Fuzzy Neural Network-Based Adaptive Control for a Class of Uncertain Nonlinear Stochastic Systems

This paper studies an adaptive tracking control for a class of nonlinear stochastic systems with unknown functions. The considered systems are in the nonaffine pure-feedback form, and it is the first to control this class of systems with stochastic disturbances. The fuzzy-neural networks are used to approximate unknown functions. Based on the backstepping design technique, the controllers and the adaptation laws are obtained. Compared to most of the existing stochastic systems, the proposed control algorithm has fewer adjustable parameters and thus, it can reduce online computation load. By using Lyapunov analysis, it is proven that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded in probability and the system output tracks the reference signal to a bounded compact set. The simulation example is given to illustrate the effectiveness of the proposed control algorithm.


Detecting Avocados to Zucchinis: What Have We Done, and Where Are We Going?

The growth of detection datasets and the multiple directions of object detection research provide both an unprecedented need and a great opportunity for a thorough evaluation of the current state of the field of categorical object detection. In this paper we strive to answer two key questions. First, where are we currently as a field: what have we done right, what still needs to be improved? Second, where should we be going in designing the next generation of object detectors? Inspired by the recent work of Hoiem et al. on the standard PASCAL VOC detection dataset, we perform a large-scale study on the Image Net Large Scale Visual Recognition Challenge (ILSVRC) data. First, we quantitatively demonstrate that this dataset provides many of the same detection challenges as the PASCAL VOC. Due to its scale of 1000 object categories, ILSVRC also provides an excellent test bed for understanding the performance of detectors as a function of several key properties of the object classes. We conduct a series of analyses looking at how different detection methods perform on a number of image-level and object-class-level properties such as texture, color, deformation, and clutter. We learn important lessons of the current object detection methods and propose a number of insights for designing the next generation object detectors.


DFD: Efficient Functional Dependency Discovery

The discovery of unknown functional dependencies in a dataset is of great importance for database redesign, anomaly detection and data cleansing applications. However, as the nature of the problem is exponential in the number of attributes none of the existing approaches can be applied on large datasets. We present a new algorithm DFD for discovering all functional dependencies in a dataset following a depth-first traversal strategy of the attribute lattice that combines aggressive pruning and efficient result verification. Our approach is able to scale far beyond existing algorithms for up to 7.5 million tuples, and is up to three orders of magnitude faster than existing approaches on smaller datasets.


Database Dependency Discovery: A Machine Learning Approach

Database dependencies, such as functional and multivalued dependencies, express the presence of structure in databas e relations, that can be utilised in the database design proce ss. The discovery of database dependencies can be viewed as an induction problem, in which general rules (dependencies) a re obtained from specific facts (the relation). This viewpoint has the advantage of abstracting away as much as possible from the particulars of the dependencies. The algorithms in this paper are designed such that they can easily be generalised to other kinds of dependencies. Like in current approaches to computational induction such as inductive logic programming, we distinguish between top down algorithms and bottom-up algorithms. In a top-down approach, hypotheses are generated in a systematic way and then tested against the given relation. In a bottom-up approach, the relation is inspected in order to see what dependencies it may satisfy or violate. We give algorithms for bot h approaches.


GORDIAN: Efficient and Scalable Discovery of Composite Keys

Identification of (composite) key attributes is of fundamental importance for many different data management tasks such as data modeling, data integration, anomaly detection, query formulation, query optimization, and indexing. However, information about keys is often missing or incomplete in many real-world database scenarios. Surprisingly, the fundamental problem of automatic key discovery has received little attention in the existing literature. Existing solutions ignore composite keys, due to the complexity associated with their discovery. Even for simple keys, current algorithms take a brute-force approach; the resulting exponential CPU and memory requirements limit the applicability of these methods to small datasets. In this paper, we describe GORDIAN, a scalable algorithm for automatic discovery of keys in large datasets, including composite keys. GORDIAN can provide exact results very efficiently for both real-world and synthetic datasets. GORDIAN can be used to find (composite) key attributes in any collection of entities, e.g., key column-groups in relational data, or key leaf-node sets in a collection of XML documents with a common schema. We show empirically that GORDIAN can be combined with sampling to efficiently obtain high quality sets of approximate keys even in very large datasets.


Fast Algorithms for Mining Association Rules in Large Databases


Logic and Databases: A Deductive Approach

ion, Databases and Conceptual Modeling (Pingree Park, Colo., June), pp. 112-114; ACM SZGMOD Rec. 11, 2 (Feb.). CODD, E. F. 1982. Relational database: A practical foundation for productivity. Commun. ACM 25, 2 (Feb.), 109-117. COLMERAUER, A. 1973. Un systeme de communication homme-machine en francais. Rapport, Groupe Intelligence Artificielle, Universite d’AixMarseille-Luminy, Marseilles, France. COLMERAUER, A., AND PIQUE, J. F. 1981. About natural logic. In Advances in Database Theory vol. 1, H. Gallaire, J. Minker, and J.-M. Nicolas, Eds. Plenum, New York, pp. 343-365. COOPER, E. C. 1980. On the expressive power of query languages for relational databases. Tech. Rep. 14-80, Computer Science Dept., Harvard Univ., Cambridge, Mass. DAHL, V. 1982. On database systems development through logic. ACM Trans. Database Syst. 7, 1 (Mar.), 102-123. DATE, C. J. 1977. An Introduction to Database Systems. Addison-Wesley, Reading, Mass. DATE, C. J. 1981. An Introduction to Database Systems, 3rd ed. Addison-Wesley, Reading, Mass. DELIYANNI, A., AND KOWALSKI, R. A. 1979. Logic and semantic networks. Commun. ACM 22, 3


Regional cerebral blood flow correlates of visuospatial tasks in Alzheimer's disease.

This study investigated the role of visuospatial tasks in identifying cognitive decline in patients with Alzheimer's disease (AD), by correlating neuropsychological performance with cerebral perfusion measures. There were 157 participants: 29 neurologically healthy controls (age: 70.3 +/- 6.6, MMSE > or = 27), 86 patients with mild AD (age: 69.18 +/- 8.28, MMSE > or = 21) and 42 patients moderate/severe AD (age: 68.86 +/- 10.69, MMSE 8-20). Single Photon Emission Computerized Tomography (SPECT) was used to derive regional perfusion ratios, and correlated using partial least squares (PLS) with neuropsychological test scores from the Benton Line Orientation (BLO) and the Rey-Osterrieth Complex Figure (RO). Cross-sectional analysis demonstrated that mean scores differed in accordance with disease status: control group (BLO 25.5, RO 33.3); mild AD (BLO 20.1, RO 25.5); moderate/severe AD (BLO 10.7, RO 16). Correlations were observed between BLO/RO and right parietal SPECT regions in the AD groups. Visuospatial performance, often undersampled in cognitive batteries for AD, is clearly impaired even in mild AD and correlates with functional deficits as indexed by cerebral perfusion ratios on SPECT implicating right hemisphere circuits. Furthermore, PLS reveals that usual spatial tasks probe a distributed brain network in both hemispheres including many areas targeted by early AD pathology.


Articulatory , positional and coarticulatory characteristics for clear / l / and dark / l / : evidence from two Catalan dialects

Electropalatographic and acoustic data reported in this study show differences in closure location and degree, dorsopalatal contact size, closure duration, relative timing of events and formant frequency between clear /l/ and dark /l/ in two dialects of Catalan (Valencian and Majorcan). The two Catalan dialects under investigation differ also regarding degree of darkness but essentially not regarding coarticulatory resistance at the word edges, i.e. the alveolar lateral is equally dark word-initially and word-finally in Majorcan, and clearer in the former position vs. than the latter in Valencian, and more resistant to vowel effects in the two positions than intervocalically in both dialects. With reference to data from the literature, it appears that languages and dialects may differ as to whether /l/ is dark or clear in all word positions or whether or not initial /l/ is clearer than final /l/, and that articulatory strengthening occurs not only wordand utterance-initially but wordand utterance-finally as well. These and other considerations confirm the hypothesis that degree of darkness in /l/ proceeds gradually rather than categorically from one language to another.


Risk factors for involvement in cyber bullying : Victims , bullies and bully – victims ☆

The use of online technology is exploding worldwide and is fast becoming a preferred method of interacting. While most online interactions are neutral or positive the Internet provides a new means through which children and youth are bullied. The aim of this grounded theory approach was to explore technology, virtual relationships and cyber bullying from the perspectives of students. Seven focus groups were held with 38 students between fifth and eighth grades. The participants considered cyber bullying to be a serious problem and some characterized online bullying as more serious than  ̳traditional‘ bullying because of the associated anonymity. Although the students depicted anonymity as integral to cyber bullying, the findings suggest that much of the cyber bullying occurred within the context of their social groups and relationships. Findings revealed five major themes: technology embraced at younger ages and becoming the dominant medium for communication; definitions and views of cyber bullying; factors unique to cyber bullying; types of cyber bullying; and telling adults. The findings highlight the complexity of the perceived anonymity provided by the Internet and how AC C EP TE D M AN U SC R IP T ACCEPTED MANUSCRIPT 3 this may impact cyber bullying. The study offers greater awareness of the meanings of online relationships for children and youth.


The Gut Microbiota and Autism Spectrum Disorders

Gastrointestinal (GI) symptoms are a common comorbidity in patients with autism spectrum disorder (ASD), but the underlying mechanisms are unknown. Many studies have shown alterations in the composition of the fecal flora and metabolic products of the gut microbiome in patients with ASD. The gut microbiota influences brain development and behaviors through the neuroendocrine, neuroimmune and autonomic nervous systems. In addition, an abnormal gut microbiota is associated with several diseases, such as inflammatory bowel disease (IBD), ASD and mood disorders. Here, we review the bidirectional interactions between the central nervous system and the gastrointestinal tract (brain-gut axis) and the role of the gut microbiota in the central nervous system (CNS) and ASD. Microbiome-mediated therapies might be a safe and effective treatment for ASD.


Compiling Knowledge into Decomposable Negation Normal Form

We propose a method for compiling proposit ional theories into a new tractable form that we refer to as decomposable negation normal form (DNNF). We show a number of results about our compilation approach. First, we show that every propositional theory can be compiled into D N N F and present an algorithm to this effect. Second, we show that if a clausal form has a bounded treewidth, then its DNNF compilation has a linear size and can be computed in linear t ime — treewidth is a graphtheoretic parameter which measures the connectivity of the clausal form. Th i rd , we show that once a propositional theory is compiled into DNNF, a number of reasoning tasks, such as satisfiability and forgett ing, can be performed in linear t ime. Finally, we propose two techniques for approximating the DNNF compilat ion of a theory when the size of such compilation is too large to be practical. One of the techniques generates a sound but incomplete compilation, while the other generates a complete but unsound compilation. Together, these approximations bound the exact compilation from below and above in terms for their abil i ty to answer queries.


Object volume estimation based on 3D point cloud

An approach to estimating the volume of an object based on 3D point cloud is proposed in this paper. Firstly, 3D point cloud is cut into slices of equal thickness along the z-axis. Bisect each slice along the y-axis and cut each slice into sub-intervals along the x-axis. Using the minimum and maximum coordinates in each sub-interval, two surface curve functions can be fitted accordingly for the bisected slices. Then, the curves are integrated to estimate the area of each slice and further integrated along the z-axis for an estimated volume of the object.


Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios

In the last decade, driven also by the availability of an unprecedented computational power and storage capabilities in cloud environments, we assisted to the proliferation of new algorithms, methods, and approaches in two areas of artificial intelligence: knowledge representation and machine learning. On the one side, the generation of a high rate of structured data on the Web led to the creation and publication of the so-called knowledge graphs. On the other side, deep learning emerged as one of the most promising approaches in the generation and training of models that can be applied to a wide variety of application fields. More recently, autoencoders have proven their strength in various scenarios, playing a fundamental role in unsupervised learning. In this paper, we instigate how to exploit the semantic information encoded in a knowledge graph to build connections between units in a Neural Network, thus leading to a new method, SEM-AUTO, to extract and weight semantic features that can eventually be used to build a recommender system. As adding content-based side information may mitigate the cold user problems, we tested how our approach behaves in the presence of a few ratings from a user on the Movielens 1M dataset and compare results with BPRSLIM.


3D Deep Learning for Efficient and Robust Landmark Detection in Volumetric Data

Recently, deep learning has demonstrated great success in computer vision with the capability to learn powerful image features from a large training set. However, most of the published work has been confined to solving 2D problems, with a few limited exceptions that treated the 3D space as a composition of 2D orthogonal planes. The challenge of 3D deep learning is due to a much larger input vector, compared to 2D, which dramatically increases the computation time and the chance of over-fitting, especially when combined with limited training samples (hundreds to thousands), typical for medical imaging applications. To address this challenge, we propose an efficient and robust deep learning algorithm capable of full 3D detection in volumetric data. A two-step approach is exploited for efficient detection. A shallow network (with one hidden layer) is used for the initial testing of all voxels to obtain a small number of promising candidates, followed by more accurate classification with a deep network. In addition, we propose two approaches, i.e., separable filter decomposition and network sparsification, to speed up the evaluation of a network. To mitigate the over-fitting issue, thereby increasing detection robustness, we extract small 3D patches from a multi-resolution image pyramid. The deeply learned image features are further combined with Haar wavelet features to increase the detection accuracy. The proposed method has been quantitatively evaluated for carotid artery bifurcation detection on a head-neck CT dataset from 455 patients. Compared to the state-ofthe-art, the mean error is reduced by more than half, from 5.97 mm to 2.64 mm, with a detection speed of less than 1 s/volume.


Residual Memory Networks in Language Modeling: Improving the Reputation of Feed-Forward Networks

We introduce the Residual Memory Network (RMN) architecture to language modeling. RMN is an architecture of feedforward neural networks that incorporates residual connections and time-delay connections that allow us to naturally incorporate information from a substantial time context. As this is the first time RMNs are applied for language modeling, we thoroughly investigate their behaviour on the well studied Penn Treebank corpus. We change the model slightly for the needs of language modeling, reducing both its time and memory consumption. Our results show that RMN is a suitable choice for small-sized neural language models: With test perplexity 112.7 and as few as 2.3M parameters, they out-perform both a much larger vanilla RNN (PPL 124, 8M parameters) and a similarly sized LSTM (PPL 115, 2.08M parameters), while being only by less than 3 perplexity points worse than twice as big LSTM.


Author Disambiguation in PubMed: Evidence on the Precision and Recall of Author-ity among NIH-Funded Scientists

We examined the usefulness (precision) and completeness (recall) of the Author-ity author disambiguation for PubMed articles by associating articles with scientists funded by the National Institutes of Health (NIH). In doing so, we exploited established unique identifiers-Principal Investigator (PI) IDs-that the NIH assigns to funded scientists. Analyzing a set of 36,987 NIH scientists who received their first R01 grant between 1985 and 2009, we identified 355,921 articles appearing in PubMed that would allow us to evaluate the precision and recall of the Author-ity disambiguation. We found that Author-ity identified the NIH scientists with 99.51% precision across the articles. It had a corresponding recall of 99.64%. Precision and recall, moreover, appeared stable across common and uncommon last names, across ethnic backgrounds, and across levels of scientist productivity.


Motion planning based on learning models of pedestrian and driver behaviors

Autonomous driving has shown the capability of providing driver convenience and enhancing safety. While introducing autonomous driving into our current traffic system, one significant issue is to make the autonomous vehicle be able to react in the same way as real human drivers. In order to ensure that an autonomous vehicle of the future will perform like human drivers, this paper proposes a vehicle motion planning model, which can represent how drivers control vehicles based on the assessment of traffic environments in the real signalized intersection. The proposed motion planning model comprises functions of pedestrian intention detection, gap detection and vehicle dynamic control. The three functions are constructed based on the analysis of actual data collected from real traffic environments. Finally, this paper demonstrates the performance of the proposed method by comparing the behaviors of our model with the behaviors of real pedestrians and human drivers. The experimental results show that our proposed model can achieve 85% recognition rate for the pedestrian crossing intention. Moreover, the vehicle controlled by the proposed motion planning model and the actual human-driven vehicle are highly similar with respect to the gap acceptance in intersections.


Facing prejudice: implicit prejudice and the perception of facial threat.

We propose that social attitudes, and in particular implicit prejudice, bias people's perceptions of the facial emotion displayed by others. To test this hypothesis, we employed a facial emotion change-detection task in which European American participants detected the offset (Study 1) or onset (Study 2) of facial anger in both Black and White targets. Higher implicit (but not explicit) prejudice was associated with a greater readiness to perceive anger in Black faces, but neither explicit nor implicit prejudice predicted anger perceptions regarding similar White faces. This pattern indicates that European Americans high in implicit racial prejudice are biased to perceive threatening affect in Black but not White faces, suggesting that the deleterious effects of stereotypes may take hold extremely early in social interaction.


Recurrent networks with attention and convolutional networks for sentence representation and classification

In this paper, we propose a bi-attention, a multi-layer attention and an attention mechanism and convolution neural network based text representation and classification model (ACNN). The bi-attention have two attention mechanism to learn two context vectors, forward RNN with attention to learn forward context vector c⃗ $\overrightarrow {\mathbf {c}}$ and backward RNN with attention to learn backward context vector c⃖ $\overleftarrow {\mathbf {c}}$, and then concatenation c⃗ $\overrightarrow {\mathbf {c}}$ and c⃖ $\overleftarrow {\mathbf {c}}$ to get context vector c. The multi-layer attention is the stack of the bi-attention. In the ACNN, the context vector c is obtained by the bi-attention, then the convolution operation is performed on the context vector c, and the max-pooling operation is used to reduce the dimension. After max-pooling operation the text is converted to low-dimensional sentence vector m. Finally, the Softmax classifier be used for text classification. We test our model on 8 benchmarks text classification datasets, and our model achieved a better or the same performance compare with the state-of-the-art methods.


Modeling changing dependency structure in multivariate time series

We show how to apply the efficient Bayesian changepoint detection techniques of Fearnhead in the multivariate setting. We model the joint density of vector-valued observations using undirected Gaussian graphical models, whose structure we estimate. We show how we can exactly compute the MAP segmentation, as well as how to draw perfect samples from the posterior over segmentations, simultaneously accounting for uncertainty about the number and location of changepoints, as well as uncertainty about the covariance structure. We illustrate the technique by applying it to financial data and to bee tracking data.


Habits — A Repeat Performance

Habits are response dispositions that are activated automatically by the context cues that co-occurred with responses during past performance. Experiencesampling diary studies indicate that much of everyday action is characterized by habitual repetition. We consider various mechanisms that could underlie the habitual control of action, and we conclude that direct cuing and motivated contexts best account for the characteristic features of habit responding—in particular, for the rigid repetition of action that can be initiated without intention and that runs to completion with minimal conscious control. We explain the utility of contemporary habit research for issues central to psychology, especially for behavior prediction, behavior change, and self-regulation. KEYWORDS—habit; automaticity; motivation; goals; behavior change; behavior prediction; self-regulation From self-help guru Anthony Robbins to the religion of Zen Buddhism, received wisdom exhorts people to be mindful, deliberative, and conscious in all they do. In contrast, contemporary research in psychology shows that it is actually people’s unthinking routines—or habits—that form the bedrock of everyday life. Without habits, people would be doomed to plan, consciously guide, and monitor every action, from making that first cup of coffee in the morning to sequencing the finger movements in a Chopin piano concerto. But what is a habit? The cognitive revolution radically reshaped the behaviorist view that habits rely on simple stimulus– response associations devoid of mental representation. Emerging is a more nuanced construct that includes roles for consciousness, goals, and motivational states. Fundamental questions persist, however, especially when comparing evidence across neuropsychology, animal-learning, and social-cognition literatures. Data from these fields support three views of habit, which we term the direct-context-cuing, implicit-goal, and motivated-context models. In this article, we consider these models and explain the relevance for psychology of a reinvigorated habit construct. HABITS AFTER BEHAVIORISM Within current theorizing, habits are automated response dispositions that are cued by aspects of the performance context (i.e., environment, preceding actions). They are learned through a process in which repetition incrementally tunes cognitive processors in procedural memory (i.e., the memory system that supports the minimally conscious control of skilled action). The relatively primitive associative learning that promotes habits is shared in some form across mammalian species. Our own interest in habits has been fueled by the recognition that much of everyday action is characterized by repetition. In experience-sampling diary studies using both student and community samples, approximately 45% of everyday behaviors tended to be repeated in the same location almost every day (Quinn & Wood, 2005; Wood, Quinn, & Kashy, 2002). In these studies, people reported a heterogeneous set of actions that varied in habit strength, including reading the newspaper, exercising, and eating fast food. Although a consensual perspective on habit mechanisms has yet to develop, common to all views is the idea that many behavioral sequences (e.g., one’s morning coffee-making routine) are performed repeatedly in similar contexts. When responses and features of context occur in contiguity, the potential exists for associations to form between them, such that contexts come to cue responses. In what follows, we outline three views of habitual control that build on this understanding. Direct Context Cuing According to the direct-context-cuing model, repeated coactivation forges direct links in memory between context and response representations. Once these links are formed via associative learning, merely perceiving a context triggers associated responses. Supporting evidence comes from research in which merely activating a construct, such as the elderly stereotype, influences the performance of relevant behaviors, such as a slow speed of walking (e.g., Bargh, Chen, & Burrows, 1996). Address correspondence to David Neal, Department of Psychology, Box 90085, Duke University, Durham, NC 27708; e-mail: dneal@ duke.edu. CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE 198 Volume 15—Number 4 Copyright r 2006 Association for Psychological Science Readers might wonder if it is realistic that contexts cue responses through this simple mechanism in the absence of an implicit or explicit goal. The answer is not clear, given that social-cognition research has thus far demonstrated only a limited version of direct-cuing effects. For example, activating the elderly stereotype influences walking speed, but it remains to be demonstrated whether such activation can initiate walking itself. However, the direct cuing of repeated action by contexts is suggested by myriad findings in cognitive neuroscience that reveal reduced involvement of goal-related neural structures, such as the prefrontal cortex, when behaviors have come under habitual control (see Daw, Niv, & Dayan, 2005). Furthermore, animal-learning research using a clever paradigm in which reinforcers are devalued suggests direct control by context. When rats initially perform an instrumental behavior (e.g., pressing a bar for a food pellet), they appear to be guided by specific goal expectations; they cease the behavior if the reward is devalued (e.g., by pairing it with a toxin; Dickinson & Balleine, 1995). In contrast, when rats have extensively repeated a behavior, their responses appear to be cued directly by contextual stimuli (e.g., the bar); reward devaluation has little impact on continued performance. These data are commonly interpreted as indicating that habit formation involves a shift to direct context cuing. Implicit Goals Associative learning explains not only the direct binding of contexts and actions but also the binding of contexts and goals. In implicit-goal models, habits develop when people repeatedly pursue a goal via a specific behavior in a given context. An indirect association then forms between the context and behavior within the broader goal system. In support, Aarts and Dijksterhuis (2000) found in several experiments that the automatic activation of habitual responses (e.g., bicycle riding) only occurs when a relevant goal has first been made accessible (e.g., the thought of attending class). These studies did not measure people’s real-world behavior, however, but focused instead on judgments about behavior. It remains to be seen whether such judgments tap the cognitive processes that actually drive behavior. In addition, there is good reason to think that habit performance itself does not depend on goal activation. Goaldriven responses tend to be dynamic and flexible, as evidenced by people sometimes substituting behaviors that serve a common goal. In contrast, habits emerge in a rigid pattern such that, for example, a habitual runner is unlikely to substitute a cycling class for running. Thus, although implicit goals provide potentially powerful guides to action, they do not plausibly explain the context cuing of habits. Motivated Contexts In another framework for understanding context-cued responses, contexts can acquire diffuse motivational value when they have preceded rewards in the past. When contexts predict rewards in this way, they energize associated responses without activating specific goals. Evidence of the motivating quality of contexts comes from animal studies of the neurotransmitters that mediate reward learning. For example, when monkeys first learn that a feature of the environment (e.g., a light) predicts a reward (e.g., a drop of juice) when a response is made (e.g., a lever press), neurotransmitter activity (i.e., dopamine release) occurs just after the reward (see Schultz, Dayan, & Montague, 1997). After repeated practice, the animal reaches for the lever when the light is illuminated. Furthermore, the neurotransmitter response is no longer elicited by the juice but instead by the light. In this way, environmental cues can acquire motivational value. Reward-predicting environments are thought to signal the cached (or long-run future) value of an action without signaling a specific outcome (e.g., juice; Daw et al., 2005). This diffuse motivation may explain the rigid nature of context cuing, given that cached values do not convey a specific desired outcome that could be met by substitutable means. Contributing further to the rigidity of habits, neural evidence indicates that, with repetition, whole sequences of responses become chunked or integrated in memory with the contexts that predict them (Barnes, Kubota, Hu, Jin, & Graybiel, 2005). Chunked responses are cued and implemented as a unit, consistent with the idea that habits require limited conscious control to proceed to completion. This quality of habitual responding is frustratingly evident when, for example, trying to fix a well-practiced but badly executed golf swing or dance-step sequence. As yet, the motivated-context idea has been tested primarily with animals. Its promise as a model of human habits comes from evidence that reward-related neurotransmitter systems are shared across species (e.g., in humans, dopamine is elicited by monetary reward). Multiple Habit Mechanisms The high degree of repetition in daily life observed in the diary research of Wood et al. (2002) is likely to be a product of multiple habit-control mechanisms that draw, in various cases, on direct context associations as well as on diffuse motivations. Although we consider implicit goals to be an implausible mediator of habitual behavior, they undoubtedly contribute to some types of repetition. Whether habits are cued directly or are diffusely motivated, they are triggered automatically by contexts and performed in a relatively rigid way. These features of responding have important implications for theories of behavior prediction, behavior cha


Digital Enterprise Architecture - Transformation for the Internet of Things

Excellence in IT is both a driver and a key enabler of the digital transformation. The digital transformation changes the way we live, work, learn, communicate, and collaborate. The Internet of Things (IoT) fundamentally influences today's digital strategies with disruptive business operating models and fast changing markets. New business information systems are integrating emerging Internet of Things infrastructures and components. With the huge diversity of Internet of Things technologies and products organizations have to leverage and extend previous Enterprise Architecture efforts to enable business value by integrating Internet of Things architectures. Both architecture engineering and management of current information systems and business models are complex and currently integrating beside the Internet of Things synergistic subjects, like Enterprise Architecture in context with services & cloud computing, semantic-based decision support through ontologies and knowledge-based systems, big data management, as well as mobility and collaboration networks. To provide adequate decision support for complex business/IT environments, we have to make transparent the impact of business and IT changes over the integral landscape of affected architectural capabilities, like directly and transitively impacted IoT-objects, business categories, processes, applications, services, platforms and infrastructures. The paper describes a new metamodel-based approach for integrating Internet of Things architectural objects, which are semi-automatically federated into a holistic Digital Enterprise Architecture environment.


A novel bridgeless buck-boost PFC converter

Conventional cascade buck-boost PFC (CBB-PFC) converter suffers from the high conduction loss in the input rectifier bridge. To resolve the above problem, a novel bridgeless buck-boost PFC topology is proposed in this paper. The proposed PFC converter which removes the input rectifier bridge has three conduction semiconductors at every moment. Comparing with CBB-PFC topology, the proposed topology reduces the conduction semiconductors, reduces conduction losses effectively, improves the efficiency of converter and is suitable for use in the wide input voltage range. In this paper, the average current mode control was implemented with UC3854, the theoretical analysis and design of detection circuits was presented. The experimental prototype with 400 V/600 W output and line input voltage range from 220 VAC to 380 VAC was built. Experimental results show that the proposed converter can improve 0.8% efficiency comparing CBB-PFC converter.


Scene text recognition and tracking to identify athletes in sport videos

We present an athlete identification module forming part of a system for the personalization of sport video broadcasts. The aim of this module is the localization of athletes in the scene, their identification through the reading of names or numbers printed on their uniforms, and the labelling of frames where athletes are visible. Building upon a previously published algorithm we extract text from individual frames and read these candidates by means of an optical character recognizer (OCR). The OCR-ed text is then compared to a known list of athletes’ names (or numbers), to provide a presence score for each athlete. Text regions are tracked in subsequent frames using a template matching technique. In this way blurred or distorted text, normally unreadable by the OCR, is exploited to provide a denser labelling of the video sequences. Extensive experiments show that the method proposed is fast, robust and reliable, out-performing results of other systems in the literature.


A Knowledge Graph based Bidirectional Recurrent Neural Network Method for Literature-based Discovery

In this paper, we present a model which incorporates biomedical knowledge graph, graph embedding and deep learning methods for literature-based discovery. Firstly, the relations between entities are extracted from biomedical abstracts and then a knowledge graph is constructed by using these obtained relations. Secondly, the graph embedding technologies are applied to convert the entities and relations in the knowledge graph into a low-dimensional vector space. Thirdly, a bidirectional Long Short-Term Memory network is trained based on the entity associations represented by the pre-trained graph embeddings. Finally, the learned model is used for open and closed literature-based discovery tasks. The experimental results show that our method could not only effectively discover hidden associations between entities, but also reveal the corresponding mechanism of interactions. It suggests that incorporating knowledge graph and deep learning methods is an effective way for capturing the underlying complex associations between entities hidden in the literature.


A system for generating and injecting indistinguishable network decoys

We propose a novel trap-based architecture for detecting passive, “silent”, attackers who are eavesdropping on enterprise networks. Motivated by the increasing number of incidents where attackers sniff the local network for interesting information, such as credit card numbers, account credentials, and passwords, we introduce a methodology for building a trap-based network that is designed to maximize the realism of bait-laced traffic. Our proposal relies on a “record, modify, replay” paradigm that can be easily adapted to different networked environments. The primary contributions of our architecture are the ease of automatically injecting large amounts of believable bait, and the integration of different detection mechanisms in the back-end. We demonstrate our methodology in a prototype platform that uses our decoy injection API to dynamically create and dispense network traps on a subset of our campus wireless network. Our network traps consist of several types of monitored passwords, authentication cookies, credit cards, and documents containing beacons to alarm when opened. The efficacy of our decoys against a model attack program is also discussed, along with results obtained from experiments in the field. In addition, we present a user study that demonstrates the believability of our decoy traffic, and finally, we provide experimental results to show that our solution causes only negligible interference to ordinary users.


Reflect , and React : A Culturally Responsive Model for Pre-service Secondary Social Studies Teachers

The purpose of this qualitative study was to design and implement a model of cultural-responsiveness within a social studies teacher education program. Specifically, we sought to understand how pre-service grades 6-12 social studies practitioners construct culturally responsive teaching (CRT) in their lesson planning. In addition, we examined the professional barriers that prevented teacher-candidates from actualizing culturally responsive pedagogy. Incorporating a conceptual model of Review, Reflect, and React, 20 teacher candidates in a social studies methods course engaged CRT theory and practice. Thematic analysis of lesson plans and clinical reflections indicated successful proponents of CRT critically analyzed their curriculum, explored the diverse needs of their students, and engaged learners in culturally appropriate social studies pedagogy. Findings also showed that unsuccessful CRT was characterized by a lack of content knowledge, resistance from the cooperating teacher, and a reliance on the textbook materials.


Deep and Broad Learning on Content-Aware POI Recommendation

POI recommendation has attracted lots of research attentions recently. There are several key factors that need to be modeled towards effective POI recommendation - POI properties, user preference and sequential momentum of check- ins. The challenge lies in how to synergistically learn multi-source heterogeneous data. Previous work tries to model multi-source information in a flat manner, using either embedding based methods or sequential prediction models in a cross-related space, which cannot generate mutually reinforce results. In this paper, a deep and broad learning approach based on a Deep Context- aware POI Recommendation (DCPR) model was proposed to structurally learn POI and user characteristics. The proposed DCPR model includes three collaborative layers, a CNN layer for POI feature mining, an RNN layer for sequential dependency and user preference modeling, and an interactive layer based on matrix factorization to jointly optimize the overall model. Experiments over three data sets demonstrate that DCPR model achieves significant improvement over state-of-the-art POI recommendation algorithms and other deep recommendation models.


A Rule Based Approach to Discourse Parsing

In this paper we present an overview of recent developments in discourse theory and parsing under the Linguistic Discourse Model (LDM) framework, a semantic theory of discourse structure. We give a novel approach to the problem of discourse segmentation based on discourse semantics and sketch a limited but robust approach to symbolic discourse parsing based on syntactic, semantic and lexical rules. To demonstrate the utility of the system in a real application, we briefly describe the architecture of the PALSUMM system, a symbolic summarization system being developed at FX Palo Alto Laboratory that uses discourse structures constructed using the theory outlined to summarize written English prose texts. 1


Emerging Principles of Gene Expression Programs and Their Regulation.

Many mechanisms contribute to regulation of gene expression to ensure coordinated cellular behaviors and fate decisions. Transcriptional responses to external signals can consist of many hundreds of genes that can be parsed into different categories based on kinetics of induction, cell-type and signal specificity, and duration of the response. Here we discuss the structure of transcription programs and suggest a basic framework to categorize gene expression programs based on characteristics related to their control mechanisms. We also discuss possible evolutionary implications of this framework.


EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces

OBJECTIVE
Brain-computer interfaces (BCI) enable direct communication with a computer, using neural activity as the control signal. This neural signal is generally chosen from a variety of well-studied electroencephalogram (EEG) signals. For a given BCI paradigm, feature extractors and classifiers are tailored to the distinct characteristics of its expected EEG control signal, limiting its application to that specific signal. Convolutional neural networks (CNNs), which have been used in computer vision and speech recognition to perform automatic feature extraction and classification, have successfully been applied to EEG-based BCIs; however, they have mainly been applied to single BCI paradigms and thus it remains unclear how these architectures generalize to other paradigms. Here, we ask if we can design a single CNN architecture to accurately classify EEG signals from different BCI paradigms, while simultaneously being as compact as possible.

APPROACH
In this work we introduce EEGNet, a compact convolutional neural network for EEG-based BCIs. We introduce the use of depthwise and separable convolutions to construct an EEG-specific model which encapsulates well-known EEG feature extraction concepts for BCI. We compare EEGNet, both for within-subject and cross-subject classification, to current state-of-the-art approaches across four BCI paradigms: P300 visual-evoked potentials, error-related negativity responses (ERN), movement-related cortical potentials (MRCP), and sensory motor rhythms (SMR).

MAIN RESULTS
We show that EEGNet generalizes across paradigms better than, and achieves comparably high performance to, the reference algorithms when only limited training data is available across all tested paradigms. In addition, we demonstrate three different approaches to visualize the contents of a trained EEGNet model to enable interpretation of the learned features.

SIGNIFICANCE
Our results suggest that EEGNet is robust enough to learn a wide variety of interpretable features over a range of BCI tasks. Our models can be found at: https://github.com/vlawhern/arl-eegmodels.


Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces

One of the major limitations of brain-computer interfaces (BCI) is their long calibration time, which limits their use in practice, both by patients and healthy users alike. Such long calibration times are due to the large between-user variability and thus to the need to collect numerous training electroencephalography (EEG) trials for the machine learning algorithms used in BCI design. In this paper, we first survey existing approaches to reduce or suppress calibration time, these approaches being notably based on regularization, user-to-user transfer, semi-supervised learning and a priori physiological information. We then propose new tools to reduce BCI calibration time. In particular, we propose to generate artificial EEG trials from the few EEG trials initially available, in order to augment the training set size. These artificial EEG trials are obtained by relevant combinations and distortions of the original trials available. We propose three different methods to do so. We also propose a new, fast and simple approach to perform user-to-user transfer for BCI. Finally, we study and compare offline different approaches, both old and new ones, on the data of 50 users from three different BCI data sets. This enables us to identify guidelines about how to reduce or suppress calibration time for BCI.


Very Deep Convolutional Networks for Large-Scale Image Recognition

In this work we investigate the effect of the convolutional n etwork depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth, which shows that a significant improvement on the prior-art configurations can be achi eved by pushing the depth to 16–19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first a nd he second places in the localisation and classification tracks respec tively. We also show that our representations generalise well to other datasets, whe re t y achieve the stateof-the-art results. Importantly, we have made our two bestp rforming ConvNet models publicly available to facilitate further research o n the use of deep visual representations in computer vision.


Going deeper with convolutions

We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.


Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Training Deep Neural Networks is complicated by the fact that the distribution of each layer’s inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a stateof-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters.


DESIGN OF AN ULTRA-WIDEBAND POWER DIVIDER VIA THE COARSE-GRAINED PARALLEL MICRO-GENETIC ALGORITHM

An ultra-wideband (UWB) power divider is designed in this paper. The UWB performance of this power divider is obtained by using a tapered microstrip line that consists of exponential and elliptic sections. The coarse grained parallel micro-genetic algorithm (PMGA) and CST Microwave Studio are combined to achieve an automated parallel design process. The method is applied to optimize the UWB power divider. The optimized power divider is fabricated and measured. The measured results show relatively low insertion loss, good return loss, and high isolation between the output ports across the whole UWB (3.1–10.6 GHz).


Visual Path Prediction in Complex Scenes with Crowded Moving Objects

This paper proposes a novel path prediction algorithm for progressing one step further than the existing works focusing on single target path prediction. In this paper, we consider moving dynamics of co-occurring objects for path prediction in a scene that includes crowded moving objects. To solve this problem, we first suggest a two-layered probabilistic model to find major movement patterns and their cooccurrence tendency. By utilizing the unsupervised learning results from the model, we present an algorithm to find the future location of any target object. Through extensive qualitative/quantitative experiments, we show that our algorithm can find a plausible future path in complex scenes with a large number of moving objects.


WHAT IS DATA MINING? WHY DATA MINING? NEED FOR HUMAN DIRECTION OF DATA MINING CROSS-INDUSTRY STANDARD PROCESS: CRISP–DM CASE STUDY 1: ANALYZING AUTOMOBILE WARRANTY CLAIMS: EXAMPLE OF THE CRISP–DM INDUSTRY STANDARD PROCESS IN ACTION FALLACIES OF DATA MINING WHAT TASKS CAN DATA MINING ACCOMPLISH? CASE STUDY 2: PREDICTING ABNORMAL STOCK MARKET RETURNS USING NEURAL NETWORKS CASE STUDY 3: MINING ASSOCIATION RULES FROM LEGAL DATABASES CASE STUDY 4: PREDICTING CORPORATE BANKRUPTCIES USING DECISION TREES CASE STUDY 5: PROFILING THE TOURISM MARKET USING k-MEANS CLUSTERING ANALYSIS


Design and implementation of server cluster dynamic load balancing based on OpenFlow

Nowadays, the Internet is flooded with huge traffic, many applications have millions users, a single server is difficult to bear a large number of clients' access, so many application providers will put several servers as a computing unit to provide support for a specific application, usually people will use distributed computing, load balancing technology to complete the work. A typical load balancing technique is to use a dedicated load balancer to forward the client requests to different servers, this technique requires dedicated hardware support, the hardware is expensive, lacks of flexibility and is easy to become a single point failure. There will be a new solution for load balancing with OpenFlow proposed., this paper mainly studies dynamic load balancing technology in the OpenFlow environment, the Controller collected the server running status through the SNMP protocol, and calculated the aggregated load of the severs according to dynamic load balancing scheduling algorithm, the OpenFlow switch will forward the client's request to the server whose aggregated load is smallest, thus minimize the response time of the web server. In the OpenFlow network environment, using this method can brings high flexibility without additional equipment.


Solving the 0-1 Knapsack Problem with Genetic Algorithms

This paper describes a research project on using Genetic Algorithms (GAs) to solve the 0-1 Knapsack Problem (KP). The Knapsack Problem is an example of a combinatorial optimization problem, which seeks to maximize the benefit of objects in a knapsack without exceeding its capacity. The paper contains three sections: brief description of the basic idea and elements of the GAs, definition of the Knapsack Problem, and implementation of the 0-1 Knapsack Problem using GAs. The main focus of the paper is on the implementation of the algorithm for solving the problem. In the program, we implemented two selection functions, roulette-wheel and group selection. The results from both of them differed depending on whether we used elitism or not. Elitism significantly improved the performance of the roulette-wheel function. Moreover, we tested the program with different crossover ratios and single and double crossover points but the results given were not that different.


Cosmetics as a Feature of the Extended Human Phenotype: Modulation of the Perception of Biologically Important Facial Signals

Research on the perception of faces has focused on the size, shape, and configuration of inherited features or the biological phenotype, and largely ignored the effects of adornment, or the extended phenotype. Research on the evolution of signaling has shown that animals frequently alter visual features, including color cues, to attract, intimidate or protect themselves from conspecifics. Humans engage in conscious manipulation of visual signals using cultural tools in real time rather than genetic changes over evolutionary time. Here, we investigate one tool, the use of color cosmetics. In two studies, we asked viewers to rate the same female faces with or without color cosmetics, and we varied the style of makeup from minimal (natural), to moderate (professional), to dramatic (glamorous). Each look provided increasing luminance contrast between the facial features and surrounding skin. Faces were shown for 250 ms or for unlimited inspection time, and subjects rated them for attractiveness, competence, likeability and trustworthiness. At 250 ms, cosmetics had significant positive effects on all outcomes. Length of inspection time did not change the effect for competence or attractiveness. However, with longer inspection time, the effect of cosmetics on likability and trust varied by specific makeup looks, indicating that cosmetics could impact automatic and deliberative judgments differently. The results suggest that cosmetics can create supernormal facial stimuli, and that one way they may do so is by exaggerating cues to sexual dimorphism. Our results provide evidence that judgments of facial trustworthiness and attractiveness are at least partially separable, that beauty has a significant positive effect on judgment of competence, a universal dimension of social cognition, but has a more nuanced effect on the other universal dimension of social warmth, and that the extended phenotype significantly influences perception of biologically important signals at first glance and at longer inspection.


Floral dip: a simplified method for Agrobacterium-mediated transformation of Arabidopsis thaliana.

The Agrobacterium vacuum infiltration method has made it possible to transform Arabidopsis thaliana without plant tissue culture or regeneration. In the present study, this method was evaluated and a substantially modified transformation method was developed. The labor-intensive vacuum infiltration process was eliminated in favor of simple dipping of developing floral tissues into a solution containing Agrobacterium tumefaciens, 5% sucrose and 500 microliters per litre of surfactant Silwet L-77. Sucrose and surfactant were critical to the success of the floral dip method. Plants inoculated when numerous immature floral buds and few siliques were present produced transformed progeny at the highest rate. Plant tissue culture media, the hormone benzylamino purine and pH adjustment were unnecessary, and Agrobacterium could be applied to plants at a range of cell densities. Repeated application of Agrobacterium improved transformation rates and overall yield of transformants approximately twofold. Covering plants for 1 day to retain humidity after inoculation also raised transformation rates twofold. Multiple ecotypes were transformable by this method. The modified method should facilitate high-throughput transformation of Arabidopsis for efforts such as T-DNA gene tagging, positional cloning, or attempts at targeted gene replacement.


Device-to-device communications: The physical layer security advantage

In systems that allow device-to-device (D2D) communications, user pairs in close proximity communicate directly without using an access point (AP) as an intermediary. D2D communications leads to improved throughput, reduced power consumption and interference, and more flexible resource allocation. We show that the D2D paradigm also provides significantly improved security at the physical layer, by reducing exposure of the information to eavesdroppers from two relatively high-power transmissions to a single low-power hop. We derive the secrecy outage probability (SOP) for the D2D and cellular systems, and compare performance for D2D scenarios in the presence of a multi-antenna eavesdropper. The cellular approach is only seen to have an advantage in certain cases when the AP has a large number of antennas and perfect channel state information.


Efficient Self-Interpretation in Lambda Calculus

We start by giving a compact representation schema for λ-terms and show how this leads to an exceedingly small and elegant self-interpreter. We then define the notion of a self-reducer, and show how this too can be written as a small λ-term. Both the self-interpreter and the self-reducer are proved correct. We finally give a constructive proof for the second fixed point theorem for the representation schema. All the constructions have been implemented on a computer, and experiments verify their correctness. Timings show that the self-interpreter and self-reducer are quite efficient, being about 35 and 50 times slower than direct execution using a call-byneed reduction strategy.


A 10000 frames/s CMOS digital pixel sensor

A 352 288 pixel CMOS image sensor chip with perpixel single-slope ADC and dynamic memory in a standard digital 0.18m CMOS process is described. The chip performs “snapshot” image acquisition, parallel 8-bit A/D conversion, and digital readout at continuous rate of 10 000 frames/s or 1 Gpixels/s with power consumption of 50 mW. Each pixel consists of a photogate circuit, a three-stage comparator, and an 8-bit 3T dynamic memory comprising a total of 37 transistors in 9.4 9.4 m with a fill factor of 15%. The photogate quantum efficiency is 13.6%, and the sensor conversion gain is 13.1 V/e . At 1000 frames/s, measured integral nonlinearity is 0.22% over a 1-V range, rms temporal noise with digital CDS is 0.15%, and rms FPN with digital CDS is 0.027%. When operated at low frame rates, on-chip power management circuits permit complete powerdown between each frame conversion and readout. The digitized pixel data is read out over a 64-bit (8-pixel) wide bus operating at 167 MHz, i.e., over 1.33 GB/s. The chip is suitable for general high-speed imaging applications as well as for the implementation of several still and standard video rate applications that benefit from high-speed capture, such as dynamic range enhancement, motion estimation and compensation, and image stabilization.


Adaptation of the Nomophobia Questionnaire (NMP-Q) to Spanish in a sample of adolescents.

INTRODUCTION
Nomophobia is the fear of being out of mobile phone contact. People suffering from this anxiety disorder have feelings of stress and nervousness when access to their mobiles or computers is not possible. This work is an adaptation and validation study of the Spanish version of the Nomophobia Questionnaire (NMP-Q).

METHODOLOGY
The study included 306 students (46.1% males and 53.9% females) with ages ranging 13 to 19 years (Md=15.41±1.22).

RESULTS
Exploratory factor analysis revealed four dimensions that accounted for 64.4% of total variance. The ordinal α-value was 0.95, ranging from 0.75 to 0.92 across factors. Measure of stability was calculated by the testretest method (r=0.823). Indicators of convergence with the Spanish versions of the “Mobile Phone Problem Use Scale” (r=0.654) and the “Generalized Problematic Internet Use Scale” (r=0.531) were identified. Problematic mobile phone use patterns were examined taking the 15P, 80P and 95P percentiles as cut-off points. Scores of 39, 87 and 116 on NMP-Q corresponded to occasional, at-risk and problematic users, respectively.

CONCLUSIONS
Psychometric analysis shows that the Spanish version of the NMP-Q is a valid and reliable tool for the study of nomophobia.


Novel air blowing control for balancing a unicycle robot

This paper presents the implementation of a novel control method of using air for balancing the roll angle of a unicycle robot. The unicycle robot is designed and built. The roll angle of the unicycle robot is controlled by blowing air while the pitch angle is also controlled by DC motors. Successful balancing performances are demonstrated.


REMAINING LIFE PREDICTION OF ELECTRONIC PRODUCTS USING LIFE CONSUMPTION MONITORING APPROACH

Various kinds of failures may occur in electronic products because of their life cycle environmental conditions including temperature, humidity, shock and vibration. Failure mechanism models are available to estimate the time to failure for most of these failures. Hence if the life cycle environment of a product can be determined, it is possible to assess the amount of damage induced and predict when the product might fail. This paper presents a life consumption monitoring methodology to determine the remaining life of a product. A battery powered data recorder is used to monitor the temperature, shock and vibration loads on a printed circuit board assembly placed under the hood of an automobile. The recorded data is used in conjunction with physics-of-failure models to determine the damage accumulation in the solder joints due to temperature and vibration loading. The remaining life of the solder joints of the test board is then obtained from the damage accumulation information. 1. Reliability Prediction Reliability is defined as the ability of a product to perform as intended (i.e., without failure and within specified performance limits) for a specified time, in its life cycle application environment. Over time, technology improvements have lead to higher I/O counts in components and circuit cards of electronic products. This has resulted in significant downsizing of interconnect thicknesses, making them more vulnerable in field applications. At the same time, increase in warranties and severe liabilities of electronic product failure have compelled manufacturers to predict the reliability of their products in field applications. An efficient reliability prediction scheme can be used for many purposes including [1]: • Logistics support (e.g., forecast warranty and life cycle costs, spare parts provisioning, availability)


You Can Run but You Can't Read: Preventing Disclosure Exploits in Executable Code

Code reuse attacks allow an adversary to impose malicious behavior on an otherwise benign program. To mitigate such attacks, a common approach is to disguise the address or content of code snippets by means of randomization or rewriting, leaving the adversary with no choice but guessing. However, disclosure attacks allow an adversary to scan a process - even remotely - and enable her to read executable memory on-the-fly, thereby allowing the just-in time assembly of exploits on the target site. In this paper, we propose an approach that fundamentally thwarts the root cause of memory disclosure exploits by preventing the inadvertent reading of code while the code itself can still be executed. We introduce a new primitive we call Execute-no-Read (XnR) which ensures that code can still be executed by the processor, but at the same time code cannot be read as data. This ultimately forfeits the self-disassembly which is necessary for just-in-time code reuse attacks (JIT-ROP) to work. To the best of our knowledge, XnR is the first approach to prevent memory disclosure attacks of executable code and JIT-ROP attacks in general. Despite the lack of hardware support for XnR in contemporary Intel x86 and ARM processors, our software emulations for Linux and Windows have a run-time overhead of only 2.2% and 3.4%, respectively.


TangiPaint: A Tangible Digital Painting System

TangiPaint is a digital painting application that provides the experience of working with real materials such as canvas and oil paint. Using fingers on the touchscreen of an iPad or iPhone, users can lay down strokes of thick, three-dimensional paint on a simulated canvas. Then using the Tangible Display technology introduced by Darling and Ferwerda [1], users can tilt the display screen to see the gloss and relief or "impasto" of the simulated surface, and modify it until they get the appearance they desire. Scene lighting can also be controlled through direct gesture-based interaction. A variety of "paints" with different color and gloss properties and substrates with different textures are available and new ones can be created or imported. The tangiPaint system represents a first step toward developing digital art media that look and behave like real materials. Introduction The development of computer-based digital art tools has had a huge impact on a wide range of creative fields. In commercial art, advertisements incorporating images, text, and graphic elements can be laid out and easily modified using digital illustration applications. In cinema, background matte elements can be digitally drawn, painted, and seamlessly integrated with live footage. In fine art, painters, printers, and engravers have also been embracing the new creative possibilities of computer-based art tools. The recent introduction of mobile, tablet-based computers with high-resolution displays, graphics processing units (GPUs) and multi-touch capabilities is also creating new possibilities for direct interaction in digital painting. However a significant limitation of most digital painting tools is that the final product is just a digital image (typically an array of RGB color values). All the colors, textures and lighting effects that we see when we look at the digital painting are “baked in” to the image by the painter. In contrast, when a painter works with real tools and media, the color, gloss, and textural properties of the work are a natural byproduct of the creative process, and lighting effects such as highlights and shadows are produced directly through interactions of the surface with light in the environment. In this paper we introduce tangiPaint, a new tablet-based digital painting system that attempts to bridge the gap between the real and digital worlds. tangiPaint is a tangible painting application that allows artists to work with digital media that look and behave like real materials. Figure 1 shows screenshots from the tangiPaint application implemented on an Apple iPad2. In Figure 1a an artist has painted a number of brushstrokes on a blank canvas. Note that in addition to color, the strokes vary in gloss, thickness, and texture, and run out just as if they were real paint laid down with a real brush. The paints also layer and mix realistically as they would on a real canvas. Figure 1. Screenshots of paintings created using the tangiPaint system. Note the gloss and relief of the brushstrokes and the texture of the underlying canvas. The system allows direct interaction with the “painted” surface both in terms of paint application and manipulation of surface orientation and


Estimating Data Integration and Cleaning Effort

Data cleaning and data integration have been the topic of intensive research for at least the past thirty years, resulting in a multitude of specialized methods and integrated tool suites. All of them require at least some and in most cases significant human input in their configuration, during processing, and for evaluation. For managers (and for developers and scientists) it would be therefore of great value to be able to estimate the effort of cleaning and integrating some given data sets and to know the pitfalls of such an integration project in advance. This helps deciding about an integration project using cost/benefit analysis, budgeting a team with funds and manpower, and monitoring its progress. Further, knowledge of how well a data source fits into a given data ecosystem improves source selection. We present an extensible framework for the automatic effort estimation for mapping and cleaning activities in data integration projects with multiple sources. It comprises a set of measures and methods for estimating integration complexity and ultimately effort, taking into account heterogeneities of both schemas and instances and regarding both integration and cleaning operations. Experiments on two real-world scenarios show that our proposal is two to four times more accurate than a current approach in estimating the time duration of an integration process, and provides a meaningful breakdown of the integration problems as well as the required integration activities. 1. COMPLEXITY OF INTEGRATION AND CLEANING Data integration and data cleaning remain among the most human-work-intensive tasks in data management. Both require a clear understanding of the semantics of schema and data – a notoriously difficult task for machines. Despite much research and development of supporting tools and algorithms, state-of-the-art integration projects involve significant human resource cost. In fact, Gartner reports that 10% of all IT cost goes into enterprise software c ©2015, Copyright is with the authors. Published in Proc. 18th International Conference on Extending Database Technology (EDBT), March 23-27, 2015, Brussels, Belgium: ISBN 978-3-89318-067-7, on OpenProceedings.org. Distribution of this paper is permitted under the terms of the Creative Commons license CC-by-nc-nd 4.0. for data integration and data quality, and it is well recognized that most of those expenses are for human labor. Thus, when embarking on a data integration and cleaning project, it is useful and important to estimate in advance the effort and cost of the project and to find out which particular difficulties cause these. Such estimations help deciding whether to pursue the project in the first place, planning and scheduling the project using estimates about the duration of integration steps, budgeting in terms of cost or manpower, and finally monitoring the progress of the project. Cost estimates can also help integration service providers, IT consultants, and IT tool vendors to generate better price quotes for integration customers. Further, automatically generated knowledge of how well and how easy a data source fits into a given data ecosystem improves source selection. However, “project estimation for [. . . ] data integration projects is especially difficult, given the number of stakeholders involved across the organization as well as the unknowns of data complexity and quality.” [14]. Any integration project has several steps and tasks, including requirements analysis, selection of data sources, determining the appropriate target database, data transformation specifications, testing, deployment, and maintenance. In this paper, we focus on exploring the database-related steps of integration and cleaning and automatically estimate their effort. 1.1 Challenges There are simple approaches to estimate in isolation the complexity of individual mapping and cleaning tasks. For the mapping, evaluating its complexity can be done by counting the matchings (i.e., correspondences) among elements. For the cleaning problem, a natural solution is to measure its complexity by counting the number of constraints on the target schema. However, as several integration approaches have shown, the interactive nature of these two problems is particularly complex [5, 11, 13]. For example, a data exchange problem takes as input two relational schemas, a transformation between them (a mapping), a set of target constraints, and answers two questions: whether it is possible to compute a valid solution for a given setting and how. Interestingly, to have a solution, certain conditions must hold on the target constraints, and extending the setting to more complex languages or data models bring tighter restrictions on the class of tractable cases [6, 12]. In our work, the main challenge is to estimate complexity and effort in a setting that goes beyond these ad-hoc studies while satisfying four main requirements: Generality: We require independence from the language used to express the data transformation. Furthermore, real cases often fail the existence of solution tests considered in formal frameworks http://www.gartner.com/ technology/research/ it-spending-forecast/ http://www.gartner.com/newsroom/ id/2292815 (e.g., weak acyclicity condition [11]), but an automatic estimation is still desirable for them in practice. Completeness: Only a subset of the constraints that hold on the data are specified over the schema. In fact, business rules are commonly enforced at the application level and are not reflected in the metadata of the schemas, but should nevertheless be considered. Granularity: Details about the integration issues are crucial for consumption of the estimation. For a real understanding and proper planning, it is important to know which source and/or target attributes are cause of problems and how (e.g., phone attributes in source and target schema have different formats). Existing estimators do not reason over actual data structures and thus make no statements about the causes of integration effort. Configurability and extensibility: The actual effort depends on subjective factors like the capabilities of available tools and the desired quality of the output. Therefore, intuitive, yet rich configuration settings for the estimation process are crucial for its applicability. Moreover, users must be able to extend the range of problems covered by the framework. These challenges cannot be tackled with existing syntactical methods to test the existence of solutions, as they work only in specific settings (Generality), are restricted to declarative specifications over the schemas (Completeness), and do not provide details about the actual problems (Granularity). On the other hand, as systems that compute solutions require human interaction to finalize the process [8, 13], they cannot be used for estimation purpose and their availability is orthogonal to our problem (Configurability). 1.2 Approaching Effort Estimation Figure 1 presents our view on the problem of estimating the effort of data integration. The starting point is an integration scenario with a target database and one or more source databases. The right-hand side of the figure shows the actual integration process performed by an integration specialist, where the goal is to move all instances of the source databases into the target database. Typically, a set of integration tools are used by the specialist. These tools have access to the source and target and support her in the tasks. The process takes a certain effort, which can be measured, for instance as amount of work in hours or days or in a monetary unit. Our goal is to find that effort without actually performing the integration. Moreover, we want to find and present the problems that cause this effort. To this end, we developed a two-phase process as shown on the left-hand side of Figure 1. The first phase, the complexity assessment, reveals concrete integration challenges for the scenario. To address generality, these problems are exclusively determined by the source and target schemas and instances; if and how an integration practitioner deals with them is not addressed at this point. Thus, this first phase is independent of external parameters, such as the level of expertise of the specialist or the available integration tools. However, it is aided by the results of schema matching and data profiling tools, which analyze the participating databases and produce metadata about them (to achieve completeness). The output of the complexity assessment is a set of clearly defined problems, such as number of violations for a constraint or number of different value representations. This detailed breakdown of the problems achieves granularity and is useful for several tasks, even if not interpreted as an input to calculate actual effort. Examples of application are source selection [9], i.e., given a set of integration candidates, find the source with the best ‘fit’; and support for data visualization [7], i.e., highlight parts of the schemas that are hard to integrate. Data


Bidirectional Decoder Networks for Attention-Based End-to-End Offline Handwriting Recognition

Recurrent neural networks that can be trained end-to-end on sequence learning tasks provide promising benefits over traditional recognition systems. In this paper, we demonstrate the application of an attention-based long short-term memory decoder network for offline handwriting recognition and analyze the segmentation, classification and decoding errors produced by the model. We further extend the decoding network by a bidirectional topology together with an integrated length estimation procedure and show that it is superior to unidirectional decoder networks. Results are presented for the word and text line recognition tasks of the RIMES handwriting recognition database. The software used in the experiments is freely available for academic research purposes.


A Piezoelectric Energy Harvester for Rotary Motion Applications: Design and Experiments

This paper investigates the analysis and design of a vibration-based energy harvester for rotary motion applications. The energy harvester consists of a cantilever beam with a tip mass and a piezoelectric ceramic attached along the beam that is mounted on a rotating shaft. Using this system, mechanical vibration energy is induced in the flexible beam due to the gravitational force applied to the tip mass while the hub is rotating. The piezoelectric transducer is used to convert the induced mechanical vibration energy into electricity. The equations of motion of the flexible structure are utilized along with the physical characteristics of the piezoelectric transducer to derive expressions for the electrical power. Furthermore, expressions for the optimum load resistance and maximum output power are obtained and validated experimentally using PVDF and PZT transducers. The results indicate that a maximum power of 6.4 mW at a shaft speed of 138 rad/s can be extracted by using a PZT transducer with dimensions 50.8 mm × 38.1 mm × 0.13 mm. This amount of power is sufficient to provide power for typical wireless sensors such as accelerometers and strain gauges.


Affective outcomes of virtual reality exposure therapy for anxiety and specific phobias: a meta-analysis.

Virtual reality exposure therapy (VRET) is an increasingly common treatment for anxiety and specific phobias. Lacking is a quantitative meta-analysis that enhances understanding of the variability and clinical significance of anxiety reduction outcomes after VRET. Searches of electronic databases yielded 52 studies, and of these, 21 studies (300 subjects) met inclusion criteria. Although meta-analysis revealed large declines in anxiety symptoms following VRET, moderator analyses were limited due to inconsistent reporting in the VRET literature. This highlights the need for future research studies that report uniform and detailed information regarding presence, immersion, anxiety and/or phobia duration, and demographics.


Hair Follicle Miniaturization in a Woolly Hair Nevus: A Novel "Root" Perspective for a Mosaic Hair Disorder.

Woolly hair nevus is a mosaic disorder characterized by unruly, tightly curled hair in a circumscribed area of the scalp. This condition may be associated with epidermal nevi. We describe an 11-year-old boy who initially presented with multiple patches of woolly hair and with epidermal nevi on his left cheek and back. He had no nail, teeth, eye, or cardiac abnormalities. Analysis of plucked hairs from patches of woolly hair showed twisting of the hair shaft and an abnormal hair cuticle. Histopathology of a woolly hair patch showed diffuse hair follicle miniaturization with increased vellus hairs.


On Using Twitter to Monitor Political Sentiment and Predict Election Results

The body of content available on Twitter undoubtedly contains a diverse range of political insight and commentary. But, to what extent is this representative of an electorate? Can we model political sentiment effectively enough to capture the voting intentions of a nation during an election capaign? We use the recent Irish General Election as a case study for investigating the potential to model political sentiment through mining of social media. Our approach combines sentiment analysis using supervised learning and volume-based measures. We evaluate against the conventional election polls and the final election result. We find that social analytics using both volume-based measures and sentiment analysis are predictive and we make a number of observations related to the task of monitoring public sentiment during an election campaign, including examining a variety of sample sizes, time periods as well as methods for qualitatively exploring the underlying content.


Robust Instance Recognition in Presence of Occlusion and Clutter

We present a robust learning based instance recognition framework from single view point clouds. Our framework is able to handle real-world instance recognition challenges, i.e, clutter, similar looking distractors and occlusion. Recent algorithms have separately tried to address the problem of clutter [9] and occlusion [16] but fail when these challenges are combined. In comparison we handle all challenges within a single framework. Our framework uses a soft label Random Forest [5] to learn discriminative shape features of an object and use them to classify both its location and pose. We propose a novel iterative training scheme for forests which maximizes the margin between classes to improve recognition accuracy, as compared to a conventional training procedure. The learnt forest outperforms template matching, DPM [7] in presence of similar looking distractors. Using occlusion information, computed from the depth data, the forest learns to emphasize the shape features from the visible regions thus making it robust to occlusion. We benchmark our system with the state-of-the-art recognition systems [9, 7] in challenging scenes drawn from the largest publicly available dataset. To complement the lack of occlusion tests in this dataset, we introduce our Desk3D dataset and demonstrate that our algorithm outperforms other methods in all settings.


Indoor Localization Using Camera Phones

Indoor localization has long been a goal of pervasive computing research. In this paper, we explore the possibility of determining user's location based on the camera images received from a smart phone. In our system, the smart phone is worn by the user as a pendant and images are periodically captured and transmitted over GPRS to a web server. The web server returns the location of the user by comparing the received images with images stored in a database. We tested our system inside the Computer Science department building. Preliminary results show that user's location can be determined correctly with more than 80% probability of success. As opposed to earlier solutions for indoor localization, this approach does not have any infrastructure requirements. The only cost is that of building an image database.


Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions

We present a practical approach for processing mobile sensor time series data for continual deep learning predictions. The approach comprises data cleaning, normalization, capping, time-based compression, and finally classification with a recurrent neural network. We demonstrate the effectiveness of the approach in a case study with 279 participants. On the basis of sparse sensor events, the network continually predicts whether the participants would attend to a notification within 10 minutes. Compared to a random baseline, the classifier achieves a 40% performance increase (AUC of 0.702) on a withheld test set. This approach allows to forgo resource-intensive, domain-specific, error-prone feature engineering, which may drastically increase the applicability of machine learning to mobile phone sensor data.


Maximum margin planning

Imitation learning of sequential, goal-directed behavior by standard supervised techniques is often difficult. We frame learning such behaviors as a maximum margin structured prediction problem over a space of policies. In this approach, we learn mappings from features to cost so an optimal policy in an MDP with these cost mimics the expert's behavior. Further, we demonstrate a simple, provably efficient approach to structured maximum margin learning, based on the subgradient method, that leverages existing fast algorithms for inference. Although the technique is general, it is particularly relevant in problems where A* and dynamic programming approaches make learning policies tractable in problems beyond the limitations of a QP formulation. We demonstrate our approach applied to route planning for outdoor mobile robots, where the behavior a designer wishes a planner to execute is often clear, while specifying cost functions that engender this behavior is a much more difficult task.


Maximum Entropy Inverse Reinforcement Learning

Recent research has shown the benefit of framing problems of imitation learning as solutions to Markov Decision Problems. This approach reduces learning to the problem of recovering a utility function that makes the behavior induced by a near-optimal policy closely mimic demonstrated behavior. In this work, we develop a probabilistic approach based on the principle of maximum entropy. Our approach provides a well-defined, globally normalized distribution over decision sequences, while providing the same performance guarantees as existing methods. We develop our technique in the context of modeling realworld navigation and driving behaviors where collected data is inherently noisy and imperfect. Our probabilistic approach enables modeling of route preferences as well as a powerful new approach to inferring destinations and routes based on partial trajectories.


Learning Agents for Uncertain Environments (Extended Abstract)

This talk proposes a very simple “baseline architecture” for a learning agent that can handle stochastic, partially observable environments. The architecture uses reinforcement learning together with a method for representing temporal processes as graphical models. I will discuss methods for leaming the parameters and structure of such representations from sensory inputs, and for computing posterior probabilities. Some open problems remain before we can try out the complete agent; more arise when we consider scaling up. A second theme of the talk will be whether reinforcement learning can provide a good model of animal and human learning. To answer this question, we must do inverse reinforcement learning: given the observed behaviour, what reward signal, if any, is being optimized? This seems to be a very interesting problem for the COLT, UAI, and ML communities, and has been addressed in econometrics under the heading of structural estimation of Markov decision processes. 1 Learning in uncertain environments AI is about the construction of intelligent agents, i.e., systems that perceive and act effectively (according to some performance measure) in an environment. I have argued elsewhere Russell and Norvig (1995) that most AI research has focused on environments that are static, deterministic, discrete, and fully observable. What is to be done when, as in the real world, the environment is dynamic, stochastic, continuous, and partially observable? ‘This paper draws on a variety of research efforts supported by NSF @I-9634215), ONR (N00014-97-l-0941), and AR0 (DAAH04-96-1-0341). Permission to make digital or hard copies of all or p.art of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for prolit or commercial adwantage and that copies bear this notice and the full citation on the first page. To copy otherwise. to republish, to post on servers or to redistribute to lists, requires prior specific pemlission and/or a fee. COLT 98 Madison WI IJSA Copyright ACM 1998 1-5X1 13-057--0/9X/ 7...$5.00 In recent years, reinforcement learning (also called neurodynamic programming) has made rapid progress as an approachfor building agents automatically (Sutton, 1988; Kaelbling et al., 1996; Bertsekas & Tsitsiklis, 1996). The basic idea is that the performance measure is made available to the agent in the form of a rewardfunction specifying the reward for each state that the agent passes through. The performance measure is then the sum of the rewards obtained. For example, when a bumble bee forages, the reward function at each time step might be some combination of the distance flown (weighted negatively) and the nectar ingested. Reinforcement learning (RL) methods are essentially online algorithmd for solving Markovdecisionprocesses (MDPs). An MDP is defined by the reward function and a model, that is, the state transition probabilities conditioned on each possible action. RL algorithms can be model-based, where the agent learns a model, or model-free-e.g., Q-learning citeWatkins: 1989, which learns just a function Q(s, a) specifying the long-term value of taking action a in state s and acting optimally thereafter. Despite their successes, RL methods have been restricted largely tofully observable MDPs, in which the sensory input at each state is sufficient to identify the state. Obviously, in the real world, we must often deal with partially observable MDPs (POMDPs). Astrom (1965) proved that optimal decisions in POMDPs depend on the belief state b at each point in time, i.e., the posterior probability distribution over all possible actual states, given all evidence to date. The functions V and Q then become functions of b instead of s. Parr and Russell (1995) describes a very simple POMDP RL algorithm using an explicit representation of b as a vector of probabilities, and McCallum (1993) shows a way to approximate the belief state using recent percept sequences. Neither approach is likely to scale up to situations with large numbers of state variables and long-term temporal dependencies. What is needed is a way of representing the model compactly and updating the belief state efficiently given the model and each new observation. Dynamic Bayesian networks (Dean & Kanazawa, 1989) seem to have some of the required properties; in particular, they have significant advantages over other approaches such as Kalman filters and hidden Markov models. Our baseline architecture, shown in Figure 1, uses DBNs to represent and update the belief state as new sensor information arrives. Given a representation for b, the reward signal is used to learn a Q-function represented by some “black-box” function approximator such as a neural network. Provided we can handle hybrid (dis-


Algorithms for Inverse Reinforcement Learning


Learning to search: Functional gradient techniques for imitation learning

Programming robot behavior remains a challenging task. While it is often easy to abstractly define or even demonstrate a desired behavior, designing a controller that embodies the same behavior is difficult, time consuming, and ultimately expensive. The machine learning paradigm offers the promise of enabling “programming by demonstration” for developing high-performance robotic systems. Unfortunately, many “behavioral cloning” (Bain & Sammut, 1995; Pomerleau, 1989; LeCun et al., 2006) approaches that utilize classical tools of supervised learning (e.g. decision trees, neural networks, or support vector machines) do not fit the needs of modern robotic systems. These systems are often built atop sophisticated planning algorithms that efficiently reason far into the future; consequently, ignoring these planning algorithms in lieu of a supervised learning approach often leads to myopic and poor-quality robot performance. While planning algorithms have shown success in many real-world applications ranging from legged locomotion (Chestnutt et al., 2003) to outdoor unstructured navigation (Kelly et al., 2004; Stentz, 2009), such algorithms rely on fully specified cost functions that map sensor readings and environment models to quantifiable costs. Such cost functions are usually manually designed and programmed. Recently, a set of techniques has been developed that explore learning these functions from expert human demonstration. These algorithms apply an inverse optimal control approach to find a cost function for which planned behavior mimics an expert’s demonstration. The work we present extends the Maximum Margin Planning (MMP) (Ratliff et al., 2006a) framework to admit learning of more powerful, non-linear cost functions. These algorithms, known collectively as LEARCH (LEArning to seaRCH ), are simpler to implement than most existing methods, more efficient than previous attempts at non-linearization (Ratliff et al., 2006b), more naturally satisfy common constraints on the cost function, and better represent our prior beliefs about the function’s form. We derive and discuss the framework both mathematically and intuitively, and demonstrate practical realworld performance with three applied case-studies including legged locomotion, grasp planning, and autonomous outdoor unstructured navigation. The latter study includes hundreds of kilometers of autonomous traversal through complex natural environments. These case-studies address key challenges in applying the algorithm in practical settings that utilize state-of-the-art planners, and which may be constrained by efficiency requirements and imperfect expert demonstration.


The benefits of hybridization

This article presents the impact of the performance of an FC and control strategies on the benefits of hybridization of fuel cell/supercapacitor hybrid sources for vehicle applications. Then, the storage device can complement the main source to produce the compatibility and performance characteristics needed in a load. The studies of two hybrid power systems for vehicle applications, FC/battery and FC/supercapacitor hybrid power sources, are explained. Experimental results with small-scale devices (a PEMFC of 500 W, 40 A, and 13 V; a lead-xicid battery module of 33 Ah and 48 V; and a supercapacitor module of 292 F, 500 A, and 30 V) in laboratory will illustrate the performance of the system during motor-drive cycles.


RF-MEMS Switches for Reconfigurable Integrated Circuits

This paper deals with a relatively new area of radio-frequency (RF) technology based on microelectromechanical systems (MEMS). RF MEMS provides a class of new devices and components which display superior high-frequency performance relative to conventional (usually semiconductor) devices, and which enable new system capabilities. In addition, MEMS devices are designed and fabricated by techniques similar to those of very large-scale integration, and can be manufactured by traditional batch-processing methods. In this paper, the only device addressed is the electrostatic microswitch—perhaps the paradigm RF-MEMS device. Through its superior performance characteristics, the microswitch is being developed in a number of existing circuits and systems, including radio front-ends, capacitor banks, and time-delay networks. The superior performance combined with ultra-low-power dissipation and large-scale integration should enable new system functionality as well. Two possibilities addressed here are quasi-optical beam steering and electrically reconfigurable antennas.


Modeling Complex Air Traffic Management Systems

In this work, we propose the use of multi-agent system (MAS) models as the basis for predictive reasoning about various safety conditions and the performance of Air Traffic Management (ATM) Systems. To this end, we describe the engineering of a domain-specific MAS model that provides constructs for creating scenarios related to ATM systems and procedures; we then instantiate the constructs in the ATM model for different scenarios. As a case study we generate a model for a concept that provides the ability to maximize departure throughput at La Guardia airport (LGA) without impacting the flow of the arrival traffic; the model consists of approximately 1.5 hours real time flight data. During this time, between 130 and 150 airplanes are managed by four en-route controllers, three TRACON controllers, and one tower controller at LGA who is responsible for departures and arrivals. The planes are landing at approximately 36 to 40 planes an hour. A key contribution of this work is that the model can be extended to various air-traffic management scenarios and can serve as a template for engineering large-scale models in other domains.