title: One pixel attack for fooling deep neural networks

abstract: Recent research has revealed that the output of deep neural networks (DNNs) can be easily altered by adding relatively small perturbations to the input vector. In this paper, we analyze an attack in an extremely limited scenario where only one pixel can be modified. For that we propose a novel method for generating one-pixel adversarial perturbations based on differential evolution (DE). It requires less adversarial information (a black-box attack) and can fool more types of networks due to the inherent features of DE. The results show that 67.97% of the natural images in Kaggle CIFAR-10 test dataset and 16.04% of the ImageNet (ILSVRC 2012) test images can be perturbed to at least one target class by modifying just one pixel with 74.03% and 22.91% confidence on average. We also show the same vulnerability on the original CIFAR-10 dataset. Thus, the proposed attack explores a different take on adversarial machine learning in an extreme limited scenario, showing that current DNNs are also vulnerable to such low dimension attacks. Besides, we also illustrate an important application of DE (or broadly speaking, evolutionary computation) in the domain of adversarial machine learning: creating tools that can effectively generate low-cost adversarial attacks against neural networks for evaluating robustness.


title: A ripple-spreading Genetic Algorithm for the airport Gate Assignment Problem

abstract: Since the Gate Assignment Problem (GAP) at airport terminals is a combinatorial optimization problem, permutation representations based on aircraft dwelling orders are typically used in the implementation of Genetic Algorithms (GAs), The design of such GAs is often confronted with feasibility and memory-efficiency problems. This paper proposes a hybrid GA, which transforms the original order based GAP solutions into value based ones, so that the basic a binary representation and all classic evolutionary operations can be applied free of the above problems. In the hybrid GA scheme, aircraft queues to gates are projected as points into a parameterized space. A deterministic model inspired by the phenomenon of natural ripple-spreading on liquid surfaces is developed which uses relative spatial parameters as input to connect all aircraft points to construct aircraft queues to gates, and then a traditional binary GA compatible to all classic evolutionary operators is used to evolve these spatial parameters in order to find an optimal or near-optimal solution. The effectiveness of the new hybrid GA based on the ripple-spreading model for the GAP problem are illustrated by experiments.


title: Reinforcement Learning of Cooperative Persuasive Dialogue Policies using Framing

abstract: In this paper, we apply reinforcement learning for automatically learning cooperative persuasive dialogue system policies using framing, the use of emotionally charged statements common in persuasive dialogue between humans. In order to apply reinforcement learning, we describe a method to construct user simulators and reward functions specifically tailored to persuasive dialogue based on a corpus of persuasive dialogues between human interlocutors. Then, we evaluate the learned policy and the effect of framing through experiments both with a user simulator and with real users. The experimental evaluation indicates that applying reinforcement learning is effective for construction of cooperative persuasive dialogue systems which use framing.


title: Dynamic Neural Turing Machine with Continuous and Discrete Addressing Schemes

abstract: In this paper, we extend neural Turing machine (NTM) into a dynamic neural Turing machine (D-NTM) by introducing a trainable memory addressing scheme. This addressing scheme maintains for each memory cell two separate vectors, content and address vectors. This allows the D-NTM to learn a wide variety of location-based addressing strategies including both linear and nonlinear ones. We implement the D-NTM with both continuous, differentiable and discrete, non-differentiable read/write mechanisms. We investigate the mechanisms and effects for learning to read and write to a memory through experiments on Facebook bAbI tasks using both a feedforward and GRU-controller. The D-NTM is evaluated on a set of Facebook bAbI tasks and shown to outperform NTM and LSTM baselines. We also provide further experimental results on sequential MNIST, associative recall and copy tasks.


title: Zebra-crossing detection based on geometric feature and vertical vanishing point

abstract: Zebra-crossing region detection from a zebra-crossing image is a challenging activity to support visually impaired navigating safely in outdoor environments. This paper proposes a zebra-crossing detection framework based on unique geometrical features of zebra-crossing. The unique geometrical feature is zebra-crossing stripe's edges are arranged in sorted order. Initially, pre-process the zebra-crossing images by applying the directional Gabor filter to eliminate the noisy and shadow's effect and improve the resolution of the zebra-crossing horizontal edges. Then Sobel edge detector is used to detect the horizontal edges. Potential horizontal edges are extracted by eliminating small and non-candidate edges. After that, the edge linking process is utilized to extract the longest horizontal edges. Extracted zebra-crossing edges are justified by arranging the horizontal edge segments in increasing parallel order. Finally, these parallel horizontal edges are used to measure the vanishing point to verify zebra-crossing edges from other analogous visual pattern and confirm the detection of zebra-crossing candidate region. Various zebra-crossing images are utilized to evaluate the proposed framework and presented outcomes demonstrate the adequacy.


title: Preparing Future College Instructors : The Role of Graduate Student Teaching Assistants (GTAs) in Successful College Calculus Programs

abstract: Graduate student Teaching Assistants (GTAs) contribute to calculus instruction in two ways: as the primary teacher and as recitation leaders. GTAs can also be viewed as the next generation of mathematics instructors. Thus, in addition to their immediate contribution to the landscape of Calculus 1 instruction, GTAs will contribute significantly to the long-term state of calculus in their future occupations. However, their preparation for these roles varies widely and is often minimal. In this study, I first compare the mathematical beliefs, instructional practices, and student success of GTAs to other Calculus 1 instructors. I then provide rich descriptions for three GTA professional development (PD) programs that prepare graduate students as course instructors, as recitation leaders, and as future faculty. I then investigate the instructional practices and mathematical beliefs of graduate students coming from these three PD programs. I conclude this work with a description of a framework for GTA-PD programs. To accomplish this work, I conducted a mixed-method analysis on national survey data and case study data from four doctoral granting institutions. These four institutions were chosen because of their higher-than -expected student success in Calculus 1. The results of these analyses indicate that graduate students teach in more innovative ways than other instructors, though their students were less successful. Among the four case study institutions, I identified three models of GTA-PD, each of which appeared successful in accomplishing their goals. These goals included transitioning graduate students into the role of instructor, preparing graduate students to implement an innovative approach to Calculus 1, and supporting graduate students as recitation leaders. These analyses also led to the development of a framework to be used to characterize, evaluate, and consider the implementation of graduate student professional development programs. This GTA-PD framework is thus one of the major contributions put forth by this dissertation


title: Eddi: interactive topic-based browsing of social status streams

abstract: Twitter streams are on overload: active users receive hundreds of items per day, and existing interfaces force us to march through a chronologically-ordered morass to find tweets of interest. We present an approach to organizing a user's own feed into coherently clustered trending topics for more directed exploration. Our Twitter client, called Eddi, groups tweets in a user's feed into topics mentioned explicitly or implicitly, which users can then browse for items of interest. To implement this topic clustering, we have developed a novel algorithm for discovering topics in short status updates powered by linguistic syntactic transformation and callouts to a search engine. An algorithm evaluation reveals that search engine callouts outperform other approaches when they employ simple syntactic transformation and backoff strategies. Active Twitter users evaluated Eddi and found it to be a more efficient and enjoyable way to browse an overwhelming status update feed than the standard chronological interface.


title: Improving function coverage with munch: a hybrid fuzzing and directed symbolic execution approach

abstract: Fuzzing and symbolic execution are popular techniques for finding vulnerabilities and generating test-cases for programs. Fuzzing, a blackbox method that mutates seed input values, is generally incapable of generating diverse inputs that exercise all paths in the program. Due to the path-explosion problem and dependence on SMT solvers, symbolic execution may also not achieve high path coverage. A hybrid technique involving fuzzing and symbolic execution may achieve better function coverage than fuzzing or symbolic execution alone. In this paper, we present Munch, an open-source framework implementing two hybrid techniques based on fuzzing and symbolic execution. We empirically show using nine large open-source programs that overall, Munch achieves higher (in-depth) function coverage than symbolic execution or fuzzing alone. Using metrics based on total analyses time and number of queries issued to the SMT solver, we also show that Munch is more efficient at achieving better function coverage.


title: Factors Contributing to the Facial Aging of Identical Twins

abstract: Background: The purpose of this study was to identify the environmental factors that contribute to facial aging in identical twins. Methods: During the Twins Day Festival in Twinsburg, Ohio, 186 pairs of identical twins completed a comprehensive questionnaire, and digital images were obtained. A panel reviewed the images independently and recorded the differences in the perceived twins' ages and their facial features. The perceived age differences were then correlated with multiple factors. Results: Four-point higher body mass index was associated with an older appearance in twins younger than age 40 but resulted in a younger appearance after age 40 (p = 0.0001). Eight-point higher body mass index was associated with an older appearance in twins younger than age 55 but was associated with a younger appearance after age 55 (p = 0.0001). The longer the twins smoked, the older they appeared (p < 0.0001). Increased sun exposure was associated with an older appearance and accelerated with age (p = 0.015), as was a history of outdoor activities and lack of sunscreen use. Twins who used hormone replacement had a younger appearance (p = 0.002). Facial rhytids were more evident in twins with a history of skin cancer (p = 0.05) and in those who smoked (p = 0.005). Dark and patchy skin discoloration was less prevalent in twins with a higher body mass index (p = 0.01) and more common in twins with a history of smoking (p = 0.005) and those with sun exposure (p = 0.005). Hair quantity was better with a higher body mass index (p = 0.01) although worse with a history of skin cancer (p = 0.005) and better with the use of hormones (p = 0.05). Conclusion: This study offers strong statistical evidence to support the role of some of the known factors that govern facial aging.


title: Socio-emotional development: From infancy to young adulthood

abstract: Results from the Uppsala Longitudinal Study (ULS), which started in 1985, are reported in two sections. The first section gives a summary of longitudinal data from infancy to middle childhood (age ...


title: An evaluation of methods for modeling contact in multibody simulation

abstract: Modeling contact in multibody simulation is a difficult problem frequently characterized by numerically brittle algorithms, long running times, and inaccurate (with respect to theory) models. We present a comprehensive evaluation of four methods for contact modeling on seven benchmark scenarios in order to quantify the performance of these methods with respect to robustness and speed. We also assess the accuracy of these methods where possible. We conclude the paper with a prescriptive description in order to guide the user of multibody simulation.


title: The Novamente artificial intelligence engine

abstract: The Novamente AI Engine, a novel AI software system, is briefly reviewed. Novamente is an integrative artificial general intelligence design, which integrates aspects of many prior AI projects and paradigms, including symbolic, probabilistic, evolutionary programming and reinforcement learning approaches; but its overall architecture is unique, drawing on system-theoretic ideas regarding complex mental dynamics and associated emergent patterns. The chapter reviews both the conceptual models of mind and intelligence which inspired the system design, and the concrete architecture of Novamente as a software system.


title: First toxicity data of chlorophenols on marine alga Dunaliella tertiolecta: correlation of marine algal toxicity with hydrophobicity and interspecies toxicity relationships.

abstract: The toxicity of phenol and 13 chlorinated phenols to the marine alga Dunaliella tertiolecta is presented for the first time. The newly generated marine algal toxicity data was found to correlate strongly with the widely used hydrophobicity parameter-the logarithm of the n-octanol-water partition coefficient (log K(OW)). Interspecies relationships using the new marine algal toxicity data of chlorophenols with the previously published data on bacterium (Vibrio fischeri), protozoan (Tetrahymena pyriformis), daphnid (Daphnia magna), freshwater alga (Pseudokirchneriella subcapitata), and fish (Pimephales promelas) revealed promising results that could be exploited in extrapolations using freshwater data to predict marine algal toxicity.


title: Coupled hidden Markov models for complex action recognition

abstract: We present algorithms for coupling and training hidden Markov models (HMMs) to model interacting processes, and demonstrate their superiority to conventional HMMs in a vision task classifying two-handed actions. HMMs are perhaps the most successful framework in perceptual computing for modeling and classifying dynamic behaviors, popular because they offer dynamic time warping, a training algorithm and a clear Bayesian semantics. However the Markovian framework makes strong restrictive assumptions about the system generating the signal-that it is a single process having a small number of states and an extremely limited state memory. The single-process model is often inappropriate for vision (and speech) applications, resulting in low ceilings on model performance. Coupled HMMs provide an efficient way to resolve many of these problems, and offer superior training speeds, model likelihoods, and robustness to initial conditions.


title: Robot Imitation: Body Schema . . .

abstract: There are two functional elements used by humans to understand and perform actions. These elements are: the body schema and the body percept. The first one is a representation of the body that contains information of the body's capabilities. The second one is a snapshot of the body and its relation with the environment at a given instant. These elements interact in order to generate, among other abilities, the ability to imitate. This paper presents an approach to robot imitation based on these two functional elements. Our approach is gradually expanded throughout three developmental stages used by humans to refine imitation. Experimental results are presented to support the feasibility of the proposed approach at the current stage for 2D movements and simple manipulation actions.


title: Progress Estimation and Phase Detection for Sequential Processes

abstract: Process modeling and understanding are fundamental for advanced human-computer interfaces and automation systems. Most recent research has focused on activity recognition, but little has been done on sensor-based detection of process progress. We introduce a real-time, sensor-based system for modeling, recognizing and estimating the progress of a work process. We implemented a multimodal deep learning structure to extract the relevant spatio-temporal features from multiple sensory inputs and used a novel deep regression structure for overall completeness estimation. Using process completeness estimation with a Gaussian mixture model, our system can predict the phase for sequential processes. The performance speed, calculated using completeness estimation, allows online estimation of the remaining time. To train our system, we introduced a novel rectified hyperbolic tangent (rtanh) activation function and conditional loss. Our system was tested on data obtained from the medical process (trauma resuscitation) and sports events (Olympic swimming competition). Our system outperformed the existing trauma-resuscitation phase detectors with a phase detection accuracy of over 86%, an F1-score of 0.67, a completeness estimation error of under 12.6%, and a remaining-time estimation error of less than 7.5 minutes. For the Olympic swimming dataset, our system achieved an accuracy of 88%, an F1-score of 0.58, a completeness estimation error of 6.3% and a remaining-time estimation error of 2.9 minutes.


title: Combining RFM model and clustering techniques for customer value analysis of a company selling online

abstract: A case study of applying RFM (recency, frequency, and monetary) model and clustering techniques in the sector of electronic commerce with a view to evaluating customers' values is presented. Self-organizing maps method (SOM) is first used to determine the best number of clusters and then K-means method is applied to classify 730 customers into eight clusters when R, F, and M are the segmenting variables, and then developing effective marketing strategies for each cluster. The average values of RFM are computed for each cluster and the overall customers. The values of RFM variables for each cluster greater than those of the overall average are identified. The results show that the cluster 7 is the most important cluster because the average values of R F and M are higher than the overall average value. In summary, the purpose of this case study is customer segmentation using RFM model and clustering algorithms (SOM and K-means) to specify loyal and profitable customers for achieving maximum benefit and a win-win situation.


title: Personality, motivation, and performance: A theory of the relationship between individual differences and information processing

abstract: We introduce a model to relate the personality dimensions of introversion- extraversion, achievement motivation, and anxiety to efficient cognitive performance. We show how these personality dimensions in combination with situational moderators (e.g., success, failure, time pressure, incentives, time of day, and stimulant drugs) affect the motivational constructs of arousal and effort. We propose a general information-processing model that accounts for the systematic effects of these motivational states on certain task components (sustained information transfer and some aspect of short-term memory). We combine empirical generalizations about task components in a structural model and derive testable predictions that differentiate alternative motivational hypotheses.


On the best rank-1 and rank-(R1, R2,...,RN ) approximation of higher-order tensor


Implementation of spectrum sensing techniques for cognitive radio systems


title: Dual-core execution: building a highly scalable single-thread instruction window

abstract: Current integration trends embrace the prosperity of single-chip multi-core processors. Although multi-core processors deliver significantly improved system throughput, single-thread performance is not addressed. In this paper, we propose a new execution paradigm that utilizes multi-cores on a single chip collaboratively to achieve high performance for single-thread memory-intensive workloads while maintaining the flexibility to support multithreaded applications. The proposed execution paradigm, dual-core execution, consists of two superscalar cores (a front and back processor) coupled with a queue. The front processor fetches and preprocesses instruction streams and retires processed instructions into the queue for the back processor to consume. The front processor executes instructions as usual except for cache-missing loads, which produce an invalid value instead of blocking the pipeline. As a result, the front processor runs far ahead to warm up the data caches and fix branch mispredictions for the back processor. In-flight instructions are distributed in the front processor, the queue, and the back processor, forming a very large instruction window for single-thread out-of-order execution. The proposed architecture incurs only minor hardware changes and does not require any large centralized structures such as large register files, issue queues, load/store queues, or reorder buffers. Experimental results show remarkable latency hiding capabilities of the proposed architecture, even outperforming more complex single-thread processors with much larger instruction windows than the front or back processor.


title: Compact CPW-Fed Planar Monopole Antenna With Wide Circular Polarization Bandwidth

abstract: The design and implementation of a coplanar waveguide-fed planar monopole antenna with circular polarization and broadband operation is presented. The antenna operates in the Industrial, Scientific, and Medical (ISM) and wireless local area network (WLAN) (5 GHz) bands with circular polarization (CP) in both bands. It is demonstrated that a fractional bandwidth for CP larger than 33% can be attained simply by introducing an inverted L-shaped slot in the ground plane and parallel-aligning an inverted-L-shaped strip. The advantages of the proposed antenna are the simple yet efficient design of the radiator, a wide 3-dB axial-ratio operating band, and a compact size.


title: A broadband noise-canceling CMOS LNA for 3.1-10.6-GHz UWB receiver

abstract: An ultra-wideband (UWB) noise-canceling low-noise amplifier (LNA) is presented. By using inductive series and shunt peaking techniques, the effective bandwidth of noise canceling is extended. This LNA has been fabricated in a 0.18/spl mu/m CMOS process. The measured noise figure is 4.5-5.1dB over 3.1-10.6-GHz, while the power gain is 9.7dB with a -3-dB bandwidth of 1.2-11.9-GHz. It consumes 20mW from a 1.8V supply and occupies only 0.59mm/sup 2/.


title: Compact Dual-Band Textile PIFA for 433-MHz/2.4-GHz ISM Bands

abstract: A textile planar inverted-F antenna (PIFA) is proposed. By co-designing the PIFA antenna and the ground plane, a wide matching bandwidth is achieved around 433 MHz for a relatively small electrical size (0.202 × 0.115 × 0.01 λ 03). A slot within the PIFA patch enables the 2.4-GHz operating band. The measured bandwidths are 35 and 309 MHz in the lower and upper bands, respectively. The corresponding radiation efficiencies are 48% and 64%. The analysis of the effect of bending and the on-body characterization show the robust performance of the antenna.


title: Creation of an IEC 62304 compliant software development plan

abstract: Organizations engaged in medical device software development are required to demonstrate compliance with a set of medical device standards and regulations before the device can be marketed. One such standard IEC 62304, Medical Device Software-Software Life Cycle Processes, defines the processes that are required in order to develop safe software. Demonstrating compliance with IEC 62304 can be problematic for organizations that are new to or have limited experience in the domain. The standard defines what processes must be carried out but does not state how. In a review of a number of such organizations, it was found that the development of a software development plan proved to be a difficult task. In this work we have created a software development plan template to assist organizations with this arduous task. The software development plan template will be validated with these organizations as part of the future work.


Filter-Context Dynamic Coattention Networks for Question Answering


Multi-Authentication for Cloud Security : A Framework


title: Preparation and characterization of expanded graphite polymer composite films for thermoelectric applications

abstract: This report demonstrates application of expanded graphite (ExG) for thermoelectric energy conversion, where it serves as a filler for both p- and n-type organic materials. Thin ExG composite films showing improved thermoelectric properties were prepared. In particular, composites with intrinsically conducting polymer poly(3,4-ethylenedioxythiophene) poly(styrenesulfonate) (PEDOT:PSS) yielding high electrical conductivity (up to 104 S m−1) and enhanced thermopower (Seebeck coefficient) provided promising p-type material. Chemical doping experiments performed on ExG dispersed in polyvinyl alcohol (PVA) revealed that the exfoliated graphitic sheets can be efficiently n-doped with polyethyleneimine (PEI). As a result, n-type ExG/PVA/PEI composite thin films showing improved n-type characteristics with thermopower values as high as −25.3 µV K−1 were prepared. With a 25 wt% ratio of PEI to ExG, the electrical conductivity was measured to be ∼103 S m−1, which is remarkably high for n-type polymer composites. Strips of composite films containing 50 wt% of ExG in PEDOT:PSS were used as p-type components, and composite films containing 20 wt% of ExG in PVA doped with PEI were used as n-type components in thermoelectric modules to demonstrate thermoelectric voltage with one, two, and three p-n couples connected in series. The testing modules produced an output voltage of ∼4 mV at a temperature gradient of 50 K. The module generated 1.7 nW power, when a load resistance matched the internal module resistance of 1 kΩ. Our results show that chemical functionalization of ExG in thin composite films resulted in more effective thermoelectric properties.


title: A Framework for the Evaluation and Management of Network Centrality

abstract: Network-analysis literature is rich in node-centrality measures that quantify the centrality of a node as a function of the (shortest) paths of the network that go through it. Existing work focuses on defining instances of such measures and designing algorithms for the specific combinatorial problems that arise for each instance. In this work, we propose a unifying definition of centrality that subsumes all path-counting based centrality definitions: e.g., stress, betweenness or paths centrality. We also define a generic algorithm for computing this generalized centrality measure for every node and every group of nodes in the network. Next, we define two optimization problems: k-Group Centrality Maximization and k-Edge Centrality Boosting. In the former, the task is to identify the subset of k nodes that have the largest group centrality. In the latter, the goal is to identify up to k edges to add to the network so that the centrality of a node is maximized. We show that both of these problems can be solved efficiently for arbitrary centrality definitions using our general framework. In a thorough experimental evaluation we show the practical utility of our framework and the efficacy of our algorithms.


abstract: Knowledge and implicature: Modeling language understanding as social cognition Noah D. Goodman (ngoodman@stanford.edu) , Department of Psychology, Stanford University Andreas Stuhlm uller (ast@mit.edu) , Department of Brain and Cognitive Sciences, MIT Abstract SOME OF THE APPLES ARE RED. Is language understanding a special case of social cognition? To help evaluate this view, we can formalize it as the ratio- nal speech-act theory: listeners assume that speakers choose their utterances approximately optimally, and listeners inter- pret an utterance by using Bayesian inference to “invert” this model of the speaker. We apply this framework to model scalar implicature (“some” implies “not all”, and “N” implies “not more than N”). This model predicts an interaction between the speaker’s knowledge state and the listener’s interpretation. We test these predictions in two experiments, and find good fit be- tween model predictions and human judgements. Keywords: Language; Bayesian model; Scalar implicature SOME ALL To what extent does language understanding rely on extra- linguistic knowledge and processes? One view of lan- guage processing suggests that it consists of largely separate, special-purpose faculties; another view, that it depends crit- ically on domain general inference mechanisms, and even on intuitive theories that are not specific to language. In- deed, many thinkers have viewed speech as an action with communicative goals, such as informing a listener (Grice, 1975; Levinson, 2000; Clark, 1996). A listener making this assumption can make stronger inferences than an utterance would allow from its literal meaning—pragmatic effects can strengthen, or change, the interpreted meaning. Recent work has aimed to formalize the social inference view of pragmatics using tools of Bayesian statistics and in- formation theory (Frank & Goodman, under review); we refer to this formal framework as the rational speech-act theory of language understanding. It views pragmatic competence as following naturally from an intuitive theory of speech produc- tion, which in turn is a special case of intuitive theory of mind. More precisely, listeners have an internal model which de- scribes speakers as choosing their utterances approximately optimally on the basis of certain social goals, such as con- veying information to the listener; listeners then interpret an utterance by using Bayesian inference to “invert” this model of the speaker, drawing conclusions about the world state and speaker’s intention from the utterance and any other relevant world knowledge. These two rationality assumptions, for lis- tener and speaker, have a role similar to those made in ideal observer models of perception (Geisler, 2003): they provide a starting point for a quantitative understanding of the complex interactions involved in language understanding. Indeed, this view has provided good quantitative models for pragmatic in- ference in a number of simple settings (Frank, Goodman, Lai, & Tenenbaum, 2009; Frank & Goodman, under review). Because rational decision making predicts that action se- lection is related to the expected utility—a quantity that de- Figure 1: How will the listener interpret the speaker’s utter- ance? How will this change if she knows that he can see only two of the objects? pends on the actor’s belief distribution—a listener who views the speaker as rational should be sensitive to the speaker’s belief state. The rational speech-act theory thus predicts an interaction between (shared) knowledge about a speaker’s knowledge state and a listener’s interpretation of his utter- ance. This is a very general prediction of the framework, which could easily prove to be false—if pragmatic inference is a highly modularized computation, for instance, we would not expect such general knowledge to affect it. Deriving and testing precise predictions about this interaction thus provides an important test of the rational speech-act theory. If you hear “some of the apples are red,” you will infer that not all of the apples are. Pragmatic effects of this sort are called scalar implicatures (Horn, 2004), and they provide a window on the interactions between the language faculty and general cognition. Consider Fig. 1: If the speaker has seen all the apples, his utterance would be interpreted as “some, but not all, of the apples are red.” However, if the speaker had only looked at two of the apples, the listener might draw a different conclusion. Indeed, we show below that the impli- cature “not all” can be canceled by facts about the speaker’s perceptual access. This interaction between language under- standing and general knowledge is not predicted by strongly modular theories of scalar implicature, and even moderately modular theories (Chierchia, Fox, & Spector, 2011) predict that such influences can only turn off scalar implicatures. We show that the interaction of knowledge and implicature is more fine grained: the details of a speaker’s belief distribu-


title: Visualizing Large-scale and High-dimensional Data

abstract: We study the problem of visualizing large-scale and high-dimensional data in a low-dimensional (typically 2D or 3D) space. Much success has been reported recently by techniques that first compute a similarity structure of the data points and then project them into a low-dimensional space with the structure preserved. These two steps suffer from considerable computational costs, preventing the state-of-the-art methods such as the t-SNE from scaling to large-scale and high-dimensional data (e.g., millions of data points and hundreds of dimensions). We propose the LargeVis, a technique that first constructs an accurately approximated K-nearest neighbor graph from the data and then layouts the graph in the low-dimensional space. Comparing to t-SNE, LargeVis significantly reduces the computational cost of the graph construction step and employs a principled probabilistic model for the visualization step, the objective of which can be effectively optimized through asynchronous stochastic gradient descent with a linear time complexity. The whole procedure thus easily scales to millions of high-dimensional data points. Experimental results on real-world data sets demonstrate that the LargeVis outperforms the state-of-the-art methods in both efficiency and effectiveness. The hyper-parameters of LargeVis are also much more stable over different data sets.


title: Improved receivers for digital high frequency waveforms using turbo equalization

abstract: We develop a turbo equalization-based receiver for serial-tone HF waveforms, where the conventional decision feedback equalizer is replaced by a soft-in soft-out (SISO) equalizer, and the receiver iterates soft information between the equalizer and the decoder. To keep the computational burden of the iterative equalization and decoding procedure as low as possible, the SISO equalizer uses a linear filter, performing soft intersymbol interference cancellation. Simulation results show that the proposed receiver performs more than 2 dB signal-to-noise ratio better than a conventional DFE-based receiver over an ITU-R poor channel.


The influence of driver fatigue on take-over performance in highly automated vehicles


title: A Semantic Approach to Host-Based Intrusion Detection Systems Using Contiguousand Discontiguous System Call Patterns

abstract: Host-based anomaly intrusion detection system design is very challenging due to the notoriously high false alarm rate. This paper introduces a new host-based anomaly intrusion detection methodology using discontiguous system call patterns, in an attempt to increase detection rates whilst reducing false alarm rates. The key concept is to apply a semantic structure to kernel level system calls in order to reflect intrinsic activities hidden in high-level programming languages, which can help understand program anomaly behaviour. Excellent results were demonstrated using a variety of decision engines, evaluating the KDD98 and UNM data sets, and a new, modern data set. The ADFA Linux data set was created as part of this research using a modern operating system and contemporary hacking methods, and is now publicly available. Furthermore, the new semantic method possesses an inherent resilience to mimicry attacks, and demonstrated a high level of portability between different operating system versions.


title: A Unified Tagging Solution: Bidirectional LSTM Recurrent Neural Network with Word Embedding

abstract: Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) has been shown to be very effective for modeling and predicting sequential data, e.g. speech utterances or handwritten documents. In this study, we propose to use BLSTM-RNN for a unified tagging solution that can be applied to various tagging tasks including part-of-speech tagging, chunking and named entity recognition. Instead of exploiting specific features carefully optimized for each task, our solution only uses one set of task-independent features and internal representations learnt from unlabeled text for all tasks.Requiring no task specific knowledge or sophisticated feature engineering, our approach gets nearly state-of-the-art performance in all these three tagging tasks.


title: The effect of lateral opening wedge distal femoral osteotomy on medial knee opening: clinical and biomechanical factors

abstract: PURPOSE ::: The case of a patient with knee valgus and instability due to combined ACL-MCL laxity who underwent lateral opening wedge distal femoral osteotomy (DFO) is presented. The symptoms of instability resolved following the surgery. It was unclear whether the increase in valgus stability was related only to a decrease in valgus moments during stance or also to a medial tensioning effect. We therefore performed a laboratory cadaveric study. The purpose of this study was to examine whether after MCL and ACL sectioning, lateral opening wedge DFO would result in decrease in medial opening under static conditions of valgus stress. ::: ::: ::: METHODS ::: Medial knee opening under valgus load of 9.8 Nm was tested in 8 cadaveric specimens in scenarios of MCL and ACL sectioning and compared before and after performing lateral opening wedge DFO. ::: ::: ::: RESULTS ::: When the superficial MCL was sectioned, medial knee opening in 30° flexion decreased after lateral opening wedge DFO compared to medial opening before the osteotomy (i.e. from 6.5 ± 0.5° to 5.6 ± 0.5°, p = 0.01). When the superficial MCL, deep MCL, and ACL were all sectioned, medial knee opening in extension decreased after lateral opening wedge DFO compared to medial opening before the osteotomy but this was not significant (i.e. from 6.8 ± 0.5° to 6.1 ± 0.5°, p = n.s.). ::: ::: ::: CONCLUSION ::: In superficial MCL-transected knees, medial laxity at 30° of knee flexion decreased after lateral opening wedge DFO. However, the clinical relevance of the laxity decrease observed remains uncertain since the reduction was small in magnitude.


title: Case report: Acute calcific discitis with intravertebral disc herniation in the dorsolumbar spine

abstract: Acute calcific discitis is a rare but well-known condition of unknown etiology. In symptomatic cases, the most common site is the cervical spine. We describe the CT scan and MRI findings in a symptomatic patient, with a lesion in the dorsolumbar spine.


The New England Journal of Medicine Special Articles RISK FACTORS FOR INJURY TO WOMEN FROM DOMESTIC VIOLENCE


title: 3DIC benefit estimation and implementation guidance from 2DIC implementation

abstract: Quantification of three-dimensional integrated circuit (3DIC) benefits over corresponding 2DIC implementation for arbitrary designs remains a critical open problem, largely due to nonexistence of any "golden" 3DIC flow. Actual design and implementation parameters and constraints affect 2DIC and 3DIC final metrics (power, slack, etc.) in highly non-monotonic ways that are difficult for engineers to comprehend and predict. We propose a novel machine learning-based methodology to estimate 3DIC power benefit (i.e., percentage power reduction) based on corresponding golden 2DIC implementation parameters. The resulting 3D Power Estimation (3DPE) models achieve small prediction errors that are bounded by construction. We are the first to perform a novel stress test of our predictive models across a wide range of implementation and design-space parameters. Further, we explore model-guided implementation of designs in 3D to achieve minimum power: that is, our models recommend a most-promising set of implementation parameters and constraints, and also provide a priori estimates of 3D power benefits, based on a given design's post-synthesis and 2D implementation parameters. We achieve ≤10% error in power benefit prediction across various 3DIC designs.


title: On learning algorithm selection for classification

abstract: This paper introduces a new method for learning algorithm evaluation and selection, with empirical results based on classification. The empirical study has been conducted among 8 algorithms/classifiers with 100 different classification problems. We evaluate the algorithms' performance in terms of a variety of accuracy and complexity measures. Consistent with the No Free Lunch theorem, we do not expect to identify the single algorithm that performs best on all datasets. Rather, we aim to determine the characteristics of datasets that lend themselves to superior modelling by certain learning algorithms. Our empirical results are used to generate rules, using the rule-based learning algorithm C5.0, to describe which types of algorithms are suited to solving which types of classification problems. Most of the rules are generated with a high confidence rating.


title: Adaptive wavelet thresholding for image denoising and compression

abstract: The first part of this paper proposes an adaptive, data-driven threshold for image denoising via wavelet soft-thresholding. The threshold is derived in a Bayesian framework, and the prior used on the wavelet coefficients is the generalized Gaussian distribution (GGD) widely used in image processing applications. The proposed threshold is simple and closed-form, and it is adaptive to each subband because it depends on data-driven estimates of the parameters. Experimental results show that the proposed method, called BayesShrink, is typically within 5% of the MSE of the best soft-thresholding benchmark with the image assumed known. It also outperforms SureShrink (Donoho and Johnstone 1994, 1995; Donoho 1995) most of the time. The second part of the paper attempts to further validate claims that lossy compression can be used for denoising. The BayesShrink threshold can aid in the parameter selection of a coder designed with the intention of denoising, and thus achieving simultaneous denoising and compression. Specifically, the zero-zone in the quantization step of compression is analogous to the threshold value in the thresholding function. The remaining coder design parameters are chosen based on a criterion derived from Rissanen's minimum description length (MDL) principle. Experiments show that this compression method does indeed remove noise significantly, especially for large noise power. However, it introduces quantization noise and should be used only if bitrate were an additional concern to denoising.


title: Design for the Value of Human Well-BeingWell-being

abstract: This chapter studies how and to what extent it is possible to design for well-being. Well-being is rarely considered in the design literature and is rarely linked to technology and design in philosophy and the social sciences. A few approaches to design for well-being have recently materialized, however, including Emotional Design, capability approaches, positive psychology approaches, and Life-Based Design. In this chapter, the notion of well-being will first be clarified and contemporary theories of and approaches to well-being will be reviewed. Next, theoretical and methodological issues in design for well-being will be discussed that must be accounted for in any successful approach. This will be followed by a review of the abovementioned four approaches to design for well-being. The chapter will conclude by considering open issues and future work in the development of design approaches for well-being.


title: Adaptive Witness Encryption and Asymmetric Password-Based Cryptography

abstract: We show by counter-example that the soundness security requirement for witness encryption given by Garg, Gentry, Sahai and Waters (STOC 2013) does not suffice for the security of their own applications. We introduce adaptively-sound (AS) witness encryption to fill the gap. We then introduce asymmetric password-based encryption (A-PBE). This offers gains over classical, symmetric password-based encryption in the face of attacks that compromise servers to recover hashed passwords. We distinguish between invasive A-PBE schemes (they introduce new password-based key-derivation functions) and non-invasive ones (they can use existing, deployed password-based key-derivation functions). We give simple and efficient invasive A-PBE schemes and use AS-secure witness encryption to give non-invasive A-PBE schemes.


title: Towards a Contextual Pragmatic Model to Detect Irony in Tweets

abstract: This paper proposes an approach to capture the pragmatic context needed to infer irony in tweets. We aim to test the validity of two main hypotheses: (1) the presence of negations, as an internal propriety of an utterance, can help to detect the disparity between the literal and the intended meaning of an utterance, (2) a tweet containing an asserted fact of the form Not(P1) is ironic if and only if one can assess the absurdity of P1. Our first results are encouraging and show that deriving a pragmatic contextual model is feasible.


title: PVANet optimization for person detection

abstract: The recent emergence of deep learning algorithms has radically influenced the object detection performances by providing very precise and robust approaches. However, the disadvantage of the recently proposed object detectors is that they are computationaly extremely intensive, making them unsuitable for being used in portable devices. In this paper we propose six models which optimize an existent deep learning-based object detector - presenting also the results obtained on current benchmarks - for the particular task of person detection. We demonstrate that we may reduce the computational requirements of the existing algorithm by using several strategies for neural network architecture optimization, while still obtaining comparable results with the state-of-the-art approaches. Moreover, on the particular pedestrian detection task, we show that we matched state-of-the-art results.


title: Optimization of Deep Neural Networks Using SoCs with OpenCL

abstract: In the optimization of deep neural networks (DNNs) via evolutionary algorithms (EAs) and the implementation of the training necessary for the creation of the objective function, there is often a trade-off between efficiency and flexibility. Pure software solutions implemented on general-purpose processors tend to be slow because they do not take advantage of the inherent parallelism of these devices, whereas hardware realizations based on heterogeneous platforms (combining central processing units (CPUs), graphics processing units (GPUs) and/or field-programmable gate arrays (FPGAs)) are designed based on different solutions using methodologies supported by different languages and using very different implementation criteria. This paper first presents a study that demonstrates the need for a heterogeneous (CPU-GPU-FPGA) platform to accelerate the optimization of artificial neural networks (ANNs) using genetic algorithms. Second, the paper presents implementations of the calculations related to the individuals evaluated in such an algorithm on different (CPU- and FPGA-based) platforms, but with the same source files written in OpenCL. The implementation of individuals on remote, low-cost FPGA systems on a chip (SoCs) is found to enable the achievement of good efficiency in terms of performance per watt.


title: Provenance-Aware Query Optimization

abstract: Data provenance is essential for debugging query results, auditing data in cloud environments, and explaining outputs of Big Data analytics. A well-established technique is to represent provenance as annotations on data and to instrument queries to propagate these annotations to produce results annotated with provenance. However, even sophisticated optimizers are often incapable of producing efficient execution plans for instrumented queries, because of their inherent complexity and unusual structure. Thus, while instrumentation enables provenance support for databases without requiring any modification to the DBMS, the performance of this approach is far from optimal. In this work, we develop provenancespecific optimizations to address this problem. Specifically, we introduce algebraic equivalences targeted at instrumented queries and discuss alternative, equivalent ways of instrumenting a query for provenance capture. Furthermore, we present an extensible heuristic and cost-based optimization (CBO) framework that governs the application of these optimizations and implement this framework in our GProM provenance system. Our CBO is agnostic to the plan space shape, uses a DBMS for cost estimation, and enables retrofitting of optimization choices into existing code by adding a few LOC. Our experiments confirm that these optimizations are highly effective, often improving performance by several orders of magnitude for diverse provenance tasks.


title: Challenges, Methodologies, and Issues in the Usability Testing of Mobile Applications

abstract: Usability testing of software applications developed for mobile devices is an emerging research area that faces a variety of challenges due to unique features of mobile devices, limited bandwidth, unreliability of wireless networks, as well as changing context (environmental factors). Traditional guidelines and methods used in usability testing of desktop applications may not be directly applicable to a mobile environment. Therefore, it is essential to develop and adopt appropriate research methodologies that can evaluate the usability of mobile applications. The contribution of this article is to propose a generic framework for conducting usability tests for mobile applications through discussing research questions, methodologies, and usability attributes. The article provides an overview of existing mobil application usability studies and discusses major research questions that have been investigated. Then, it proposes a generic framework and provides detailed guidelines on how to conduct such usability studies.


title: Correction to ‘Neural systems for landmark-based wayfinding in humans’

abstract: [ Phil. Trans. R. Soc. B . 369 , 20120533. (Published 23 December 2013) ([doi:10.1098/rstb.2012.0533][2])][2] ::: ::: The publisher regrets that the reference list is incorrectly numbered. The references between 1–25 and 50–62 are correct, but references 26–49 should appear as follows. ::: ::: 1. 26.1.


title: An Information Architecture to Support the Visualization of Personal Histories

abstract: This paper proposes an information architecture for personal history data and describes how the data model can be extended to a runtime model for a compact visualization using graphical timelines. Our information architecture was developed for juvenile justice and medical patient records, but is usable in other application domains such as personal resumes, financial histories, or customer support. Our model groups personal history events into aggregates that are contained in facets (e.g., doctor visits, hospitalizations, or lab tests). Crosslinks enable representation of arbitrary relationships across events and aggregates. Data attributes, such as severity, can be mapped by data administrators to visual attributes such as color and line thickness. End-users have powerful controls over the display contents, and they can modify the mapping to fit their tasks.


title: Gender-related consequences of Internet use perceived by parents in a representative quota sample of adolescents

abstract: Only few studies have investigated gender differences in consequences of adolescent Internet use in a general population sample. In this study, we surveyed a representative German quota sample of N=1744 adolescents aged between 14 and 17 years and their caregivers with standardised questionnaires. Parents’ reports showed significant differences between male and female youth in 8 out of 10 problem areas (summarised in a ‘difficulties index’, Cronbach's alpha=0.89) caused by adolescent Internet use. According to parents’ assessment, male adolescents neglected school obligations and friendships outside the Internet more frequently than female adolescents. Compared to girls, boys spent more time and money using the Internet, regarded media use as more important, set wrong priorities in selecting online content, and rather ran into cost traps or legal consequences. More parents of boys than of girls observed adverse effects on adolescents’ physical and mental development. Both parents and adolescents consisten...


title: Split Hamiltonian Monte Carlo

abstract: We show how the Hamiltonian Monte Carlo algorithm can sometimes be speeded up by "splitting" the Hamiltonian in a way that allows much of the movement around the state space to be done at low computational cost. One context where this is possible is when the log density of the distribution of interest (the potential energy function) can be written as the log of a Gaussian density, which is a quadratic function, plus a slowly varying function. Hamiltonian dynamics for quadratic energy functions can be analytically solved. With the splitting technique, only the slowly-varying part of the energy needs to be handled numerically, and this can be done with a larger stepsize (and hence fewer steps) than would be necessary with a direct simulation of the dynamics. Another context where splitting helps is when the most important terms of the potential energy function and its gradient can be evaluated quickly, with only a slowly-varying part requiring costly computations. With splitting, the quick portion can be handled with a small stepsize, while the costly portion uses a larger stepsize. We show that both of these splitting approaches can reduce the computational cost of sampling from the posterior distribution for a logistic regression model, using either a Gaussian approximation centered on the posterior mode, or a Hamiltonian split into a term that depends on only a small number of critical cases, and another term that involves the larger number of cases whose influence on the posterior distribution is small. Supplemental materials for this paper are available online.


title: Dispositional factors in internet use: personality versus cognitive style

abstract: This study directly tests the effect of personality and cognitive style on three measures of Internet use. The results support the use of personality-but not cognitive style-as an antecedent variable. After controlling for computer anxiety, self-efficacy, and gender, including the "Big Five" personality factors in the analysis significantly adds to the predictive capabilities of the dependent variables. Including cognitive style does not. The results are discussed in terms of the role of personality and cognitive style in models of technology adoption and use.


Forgetting-Based Abduction in ALC


title: Author ' s personal copy Automatic medical image annotation in ImageCLEF 2007 : Overview , results , and discussion

abstract: In this paper, the automatic medical annotation task of the 2007 CLEF cross language image retrieval campaign (ImageCLEF) is described. The paper focusses on the images used, the task setup, and the results obtained in the evaluation campaign. Since 2005, the medical automatic image annotation task exists in ImageCLEF with increasing complexity to evaluate the performance of state-of-the-art methods for completely automatic annotation of medical images based on visual properties. The paper also describes the evolution of the task from its origin in 2005-2007. The 2007 task, comprising 11,000 fully annotated training images and 1000 test images to be annotated, is a realistic task with a large number of possible classes at different levels of detail. Detailed analysis of the methods across participating groups is presented with respect to the (i) image representation, (ii) classification method, and (iii) use of the class hierarchy. The results show that methods which build on local image descriptors and discriminative models are able to provide good predictions of the image classes, mostly by using techniques that were originally developed in the machine learning and computer vision domain for object recognition in non-medical images.


title: Moodle-based data mining potentials of MOOC systems at the University of Szeged

abstract: In today's world virtual online educational platforms emerge literally on daily bases and many offer MOOC-based courses. With the appearance of MOOC, educational platforms have gained an additional boost, a new aspect in their evolutionary process, which has opened a new field of research thanking to the extraction of logging information within the frames of data mining. It has become clear that educators will be able to tailor their courses by merging the two previously mentioned fields and by carrying out MOOC-based data mining, targeting pedagogical aspects. This field of research seems promising and important, thus a faculty at the University of Szeged has created its own MOOC educational platform which has been set to facilitate data mining by implementing a wide range of logging algorithms. The data would be processed through a complex Artificial Intelligence program, which, in the short term, could reveal new and exciting pedagogical findings, while in the long run, the supervisors could put together a platform that would help and notify educators about relevant information. It would become possible to create adaptive educational materials, as well. This work aims at clarifying how such platforms function and what the steps of data collection and evaluation are.


title: A 2–15-GHz Accurate Built-in-Self-Test System for Wideband Phased Arrays Using Self-Correcting Eight-State $I/Q$ Mixers

abstract: A built-in-self-test (BIST) system for wideband phase arrays channels is presented. The BIST is implemented using an on-chip in-phase/quadrature ( $I/Q$ ) receiver with an integrated ring oscillator that provides both the channel test signal and the mixer local oscillator (LO). The BIST achieves wideband accuracy for relative phase and gain measurements at 2–15 GHz with a one-time self-correction algorithm with eight LO phases. The sequential algorithm determines the $I/Q$ errors, such as dc offset, gain and phase imbalances from the $I/Q$ outputs resulting from different LO phase states. An rms power detector network is also implemented for absolute gain measurements. The BIST can operate at rates >1 MHz (less than 1- $\mu \text{s}$ sampling time) with signal-to-noise ratio greater than 50 dB and provides measurements that agree well with the vector network analyzer S-parameter data over a wide frequency range. To the best of our knowledge, this is the first implementation of high accuracy wideband BIST system for phased-array channels.


title: Robustness of the Black and Scholes Formula

abstract: Consider an option on a stock whose volatility is unknown and stochastic. An agent assumes this volatility to be a specific function of time and the stock price, knowing that this assumption may result in a misspecification of the volatility. However, if the misspecified volatility dominates the true volatility, then the misspecified price of the option dominates its true price. Moreover, the option hedging strategy computed under the assumption of the misspecified volatility provides an almost sure one-sided hedge for the option under the true volatility. Analogous results hold if the true volatility dominates the misspecified volatility. These comparisons can fail, however, if the misspecified volatility is not assumed to be a function of time and the stock price. The positive results, which apply to both European and American options, are used to obtain a bound and hedge for Asian options. Copyright Blackwell Publishers 1998.


title: An electricity supplier bidding strategy through Q-Learning

abstract: One of the most important issues for power suppliers in the deregulated electric industry is how to bid into the electricity auction market to satisfy their profit-maximizing goals. Based on the Q-Learning algorithm, this paper presents a novel supplier bidding strategy to maximize supplier's profit in a long term. A perfectly competitive day-ahead electricity auction market, where no supplier possess the market power and all suppliers winning the market are paid on their own bids, is assumed here. The dynamics and the incomplete information of the market are emphasized. The impact of suppliers' strategic biddings on the market price is analyzed. Agent-based simulations are presented in this paper. The simulation results show the feasibility of the proposed bidding strategy.


title: Review of Inflatable Booms for Deployable Space Structures: Packing and Rigidization

abstract: Inflatable structures offer the potential of compactly stowing lightweight structures, ::: which assume a fully deployed state in space. An important category of space inflatables ::: are cylindrical booms, which may form the structural members of trusses or the support ::: structure for solar sails. Two critical and interdependent aspects of designing inflatable ::: cylindrical booms for space applications are i) packaging methods that enable compact ::: stowage and ensure reliable deployment, and ii) rigidization techniques that provide ::: long-term structural ridigity after deployment. The vast literature in these two fields ::: is summarized to establish the state of the art.


title: A Multiple Attribute-based Decision Making model for autonomous vehicle in urban environment

abstract: In this paper, a maneuver decision making method for autonomous vehicle in complex urban environment is studied. We decompose the decision making problem into three steps. The first step is for selecting the logical maneuvers, in the second step we remove the maneuvers which break the traffic rules. In the third step, Multiple Attribute Decision Making (MADM) methods such as Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are used in the process of selecting the optimum driving maneuver in the scenario considering safety and efficiency. AHP is used for obtaining the weights of attributes, TOPSIS is responsible for calculating the ratings and ranking the alternatives. Road test indicates that the proposed method helps the autonomous vehicle to make reasonable decisions in complex environment. In general, the experiment results show that this method is efficient and reliable.


DESIGN AND DEVELOPMENT OF 3D PRINTED MYOELECTRIC ROBOTIC EXOSKELETON FOR HAND REHABILITATION


title: Development of a controllable and continuous jumping robot

abstract: A miniature robot with continuous jumping ability is presented in this paper. The robot has a dimension about 6cm×8cm×2cm and weighs 20 grams. To achieve continuous jumping, various mechanisms are needed including the jumping mechanism, energy store and release mechanism, self-righting mechanism, and jumping direction changing mechanism. The design and analysis for those mechanisms are elaborated in this paper. Moreover, implementation and experimental results are also presented. It is shown that the robot can jump higher than 55cm with a 75° takeoff angle. The robot can be used as mobile sensors and deployed in the areas of rugged terrain and natural obstacles which are not suitable for sensors with wheels.


title: Power System with PSS and FACTS Controller: Modelling, Simulation and Simultaneous Tuning Employing Genetic Algorithm

abstract: This paper presents a systematic procedure for modelling and simulation of a power system installed with a power system stabilizer (PSS) and a flexible ac transmission system (FACTS)-based controller. For the design purpose, the model of example power system which is a single-machine infinite-bus power system installed with the proposed controllers is developed in MATLAB/SIMULINK. In the developed model synchronous generator is represented by model 1.1. which includes both the generator main field winding and the damper winding in q-axis so as to evaluate the impact of PSS and FACTS-based controller on power system stability. The model can be can be used for teaching the power system stability phenomena, and also for research works especially to develop generator controllers using advanced technologies. Further, to avoid adverse interactions, PSS and FACTS-based controller are simultaneously designed employing genetic algorithm (GA). The non-linear simulation results are presented for the example power system under various disturbance conditions to validate the effectiveness of the proposed modelling and simultaneous design approach.


title: 3D printed cardiovascular models for surgical planning in complex congenital heart diseases

abstract: Background A precise understanding of the anatomical structures of the heart and great vessels is essential for surgical planning in order to avoid unexpected findings. Rapid prototyping techniques are used to print three-dimensional (3D) replicas of patients’ cardiovascular anatomy based on 3D clinical images such as MRI. The purpose of this study is to explore the use of 3D patient-specific cardiovascular models using rapid prototyping techniques to improve surgical planning in patients with complex congenital heart disease.


title: Target Blood Pressure Level Before Antihypertensive Class to Improve Outcomes More Quickly in Uncomplicated Hypertension

abstract: To the Editor: ::: ::: The article by Ma et al,1 regarding changes in antihypertensive prescribing during US outpatient visits for uncomplicated hypertension, provides important information on the prescribing patterns and use of guideline recommendations for the treatment of hypertension in the United States. However, although the ongoing debate as to which class of antihypertensive to prescribe, especially in patients with uncomplicated hypertension, continues unabated, the importance of blood pressure reduction is too …


title: DeepHand: Robust Hand Pose Estimation by Completing a Matrix Imputed with Deep Features

abstract: We propose DeepHand to estimate the 3D pose of a hand using depth data from commercial 3D sensors. We discriminatively train convolutional neural networks to output a low dimensional activation feature given a depth map. This activation feature vector is representative of the global or local joint angle parameters of a hand pose. We efficiently identify 'spatial' nearest neighbors to the activation feature, from a database of features corresponding to synthetic depth maps, and store some 'temporal' neighbors from previous frames. Our matrix completion algorithm uses these 'spatio-temporal' activation features and the corresponding known pose parameter values to estimate the unknown pose parameters of the input feature vector. Our database of activation features supplements large viewpoint coverage and our hierarchical estimation of pose parameters is robust to occlusions. We show that our approach compares favorably to state-of-the-art methods while achieving real time performance (≈ 32 FPS) on a standard computer.


title: Detection tube for small HF RFID tags, based on mutual coupling with a coil resonator

abstract: This communication concerns the detection of 13.56 MHz (HF) RFID “small” tags. Herein, the term “small” refer to an effective area below 1 cm2 and the detection principle is in volume, especially inside a tube of 9 cm in diameter and 2m in length. The ability to detect the “small” tags in the tube is achieved by using a coil resonator conformed on the tube surface, following a principle of multiple magnetic coupling, also referred as magnetic field guide. Theoretical considerations on mutual coupling formula and electrical model fit to CST simulations and VNA measurements concerning the evaluation of impedance and coupling factors range. Detection tests with an RFID reader (from IB technology) and NXP SLI-X chip confirm the possibility of detection by providing a first result of 2 cm range. This detection was impossible inside the tube without using the resonator. Perspectives of improvement evocated at the end of the paper are numerous for that structure.


title: Large-scale field-programmable analog arrays for analog signal processing

abstract: Field-programmable analog arrays (FPAAs) provide a method for rapidly prototyping analog systems. Currently available commercial and academic FPAAs are typically based on operational amplifiers (or other similar analog primitives) with only a few computational elements per chip. While their specific architectures vary, their small sizes and often restrictive interconnect designs leave current FPAAs limited in functionality and flexibility. For FPAAs to enter the realm of large-scale reconfigurable devices such as modern field-programmable gate arrays (FPGAs), new technologies must be explored to provide area-efficient accurately programmable analog circuitry that can be easily integrated into a larger digital/mixed-signal system. Recent advances in the area of floating-gate transistors have led to a core technology that exhibits many of these qualities, and current research promises a digitally controllable analog technology that can be directly mated to commercial FPGAs. By leveraging these advances, a new generation of FPAAs is introduced in this paper that will dramatically advance the current state of the art in terms of size, functionality, and flexibility. FPAAs have been fabricated using floating-gate transistors as the sole programmable element, and the results of characterization and system-level experiments on the most recent FPAA are shown.


title: Large-scale cluster management at Google with Borg

abstract: Google's Borg system is a cluster manager that runs hundreds of thousands of jobs, from many thousands of different applications, across a number of clusters each with up to tens of thousands of machines. It achieves high utilization by combining admission control, efficient task-packing, over-commitment, and machine sharing with process-level performance isolation. It supports high-availability applications with runtime features that minimize fault-recovery time, and scheduling policies that reduce the probability of correlated failures. Borg simplifies life for its users by offering a declarative job specification language, name service integration, real-time job monitoring, and tools to analyze and simulate system behavior. We present a summary of the Borg system architecture and features, important design decisions, a quantitative analysis of some of its policy decisions, and a qualitative examination of lessons learned from a decade of operational experience with it.


title: A long-term outcome study of selective mutism in childhood

abstract: OBJECTIVE: Controlled study of the long-term outcome of selective mutism (SM) in childhood. METHOD: A sample of 33 young adults with SM in childhood and two age- and gender-matched comparison groups were studied. The latter comprised 26 young adults with anxiety disorders in childhood (ANX) and 30 young adults with no psychiatric disorders during childhood. The three groups were compared with regard to psychiatric disorder in young adulthood by use of the Composite International Diagnostic Interview (CIDI). In addition, the effect of various predictors on outcome of SM was studied. RESULTS: The symptoms of SM improved considerably in the entire SM sample. However, both SM and ANX had significantly higher rates for phobic disorder and any psychiatric disorder than controls at outcome. Taciturnity in the family and, by trend, immigrant status and a severity indicator of SM had an impact on psychopathology and symptomatic outcome in young adulthood. CONCLUSION: This first controlled long-term outcome study of SM provides evidence of symptomatic improvement of SM in young adulthood. However, a high rate of phobic disorder at outcome points to the fact that SM may be regarded as an anxiety disorder variant.


title: A Review on Different Security Issues and Challenges in Cloud Computing

abstract: In computer’s world these days cloud computing plays very important role. It gives user facilities like group of things such as software, platform and infrastructure services. Virtualization is the backbone of cloud resource sharing. Security is also a main problem of cloud. Multiple users have their own perception related to the cloud. By using cloud computing, user can access resources anywhere by using internet. So this technique is very useful in user’s daily life. One of the factors for cloud computing is cloud services which were provided by the cloud (IAAS, PAAS, and SAAS). These services enable users to access infrastructure, platform and software. Even resources are allocated to users according to their requirements. But many people think it is unsafe to use cloud resources and its services. It is unsafe to use cloud because there is no guarantee of information which is controlled or maintained by the vendors. There are some security issues that are noticed in cloud computing. In this paper, we have discussed a few issues with cloud computing and the challenges of cloud computing. This paper gives overall investigation of security on data, protection and issues in the cloud. The paper also defines the literature review related to the cloud computing issues and threats and also the various security concerns are discussed.


Growth and Characterization of M-Plane GaN Thin Films Grown on γ-LiAlO2 (100) Substrates


title: Multi-factor authentication: A survey and challenges in V2X applications

abstract: Today, the digitalization strides tremendously on all the sides of the modern society. One of the enablers to keep this process secure is the authentication. It touches many different areas of the connected world including payments, communications, and access right management. This manuscript attempts to shed the light on the authentication systems' evolution towards Multi-factor Authentication (MFA) from Singlefactor Authentication (SFA) and through Two-factor Authentication (2FA). Particularly, MFA is expected to be utilized for the user and vehicle-to-everything (V2X) interaction which is selected as descriptive scenario. The manuscript is focused on already available and potentially integrated sensors (factor providers) to authenticate the occupant from inside the vehicle. The survey on existing vehicular systems suitable for MFA is given. Finally, the MFA system based on reversed Lagrange polynomial, utilized in Shamir's Secret Sharing (SSS), was proposed to enable flexible in-car authentication. The solution was further extended covering the cases of authenticating the user even if some of the factors are mismatched or absent. The framework allows to qualify the missing factor and authenticate the user without providing the sensitive biometric data to the verification entity. The proposed is finally compared to conventional SSS.


title: Social media effects on fostering online civic engagement and building citizen trust and trust in institutions

abstract: Abstract This paper tests the extent to which social media is shaping civic engagement initiatives to build trust among people and increase trust in their institutions, particularly the government, police and justice systems. A survey of 502 citizens showed that using social media for civic engagement has a significant positive impact on trust propensity and that this trust had led to an increase in trust towards institutions. Interestingly, while group incentives encouraged citizens to engage online for civic matters, it is civic publications through postings on social media that intensify the urge of citizens for civic action to address social issues. Post-hoc analysis via ten interviews with social activists was conducted to further examine their perceptions on trust towards institutions. The overall findings suggest that institutions, in their effort to promote a meaningful and trusting citizen engagement, need to enhance trust among the public by fostering social capital via online civic engagement and closing the public–police disengagement gap.


Capturing wide field of view light fields using spherical mirrors


title: Foreground–Background Separation From Video Clips via Motion-Assisted Matrix Restoration

abstract: Separation of video clips into foreground and background components is a useful and important technique, making recognition, classification, and scene analysis more efficient. In this paper, we propose a motion-assisted matrix restoration (MAMR) model for foreground–background separation in video clips. In the proposed MAMR model, the backgrounds across frames are modeled by a low-rank matrix, while the foreground objects are modeled by a sparse matrix. To facilitate efficient foreground-background separation, a dense motion field is estimated for each frame, and mapped into a weighting matrix which indicates the likelihood that each pixel belongs to the background. Anchor frames are selected in the dense motion estimation to overcome the difficulty of detecting slowly moving objects and camouflages. In addition, we extend our model to a robust MAMR model against noise for practical applications. Evaluations on challenging datasets demonstrate that our method outperforms many other state-of-the-art methods, and is versatile for a wide range of surveillance videos.


NONLINEAR FINITE ELEMENT ANALYSIS OF REINFORCED CONCRETE SLABS UNDER IMPACT LOADS


title: Non-Foster Impedance Matching for Transmit Applications

abstract: We give experimental results showing the gain-bandwidth advantages of using non-Foster networks (i.e. containing negative inductors and capacitors) to impedance match an electrically-small antenna to a transmitter. Over a given bandwidth, non-Foster achieves up to 20 dB more transducer power gain than any passive design. The overall power efficiency advantage of non-Foster networks is limited by the required DC bias power. We demonstrate a class-B biasing strategy that, when fully developed, will make non-Foster transmit matching more efficient than any passive design. BACKGROUND


title: Emotions, Partisanship, and Misperceptions: How Anger and Anxiety Moderate the Effect of Partisan Bias on Susceptibility to Political Misinformation

abstract: Citizens are frequently misinformed about political issues and candidates but the circumstances under which inaccurate beliefs emerge are not fully understood. This experimental study demonstrates that the independent experience of two emotions, anger and anxiety, in part determines whether citizens consider misinformation in a partisan or open-minded fashion. Anger encourages partisan, motivated evaluation of uncorrected misinformation that results in beliefs consistent with the supported political party, while anxiety at times promotes initial beliefs based less on partisanship and more on the information environment. However, exposure to corrections improves belief accuracy, regardless of emotion or partisanship. The results indicate that the unique experience of anger and anxiety can affect the accuracy of political beliefs by strengthening or attenuating the influence of partisanship


title: Analysis and design methodology for Planar Transformer with low self-capacitance used in high voltage flyback charging circuit

abstract: In high voltage (HV) flyback charging circuits, the importance of transformer parasitics holds a significant part in the overall system parasitics. The HV transformers have a larger number of turns on the secondary side that leads to higher self-capacitance which is inevitable. The conventional wire-wound transformer (CWT) has limitation over the design with larger self-capacitance including increased size and volume. For capacitive load in flyback charging circuit these self-capacitances on the secondary side gets added with device capacitances and dominates the load. For such applications the requirement is to have a transformer with minimum self-capacitances and low profile. In order to achieve the above requirements Planar Transformer (PT) design can be implemented with windings as tracks in Printed Circuit Boards (PCB) each layer is insulated by the FR4 material which aids better insulation. Finite Element Model (FEM) has been developed to obtain the self-capacitance in between the layers for larger turns on the secondary side. The modelled hardware prototype of the Planar Transformer has been characterised for open circuit and short circuit test using Frequency Response Analyser (FRA). The results obtained from FEM and FRA are compared and presented.


title: Efficient search and mode prediction algorithms for motion estimation in H.264/AVC

abstract: The H.264 video coding standard adopts different coding schemes like variable block sizes and multiple reference frames for motion estimation. Hence, H.264/AVC provides gains in compression efficiency of up to 50% over a wide range of bit rates and video resolutions compared to previous standards. However, these features incur a considerable increase in encoder complexity, mainly with regards to motion estimation and mode decision. In this paper, efficient methods aimed at speed up of motion estimation and mode decision are proposed. Experimental results show that our proposed algorithms can save a coding time of about 88% compared with the reference software JM80 while keeping the video quality.


title: CM-Builder: an automated NL-based CASE tool

abstract: This paper describes a natural language-based CASE tool called CM-Builder which aims at supporting the analysis stage of software development in an object-oriented framework. CM-Builder uses robust natural language processing techniques to analyse software requirements texts written in English and build an integrated discourse model of the processed text, represented in a semantic network. This semantic network is then used to automatically construct an initial UML class model representing the object classes mentioned in the text and the relationships among them. The initial model can be directly input to a graphical CASE tool for further refinement by a human analyst. CM-Builder has been quantitatively evaluated in blind trials against a collection of unseen software requirements texts and we present the results of this evaluation, together with the evaluation methodology. The results are very encouraging and demonstrate that tools such as CM-Builder have the potential to play an important role in the software development process.


title: Utility of impulse oscillometry in young children with asthma.

abstract: Impulse Oscillometry (IOS) was developed as a tool to measure lung function, and as it only requires passive cooperation, it has been successfully used in younger children. The aim of this study was to assess the utility of IOS compared to conventional spirometry for lung function measures in asthmatic Korean children aged 3 to 6 yrs. Total serum IgE levels, total eosinophil counts, and specific IgE levels were measured in 77 children with asthma and 55 control subjects. IOS and spirometry were performed in the children before and after bronchodilator administration. Asthmatic children significantly differed from control subjects in baseline resistance at 10 Hz and in their IOS-assessed bronchodilator responses through a Delta resistance at 5 Hz (mean, -27.4% vs. -13.1%; p = 0.002), 10 Hz (mean, -20.4% vs. -11.5%; p = 0.012), 20 Hz (mean, -16.4% vs. -9.4%; p = 0.016), and 35 Hz (mean, -17.2% vs. -10.2%; p = 0.020). Conventional spirometry did not show statistically significant findings. Asthmatic children significantly differed from control subjects in IOS-assessed bronchodilator response through a Delta resistance at 5 Hz, both in atopic children and in non-atopic children. For atopic children, atopic asthmatic children (n = 58) also significantly differed from atopic controls (n = 30) in baseline resistance at 10 Hz, 20 Hz, 35 Hz and impedance and in IOS-assessed bronchodilator responses through a Delta resistance at 10 Hz and 35 Hz. There were some significant correlations between bronchodilator responses of spirometric parameters and IOS parameters. IOS is useful diagnostic tool in early asthma development and might be a helpful objective outcome measure of early interventions.


title: Model Predictive Control for Distributed Microgrid Battery Energy Storage Systems

abstract: This brief proposes a new convex model predictive control (MPC) strategy for dynamic optimal power flow between battery energy storage (ES) systems distributed in an ac microgrid. The proposed control strategy uses a new problem formulation, based on a linear $d$ – $q$ reference frame voltage-current model and linearized power flow approximations. This allows the optimal power flows to be solved as a convex optimization problem, for which fast and robust solvers exist. The proposed method does not assume that real and reactive power flows are decoupled, allowing line losses, voltage constraints, and converter current constraints to be addressed. In addition, nonlinear variations in the charge and discharge efficiencies of lithium ion batteries are analyzed and included in the control strategy. Real-time digital simulations were carried out for an islanded microgrid based on the IEEE 13 bus prototypical feeder, with distributed battery ES systems and intermittent photovoltaic generation. It is shown that the proposed control strategy approaches the performance of a strategy based on nonconvex optimization, while reducing the required computation time by a factor of 1000, making it suitable for a real-time MPC implementation.


title: Second homes and local socio-economic development: the case of Croatia

abstract: This paper contributes to the discussion on the impact of second homes growth on host areas by providing an analysis of the relationship of second home development and local socio-economic development in Croatia. For that purpose, we used regression and cluster analysis while the local government level was used as the unit of analysis. The results show that second home development in Croatia is associated with increased dynamism of the local economy, particularly with higher local budget income per capita. Concerning the social components of the local development, the analysis revealed that second home development is positively connected to the local educational structure and the population index change. The research findings also indicate that second homes and tourism have independently a positive effect on the local socio-economic development, but the combination of the intensive second home development with the intensive tourism development produces the effect of synergy that additionally stimulates local socio-economic development.


title: Effects of Great Barrier Reef Degradation on Recreational Demand: A Contingent Behaviour Approach

abstract: Agricultural run-off from the Great Barrier Reef catchment area may cause degradation of coral reefs, affecting the tourism sector that relies on healthy reefs for its income generation. A Contingent Behaviour approach is used to determine the effect of reef degradation on demand for recreational dive and snorkel trips, for a case study of the Great Barrier Reef (GBR) in Australia. We assessed how reef degradation affects GBR tourism and to what extent reef-trip demand depends on the visitors' socio-economic characteristics. A count data model is developed, and results indicate that an average visitor would undertake about 60% less trips to the GBR per year, given a combined 80%, 30% and 70% decrease in coral cover, coral diversity and fish diversity, respectively. This corresponds to a decrease in tourism expenditure for reef trips in the Great Barrier Reef Marine Park area of about A$ 136 million per year.


title: Introducing Coding and Computational Thinking in the Schools: The TACCLE 3 – Coding Project Experience

abstract: Several countries have usually adopted several priorities for developing ICT competences from kindergarten to secondary education. Most of them are focused on the development of key competences and/or coding skills. Although coding may be very attractive for young students and a very good practice or experience, it could be more interesting to develop students’ logical thinking skills and problem-solving skills throughout programming approaches or computational thinking. This is a very exciting challenge with lots of possibilities regarding coding, robots, mobile devices, Arduino-based application, game-based learning, and so on. TACCLE 3 – Coding is a European Union Erasmus+ KA2 Programme project that supports primary school staff and others who are teaching computing to 4–14-year-olds. Specifically, TACCLE 3 project has three main objectives: (1) to equip fellow classroom teachers, whatever their level of confidence, with the knowledge and the materials they need to teach coding effectively; (2) to develop a website of easy-to-follow and innovative ideas and resources to aid teachers in teaching coding (they will also find a review of the current academic research and an overview of the resources currently available for teaching coding); and (3) to provide national and international in-service training courses and other staff development events to help support and develop confidence and competences in teaching coding. This chapter explains the work done in TACCLE 3 and the first experiences we have to introduce the computational thinking to the primary school teachers, with a special attention to the use of smart textile objects.


title: High-intensity endurance training in 20- to 30- and 60- to 70-yr-old healthy men.

abstract: Factors contributing to maximal incremental and short-term exercise capacity were measured before and after 12 wk of high-intensity endurance training in 12 old (60-70 yr) and 10 young (20-30 yr) sedentary healthy males. Peak O2 uptake in incremental cycle ergometer exercise increased from 1.60 +/- 0.073 to 2.21 +/- 0.073 (SE) l/min (38% increase) in the old subjects and from 2.54 +/- 0.141 to 3.26 +/- 0.181 l/min (29%) in the young subjects. Peak cardiac output, estimated by extrapolation from a series of submaximal measurements by the CO2 rebreathing method, increased by 30% (from 12.7 to 16.5 l/min) in the old subjects, associated with a 6% increase (from 126 to 135 ml/l) in arteriovenous O2 difference; in the young subjects there were equal 14% increases in both variables (18.0 to 20.5 l/min and 140 to 159 ml/l, respectively). Submaximal mean arterial pressure and cardiac output were lower posttraining in the old subjects; total vascular conductance and cardiac stroke volume increased. Although peak power at the start of a short-term maximal isokinetic test did not change, total work accomplished in 30 s at a pedaling frequency of 110 revolutions/min increased in both groups, from 11.2 to 12.6 kJ and from 15.7 to 16.9 kJ in the old and young, respectively; fatigue during the 30-s test was less, and postexercise plasma lactate concentrations were lower. In older subjects, increases in aerobic power after high-intensity endurance training are at least as large as in younger subjects and are associated with increases in vascular conductance, maximal cardiac output, and stroke volume.


title: Lossless data compression techniques and their performance

abstract: This paper provides the study of various lossless data compression techniques and compares their performance and efficiency using time and space complexity. Data compression is itself a huge field in a computer science and it is used in reducing the size of data by removing the redundant characters or encoding the bits in data. We have used only lossless data compression techniques for compressing various media format files like text, doc, bmp, png and wav. Huffman coding which generates variable length codes for each symbol. LZW is a dictionary based technique and Shannon-Fano coding is used to generate binary code based on the frequency of each symbol. So we calculated the mean compression ratio, compression factor and compression time for various media files and result analysis of above data compression techniques will help to select the most appropriate method of compressing of any given file format in real world.


The Unleashed Scandal: The End of Control in the Digital Age


title: Is science me? High school students' identities, participation and aspirations in science, engineering, and medicine

abstract: This study follows an ethnically and economically diverse sample of 33 high school students to explore why some who were once very interested in science, engineering, or medicine (SEM) majors or careers decided to leave the pipeline in high school while others persisted. Through longitudinal interviews and surveys, students shared narratives about their developing science identities, SEM participation and aspirations. In analysis, three groups emerged (High Achieving Persisters, Low Achieving Persisters, and Lost Potentials), each experiencing different interactions and experiences within science communities of practice in and outside of school and within the extended family. These different microclimates framed students' perceptions of their SEM study, abilities, career options, and expected success, thereby shaping their science identities and consequent SEM trajectories. School science was often hard and discouraging; there were very few science advocates at school or home; and meaningful opportunities to work with real science professionals were scarce, even in schools with science or health academies. Students expressed positive attitudes toward science and non-science pursuits where they experienced success and received support from important people in their lives. Results underscore the key role communities of practice play in career and identity development and suggest a need for interventions to help socializers better understand the value and purpose of science literacy themselves so as to encourage students to appreciate science, be aware of possible career options in science and enjoy learning and doing science. © 2009 Wiley Periodicals, Inc. J Res Sci Teach 47: 564–582, 2010


title: Halal Kit Identifier Using Radio Frequency Identification Technology

abstract: Halal certification is very crucial these days. In Malaysia, JAKIM plays very important role to certify halal product. Products with halal certification become highest priority in users' demands in Muslim country, particularly in Malaysia. However, due to the rapid development of advanced technology, irresponsible manufactures resort to produce fake halal to attract Muslim consumers and overshadow their decision. Therefore this project is to develop a halal kit identifier to help consumer to identify the halal status of the product. The main focus of this project is to validate the Halal status for products by using Radio Frequency Identification (RFID) technology to improve the existing systems available nowadays. Using this approach, the Halal status of the product can be easily validated, because every tag that will be embedded into food packages has its unique identification number. From various tests conducted, the portable Halal kit identifier using RFID technology developed in this research is able to help consumers to validate Halal status of products wirelessly in very efficient way.


title: Psychiatric comorbidity in gender dysphoric adolescents

abstract: Background: This study examined psychiatric comorbidity in adolescents with a gender identity disorder (GID). We focused on its relation to gender, type of GID diagnosis and eligibility for medical interventions (puberty suppression and cross-sex hormones). Methods: To ascertain DSM-IV diagnoses, the Diagnostic Interview Schedule for Children (DISC) was administered to parents of 105 gender dysphoric adolescents. Results: 67.6% had no concurrent psychiatric disorder. Anxiety disorders occurred in 21%, mood disorders in 12.4% and disruptive disorders in 11.4% of the adolescents. Compared with natal females (n = 52), natal males (n = 53) suffered more often from two or more comorbid diagnoses (22.6% vs. 7.7%, p = .03), mood disorders (20.8% vs. 3.8%, p = .008) and social anxiety disorder (15.1% vs. 3.8%, p = .049). Adolescents with GID considered to be ‘delayed eligible’ for medical treatment were older [15.6 years (SD = 1.6) vs. 14.1 years (SD = 2.2), p = .001], their intelligence was lower [91.6 (SD = 12.4) vs. 99.1 (SD = 12.8), p = .011] and a lower percentage was living with both parents (23% vs. 64%, p 1.0 for all psychiatric diagnoses except specific phobia. Conclusions: Despite the suffering resulting from the incongruence between experienced and assigned gender at the start of puberty, the majority of gender dysphoric adolescents do not have co-occurring psychiatric problems. Delayed eligibility for medical interventions is associated with psychiatric comorbidity although other factors are of importance as well. Keywords: Gender identity disorder, gender dysphoria, adolescents, psychiatric comorbidity, puberty suppression, cross-sex hormones.