title: Radar and RGB-Depth Sensors for Fall Detection: A Review

abstract: This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing.


Coarse Clustering and Classification of Images with CNN Features for Participatory Sensing in Agriculture


title: Unsupervised Segmentation of 3D Medical Images Based on Clustering and Deep Representation Learning

abstract: This paper presents a novel unsupervised segmentation method for 3D medical images. Convolutional neural networks (CNNs) have brought significant advances in image segmentation. However, most of the recent methods rely on supervised learning, which requires large amounts of manually annotated data. Thus, it is challenging for these methods to cope with the growing amount of medical images. This paper proposes a unified approach to unsupervised deep representation learning and clustering for segmentation. Our proposed method consists of two phases. In the first phase, we learn deep feature representations of training patches from a target image using joint unsupervised learning (JULE) that alternately clusters representations generated by a CNN and updates the CNN parameters using cluster labels as supervisory signals. We extend JULE to 3D medical images by utilizing 3D convolutions throughout the CNN architecture. In the second phase, we apply k-means to the deep representations from the trained CNN and then project cluster labels to the target image in order to obtain the fully segmented image. We evaluated our methods on three images of lung cancer specimens scanned with micro-computed tomography (micro-CT). The automatic segmentation of pathological regions in micro-CT could further contribute to the pathological examination process. Hence, we aim to automatically divide each image into the regions of invasive carcinoma, noninvasive carcinoma, and normal tissue. Our experiments show the potential abilities of unsupervised deep representation learning for medical image segmentation.


title: Automatic Speech Recognition with Sparse Training Data for Dysarthric Speakers

abstract: We describe an unusual ASR application: recognition of command words from severely dysarthric speakers, who have poor control of their articulators. The goal is to allow these clients to control assistive technology by voice. While this is a small vocabulary, speaker-dependent, isolated-word application, the speech material is more variable than normal, and only a small amount of data is available for training. After training a CDHMM recogniser, it is necessary to predict its likely performance without using an independent test set,so that confusable words can be replaced by alternatives. We present a battery of measures of consistency and confusability, based on forced-alignment, which can be used to predict recogniser performance. We show how these measures perform, and how they are presented to the clinicians who are the users of the system.


title: Double Integral Sliding Mode MPPT Control of a Photovoltaic System

abstract: This brief proposes a new maximum power point tracker (MPPT) for a stand-alone photovoltaic (PV) system using the concept of double integral sliding mode controller (DISMC). Performance of a sliding mode controller (SMC) is greatly influenced by the choice of the sliding surface. A double integral SMC uses double integral of tracking voltage error term in its sliding surface to eliminate steady-state error apart from providing robust control actions in face of system uncertainties. However, there is still difficulty of increased chattering and slow transient response in DISMC. In this brief, the objective is to exploit the merits of the DISMC for designing an MPPT for PV system whilst the disadvantages of chattering and slow transient response are alleviated by choosing a new sliding surface. The design of this new DISMC-MPPT system considers the uncertainties in weather conditions and variations in load. This MPPT has been implemented using a pulsewidth modulator controlled dc/dc converter to keep the switching frequency constant. Thus, designing the control and filter circuits becomes simpler. The effectiveness of the proposed DISMC has been validated using simulation and experimental results using a prototype PV control system setup.


title: Interleaved Phase-Shift Full-Bridge Converter With Transformer Winding Series–Parallel Autoregulated (SPAR) Current Doubler Rectifier

abstract: The analysis and design guidelines for a two-phase interleaved phase-shift full-bridge converter with transformer winding series–parallel autoregulated current doubler rectifier are presented in this paper. The secondary windings of two transformers work in parallel when the equivalent duty cycle is smaller than 0.25 but in series when the duty cycle is larger than 0.25 owing to the series–parallel autoregulated rectifier. With the proposed rectifying structure, the voltage stress of the rectifier is reduced. Also, the interleaving operation reduces the output current ripple. A 1-kW prototype with 200–400-V input and 50-V/20-A output is built up to verify the theoretical analysis.


title: Learning A Physical Long-term Predictor

abstract: Evolution has resulted in highly developed abilities in many natural intelligences to quickly and accurately predict mechanical phenomena. Humans have successfully developed laws of physics to abstract and model such mechanical phenomena. In the context of artificial intelligence, a recent line of work has focused on estimating physical parameters based on sensory data and use them in physical simulators to make long-term predictions. In contrast, we investigate the effectiveness of a single neural network for end-to-end long-term prediction of mechanical phenomena. Based on extensive evaluation, we demonstrate that such networks can outperform alternate approaches having even access to ground-truth physical simulators, especially when some physical parameters are unobserved or not known a-priori. Further, our network outputs a distribution of outcomes to capture the inherent uncertainty in the data. Our approach demonstrates for the first time the possibility of making actionable long-term predictions from sensor data without requiring to explicitly model the underlying physical laws.


title: Bandwidth and gain improvement of a circularly polarized dual-rhombic loop antenna

abstract: We present a novel way to improve the performance of a circularly polarized (CP) dual-rhombic loop antenna. The bandwidth is enhanced by adding a pair of parasitic rhombic loops inside the dual-rhombic loop while the gain is increased with the help of a short backfire antenna (SBA). Good impedance matching is obtained by introducing a broadband balun. The addition of the parasitic elements leads to a bandwidth of 46% for $ axial ~ratio ~( AR) leq 2~ dB$ with an impedance bandwidth of 50% for $ VSWRleq 2$. The dual-rhombic loop excited SBA achieves an AR ( $leq 3~ dB$ ) bandwidth of 45% with an antenna gain of 11 dBi.


Spectrum sharing in Cognitive Radio Networks : A Survey


NATURAL ANSWER GENERATION WITH HETEROGENEOUS MEMORY


title: Social computing for image matching

abstract: One of the main technological trends in the last five years is mass data analysis. This trend is due in part to the emergence of concepts such as social networks, which generate a large volume of data that can provide added value through their analysis. This article is focused on a business and employment-oriented social network. More specifically, it focuses on the analysis of information provided by different users in image form. The images are analyzed to detect whether other existing users have posted or talked about the same image, even if the image has undergone some type of modification such as watermarks or color filters. This makes it possible to establish new connections among unknown users by detecting what they are posting or whether they are talking about the same images. The proposed solution consists of an image matching algorithm, which is based on the rapid calculation and comparison of hashes. However, there is a computationally expensive aspect in charge of revoking possible image transformations. As a result, the image matching process is supported by a distributed forecasting system that enables or disables nodes to serve all the possible requests. The proposed system has shown promising results for matching modified images, especially when compared with other existing systems.


title: DeepProbLog: Neural Probabilistic Logic Programming

abstract: We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques can be adapted for the new language. Our experiments demonstrate that DeepProbLog supports (i) both symbolic and subsymbolic representations and inference, (ii) program induction, (iii) probabilistic (logic) programming, and (iv) (deep) learning from examples. To the best of our knowledge, this work is the first to propose a framework where general-purpose neural networks and expressive probabilistic-logical modeling and reasoning are integrated in a way that exploits the full expressiveness and strengths of both worlds and can be trained end-to-end based on examples.


title: Adding New Tasks to a Single Network with Weight Transformations using Binary Masks

abstract: Visual recognition algorithms are required today to exhibit adaptive abilities. Given a deep model trained on a specific, given task, it would be highly desirable to be able to adapt incrementally to new tasks, preserving scalability as the number of new tasks increases, while at the same time avoiding catastrophic forgetting issues. Recent work has shown that masking the internal weights of a given original conv-net through learned binary variables is a promising strategy. We build upon this intuition and take into account more elaborated affine transformations of the convolutional weights that include learned binary masks. We show that with our generalization it is possible to achieve significantly higher levels of adaptation to new tasks, enabling the approach to compete with fine tuning strategies by requiring slightly more than 1 bit per network parameter per additional task. Experiments on two popular benchmarks showcase the power of our approach, that achieves the new state of the art on the Visual Decathlon Challenge.


title: A multifunctional AAV–CRISPR–Cas9 and its host response

abstract: Packaging split Cas9 into AAVs increases cargo capacity and allows for efficient genome editing and gene activation in vivo. AAV–split-Cas9 activates the host immune system but does not trigger the extensive cellular damage observed with delivery of Cas9 via DNA electroporation.


title: Fast and accurate short read alignment with Burrows–Wheeler transform

abstract: Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. ::: ::: Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ~10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. ::: ::: Availability: http://maq.sourceforge.net ::: ::: Contact: [email protected]


title: An Efficient Character-Level Neural Machine Translation

abstract: Neural machine translation aims at building a single large neural network that can be trained to maximize translation performance. The encoder-decoder architecture with an attention mechanism achieves a translation performance comparable to the existing state-of-the-art phrase-based systems on the task of English-to-French translation. However, the use of large vocabulary becomes the bottleneck in both training and improving the performance. In this paper, we propose an efficient architecture to train a deep character-level neural machine translation by introducing a decimator and an interpolator. The decimator is used to sample the source sequence before encoding while the interpolator is used to resample after decoding. Such a deep model has two major advantages. It avoids the large vocabulary issue radically; at the same time, it is much faster and more memory-efficient in training than conventional character-based models. More interestingly, our model is able to translate the misspelled word like human beings.


title: A learning-based fuzzy LQR control scheme for height control of an unmanned quadrotor helicopter

abstract: In this paper, a novel learning-based fuzzy Linear Quadratic Regulator (LQR) control method using Extended Kalman Filter (EKF) to optimize a Mamdani fuzzy LQR controller is presented. The EKF is used to adjust the shape of membership functions and rules of the fuzzy controller to adapt with the working conditions automatically during the operation process to minimize the control error. Then, the LQR controller is tuned according to the modified fuzzy membership functions and rules. The proposed approach in this paper is verified by testing and comparing performance of the height control problem of an unmanned quadrotor helicopter between the conventional LQR and learning-based fuzzy LQR controllers in the Matlab/Simulink. Simulation results show that developed method is effective for online optimization of fuzzy LQR controllers, improving control performance significantly.


title: Lipreading Approach for Isolated Digits Recognition under Whisper and Neutral Speech

abstract: Whisper is a speech production mode normally used to protect confidential information. Given the differences in the acoustic domain, the performance of automatic speech recognition (ASR) systems decreases with whisper speech. An appealing approach to improve the performance is the use of lipreading. This study explores the use of visual features characterizing the lips’ geometry and appearance to recognize digits under normal and whisper speech conditions using hidden Markov models (HMMs). We evaluate the proposed features on the digit part of the audiovisual whisper (AVW) corpus. While the proposed system achieves high accuracy in speaker dependent conditions (80.8%), the performance decreases when we evaluate speaker independent models (52.9%). We propose supervised adaptation schemes to reduce the mismatch between speakers. Across all conditions, the performance of the classifiers remain competitive even in the presence of whisper speech, highlighting the benefits of using visual features. Index Terms: Lipreading, whisper speech, multimodal corpus


title: Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks

abstract: The clustering Algorithm is a kind of key technique used to reduce energy consumption. It can increase the scalability and lifetime of the network. Energy-efficient clustering protocols should be designed for the characteristic of heterogeneous wireless sensor networks. We propose and evaluate a new distributed energy-efficient clustering scheme for heterogeneous wireless sensor networks, which is called DEEC. In DEEC, the cluster-heads are elected by a probability based on the ratio between residual energy of each node and the average energy of the network. The epochs of being cluster-heads for nodes are different according to their initial and residual energy. The nodes with high initial and residual energy will have more chances to be the cluster-heads than the nodes with low energy. Finally, the simulation results show that DEEC achieves longer lifetime and more effective messages than current important clustering protocols in heterogeneous environments.


title: World-Championship-Caliber Scrabble

abstract: Computer Scrabble programs have achieved a level of performance that exceeds that of the strongest human players. MAVEN was the first program to demonstrate this against human opposition. Scrabble is a game of imperfect information with a large branching factor. The techniques successfully applied in two-player games such as chess do not work here. MAVEN combines a selective move generator, simulations of likely game scenarios, and the B* algorithm to produce a world-championship-caliber Scrabble-playing program.


title: Online sketching hashing

abstract: Recently, hashing based approximate nearest neighbor (ANN) search has attracted much attention. Extensive new algorithms have been developed and successfully applied to different applications. However, two critical problems are rarely mentioned. First, in real-world applications, the data often comes in a streaming fashion but most of existing hashing methods are batch based models. Second, when the dataset becomes huge, it is almost impossible to load all the data into memory to train hashing models. In this paper, we propose a novel approach to handle these two problems simultaneously based on the idea of data sketching. A sketch of one dataset preserves its major characters but with significantly smaller size. With a small size sketch, our method can learn hash functions in an online fashion, while needs rather low computational complexity and storage space. Extensive experiments on two large scale benchmarks and one synthetic dataset demonstrate the efficacy of the proposed method.


title: Life on the outside: role of biofilms in environmental persistence of Shiga-toxin producing Escherichia coli

abstract: Escherichia coli is a heterogeneous species that can be part of the normal flora of humans but also include strains of medical importance. Among pathogenic members, Shiga-toxin producing E. coli (STEC) are some of the more prominent pathogenic E. coli within the public sphere. STEC disease outbreaks are typically associated with contaminated beef, contaminated drinking water, and contaminated fresh produce. These water- and food-borne pathogens usually colonize cattle asymptomatically; cows will shed STEC in their feces and the subsequent fecal contamination of the environment and processing plants is a major concern for food and public safety. This is especially important because STEC can survive for prolonged periods of time outside its host in environments such as water, produce, and farm soil. Biofilms are hypothesized to be important for survival in the environment especially on produce, in rivers, and in processing plants. Several factors involved in biofilm formation such as curli, cellulose, poly-N-acetyl glucosamine, and colanic acid are involved in plant colonization and adherence to different surfaces often found in meat processing plants. In food processing plants, contamination of beef carcasses occurs at different stages of processing and this is often caused by the formation of STEC biofilms on the surface of several pieces of equipment associated with slaughtering and processing. Biofilms protect bacteria against several challenges, including biocides used in industrial processes. STEC biofilms are less sensitive than planktonic cells to several chemical sanitizers such as quaternary ammonium compounds, peroxyacetic acid, and chlorine compounds. Increased resistance to sanitizers by STEC growing in a biofilm is likely to be a source of contamination in the processing plant. This review focuses on the role of biofilm formation by STEC as a means of persistence outside their animal host and factors associated with biofilm formation.


title: An Adaptive Robust Unscented Kalman Filter based Matching Algorithm for Underwater Gravity Aided Navigation

abstract: Gravity matching is the key technology of gravity aided inertial navigation. Traditional single point matching algorithm, SITAN algorithm, introduces large linearization error. The single point matching of UKF can reduce the linearization error and improve the matching accuracy effectively. However, under the situation of strong uncertainty of system process noise and the polluted measurement noise, UKF has poor performance. An adaptive robust Unscented Kalman Filter (ARUKF) based matching algorithm for gravity aided inertial navigation is proposed, which improves the robustness by introducing adaptive factor and robust function. Simulation results indicate that compared with algorithm based on standard UKF, the proposed algorithm can reduce the matching error more effectively, higher matching accuracy can be achieved ultimately.


title: Health applications of bioactive compounds from marine microalgae.

abstract: Marine microalgae and cyanobacteria are very rich in several chemical compounds and, therefore, they may be used in several biological applications related with health benefits, among others. This review brings the research up-to-date on the bioactive compounds produced by marine unicellular algae, directly or indirectly related to human health. It covers and goes through the most studied applications of substances such as PUFA, sterols, proteins and enzymes, vitamins and pigments, in areas so diverse as human and animal nutrition, therapeutics, and aquaculture. The great potential of marine microalgae and the biocoumpounds they produce are discussed in this review.


title: Text rewriting improves semantic role labeling

abstract: Large-scale annotated corpora are a prerequisite to developing high-performance NLP systems. Such corpora are expensive to produce, limited in size, often demanding linguistic expertise. In this paper we use text rewriting as a means of increasing the amount of labeled data available for model training. Our method uses automatically extracted rewrite rules from comparable corpora and bitexts to generate multiple versions of sentences annotated with gold standard labels. We apply this idea to semantic role labeling and show that a model trained on rewritten data outperforms the state of the art on the CoNLL-2009 benchmark dataset.


title: Unveiling a Novel, Cation-Rich Compound in a High-Pressure Pb–Te Binary System

abstract: Because of the common oxidation states of group IV elements (+2 or +4) and group VI elements (−2), 1:1 and 1:2 are two typical stoichiometries found in the IV–VI compounds. Particularly, in the Pb–Te binary system, the 1:1 stoichiometric PbTe is believed to be the only stable compound. Herein, using evolutionary algorithms, density functional theory, a laser-heated diamond anvil cell, and synchrotron X-ray diffraction experiments, we discovered a novel Pb–Te compound with an unexpected stoichiometry of 3:2 above 20 GPa. This tetragonal Pb3Te2 is the one of the very few cation-rich compounds that has ever been discovered in the entire IV–VI binary system. Further analyses based on electron density distribution, electron localization function, and Bader charge have shown that this newly discovered compound has a mixed character of chemical bonding with a decreased ionicity. By further calculating the electron–phonon interaction, Pb3Te2 is predicted to exhibit a superconducting transition at low temperatures...


title: Avoiding pathologies in very deep networks

abstract: Choosing appropriate architectures and regularization strategies of deep networks is crucial to good predictive performance. To shed light on this problem, we analyze the analogous problem of constructing useful priors on compositions of functions. Specifically, we study the deep Gaussian process, a type of infinitely-wide, deep neural network. We show that in standard architectures, the representational capacity of the network tends to capture fewer degrees of freedom as the number of layers increases, retaining only a single degree of freedom in the limit. We propose an alternate network architecture which does not suffer from this pathology. We also examine deep covariance functions, obtained by composing infinitely many feature transforms. Lastly, we characterize the class of models obtained by performing dropout on Gaussian processes.


Angle closure glaucoma due to iris and ciliary body cysts


title: Sketch-based Facial Expression Recognition for Human Figure Drawing Psychological Test

abstract: Drawing tests have been long used by practitioners for early screening of a number of psychological and neurological impairments. These brain functioning tests are used by psychologists to understand feelings, personality and reactions of individuals to different circumstances. Among these, Human Figure Drawing Test (HFDT) is a popular instrument for the assessment of cognitive functioning of individuals. While the HFDT has various dimensions, the focus of this study lies on the face of the drawn figure. A computerized system that analyzes the hand-drawn facial images to extract the expressions from the image is proposed. Sketch of human face is drawn by the subject and then fed to the system, the image is then binarized and segmented into different facial components. Features (based on local binary patterns, gray level co-occurrence matrices and histogram of oriented gradients) computed from the facial components are used to train an SVM classifier to learn to distinguish between four expression classes, ‘happy’, ‘sad’, ‘angry’ and ‘neutral’. The system evaluated on a custom developed database of sketches realized promising results. The developed system could serve as a useful module toward development of a complete automated system to score human figure drawing test.


Document-Level Adaptation for Neural Machine Translation


title: Decision neuroscience and consumer decision making

abstract: This article proposes that neuroscience can shape future theory and models in consumer decision making and suggests ways that neuroscience methods can be used in decision-making research. The article argues that neuroscience facilitates better theory development and empirical testing by considering the physiological context and the role of constructs such as hunger, stress, and social influence on consumer choice and preferences. Neuroscience can also provide new explanations for different sources of heterogeneity within and across populations, suggest novel hypotheses with respect to choices and underlying mechanisms that accord with an understanding of biology, and allow for the use of neural data to make better predictions about consumer behavior. The article suggests that despite some challenges associated with incorporating neuroscience into research on consumer decision processes, the use of neuroscience paradigms will produce a deeper understanding of decision making that can lead to the development of more effective decision aids and interventions.


title: Evaluation of physical performance by rectangular-triangular bicycle ergometry and computer-assisted ergospirometry

abstract: A great deal of information about physical adjustment to work can be obtained from quantitative stress testing. Maximal stress limited by symptoms of exertional intolerance is the concept of the 2 min duration work increment test (rectangular-triangular exercise test). Compared to steady-state work tests strict observation of the standardized procedure- and computer assisted evaluation of ergospirometric parameters offer innovatory opportunities: (1) the test is of short duration (8–14 min), (2) the subjects recover rapidly, even from an exhausting test, (3) one is more likely to be able to observe plateauing of\(\dot V_{O_2 } \), should determination of maximal\(\dot V_{O_2 } \) be desired, (4) adaptation to increasing work rates and maximal work capacity is assessable, (5) computer technics provide on-line assessment of aerobic and anaerobic power in quantitative terms, (65) measurements proved to be highly reproducible, (7) the relationship between variables such as increments of heart rate and systolic blood pressure, respiratory minute volume, oxygen uptake during the early phase of the nonsteady-state condition and the index of anaerobic power, and the influence of factors such as work load and work output, has been studied to derive standard values.


title: Effective Decision-Making/Problem-Solving Training with Mothers Who Have Mental Retardation.

abstract: Nine mothers with mental retardation received group training in decision-making using vignettes describing child-raising situations. A multiple baseline procedure was used to assess the effectiveness of training in how to identify and apply decision-making steps to trained and untrained vignettes. Significant changes in step identification and use were observed and maintained for a month for both types of vignettes. Although training included nothing to improve the appropriateness of decisions, there was significant improvement for the high-risk situations used in training. Results suggest that parents with mental retardation are able to learn and apply basic steps of decision-making.


title: Fair Enough: Guaranteeing Approximate Maximin Shares

abstract: We consider the problem of fairly allocating indivisible goods, focusing on a recently introduced notion of fairness called maximin share guarantee: each player’s value for his allocation should be at least as high as what he can guarantee by dividing the items into as many bundles as there are players and receiving his least desirable bundle. Assuming additive valuation functions, we show that such allocations may not exist, but allocations guaranteeing each player 2/3 of the above value always exist. These theoretical results have direct practical implications.


title: U.S. College Students’ Internet Use: Race, Gender and Digital Divides

abstract: The goal of this study was to learn about whether race and gender make a difference in Internet use among U.S. college students. A survey of college students at 40 U.S. higher education institutions was conducted, along with observations and interviews at several Midwestern U.S. universities. For comparison to the general U.S. population a nationwide telephone survey was undertaken. The study presents new data on Internet use among male and female college students, as well as trends in use across racial lines. Data on non-White Hispanic college student users of the Internet provides insight into Internet use among a group that appears to be underrepresented in the literature on college students and Internet use. The data analysis presents a complex picture of differential Internet use along gender lines, one that is generally consistent with the existing scholarly literature. Differential use based on race is a bit more complex. Stronger points of contrast emerge amongst White non-Hispanic, Hispanic, and Black non-Hispanic college students than they do when the respondents are grouped by gender.


title: Reducing memory access latency with asymmetric DRAM bank organizations

abstract: DRAM has been a de facto standard for main memory, and advances in process technology have led to a rapid increase in its capacity and bandwidth. In contrast, its random access latency has remained relatively stagnant, as it is still around 100 CPU clock cycles. Modern computer systems rely on caches or other latency tolerance techniques to lower the average access latency. However, not all applications have ample parallelism or locality that would help hide or reduce the latency. Moreover, applications' demands for memory space continue to grow, while the capacity gap between last-level caches and main memory is unlikely to shrink. Consequently, reducing the main-memory latency is important for application performance. Unfortunately, previous proposals have not adequately addressed this problem, as they have focused only on improving the bandwidth and capacity or reduced the latency at the cost of significant area overhead. We propose asymmetric DRAM bank organizations to reduce the average main-memory access latency. We first analyze the access and cycle times of a modern DRAM device to identify key delay components for latency reduction. Then we reorganize a subset of DRAM banks to reduce their access and cycle times by half with low area overhead. By synergistically combining these reorganized DRAM banks with support for non-uniform bank accesses, we introduce a novel DRAM bank organization with center high-aspect-ratio mats called CHARM. Experiments on a simulated chip-multiprocessor system show that CHARM improves both the instructions per cycle and system-wide energy-delay product up to 21% and 32%, respectively, with only a 3% increase in die area.


title: Tunable Self-Oscillating Switching Technique for Current Source Induction Heating Systems

abstract: This paper presents a tunable self-oscillating switching (TSOS) technique for current source parallel resonant inverters. Using this technique, the inverter has lower voltage stress in comparison with conventional methods such as phase-locked loop (PLL) circuits or integral controllers. The new switching technique instantly tracks the variation of resonant frequency. In addition, the TSOS has the capability of phase error tuning which is essential for parallel operation and zone control induction heating systems. The TSOS method has simple structure and operates in wide range of frequencies. Hence, the inverter can be utilized as a general-purpose inverter to supply different coils, loads and self-switched capacitor banks. The dynamics of the new tuning system is compared with the PLL method in transient conditions. A laboratory prototype with operating frequency of 25-100 kHz is implemented to verify the performance of the new tuning system.


title: Gradient Episodic Memory for Continual Learning

abstract: One major obstacle towards AI is the poor ability of models to solve new problems quicker, and without forgetting previously acquired knowledge. To better understand this issue, we study the problem of continual learning, where the model observes, once and one by one, examples concerning a sequence of tasks. First, we propose a set of metrics to evaluate models learning over a continuum of data. These metrics characterize models not only by their test accuracy, but also in terms of their ability to transfer knowledge across tasks. Second, we propose a model for continual learning, called Gradient Episodic Memory (GEM) that alleviates forgetting, while allowing beneficial transfer of knowledge to previous tasks. Our experiments on variants of the MNIST and CIFAR-100 datasets demonstrate the strong performance of GEM when compared to the state-of-the-art.


title: Chronic pain in Australia: a prevalence study

abstract: This study reports chronic pain prevalence in a randomly selected sample of the adult Australian population. Data were collected by Computer-Assisted Telephone Interview (CATI) using randomly generated telephone numbers and a two-stage stratified sample design. Chronic pain was defined as pain experienced every day for three months in the six months prior to interview. There were 17,543 completed interviews (response rate=70.8%). Chronic pain was reported by 17.1% of males and 20.0% of females. For males, prevalence peaked at 27.0% in the 65–69 year age group and for females, prevalence peaked at 31.0% in the oldest age group (80–84 years). Having chronic pain was significantly associated with older age, female gender, lower levels of completed education, and not having private health insurance; it was also strongly associated with receiving a disability benefit (adjusted OR=3.89, P<0.001) or unemployment benefit (adjusted OR=1.99, P<0.001); being unemployed for health reasons (adjusted OR=6.41, P<0.001); having poor self-rated health (adjusted OR=7.24, P<0.001); and high levels of psychological distress (adjusted OR=3.16, P<0.001). Eleven per cent of males and 13.5% of females in the survey reported some degree of interference with daily activities caused by their pain. Prevalence of interference was highest in the 55–59 year age group in both males (17.2%) and females (19.7%). Younger respondents with chronic pain were proportionately most likely to report interference due to pain, affecting 84.3% of females and 75.9% of males aged 20–24 years with chronic pain. Within the subgroup of respondents reporting chronic pain, the presence of interference with daily activities caused by pain was significantly associated with younger age; female gender; and not having private health insurance. There were strong associations between having interfering chronic pain and receiving disability benefits (adjusted OR=3.31, P<0.001) or being unemployed due to health reasons (adjusted OR=7.94, P<0.001, respectively). The results show that chronic pain impacts upon a large proportion of the adult Australian population, including the working age population, and is strongly associated with markers of social disadvantage.


title: Design of a modular mobile multi robot system: ULGEN (universal-generative robot)

abstract: This paper presents a chain type, homogenous, mobile and modular multi-robot system (ULGEN) with self-assembly, self-reconfigurability, localization and high mobility capabilities. The main goal of the work is to have high maneuverable modules which can travel autonomously to explore areas individually and change their configuration when encountering obstacles. Once the optimal path is found by any of the individual modules, the whole system can re-assemble and follow that path. Five degrees of freedom and six active docking faces makes a single module adapt to different tasks, avoid obstacles, and reconfigure as a structure. A single module can achieve locomotion in crawler and differential drive modes on its wheels. The rotating central joint brings up different capabilities, such as - recovering itself when the module falls on its side, changing the locomotion type without reconfiguring the whole structure while switching from the skid-steering mode to 4-wheel steering mode or insect-like four-legged robot mode to mammal-like four-legged robot mode. In this paper, the hardware and software architectures are discussed. Locomotion types and possible configurations are modelled in a simulation environment. Finally, single module locomotion types are implemented on the prototype.


title: A Bayesian Framework for Modeling Human Evaluations.

abstract: Several situations that we come across in our daily lives involve some form of evaluation: a process where an evaluator chooses a correct label for a given item. Examples of such situations include a crowd-worker labeling an image or a student answering a multiple-choice question. Gaining insights into human evaluations is important for determining the quality of individual evaluators as well as identifying true labels of items. Here, we generalize the question of estimating the quality of individual evaluators, extending it to obtain diagnostic insights into how various evaluators label different kinds of items. We propose a series of increasingly powerful hierarchical Bayesian models which infer latent groups of evaluators and items with the goal of obtaining insights into the underlying evaluation process. We apply our framework to a wide range of real-world domains, and demonstrate that our approach can accurately predict evaluator decisions, diagnose types of mistakes evaluators tend to make, and infer true labels of items.


title: Towards a Maturity Model for the Adoption of Social Media as a Means of Organizational Innovation

abstract: Social Media applications as a contemporary phenomenon attract the attention of organizations and researchers. While these technologies are mainstream in a private setting, there is still uncertainty among organizations of operating them. The corporate perspective aims at deploying these technologies for value creation, i.e. improvement and even innovation. Conceptual guidance for a proper management to professionalize Social Media management is missing. Maturity models provide support to this challenge. This paper deals with the development of a maturity model for the organizational adoption of Social Media. By means of a design science research approach, the model is defined along the dimensions strategy, processes, IT systems, culture, and governance and measured against five maturity stages. Thereby a contribution is made to the knowledge base of corporate Social Media usage as well as to the development of a maturity model along a defined design methodology.


title: Coupled Hidden Markov Model-Based Method for Apnea Bradycardia Detection

abstract: In this paper, we present a novel framework for the coupled hidden Markov model (CHMM), based on the forward and backward recursions and conditional probabilities, given a multidimensional observation. In the proposed framework, the interdependencies of states networks are modeled with Markovian-like transition laws that influence the evolution of hidden states in all channels. Moreover, an offline inference approach by maximum likelihood estimation is proposed for the learning procedure of model parameters. To evaluate its performance, we first apply the CHMM model to classify and detect disturbances using synthetic data generated by the FitzHugh–Nagumo model. The average sensitivity and specificity of the classification are above $93.98\%$ and $95.38\%$ and those of the detection reach $94.49\%$ and $99.34\%$ , respectively. The method is also evaluated using a clinical database composed of annotated physiological signal recordings of neonates suffering from apnea-bradycardia. Different combinations of beat-to-beat features extracted from electrocardiographic signals constitute the multidimensional observations for which the proposed CHMM model is applied, to detect each apnea bradycardia episode. The proposed approach is finally compared to other previously proposed HMM-based detection methods. Our CHMM provides the best performance on this clinical database, presenting an average sensitivity of $95.74\%$ and specificity of $91.88\%$ while it reduces the detection delay by $-$ 0.59 s.


title: FChain: Toward Black-Box Online Fault Localization for Cloud Systems

abstract: Distributed applications running inside cloud systems are prone to performance anomalies due to various reasons such as resource contentions, software bugs, and hardware failures. One big challenge for diagnosing an abnormal distributed application is to pinpoint the faulty components. In this paper, we present a black-box online fault localization system called FChain that can pinpoint faulty components immediately after a performance anomaly is detected. FChain first discovers the onset time of abnormal behaviors at different components by distinguishing the abnormal change point from many change points caused by normal workload fluctuations. Faulty components are then pinpointed based on the abnormal change propagation patterns and inter-component dependency relationships. FChain performs runtime validation to further filter out false alarms. We have implemented FChain on top of the Xen platform and tested it using several benchmark applications (RUBiS, Hadoop, and IBM System S). Our experimental results show that FChain can quickly pinpoint the faulty components with high accuracy within a few seconds. FChain can achieve up to 90% higher precision and 20% higher recall than existing schemes. FChain is non-intrusive and light-weight, which imposes less than 1% overhead to the cloud system.


o-Nitroaniline Derivatives. Part 10. 5- and 6-Amino-1H-benzimidazole 3-Oxides


title: Approximation Schemes for Geometric NP-Hard Problems: A Survey

abstract: Traveling Salesman, Steiner Tree, and many other famous geometric optimization problems are NP-hard. Since we do not expect to design efficient algorithms that solve these problems optimally, researchers have tried to design approximation algorithms, which can compute a provably near-optimal solution in polynomial time.


3 A : A PERSON RE-IDENTIFICATION SYSTEM VIA ATTRIBUTE AUGMENTATION AND AGGREGATION


title: Development and validation of the mindful eating questionnaire.

abstract: Abstract "Mindful eating" describes a nonjudgmental awareness of physical and emotional sensations associated with eating. This article reports the development of a mindful eating questionnaire (MEQ) to support rigorous scientific inquiry into this concept. An item pool was developed based on hypothesized domains of mindful eating. A cross-sectional survey examined associations of MEQ scores with demographic and health-related characteristics. The MEQ was distributed to seven convenience samples between January and May 2007, with an overall response rate of 62% (n=303). Participants were mostly women (81%) and white (90%), and had a mean age of 42±14.4 years (range 18 to 80 years). Exploratory factor analysis was used to identify factors, which were defined as the mean of items scored one to four, where four indicated higher mindfulness; the mean of all factors was the summary MEQ score. Multiple regression analysis was used to measure associations of demographic characteristics, obesity, yoga practice, and physical activity with MEQ scores. Domains of the final 28-item questionnaire were: disinhibition, awareness, external cues, emotional response, and distraction. The mean MEQ score was 2.92±0.37, with a reliability (Chronbach's α) of .64. The covariate-adjusted MEQ score was inversely associated with body mass index (3.02 for body mass index 30, P


title: Attended End-to-End Architecture for Age Estimation From Facial Expression Videos

abstract: The main challenges of age estimation from facial expression videos lie not only in the modeling of the static facial appearance, but also in the capturing of the temporal facial dynamics. Traditional techniques to this problem focus on constructing handcrafted features to explore the discriminative information contained in facial appearance and dynamics separately. This relies on sophisticated feature-refinement and framework-design. In this paper, we present an end-to-end architecture for age estimation, called Spatially-Indexed Attention Model (SIAM), which is able to simultaneously learn both the appearance and dynamics of age from raw videos of facial expressions. Specifically, we employ convolutional neural networks to extract effective latent appearance representations and feed them into recurrent networks to model the temporal dynamics. More importantly, we propose to leverage attention models for salience detection in both the spatial domain for each single image and the temporal domain for the whole video as well. We design a specific spatially-indexed attention mechanism among the convolutional layers to extract the salient facial regions in each individual image, and a temporal attention layer to assign attention weights to each frame. This two-pronged approach not only improves the performance by allowing the model to focus on informative frames and facial areas, but it also offers an interpretable correspondence between the spatial facial regions as well as temporal frames, and the task of age estimation. We demonstrate the strong performance of our model in experiments on a large, gender-balanced database with 400 subjects with ages spanning from 8 to 76 years. Experiments reveal that our model exhibits significant superiority over the state-of-the-art methods given sufficient training data.


title: MicroRNAs en route to the clinic: progress in validating and targeting microRNAs for cancer therapy

abstract: In normal cells multiple microRNAs (miRNAs) converge to maintain a proper balance of various processes, including proliferation, differentiation and cell death. miRNA dysregulation can have profound cellular consequences, especially because individual miRNAs can bind to and regulate multiple mRNAs. In cancer, the loss of tumour-suppressive miRNAs enhances the expression of target oncogenes, whereas increased expression of oncogenic miRNAs (known as oncomirs) can repress target tumour suppressor genes. This realization has resulted in a quest to understand the pathways that are regulated by these miRNAs using in vivo model systems, and to comprehend the feasibility of targeting oncogenic miRNAs and restoring tumour-suppressive miRNAs for cancer therapy. Here we discuss progress in using mouse models to understand the roles of miRNAs in cancer and the potential for manipulating miRNAs for cancer therapy as these molecules make their way towards clinical trials.


title: Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks

abstract: Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision based problems. However, deep models are perceived as "black box" methods considering the lack of understanding of their internal functioning. There has been a significant recent interest to develop explainable deep learning models, and this paper is an effort in this direction. Building on a recently proposed method called Grad-CAM, we propose Grad-CAM++ to provide better visual explanations of CNN model predictions (when compared to Grad-CAM), in terms of better localization of objects as well as explaining occurrences of multiple objects of a class in a single image. We provide a mathematical explanation for the proposed method, Grad-CAM++, which uses a weighted combination of the positive partial derivatives of the last convolutional layer feature maps with respect to a specific class score as weights to generate a visual explanation for the class label under consideration. Our extensive experiments and evaluations, both subjective and objective, on standard datasets showed that Grad-CAM++ indeed provides better visual explanations for a given CNN architecture when compared to Grad-CAM.


title: Skin lesions on yellowfin tuna Thunnus albacares from Gulf of Mexico outer continental shelf: Morphological, molecular, and histological diagnosis of infection by a capsalid monogenoid

abstract: We characterize lesion-associated capsaline infections on yellowfin tuna, Thunnus albacares, in the Gulf of Mexico by comparing our specimens with published descriptions and museum specimens ascribed to Capsala biparasiticum and its synonyms: vouchers of C. biparasiticum from parasitic copepods; the holotype of Capsala neothunni; and vouchers of Capsala abidjani. Those from parasitic copepods differed by having a small, rounded body, large anterior attachment organs, closely spaced dorsomarginal body sclerites, small testes, and a short and wide testicular field. No morphometric feature in the holotype of C. neothunni ranged outside of that reported for the newly-collected specimens, indicating conspecificity of our specimens. The specimens of C. abidjani differed by having a large anterior attachment organ, few and dendritic testes, and a short, wide testicular field. Large subunit ribosomal DNA (28S) sequences grouped our specimens and Capsala sp. as sister taxa and indicated a phylogenetic affinity of Nasicola klawei. The haptoral attachment site comprised a crater-like depression surrounded by a blackish-colored halo of extensively rugose skin, with abundant pockmarked-like, irregularly-shaped oblong or semi-circular epidermal pits surrounding these attachment sites. Histology confirmed extensive folding of epidermis and underlying stratum laxum, likely epidermal hyperplasia, foci of weak cell-to-cell adhesions among apical malpighian cells as well as that between stratum germinativum and stratum laxum, myriad goblet cells in epidermis, rodlet cells in apical layer of epidermis, and lymphocytic infiltrates and melanin in dermis. The present study comprises (i) the first published report of this parasite from yellowfin tuna captured in the Gulf of Mexico-NW Atlantic Ocean Basin, (ii) confirmation of its infection on the skin (rather than on a parasitic copepod), (iii) the first molecular data for this capsaline, and (iv) the first observations of histopathological changes associated with a capsalid infection on a wild-caught epipelagic fish.


title: The Long War and Parental CombatDeployment: Effects on MilitaryChildren and At-Home Spouses

abstract: Objective: Given the growing number of military service members with families and the multiple combat deployments characterizing current war time duties, the impact of deployments on military children requires clarification. Behavioral and emotional adjustment problems were examined in children (aged 6 through 12) of an active duty Army or Marine Corps parent currently deployed (CD) or recently returned (RR) from Afghanistan or Iraq. Method: Children (N 272) and their at-home civilian (AHC) (N 163) and/or recently returned active duty (AD) parent (N 65) were interviewed. Child adjustment outcomes were examined in relation to parental psychological distress and months of combat deployment (of the AD) using mixed effects linear models. Results: Parental distress (AHC and AD) and cumulative length of parental combat‐related deployments during the child’s lifetime independently predicted increased child depression and externalizing symptoms. Although behavioral adjustment and depression levels were comparable to community norms, anxiety was significantly elevated in children in both deployment groups. In contrast, AHC parental distress was greater in those with a CD (vs. RR) spouse. Conclusions: Findings indicate that parental combat deployment has a cumulative effect on children that remains even after the deployed parent returns home, and that is predicted by psychological distress of both the AD and AHC parent. Such data may be informative for screening, prevention, and intervention strategies. J. Am. Acad. Child Adolesc. Psychiatry, 2010;49(4):310‐320. Key Words: military children, deployment stress, family risk


title: Hemodynamic and affective correlates assessed during performance on the Columbia Card Task (CCT)

abstract: The study aimed to test the potential of functional near-infrared spectroscopy (fNIRS) in combination with electrodermal activity (EDA) in a decision paradigm by means of the Columbia Card Task (CCT). The CCT is a dynamic decision task characterized by assessing subjects’ risk-taking via eliciting voluntary stopping points in a series of incrementally increasingly risky choices. Using the combined fNIRS-EDA approach, we aim to examine the hemodynamic and affective correlates of both decision and outcome responses during performance on the CCT. Twenty healthy subjects completed the Cold and Hot CCT version while fNIRS over prefrontal cortex and EDA were recorded. Results showed that (1) in the decision phase fNIRS revealed larger total hemoglobin concentration changes [tHb] in the Cold as compared to the Hot CCT, whereas EDA revealed an opposite pattern with larger skin conductance responses (SCRs) to the Hot as compared to the Cold CCT. (2) No significant [tHb] signals or SCRs were found in the outcome phase. (3) Coherence calculations between fNIRS and EDA in the heart rate frequency showed a significant increase during the Hot as compared to the Cold CCT. Our findings designate fNIRS as suitable tool for monitoring decision-making processes. The combination of fNIRS and EDA demonstrates the potential of simultaneously assessing the interaction between hemodynamic and affective responses which can provide additional information concerning the relationship between these two physiological systems for various research areas.


title: Applying Machine Learning Techniques to Transportation Mode Recognition Using Mobile Phone Sensor Data

abstract: This paper adopts different supervised learning methods from the field of machine learning to develop multiclass classifiers that identify the transportation mode, including driving a car, riding a bicycle, riding a bus, walking, and running. Methods that were considered include K-nearest neighbor, support vector machines (SVMs), and tree-based models that comprise a single decision tree, bagging, and random forest (RF) methods. For training and validating purposes, data were obtained from smartphone sensors, including accelerometer, gyroscope, and rotation vector sensors. K-fold cross-validation as well as out-of-bag error was used for model selection and validation purposes. Several features were created from which a subset was identified through the minimum redundancy maximum relevance method. Data obtained from the smartphone sensors were found to provide important information to distinguish between transportation modes. The performance of different methods was evaluated and compared. The RF and SVM methods were found to produce the best performance. Furthermore, an effort was made to develop a new additional feature that entails creating a combination of other features by adopting a simulated annealing algorithm and a random forest method.


title: BigHouse: A simulation infrastructure for data center systems

abstract: Recently, there has been an explosive growth in Internet services, greatly increasing the importance of data center systems. Applications served from “the cloud” are driving data center growth and quickly overtaking traditional workstations. Although there are a many tools for evaluating components of desktop and server architectures in detail, scalable modeling tools are noticeably missing. We describe BigHouse a simulation infrastructure for data center systems. Instead of simulating servers using detailed microarchitectural models, BigHouse raises the level of abstraction. Using a combination of queuing theory and stochastic modeling, BigHouse can simulate server systems in minutes rather than hours. BigHouse leverages statistical simulation techniques to limit simulation turnaround time to the minimum runtime needed for a desired accuracy. In this paper, we introduce BigHouse, describe its design, and present case studies for how it has already been applied to build and validate models of data center workloads and systems. Furthermore, we describe statistical techniques incorporated into BigHouse to accelerate and parallelize its simulations, and demonstrate its scalability to model large cluster systems while maintaining reasonable simulation time.


title: A review report on low power VLSI systems analysis and modeling techniques

abstract: In the present trend of CMOS IC technology, Low Power Design is a major issue in designing of a system. In order to achieve that criterion, power consumption should be minimized. In this paper, techniques which are available for reduction of power consumption at different abstraction levels are discussed in detail. With this paper, Designer can easily choose the proper methodology with the appropriate optimization technique to achieve the required goal.


title: A non-differential elastomer curvature sensor for softer-than-skin electronics

abstract: We extend soft lithography microfabrication and design methods to introduce curvature sensors that are elastically soft (modulus 0.1‐1 MPa) and stretchable (100‐1000% strain). In contrast to existing curvature sensors that measure differential strain, sensors in this new class measure curvature directly and allow for arbitrary gauge factor and film thickness. Moreover, each sensor is composed entirely of a soft elastomer (PDMS (polydimethylsiloxane) or Ecoflex ® ) and conductive liquid (eutectic gallium indium, eGaIn) and thus remains functional even when stretched to several times its natural length. The electrical resistance in the embedded eGaIn microchannel is measured as a function of the bending curvature for a variety of sensor designs. In all cases, the experimental measurements are in reasonable agreement with closed-form algebraic approximations derived from elastic plate theory and Ohm’s law. (Some figures in this article are in colour only in the electronic version)


title: Automatic Electric Meter Reading System: A Cost-Feasible Alternative Approach In Meter Reading For Bangladesh Perspective Using Low-Cost Digital Wattmeter And Wimax Technology

abstract: Energy meter reading is a monotonous and an expensive task. Now the meter reader people goes to each meter and take the meter reading manually to issue the bill which will later be entered in the billing software for billing and payment automation. If the manual meter reading and bill data entry process can be automated then it would reduced the laborious task and financial wastage. "Automatic Electric Meter Reading (AMR) System" is a metering system that is to be used for data collecting from the meter and processing the collected data for billing and other decision purposes. In this paper we have proposed an automatic meter reading system which is low cost, high performance, highest data rate, highest coverage area and most appropriate for Bangladesh perspective. In this AMR system there are four basic units. They are reading unit, communication unit, data receiving and processing unit and billing system. For reading unit we identified the disk rotation of the energy meter and stored the data in microcontroller. So it is not required to change the current analog energy meter. An external module will be added with the current energy meter. In the communication unit Wimax transceiver was used for wireless communication between meter end and the server end because of its wide coverage area. In the data receiving and processing unit meter reading will be collected from the transceiver which is controlled by another microcontroller. There will be a computer application that will take the data from the microcontroller. This will also help to avoid any tampering or break down of energy meter. There are various AMR system exists all over the world. Those systems were analyzed and we found they are not feasible for Bangladesh.


title: Biomimetic Spider Leg Joints: A Review from Biomechanical Research to Compliant Robotic Actuators

abstract: Due to their inherent compliance, soft actuated joints are becoming increasingly important for robotic applications, especially when human-robot-interactions are expected. Several of these flexible actuators are inspired by biological models. One perfect showpiece for biomimetic robots is the spider leg, because it combines lightweight design and graceful movements with powerful and dynamic actuation. Building on this motivation, the review article focuses on compliant robotic joints inspired by the function principle of the spider leg. The mechanism is introduced by an overview of existing biological and biomechanical research. Thereupon a classification of robots that are bio-inspired by spider joints is presented. Based on this, the biomimetic robot applications referring to the spider principle are identified and discussed.


EVIEW ON M ICROSTRIP P ATCH A NTENNA


title: Why social networks are different from other types of networks.

abstract: We argue that social networks differ from most other types of networks, including technological and biological networks, in two important ways. First, they have nontrivial clustering or network transitivity and second, they show positive correlations, also called assortative mixing, between the degrees of adjacent vertices. Social networks are often divided into groups or communities, and it has recently been suggested that this division could account for the observed clustering. We demonstrate that group structure in networks can also account for degree correlations. We show using a simple model that we should expect assortative mixing in such networks whenever there is variation in the sizes of the groups and that the predicted level of assortative mixing compares well with that observed in real-world networks.


title: Central Lip Lift as Aesthetic and Physiognomic Plastic Surgery: The Effect on Lower Facial Profile

abstract: Background A central lip lift was introduced to Westerners in 1980s. However, no studies have been conducted on the facial aesthetic and physiognomic perspectives of a central lip lift in the Asian population. ::: ::: Objectives The authors presented the central lip lift as aesthetic and physiognomic treatment in Asians and explained its effect on lower facial profile. ::: ::: Methods A retrospective chart review was performed in 202 cases of asians. The authors analyzed patient age, cause of long philtrum, purpose of the treatment, and postoperative satisfaction. The authors then performed an anthropometric assessment and a photographic analysis. ::: ::: Results The vertical disproportion of the lower face was improved after the treatment, and there was significant shortening of the philtrum length ( P < .001) and an increase in a visible upper vermilion ( P < .001). In Westerners, a long philtrum was mainly caused by the aging process. Aging patients (range, 40-59 years) underwent the central lip lift for upper lip rejuvenation. In contrast, in Asia, a long philtrum was primarily caused by bone retraction after an orthognathic surgery or orthodontic procedure. Young patients (range, 20-39 years old) underwent the central lip lift to correct a relatively lengthened philtrum after 2-jaw surgery. Furthermore, about half of the patients (52.0%) underwent the central lip lift for facial physiognomic improvement. ::: ::: Conclusions In today's multiracial society, plastic surgeons planning a central lip lift in Asian patients should consider both aesthetic and physiognomic perspectives. Regardless of the aesthetic outcome, the surgeon should strive to maximize patient satisfaction. ::: ::: Level of Evidence 4![Graphic][1] Therapeutic ::: ::: [1]: /embed/inline-graphic-1.gif


title: Connectivity and thought : The influence of semantic network structure in a neurodynamical model of thinking

abstract: Understanding cognition has been a central focus for psychologists, neuroscientists and philosophers for thousands of years, but many of its most fundamental processes remain very poorly understood. Chief among these is the process of thought itself: the spontaneous emergence of specific ideas within the stream of consciousness. It is widely accepted that ideas, both familiar and novel, arise from the combination of existing concepts. From this perspective, thought is an emergent attribute of memory, arising from the intrinsic dynamics of the neural substrate in which information is embedded. An important issue in any understanding of this process is the relationship between the emergence of conceptual combinations and the dynamics of the underlying neural networks. Virtually all theories of ideation hypothesize that ideas arise during the thought process through association, each one triggering the next through some type of linkage, e.g., structural analogy, semantic similarity, polysemy, etc. In particular, it has been suggested that the creativity of ideation in individuals reflects the qualitative structure of conceptual associations in their minds. Interestingly, psycholinguistic studies have shown that semantic networks across many languages have a particular type of structure with small-world, scale free connectivity. So far, however, these related insights have not been brought together, in part because there has been no explicitly neural model for the dynamics of spontaneous thought. Recently, we have developed such a model. Though simplistic and abstract, this model attempts to capture the most basic aspects of the process hypothesized by theoretical models within a neurodynamical framework. It represents semantic memory as a recurrent semantic neural network with itinerant dynamics. Conceptual combinations arise through this dynamics as co-active groups of neural units, and either dissolve quickly or persist for a time as emergent metastable attractors and are recognized consciously as ideas. The work presented in this paper describes this model in detail, and uses it to systematically study the relationship between the structure of conceptual associations in the neural substrate and the ideas arising from this system's dynamics. In particular, we consider how the small-world and scale-free characteristics influence the effectiveness of the thought process under several metrics, and show that networks with both attributes indeed provide significant advantages in generating unique conceptual combinations.


title: BER of OFDM systems impaired by carrier frequency offset in multipath fading channels

abstract: We introduce an analytical approach to evaluate the error probability of orthogonal frequency-division-multiplexing (OFDM) systems subject to carrier frequency offset (CFO) in frequency-selective channels, characterized by Rayleigh or Rician fading. By properly exploiting the Gaussian approximation of the intercarrier interference (ICI), we show that the bit-error rate (BER) for an uncoded OFDM system with quadrature amplitude modulation (QAM) can be expressed by the sum of a few integrals, whose number depends on the constellation size. Each integral can be evaluated numerically, or, in Rayleigh fading, by using a series expansion that involves generalized hypergeometric functions. Simulation results illustrate that the theoretical analysis is quite accurate, especially for Rayleigh channels, and also with nonlinear amplifiers.


title: A commutation error correction method for high-speed brushless dc motor based on the phase current

abstract: In brushless dc motor (BLDCM) precise commutation point and commutation interval, which impact motor torque ripple and motor efficiency are significant issue. Especially for high-speed high-power BLDCM drive without rotor position sensors (i.e. sensorless drive), the impact of commutation electrical angle error becomes more important. However commutation error is inevitable due to interference, detection error, motor parameters change, and correction of commutation error is difficult in very shortly commutation interval. In this paper, a novel current control strategy for the middle speed and high speed range is presented. A phase current closed-loop control and optimized current hysteresis controller based on adjacent half conduction period of phase current is adopted to autocorrect commutation electrical angle error. The proposed method has the characteristics of simple and rapid applying for high-speed BLDCM. Lastly, the feasibility and effectiveness of the proposed method is proven by experimental results.


title: Dynamic power minimization during combinational circuit testing as a traveling salesman problem

abstract: Testing of VLSI circuits can cause generation of excessive heat which can damage the chips under test. In the random testing environment, high-performance CMOS circuits consume significant dynamic power during testing because of enhanced switching activity in the internal nodes. Our work focuses on the fact that power minimization is a traveling salesman problem (TSP). We explore application of local search and genetic algorithms to test set reordering and perform a quantitative comparison to previously used deterministic techniques. We also consider reduction of the original test set as a dual-objective optimization problem, where switching activity and fault coverage are the two objective functions.


Training Invariant Support Vector Machines using Selective Sampling


title: Breaking Cryptographic Implementations Using Deep Learning Techniques

abstract: Template attack is the most common and powerful profiled side channel attack. It relies on a realistic assumption regarding the noise of the device under attack: the probability density function of the data is a multivariate Gaussian distribution. To relax this assumption, a recent line of research has investigated new profiling approaches mainly by applying machine learning techniques. The obtained results are commensurate, and in some particular cases better, compared to template attack. In this work, we propose to continue this recent line of research by applying more sophisticated profiling techniques based on deep learning. Our experimental results confirm the overwhelming advantages of the resulting new attacks when targeting both unprotected and protected cryptographic implementations.


title: A Wideband Base Station Antenna Element With Stable Radiation Pattern and Reduced Beam Squint

abstract: This paper presents the design procedure, optimization strategy, theoretical analysis, and experimental results of a wideband dual-polarized base station antenna element with superior performance. The proposed antenna element consists of four electric folded dipoles arranged in an octagon shape that are excited simultaneously for each polarization. It provides ±45° slant-polarized radiation that meets all the requirements for base station antenna elements, including stable radiation patterns, low cross polarization level, high port-to-port isolation, and excellent matching across the wide band. The problem of beam squint for beam-tilted arrays is discussed and it is found that the geometry of this element serves to reduce beam squint. Experimental results show that this element has a wide bandwidth of 46.4% from 1.69 to 2.71 GHz with ≥15-dB return loss and 9.8 ± 0.9-dBi gain. Across this wide band, the variations of the half-power-beamwidths of the two polarizations are all within 66.5° ± 5.5°, the port-to-port isolation is >28 dB, the cross-polarization discrimination is >25 dB, and most importantly, the beam squint is <4° with a maximum 10° down-tilt.


title: Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer's Disease

abstract: Recent evidence suggests that some brain areas act as hubs interconnecting distinct, functionally specialized systems. These nexuses are intriguing because of their potential role in integration and also because they may augment metabolic cascades relevant to brain disease. To identify regions of high connectivity in the human cerebral cortex, we applied a computationally efficient approach to map the degree of intrinsic functional connectivity across the brain. Analysis of two separate functional magnetic resonance imaging datasets (each n = 24) demonstrated hubs throughout heteromodal areas of association cortex. Prominent hubs were located within posterior cingulate, lateral temporal, lateral parietal, and medial/lateral prefrontal cortices. Network analysis revealed that many, but not all, hubs were located within regions previously implicated as components of the default network. A third dataset ( n = 12) demonstrated that the locations of hubs were present across passive and active task states, suggesting that they reflect a stable property of cortical network architecture. To obtain an accurate reference map, data were combined across 127 participants to yield a consensus estimate of cortical hubs. Using this consensus estimate, we explored whether the topography of hubs could explain the pattern of vulnerability in Alzheimer9s disease (AD) because some models suggest that regions of high activity and metabolism accelerate pathology. Positron emission tomography amyloid imaging in AD ( n = 10) compared with older controls ( n = 29) showed high amyloid-β deposition in the locations of cortical hubs consistent with the possibility that hubs, while acting as critical way stations for information processing, may also augment the underlying pathological cascade in AD.


title: Dynamic variation monitor for measuring the impact of voltage droops on microprocessor clock frequency

abstract: A 45nm microprocessor integrates an all-digital dynamic variation monitor (DVM), consisting of a tunable replica circuit with a time-to-digital converter, to measure the impact of dynamic variations on path-level delay or frequency. Measurements reveal the high sensitivity of the microprocessor maximum clock frequency (F MAX ) to the placement and magnitude of a high-frequency supply voltage (V CC ) droop and demonstrate the DVM capability of tracking F MAX changes to within 1% for a wide range of V CC droop profiles. Furthermore, the DVM interfaces with an adaptive clock control circuit to dynamically change the clock frequency in response to dynamic variations, enabling the microprocessor to operate at maximum efficiency.


title: UAV-based crop and weed classification for smart farming

abstract: Unmanned aerial vehicles (UAVs) and other robots in smart farming applications offer the potential to monitor farm land on a per-plant basis, which in turn can reduce the amount of herbicides and pesticides that must be applied. A central information for the farmer as well as for autonomous agriculture robots is the knowledge about the type and distribution of the weeds in the field. In this regard, UAVs offer excellent survey capabilities at low cost. In this paper, we address the problem of detecting value crops such as sugar beets as well as typical weeds using a camera installed on a light-weight UAV. We propose a system that performs vegetation detection, plant-tailored feature extraction, and classification to obtain an estimate of the distribution of crops and weeds in the field. We implemented and evaluated our system using UAVs on two farms, one in Germany and one in Switzerland and demonstrate that our approach allows for analyzing the field and classifying individual plants.


title: Leveraging Natural Load Dynamics With Variable Gear-Ratio Actuators

abstract: This paper presents a robotic system where the gear-ratio of an actuator is dynamically changed to either leverage or attenuate the natural load dynamics. Based on this principle, lightweight robotic systems can be made fast and strong; exploiting the natural load dynamics for moving at higher speeds (small reduction ratio), while also able to bear a large load through the attenuation of the load dynamics (large reduction ratio). A model-based control algorithm to automatically select the optimal gear-ratios that minimize the total actuator torques for an arbitrary dynamic state and expected uncertainty level is proposed. Also, a novel 3-DoF robot arm using custom actuators with two discrete gear-ratios is presented. The advantages of gear-shifting dynamically are demonstrated through experiments and simulations. Results show that actively changing the gear-ratio using the proposed control algorithms can lead to an order-of-magnitude reduction of necessary actuator torque and power, and also increase robustness to disturbances.


title: A Formal Basis for the Heuristic Determination of Minimum Cost Paths

abstract: Although the problem of determining the minimum cost path through a graph arises naturally in a number of interesting applications, there has been no underlying theory to guide the development of efficient search procedures. Moreover, there is no adequate conceptual framework within which the various ad hoc search strategies proposed to date can be compared. This paper describes how heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching and demonstrates an optimality property of a class of search strategies.


title: Survey on security in intra-body area network communication

abstract: With the advances in microelectronics, embedded computing, and wireless communications, the interest in Body Sensor Networks has risen sharply and has enabled the development and implementation of such networks. A Body Sensor Network is constructed from sensor nodes distributed in and on the user's body. The nodes form a wireless network that collects physiological data and forwards it on. This sort of network has wide application prospects in the future of healthcare. The collected data is highly private and must, therefore, be protected adequately. The security mechanisms usually deployed depend heavily on the key agreement scheme. Because of the reliability requirements, energy efficiency, and hardware constraints, building a key agreement scheme for a Body Sensor Network can be quite a challenge. This paper presents a state-of-the-art overview of security in Body Sensor Networks, focusing on proposed key agreement schemes, ways they are built in, and the methods used to evaluate their security and performance. Results show that the community is very much split between the traditional key agreement schemes and schemes that take advantage of physiological or other signals to exchange a key. Security analysis is rarely performed with formal methods; instead, descriptive analysis is commonplace. There are no standards or guidelines on measuring a scheme`s efficiency. The authors therefore used different methods and, consequently, schemes can be difficult to compare.


title: Cardiovascular and endocrine alterations after masturbation-induced orgasm in women.

abstract: ObjectiveThe present study investigated the cardiovascular, genital, and endocrine changes in women after masturbation-induced orgasm because the neuroendocrine response to sexual arousal in humans is equivocal.MethodsHealthy women (N = 10) completed an experimental session, in which a documentary f


title: Optimized GAF protocol based sleep/awake protocol for WSN to improve Network Lifetime

abstract: In recent time, Wireless Sensor Networks (WSNs) is getting popular in wide range of applications. WSN is the network of Sensor Nodes (SNs) in which each node have sensing, communication and computation facility. The main limitation of WSN is that SNs have limited energy. So the main focus of research in WSN is to improve the Network Lifetime by plummeting energy consumption. Several locations aware routing protocol has been proposed in literature for this purpose. Geographic Adaptive Fidelity (GAF) is a location aware routing protocol that divides the network into grids and make one node active at a time from each grid to sense data. Its enhancement, Optimized GAF, proposed earlier, improves the network lifetime by improving the discovery phase. In discovery phase Optimized GAF saves the sequence of next active nodes in advance on the basis of higher residual energy of nodes. This paper improves the selection further considering their distance from Base Station (BS) thus improving the overall Network Lifetime. Comparison is done between Optimised GAF (Base Protocol) and Proposed Protocol on the basis of Network Lifetime and Energy Consumption. Both the protocols are implemented in MATLAB. Results show that the proposed protocol is better than basic protocol.


Learning Graph Representations with Recurrent Neural Network Autoencoders


title: A communication system on smart phones and tablets for non-verbal children with autism

abstract: We designed, developed and evaluated an Augmentative and Alternative Communication (AAC) system, AutVisComm, for children with autism that can run on smart phones and tablets. An iterative design and development process was followed, where the prototypes were developed in close collaboration with the user group, and the usability testing was gradually expanded to larger groups. In the last evaluation stage described here, twenty-four children with autism used AutVisComm to learn to request the desired object. We measured their learning rates and correlated them with their behavior traits (as observed by their teachers) like joint attention, symbolic processing and imitation. We found that their ability for symbolic processing did not correlate with the learning rate, but their ability for joint attention did. This suggests that this system (and this class of AACs) helps to compensate for a lack of symbolic processing, but not for a lack of joint-attention mechanism.


title: SoftNIC: A Software NIC to Augment Hardware

abstract: As the main gateway for network traffic to a server, the network interface card (NIC) is an ideal place to incorporate diverse network functionality, such as traffic control, protocol offloading, and virtualization. However, the slow evolution and inherent inflexibility of NIC hardware have failed to support evolving protocols, emerging applications, and rapidly changing system/network architectures. The traditional software approach to this problem—implementing NIC features in the host network stack—is unable to meet increasingly challenging performance requirements. In this paper we present SoftNIC, a hybrid softwarehardware architecture to bridge the gap between limited hardware capabilities and ever changing user demands. SoftNIC provides a programmable platform that allows applications to leverage NIC features implemented in software and hardware, without sacrificing performance. Our evaluation results show that SoftNIC achieves multi-10G performance even on a single core and scales further with multiple cores. We also present a variety of use cases to show the potential of software NIC augmentation.


title: Automated accident detection and rescue system

abstract: Vehicle accidents cause lot of damage. Early detection and timely action will help a lot in accident situations. Researchers proposed methods to analyze vehicle mounted sensor data to detect accidents. To get a big clear picture, we need to correlate and analyze pieces of sensor data instead of analyzing them independently. Along with it, if we also analyze traveler's health data, it will be helpful to take rescue actions. It may not be straightforward to correlate and analyze all these data sets in real time. A big system and framework is required to do so. An architecture and design methodology is proposed in this paper to analyze the data provided by the vehicle mounted sensors and health data sent by passenger's wearable devices to detect accidents in real time. After detecting the accidents, system assesses the impact of the accident. Then it either alerts relevant people for rescue action or it does needful actions (as described by the domain experts) on its own.


title: Opinion–Aspect Relations in Cognizing Customer Feelings via Reviews

abstract: Determining a consensus opinion on a product sold online is no longer easy, because assessments have become more and more numerous on the Internet. To address this problem, researchers have used various approaches, such as looking for feelings expressed in the documents and exploring the appearance and syntax of reviews. Aspect-based evaluation is the most important aspect of opinion mining, and researchers are becoming more interested in product aspect extraction; however, more complex algorithms are needed to address this issue precisely with large data sets. This paper introduces a method to extract and summarize product aspects and corresponding opinions from a large number of product reviews in a specific domain. We maximize the accuracy and usefulness of the review summaries by leveraging knowledge about product aspect extraction and providing both an appropriate level of detail and rich representation capabilities. The results show that the proposed system achieves F1-scores of 0.714 for camera reviews and 0.774 for laptop reviews.


title: A probabilistic method for inferring preferences from clicks

abstract: Evaluating rankers using implicit feedback, such as clicks on documents in a result list, is an increasingly popular alternative to traditional evaluation methods based on explicit relevance judgments. Previous work has shown that so-called interleaved comparison methods can utilize click data to detect small differences between rankers and can be applied to learn ranking functions online. In this paper, we analyze three existing interleaved comparison methods and find that they are all either biased or insensitive to some differences between rankers. To address these problems, we present a new method based on a probabilistic interleaving process. We derive an unbiased estimator of comparison outcomes and show how marginalizing over possible comparison outcomes given the observed click data can make this estimator even more effective. We validate our approach using a recently developed simulation framework based on a learning to rank dataset and a model of click behavior. Our experiments confirm the results of our analysis and show that our method is both more accurate and more robust to noise than existing methods.


title: Rotation Invariant Texture Retrieval Considering the Scale Dependence of Gabor Wavelet

abstract: Obtaining robust and efficient rotation-invariant texture features in content-based image retrieval field is a challenging work. We propose three efficient rotation-invariant methods for texture image retrieval using copula model based in the domains of Gabor wavelet (GW) and circularly symmetric GW (CSGW). The proposed copula models use copula function to capture the scale dependence of GW/CSGW for improving the retrieval performance. It is well known that the Kullback–Leibler distance (KLD) is the commonly used similarity measurement between probability models. However, it is difficult to deduce the closed-form of KLD between two copula models due to the complexity of the copula model. We also put forward a kind of retrieval scheme using the KLDs of marginal distributions and the KLD of copula function to calculate the KLD of copula model. The proposed texture retrieval method has low computational complexity and high retrieval precision. The experimental results on VisTex and Brodatz data sets show that the proposed retrieval method is more effective compared with the state-of-the-art methods.


title: Leveraging Synthetic Discourse Data via Multi-task Learning for Implicit Discourse Relation Recognition

abstract: To overcome the shortage of labeled data for implicit discourse relation recognition, previous works attempted to automatically generate training data by removing explicit discourse connectives from sentences and then built models on these synthetic implicit examples. However, a previous study (Sporleder and Lascarides, 2008) showed that models trained on these synthetic data do not generalize very well to natural (i.e. genuine) implicit discourse data. In this work we revisit this issue and present a multi-task learning based system which can effectively use synthetic data for implicit discourse relation recognition. Results on PDTB data show that under the multi-task learning framework our models with the use of the prediction of explicit discourse connectives as auxiliary learning tasks, can achieve an averaged F1 improvement of 5.86% over baseline models.


NuCypher : A proxy re-encryption network to empower privacy in decentralized systems


title: Surface Aesthetics in Tip Rhinoplasty: A Step-by-Step Guide

abstract: Tip rhinoplasty is a key component of aesthetic rhinoplasty. An understanding of the correlation between tip surface aesthetics and the underlying anatomic structures enables proper identification and correction of tip abnormalities. Surface aesthetics of the attractive nose are created by certain lines, shadows, and highlights with specific proportions and breakpoints. In this Featured Operative Technique, the authors describe a stepwise process for tip rhinoplasty that conceptualizes aesthetic subunits as geometric polygons to define the existing deformity, the operative plan, and the aesthetic goals. Tip rhinoplasty is described in detail, from initial markings through incisions and dissection. The autorim graft concept is explained, and lateral crural steal and footplate setback techniques are described for the attainment of symmetric domes with correct lateral crural resting angles. Methods in columellar reconstruction are described, including creating the columella (C') breakpoint and the infralobular caudal contour graft. The principal author (B.Ç.) has applied these techniques to 257 consecutive "polygon rhinoplasties" over the past 3 years.


Computer-Assisted Detection of Retinal Nerve Fiber Layer Defect on Fundus Photography with the Average Curvature of Polarimetric Image


title: Organizing and the process of sensemaking

abstract: Sensemaking involves turning circumstances into a situation that is comprehended explicitly in words and that serves as a springboard into action. In this paper we take the position that the concept of sensemaking fills important gaps in organizational theory. The seemingly transient nature of sensemaking belies its central role in the determination of human behavior, whether people are acting in formal organizations or elsewhere. Sensemaking is central because it is the primary site where meanings materialize that inform and constrain identity and action. The purpose of this paper is to take stock of the concept of sensemaking. We do so by pinpointing central features of sensemaking, some of which have been explicated but neglected, some of which have been assumed but not made explicit, some of which have changed in significance over time, and some of which have been missing all along or have gone awry. We sense joint enthusiasm to restate sensemaking in ways that make it more future oriented, more action oriented, more macro, more closely tied to organizing, meshed more boldly with identity, more visible, more behaviorally defined, less sedentary and backward looking, more infused with emotion and with issues of sensegiving and persuasion. These key enhancements provide a foundation upon which to build future studies that can strengthen the sensemaking perspective.


title: MR-DBSCAN: An Efficient Parallel Density-Based Clustering Algorithm Using MapReduce

abstract: Data clustering is an important data mining technology that plays a crucial role in numerous scientific applications. However, it is challenging due to the size of datasets has been growing rapidly to extra-large scale in the real world. Meanwhile, MapReduce is a desirable parallel programming platform that is widely applied in kinds of data process fields. In this paper, we propose an efficient parallel density-based clustering algorithm and implement it by a 4-stages MapReduce paradigm. Furthermore, we adopt a quick partitioning strategy for large scale non-indexed data. We study the metric of merge among bordering partitions and make optimizations on it. At last, we evaluate our work on real large scale datasets using Hadoop platform. Results reveal that the speedup and scale up of our work are very efficient.


An Analysis of Undergraduate Information Systems Curricula: Adoption of the IS 2010 Curriculum Guidelines


title: Low-power twiddle factor unit for FFT computation

abstract: An integral part of FFT computation are the twiddle factors, which, in software implementations, are typically stored into RAM memory implying large memory footprint and power consumption. In this paper, we propose a novel twiddle factor generator based on reduced ROM tables. The unit supports both radix-4 and mixed-radix-4/2 FFT algorithms and several transform lengths. The unit operates at a rate of one factor per clock cycle.


title: Optimizing Long Short-Term Memory Recurrent Neural Networks Using Ant Colony Optimization to Predict Turbine Engine Vibration

abstract: This article expands on research that has been done to develop a recurrent neural network (RNN) capable of predicting aircraft engine vibrations using long short-term memory (LSTM) neurons. LSTM RNNs can provide a more generalizable and robust method for prediction over analytical calculations of engine vibration, as analytical calculations must be solved iteratively based on specific empirical engine parameters, making this approach ungeneralizable across multiple engines. In initial work, multiple LSTM RNN architectures were proposed, evaluated and compared. This research improves the performance of the most effective LSTM network design proposed in the previous work by using a promising neuroevolution method based on ant colony optimization (ACO) to develop and enhance the LSTM cell structure of the network. A parallelized version of the ACO neuroevolution algorithm has been developed and the evolved LSTM RNNs were compared to the previously used fixed topology. The evolved networks were trained on a large database of flight data records obtained from an airline containing flights that suffered from excessive vibration. Results were obtained using MPI (Message Passing Interface) on a high performance computing (HPC) cluster, evolving 1000 different LSTM cell structures using 168 cores over 4 days. The new evolved LSTM cells showed an improvement of 1.35%, reducing prediction error from 5.51% to 4.17% when predicting excessive engine vibrations 10 seconds in the future, while at the same time dramatically reducing the number of weights from 21,170 to 11,810.


Cyber-Physical System Security of a Power Grid : State-ofthe-Art


title: Self-similarity of complex networks and hidden metric spaces

abstract: We demonstrate that the self-similarity of some scale-free networks with respect to a simple degree-thresholding renormalization scheme finds a natural interpretation in the assumption that network nodes exist in hidden metric spaces. Clustering, i.e., cycles of length three, plays a crucial role in this framework as a topological reflection of the triangle inequality in the hidden geometry. We prove that a class of hidden variable models with underlying metric spaces are able to accurately reproduce the self-similarity properties that we measured in the real networks. Our findings indicate that hidden geometries underlying these real networks are a plausible explanation for their observed topologies and, in particular, for their self-similarity with respect to the degree-based renormalization.


Mining Paraphrasal Typed Templates from a Plain Text Corpus