loosely classified here as two different types: biofuel cells that generate electricity from the addition of artificial electron shuttles (mediators) (8-12) and microbial fuel cells (MFCs) that do not require the addition of a mediator (5, 7, 13-15). It has recently been shown that certain metal-reducing bacteria, belonging primarily to the family Geobacteraceae, can directly transfer electrons to electrodes using electrochemically active redox enzymes such as cytochromes on their outer membrane (16-18). These so-called mediatorless MFCs are considered to have more commercial application potential than biofuel cells because the mediators used in biofuel cells are expensive and toxic to the microorganisms (7). MFCs typically produce power at a density of less than 50 mW/m2 (normalized to anode projected surface area) (7, 13, 19). In a MFC, two electrodes (anode and cathode) are each placed in water in two chambers joined by a proton exchange membrane (PEM). The main disadvantage of a twochamber MFC is that the solution cathode must be aerated to provide oxygen to the cathode. It is known that the power output of an MFC can be improved by increasing the efficiency of the cathode. For example, power is increased by adding ferricyanide (20) to the cathode chamber. It is possible, however, to design a MFC that does not require that the cathode be placed in water. In hydrogen fuel cells, the cathode is bonded directly to the PEM so that oxygen in the air can directly react at the electrode (21). This technique was successfully used to produce electricity from wastewater in a single chamber MFC by Liu et al. (15). Park and Zeikus (20) produced a maximum of 788 mW/m2 using a unique system with a Mn4+ graphite anode and a direct-air Fe3+ graphite cathode (20). Because the power output of MFCs is low relative to other types of fuel cells, reducing their cost is essential if power generation using this technology is to be an economical method of energy production. Most studies have used relatively expensive solid graphite electrodes (7, 19), but graphite-felt (14) and carbon cloth (15) can also be used. The use of air-driven cathodes can reduce MFC costs because passive oxygen transfer to the cathode using air does not require energy intensive air sparging of the water. Finally, PEMs such as Nafion are quite expensive. We wondered if this material was essential for power production in an MFC. We therefore designed and constructed a carbon-cloth, aircathode fuel cell to try to increase power density to levels not previously achieved with aqueous cathode systems. To study the effect of the PEM on power production, we compared power density for glucose and wastewater feeds with the system in the presence and absence of a polymeric PEM.

Advanced Machine Learning Technologies and Applications <=SEP=> The recognition of a character begins with analyzing its form and extracting the features that will be exploited for the identification. Primitives can be described as a tool to distinguish an object of one class from another object of another class. It is necessary to define the significant primitives. The size of vector primitives can be large if a large number of primitives are extracted including redundant and irrelevant features. As a result, the performance of the recognition system becomes poor, and as the number of features increases, so does the computing time. Feature selection, therefore, is required to ensure the selection of a subset of features that gives accurate recognition. In our work we propose a feature selection approach based genetic algorithm to improve the discrimination capacity of the Multilayer Perceptron Neural Networks (MLP).

Effects of birth spacing on maternal, perinatal, infant, and child health: a systematic review of causal mechanisms. <=SEP=> This systematic review of 58 observational studies identified hypothetical causal mechanisms explaining the effects of short and long intervals between pregnancies on maternal, perinatal, infant, and child health, and critically examined the scientific evidence for each causal mechanism hypothesized. The following hypothetical causal mechanisms for explaining the association between short intervals and adverse outcomes were identified: maternal nutritional depletion, folate depletion, cervical insufficiency, vertical transmission of infections, suboptimal lactation related to breastfeeding-pregnancy overlap, sibling competition, transmission of infectious diseases among siblings, incomplete healing of uterine scar from previous cesarean delivery, and abnormal remodeling of endometrial blood vessels. Women's physiological regression is the only hypothetical causal mechanism that has been proposed to explain the association between long intervals and adverse outcomes. We found growing evidence supporting most of these hypotheses.

Paternal filicide in Québec. <=SEP=> In this retrospective study, relevant demographic, social, and clinical variables were examined in 77 cases of paternal filicide. Between 1991 and 2001, all consecutive coroners' files on domestic homicide in Québec, Canada, were reviewed, and 77 child victims of 60 male parent perpetrators were identified. The results support data indicating that more fathers commit filicide than do mothers. A history of family abuse was characteristic of a substantial number of cases, and most of the cases involved violent means of homicide. Filicide was frequently (60%) followed by the suicide of the perpetrator and more so (86%) in cases involving multiple sibling victims. The abuse of drugs and alcohol was rare. At the time of the offense, most of the perpetrators were suffering from a psychiatric illness, usually depressive disorder. Nearly one-third were in a psychotic state. The proportion of fatal abuse cases was comparatively low. Many of the perpetrators had had contact with health professionals prior to the offense, although none had received treatment for a psychiatric illness.

Adaptive Comparator Bias-Current Control of 0.6 V Input Boost Converter for ReRAM Program Voltages in Low Power Embedded Applications <=SEP=> A novel method adaptively controls the comparator bias current in a boost converter for sensor-data storage on Resistive Random Access Memory (ReRAM). ReRAM is ideal for low-power sensors because it is faster and lower voltage than NAND flash. However, ReRAM's voltage design needs to consider program current variation due to data pattern dependency and cell reliability is sensitive to small voltage fluctuations. The proposed control method of the boost converter architecture has three proposals. First, low program voltage ripple (VRIPPLE) and high program energy efficiency is obtained with an adaptive 2 to 8 μA comparator bias-current (ICMP) control method. ICMP is adjusted depending on the number of program bits (NBITS) in the SET data. In 2 cases of NBITS = 1 and 16, and compared to a conventional scheme (ICMP = 8 μA and 2 μA), respectively, VRIPPLE decreases by 41.2% and energy efficiency improves by 6.6%, respectively. Secondly, as NBITS decrease during SET verify, ICMP also decreases, which further reduces SET energy by 8.9 pJ. Thirdly, although the conventional boost converter fails in the `SS' process condition, an additional cross-coupled charge-pump boosts the buffer circuit, which provides full functional operation over the input range of 0.6 to 1.0 V.

ECOFEN: An End-to-end energy Cost mOdel and simulator For Evaluating power consumption in large-scale Networks <=SEP=> Wired networks are increasing in size and their power consumption is becoming a matter of concern. Evaluating the end-to-end electrical cost of new network architectures and protocols is difficult due to the lack of monitored realistic infrastructures. We propose an End-to-End energy Cost mOdel and simulator For Evaluating power consumption in large-scale Networks (ECOFEN) whose user's entries are the network topology and traffic. Based on configurable measurement of different network components (routers, switches, NICs, etc.), it provides the power consumption of the overall network including the end-hosts as well as the power consumption of each equipment over time.