title: A Comparison of GMM-HMM and DNN-HMM Based Pronunciation Verification Techniques for Use in the Assessment of Childhood Apraxia of Speech

abstract: This paper introduces a pronunciation verification method to be used in an automatic assessment therapy tool of child disordered speech. The proposed method creates a phonebased search lattice that is flexible enough to cover all probable mispronunciations. This allows us to verify the correctness of the pronunciation and detect the incorrect phonemes produced by the child. We compare between two different acoustic models, the conventional GMM-HMM and the hybrid DNN-HMM. Results show that the hybrid DNNHMM outperforms the conventional GMM-HMM for all experiments on both normal and disordered speech. The total correctness accuracy of the system at the phoneme level is above 85% when used with disordered speech.


title: Paraphrase Identification and Semantic Similarity in Twitter with Simple Features

abstract: Paraphrase Identification and Semantic Similarity are two different yet well related tasks in NLP. There are many studies on these two tasks extensively on structured texts in the past. However, with the strong rise of social media data, studying these tasks on unstructured texts, particularly, social texts in Twitter is very interesting as it could be more complicated problems to deal with. We investigate and find a set of simple features which enables us to achieve very competitive performance on both tasks in Twitter data. Interestingly, we also confirm the significance of using word alignment techniques from evaluation metrics in machine translation in the overall performance of these tasks.


title: Complex Luminaires: Illumination and Appearance Rendering

abstract: Simulating a complex luminaire such as a chandelier is expensive and slow, even using state-of-the-art algorithms. A more practical alternative is to use precomputation to accelerate rendering. Prior approaches cached information on an aperture surface that separates the luminaire from the scene, but many luminaires have large or ill-defined apertures leading to excessive data storage and inaccurate results. In this article, we separate luminaire rendering into illumination and appearance components. A precomputation stage simulates the complex light flow inside the luminaire to generate two data structures: a set of anisotropic point lights (APLs) and a radiance volume. The APLs are located near apparent sources and represent the light leaving the luminaire, allowing its nearand far-field illumination to be accurately and efficiently computed at render time. The luminaire's appearance consists of high- and low-frequency components, which are both visually important. High-frequency components are computed dynamically at render time, while the more computationally expensive low-frequency components are approximated using the precomputed radiance volume. Results are shown for several complex luminaires, demonstrating orders of magnitude faster rendering compared to the best global illumination algorithms and higher fidelity with greatly reduced storage requirements compared to previous precomputed approaches.


title: Looking back to move forward: Nostalgia as a psychological resource for promoting relationship goals and overcoming relationship challenges.

abstract: Previous research has shown that nostalgia is a highly social emotion that provides a sense of social connectedness. In the present research, we tested a social motivational function of nostalgia. Specifically, across 7 studies we found converging evidence that nostalgia mobilizes social goals. In Study 1, nostalgia increased the importance people assigned to relationship goals and how optimistic they felt about achieving these goals. In Study 2, nostalgia increased intentions to pursue goals of connecting with friends. In Study 3, experimentally induced pessimism about achieving relationship goals instigated nostalgia. In Study 4, we found evidence that it is the interpersonal nature of nostalgia that is associated with striving to connect with others. Specifically, nostalgia about aspects of the past that were high in sociality was associated with intentions to interact with others, whereas nostalgia for aspects of the past that were low in sociality was not. In Study 5, nostalgic reflection increased friendship-approach goal striving relative to reflecting on ordinary social memories, but did not increase friendship-avoidant goal striving. Finally, in Studies 6 and 7, we found evidence that social-efficacy mediated the effect of nostalgia on striving to connect with others and striving to overcome interpersonal challenges. Together, these findings establish nostalgia as catalyst for social goal pursuit and growth. (PsycINFO Database Record (c) 2015 APA, all rights reserved)


Biologics for tendon repair☆


title: Natural language processing to extract symptoms of severe mental illness from clinical text: the Clinical Record Interactive Search Comprehensive Data Extraction (CRIS-CODE) project

abstract: OBJECTIVES: We sought to use natural language processing to develop a suite of language models to capture key symptoms of severe mental illness (SMI) from clinical text, to facilitate the secondary use of mental healthcare data in research. DESIGN: Development and validation of information extraction applications for ascertaining symptoms of SMI in routine mental health records using the Clinical Record Interactive Search (CRIS) data resource; description of their distribution in a corpus of discharge summaries. SETTING: Electronic records from a large mental healthcare provider serving a geographic catchment of 1.2 million residents in four boroughs of south London, UK. PARTICIPANTS: The distribution of derived symptoms was described in 23 128 discharge summaries from 7962 patients who had received an SMI diagnosis, and 13 496 discharge summaries from 7575 patients who had received a non-SMI diagnosis. OUTCOME MEASURES: Fifty SMI symptoms were identified by a team of psychiatrists for extraction based on salience and linguistic consistency in records, broadly categorised under positive, negative, disorganisation, manic and catatonic subgroups. Text models for each symptom were generated using the TextHunter tool and the CRIS database. RESULTS: We extracted data for 46 symptoms with a median F1 score of 0.88. Four symptom models performed poorly and were excluded. From the corpus of discharge summaries, it was possible to extract symptomatology in 87% of patients with SMI and 60% of patients with non-SMI diagnosis. CONCLUSIONS: This work demonstrates the possibility of automatically extracting a broad range of SMI symptoms from English text discharge summaries for patients with an SMI diagnosis. Descriptive data also indicated that most symptoms cut across diagnoses, rather than being restricted to particular groups.


title: Real-World Recommender Systems for Academia: The Pain and Gain in Building, Operating, and Researching them [Long Version]

abstract: Research on recommender systems is a challenging task, as is building and operating such systems. Major challenges include non-reproducible research results, dealing with noisy data, and answering many questions such as how many recommendations to display, how often, and, of course, how to generate recommendations most effectively. In the past six years, we built three research-article recommender systems for digital libraries and reference managers, and conducted research on these systems. In this paper, we share some experiences we made during that time. Among others, we discuss the required skills to build recommender systems, and why the literature provides little help in identifying promising recommendation approaches. We explain the challenge in creating a randomization engine to run A/B tests, and how low data quality impacts the calculation of bibliometrics. We further discuss why several of our experiments delivered disappointing results, and provide statistics on how many researchers showed interest in our recommendation dataset.


title: Dark web forums portal: Searching and analyzing jihadist forums

abstract: With the advent of Web 2.0, the Web is acting as a platform which enables end-user content generation. As a major type of social media in Web 2.0, Web forums facilitate intensive interactions among participants. International Jihadist groups often use Web forums to promote violence and distribute propaganda materials. These Dark Web forums are heterogeneous and widely distributed. Therefore, how to access and analyze the forum messages and interactions among participants is becoming an issue. This paper presents a general framework for Web forum data integration. Specifically, a Web-based knowledge portal, the Dark Web Forums Portal, is built based on the framework. The portal incorporates the data collected from different international Jihadist forums and provides several important analysis functions, including forum browsing and searching (in single forum and across multiple forums), forum statistics analysis, multilingual translation, and social network visualization. Preliminary results of our user study show that the Dark Web Forums Portal helps users locate information quickly and effectively. Users found the forum statistics analysis, multilingual translation, and social network visualization functions of the portal to be particularly valuable.


title: ‘ ‘ Beating ’ ’ Fitts ’ law : virtual enhancements for pointing facilitation

abstract: We survey recent research into new techniques for artificially facilitating pointing at targets in graphical user interfaces. While pointing in the physical world is governed by Fitts' law and constrained by physical laws, pointing in the virtual world does not necessarily have to abide by the same constraints, opening the possibility for "beating" Fitts' law with the aid of the computer by artificially reducing the target distance, increasing the target width, or both. The survey suggests that while the techniques developed to date are promising, particularly when applied to the selection of single isolated targets, many of them do not scale well to the common situation in graphical user interfaces where multiple targets are located in close proximity.


title: EVALUATION: FROM PRECISION, RECALL AND F-MEASURE TO ROC, INFORMEDNESS, MARKEDNESS & CORRELATION

abstract: Commonly used evaluation measures including Recall, Precision, F-Factor and Rand Accuracy are biased and should not be used without clear understanding of the biases, and corresponding identification of chance or base case levels of the statistic. Using these measures a system that performs worse in the objective sense of Informedness, can appear to perform better under any of these commonly used measures. We discuss several concepts and measures that reflect the probability that prediction is informed versus chance. Informedness and introduce Markedness as a dual measure for the probability that prediction is marked versus chance. Finally we demonstrate elegant connections between the concepts of Informedness, Markedness, Correlation and Significance as well as their intuitive relationships with Recall and Precision, and outline the extension from the dichotomous case to the general multi-class case. .


title: Review on Computer Vision Techniques in Emergency Situation

abstract: In emergency situations, actions that save lives and limit the impact of hazards are crucial. In order to act, situational awareness is needed to decide what to do. Geolocalized photos and video of the situations as they evolve can be crucial in better understanding them and making decisions faster. Cameras are almost everywhere these days, either in terms of smartphones, installed CCTV cameras, UAVs or others. However, this poses challenges in big data and information overflow. Moreover, most of the time there are no disasters at any given location, so humans aiming to detect sudden situations may not be as alert as needed at any point in time. Consequently, computer vision tools can be an excellent decision support. The number of emergencies where computer vision tools has been considered or used is very wide, and there is a great overlap across related emergency research. Researchers tend to focus on state-of-the-art systems that cover the same emergency as they are studying, obviating important research in other fields. In order to unveil this overlap, the survey is divided along four main axes: the types of emergencies that have been studied in computer vision, the objective that the algorithms can address, the type of hardware needed and the algorithms used. Therefore, this review provides a broad overview of the progress of computer vision covering all sorts of emergencies.


title: Learning Bayesian networks for clinical time series analysis

abstract: Display Omitted An algorithm to learn models from sparse time series data is formulated.Augmented temporal naive Bayes networks are proposed as an effective model.Predictive models for chronic obstructive pulmonary disease are built.Extensive evaluation results indicate the predictive power of the models. IntroductionAutonomous chronic disease management requires models that are able to interpret time series data from patients. However, construction of such models by means of machine learning requires the availability of costly health-care data, often resulting in small samples. We analysed data from chronic obstructive pulmonary disease (COPD) patients with the goal of constructing a model to predict the occurrence of exacerbation events, i.e., episodes of decreased pulmonary health status. MethodsData from 10 COPD patients, gathered with our home monitoring system, were used for temporal Bayesian network learning, combined with bootstrapping methods for data analysis of small data samples. For comparison a temporal variant of augmented naive Bayes models and a temporal nodes Bayesian network (TNBN) were constructed. The performances of the methods were first tested with synthetic data. Subsequently, different COPD models were compared to each other using an external validation data set. ResultsThe model learning methods are capable of finding good predictive models for our COPD data. Model averaging over models based on bootstrap replications is able to find a good balance between true and false positive rates on predicting COPD exacerbation events. Temporal naive Bayes offers an alternative that trades some performance for a reduction in computation time and easier interpretation.


title: Stream-A-Game: An open-source mobile Cloud Gaming platform

abstract: Cloud Gaming is a paradigm shift in that games are not executed on the player's device but on a remote server, which delivers a live video stream of the game over the network to the client and takes input commands in return. We present the first truly mobile open-source cloud gaming system, which runs instances of the Android Open Source Project (AOSP) mobile operating system in the cloud and delivers their output in real time to clients running a lightweight display software. Incorporating AOSP allows using this platform to stream innumerable existing Android mobile games which are designed with touch interfaces and mobile usage scenarios in mind. Utilizing a base of mature open-source components, our platform can be configured and manipulated in any conceivable manner.


title: lambda-Connectedness Determination for Image Segmentation

abstract: Image segmentation is to separate an image into distinct homogeneous regions belonging to different objects. It is an essential step in image analysis and computer vision. This paper compares some segmentation technologies and attempts to find an automated way to better determine the parameters for image segmentation, especially the connectivity value of lambda in lambda-connected segmentation. Based on the theories on the maximum entropy method and Otsu's minimum variance method, we propose:(1)maximum entropy connectedness determination: a method that uses maximum entropy to determine the best lambda value in lambda-connected segmentation, and (2) minimum variance connectedness determination: a method that uses the principle of minimum variance to determine lambda value. Applying these optimization techniques in real images, the experimental results have shown great promise in the development of the new methods. In the end, we extend the above method to more general case in order to compare it with the famous Mumford-Shah method that uses variational principle and geometric measure.


title: Sparse and powerful cortical spikes

abstract: Activity in cortical networks is heterogeneous, sparse and often precisely timed. The functional significance of sparseness and precise spike timing is debated, but our understanding of the developmental and synaptic mechanisms that shape neuronal discharge patterns has improved. Evidence for highly specialized, selective and abstract cortical response properties is accumulating. Singe-cell stimulation experiments demonstrate a high sensitivity of cortical networks to the action potentials of some, but not all, single neurons. It is unclear how this sensitivity of cortical networks to small perturbations comes about and whether it is a generic property of cortex. The unforeseen sensitivity to cortical spikes puts serious constraints on the nature of neural coding schemes.


title: Easy Hyperparameter Search Using Optunity

abstract: Optunity is a free software package dedicated to hyperparameter optimization. It contains various types of solvers, ranging from undirected methods to direct search, particle swarm and evolutionary optimization. The design focuses on ease of use, flexibility, code clarity and interoperability with existing software in all machine learning environments. Optunity is written in Python and contains interfaces to environments such as R and MATLAB. Optunity uses a BSD license and is freely available online at this http URL


title: Disorders of sexual differentiation: II. Diagnosis and treatment

abstract: OBJECTIVES ::: To provide a review and summary of recent advances in the diagnosis and management of disorder(s) of sexual differentiation (DSD), an area that has developed over recent years with implications for the management of children with DSD; and to assess the refinements in the surgical techniques used for genital reconstruction. ::: ::: ::: METHODS ::: Recent publications (in the previous 10 years) were identified using PubMed, as were relevant previous studies, using following keywords; 'diagnosis and management', 'ambiguous genitalia', 'intersex', 'disorders of sexual differentiation', 'genitogram', 'endocrine assessment', 'gender assignment', 'genitoplasty', and 'urogenital sinus'. The findings were reviewed. ::: ::: ::: RESULTS ::: Arbitrary criteria have been developed to select patients likely to have DSD. Unnecessary tests, especially those that require anaesthesia or are associated with radiation exposure, should be limited to situations where a specific question needs to be answered. Laparoscopy is an important diagnostic tool in selected patients. The routine use of multidisciplinary diagnostic and expert surgical teams has become standard. Full disclosure of different therapeutic approaches and their timing is recommended. ::: ::: ::: CONCLUSIONS ::: Diagnostic tests should be tailored according to the available information. Parents and/or patients should be made aware of the paucity of well-designed studies, as these conditions are rare. Unnecessary irreversible surgery should be postponed until a multidisciplinary experienced team, with the parents' and or patients' approval, can make a well-judged decision.


title: Political Bots and the Swedish General Election

abstract: In this paper we present a study of political bots on Twitter and their influence on the Swedish general election in September 2018. We have not restricted our study to bots that are a software program, instead we are interested in any type of bot-like automated behaviour. This includes a human being manually copying or retweeting content repeatedly in a robot-like way with the aim of influencing the interaction between a user and content or with other users. We examined how bots influence discussions about the Swedish general election on Twitter during a three month period starting half a year before the election. For this purpose we trained a machine learning model to recognize automatic behaviour and studied accounts that tweet about the Swedish election. Our results show, among other things, that bot activity is at a similar level to that found in other studies, and that distinctive clusters, for example among right-wing Twitter accounts, can be identified.


title: The surprising secret identity of the semidefinite relaxation of K-means: manifold learning

abstract: In recent years, semidefinite programs (SDP) have been the subject of interesting research in the field of clustering. In many cases, these convex programs deliver the same answers as non-convex alternatives and come with a guarantee of optimality. Unexpectedly, we find that a popular semidefinite relaxation of K-means (SDP-KM), learns manifolds present in the data, something not possible with the original K-means formulation. To build an intuitive understanding of its manifold learning capabilities, we develop a theoretical analysis of SDP-KM on idealized datasets. Additionally, we show that SDP-KM even segregates linearly non-separable manifolds. SDP-KM is convex and the globally optimal solution can be found by generic SDP solvers with polynomial time complexity. To overcome poor performance of these solvers on large datasets, we explore efficient algorithms based on the explicit Gramian representation of the problem. These features render SDP-KM a versatile and interesting tool for manifold learning while remaining amenable to theoretical analysis.


title: Surface Accuracy Improvement of the Yebes 40 Meter Radiotelescope Using Microwave Holography

abstract: The surface improvement of the Centro Astronomico de Yebes (CAY) 40 m radiotelescope with the use of coherent (with-phase) microwave holography is shown. For this purpose, a dual channel Ku-band receiver in prime focus has been developed. It allows holography measurements with unmodulated beacons from geostationary satellites in the Clarke belt. Initial surface measurements revealed an accuracy of 485 μm amplitude-weighted RMS (WRMS) with respect to the best-fit paraboloid. After a few iterations of the measurement and surface adjustment cycle, the accuracy has been improved to 194 μm WRMS. This means an estimated aperture efficiency improvement from 2.6% to 38.2% at 86 GHz for a prime-focus receiver with an illumination equal to the one produced by the holography receiver test feed.


title: LiDAR point clouds correction acquired from a moving car based on CAN-bus data

abstract: In this paper, we investigate the impact of different kind of car trajectories on LiDAR scans. In fact, LiDAR scanning speeds are considerably slower than car speeds introducing distortions. We propose a method to overcome this issue as well as new metrics based on CAN bus data. Our results suggest that the vehicle trajectory should be taken into account when building 3D large-scale maps from a LiDAR mounted on a moving vehicle.


title: Visual Similarity Based 3D Shape Retrieval Using Bag-of-Features

abstract: This paper presents a novel 3D shape retrieval method, which uses Bag-of-Features and an efficient multi-view shape matching scheme. In our approach, a properly normalized object is first described by a set of depth-buffer views captured on the surrounding vertices of a given unit geodesic sphere. We then represent each view as a word histogram generated by the vector quantization of the view’s salient local features. The dissimilarity between two 3D models is measured by the minimum distance of their all (24) possible matching pairs. This paper also investigates several critical issues including the influence of the number of views, codebook, training data, and distance function. Experiments on four commonly-used benchmarks demonstrate that: 1) Our approach obtains superior performance in searching for rigid models. 2) The local feature and global feature based methods are somehow complementary. Moreover, a linear combination of them significantly outperforms the state-of-the-art in terms of retrieval accuracy.


title: Learning Euclidean-to-Riemannian Metric for Point-to-Set Classification

abstract: In this paper, we focus on the problem of point-to-set classification, where single points are matched against sets of correlated points. Since the points commonly lie in Euclidean space while the sets are typically modeled as elements on Riemannian manifold, they can be treated as Euclidean points and Riemannian points respectively. To learn a metric between the heterogeneous points, we propose a novel Euclidean-to-Riemannian metric learning framework. Specifically, by exploiting typical Riemannian metrics, the Riemannian manifold is first embedded into a high dimensional Hilbert space to reduce the gaps between the heterogeneous spaces and meanwhile respect the Riemannian geometry of the manifold. The final distance metric is then learned by pursuing multiple transformations from the Hilbert space and the original Euclidean space (or its corresponding Hilbert space) to a common Euclidean subspace, where classical Euclidean distances of transformed heterogeneous points can be measured. Extensive experiments clearly demonstrate the superiority of our proposed approach over the state-of-the-art methods.


title: Seismic surveying with drone-mounted geophones

abstract: Seismic imaging is the primary technique for subsurface exploration. Traditional seismic imaging techniques rely heavily on manual labor to plant sensors, lay miles of cabling, and then recover the sensors. Often sites of resource or rescue interest may be difficult or hazardous to access. Thus, there is a substantial need for unmanned sensors that can be deployed by air and potentially in large numbers. This paper presents working prototypes of autonomous drones equipped with geophones (vibration sensors) that can fly to a site, land, listen for echoes and vibrations, store the information on-board, and subsequently return to home base. The design uses four geophone sensors (with spikes) in place of the landing gear. This provides a stable landing attitude, redundancy in sensing, and ensures the geophones are oriented perpendicular to the ground. The paper describes hardware experiments demonstrating the efficacy of this technique and a comparison with traditional manual techniques. The performance of the seismic drone was comparable to a well planted geophone, proving the drone mount system is a feasible alternative to traditional seismic sensors.


The Impact of Blockchain Technology on Business Models in the Payments Industry


title: Recent Developments and Future Trends in Volunteered Geographic Information Research: The Case of OpenStreetMap

abstract: User-generated content (UGC) platforms on the Internet have experienced a steep increase in data contributions in recent years. The ubiquitous usage of location-enabled devices, such as smartphones, allows contributors to share their geographic information on a number of selected online portals. The collected information is oftentimes referred to as volunteered geographic information (VGI). One of the most utilized, analyzed and cited VGI-platforms, with an increasing popularity over the past few years, is OpenStreetMap (OSM), whose main goal it is to create a freely available geographic database of the world. This paper presents a comprehensive overview of the latest developments in VGI research, focusing on its collaboratively collected geodata and corresponding contributor patterns. Additionally, trends in the realm of OSM research are discussed, highlighting which aspects need to be investigated more closely in the near future.


title: DCU: Aspect-based Polarity Classification for SemEval Task 4

abstract: We describe the work carried out by DCU on the Aspect Based Sentiment Analysis task at SemEval 2014. Our team submitted one constrained run for the restaurant domain and one for the laptop domain for sub-task B (aspect term polarity prediction), ranking highest out of 36 systems on the restaurant test set and joint highest out of 32 systems on the laptop test set.


title: A vector space model for automatic indexing

abstract: In a document retrieval, or other pattern matching environment where stored entities (documents) are compared with each other or with incoming patterns (search requests), it appears that the best indexing (property) space is one where each entity lies as far away from the others as possible; in these circumstances the value of an indexing system may be expressible as a function of the density of the object space; in particular, retrieval performance may correlate inversely with space density. An approach based on space density computations is used to choose an optimum indexing vocabulary for a collection of documents. Typical evaluation results are shown, demonstating the usefulness of the model.


title: Accountable Privacy for Decentralized Anonymous Payments

abstract: Decentralized ledger-based currencies such as Bitcoin provide a means to construct payment systems without requiring a trusted bank. Removing this trust assumption comes at the significant cost of transaction privacy. A number of academic works have sought to improve the privacy offered by ledger-based currencies using anonymous electronic cash (e-cash) techniques. Unfortunately, this strong degree of privacy creates new regulatory concerns, since the new private transactions cannot be subject to the same controls used to prevent individuals from conducting illegal transactions such as money laundering. We propose an initial approach to addressing this issue by adding privacy preserving policy-enforcement mechanisms that guarantee regulatory compliance, allow selective user tracing, and admit tracing of tainted coins (e.g., ransom payments). To accomplish this new functionality we also provide improved definitions for Zerocash and, of independent interest, an efficient construction for simulation sound zk-SNARKs.


title: Paraptosis-like cell death induced by yessotoxin.

abstract: This study shows that BC3H1 myoblast cell lines exposed to 100 nM yessotoxin (YTX) undergo a form of programmed cell death distinct from apoptosis and with features resembling paraptosis. Morphologically, cells treated with YTX reveal extensive cytoplasmic vacuolation, mitochondrial and endoplasmic reticulum swelling, uncondensed chromatin and cytoskeletal alterations. DNA electrophoresis evidences lack of DNA fragmentation and Western blotting analysis demonstrates activation of the mitogen-activated protein kinase JNK/SAPK1. Further characterisation of this form of programmed cell death may have interest within medicine and cancer therapy.


title: Data analytics for forecasting cell congestion on LTE networks

abstract: This paper presents a methodology for forecasting the average downlink throughput for an LTE cell by using real measurement data collected by multiple LTE probes. The approach uses data analytics techniques, namely forecasting algorithms to anticipate cell congestion events which can then be used by Self-Organizing Network (SON) strategies for triggering network re-configurations, such as shifting coverage and capacity to areas where they are most needed, before subscribers have been impacted by dropped calls or reduced data speeds. The presented implementation results show the prediction of network behaviour is possible with a high level of accuracy, effectively allowing SON strategies to be enforced in time.


title: Fast rumor source identification via random walks

abstract: We consider the problem of inferring the source of a rumor in a given large network. We assume that the rumor propagates in the network through a discrete time susceptible-infected model. Input to our problem includes information regarding the entire network, an infected subgraph of the network observed at some known time instant, and the probability of one-hop rumor propagation. We propose a heuristic based on the hitting time statistics of a surrogate random walk process that can be used to approximate the maximum likelihood estimator of the rumor source. We test the performance of our heuristic on some standard synthetic and real-world network datasets and show that it outperforms many centrality-based heuristics that have traditionally been used in rumor source inference literature. Through time complexity analysis and extensive experimental evaluation, we demonstrate that our heuristic is computationally efficient for large, undirected and dense non-tree networks.


title: Development of an Ultra-Wideband Suspended Stripline to Shielded Microstrip Transition

abstract: A novel ultra-wideband suspended stripline to shielded microstrip transition is presented here with measurement results. The novel transition has less than 0.2 dB insertion loss and better than 15 dB return loss over dc to 40 GHz. This transition design addresses the interconnection challenge between suspended stripline and shielded microstrip circuits, such as in assemblies where the active circuitry is in MS environment, and the passive circuitry is in low-loss SS environment. The described design allows for less critical mechanical tolerance and is less sensitive to misalignment.


title: Nostalgia: content, triggers, functions

abstract: Approximately 3 millennia ago, Homer (1921) composed his epic poem The Odyssey and with it created one of the most gripping literary accounts of nostalgia. The poem revolves around the adventures of Odysseus who, after emerging victoriously from the Trojan War, embarks on a quest to return to his homeland, the island of Ithaca, and reunite with his faithful wife, Penelope. This quest was to last 10 years, 7 of which were spent in the possessive arms of the seductive sea nymph Calypso. In an attempt to persuade Calypso to set him free, Odysseus confides to her, “Full well I acknowledge Prudent Penelope cannot compare with your stature of beauty, for she is only a mortal, and you are immortal and ageless. Nevertheless it is she whom I daily desire and pine for. Therefore I long for my home and to see the day of returning” (Homer, 1921, Book V, pp. 78–79). The Greek words for return and suffering are nostos and algos, respectively. The literal meaning of nostalgia, then, is the suffering caused by the yearning to return to one’s place of origin.


title: The irregular cutting-stock problem - a new procedure for deriving the no-fit polygon

abstract: The nofit polygon is a powerful and effective tool for handling the geometry required for a range of solution approaches to two-dimensional irregular cutting-stock problems. However, unless all the pieces are convex, it is widely perceived as being difficult to implement, and its use has therefore been somewhat limited. The primary purpose of this paper is to correct this misconception by introducing a new method of calculating the nofit polygon. Although it is based on previous approaches which use the mathematical concept of Minkowski sums, this new method can be stated as a set of simple rules that can be implemented without an indepth understanding of the underlying mathematics. The result is an approach that is both very general and easy to use.


title: A digital neurosynaptic core using embedded crossbar memory with 45pJ per spike in 45nm

abstract: The grand challenge of neuromorphic computation is to develop a flexible brain-like architecture capable of a wide array of real-time applications, while striving towards the ultra-low power consumption and compact size of the human brain—within the constraints of existing silicon and post-silicon technologies. To this end, we fabricated a key building block of a modular neuromorphic architecture, a neurosynaptic core, with 256 digital integrate-and-fire neurons and a 1024×256 bit SRAM crossbar memory for synapses using IBM's 45nm SOI process. Our fully digital implementation is able to leverage favorable CMOS scaling trends, while ensuring one-to-one correspondence between hardware and software. In contrast to a conventional von Neumann architecture, our core tightly integrates computation (neurons) alongside memory (synapses), which allows us to implement efficient fan-out (communication) in a naturally parallel and event-driven manner, leading to ultra-low active power consumption of 45pJ/spike. The core is fully configurable in terms of neuron parameters, axon types, and synapse states and is thus amenable to a wide range of applications. As an example, we trained a restricted Boltzmann machine offline to perform a visual digit recognition task, and mapped the learned weights to our chip.


title: Non-Contact Stress Measurement during Tensile Testing Using an Emat for SH0-Plate Wave and Lamb Wave

abstract: The stress on a test specimen during tensile testing is generally measured by a strain gauge. This method has some problems in that it would influence the measurement conditions of the tensile test and can evaluate only the position at which the strain gauge is attached. The acoustoelastic method is proposed as a method replacing the strain gauge method. However, an ultrasonic sensor with a piezoelectric oscillator requires a coupling medium to inject an ultrasonic wave into a solid material. This condition, due to the error factor of the stress measurement, makes it difficult for the ultrasonic sensor to move on the specimen. We then tried to develop a non-contact stress measurement system during tensile testing using an electromagnetic acoustic transducer (EMAT) with an SH0-plate wave and S0-Lamb wave. The EMAT can measure the propagation time in which the ultrasonic wave travels between a receiver and a transmitter without a coupling medium during the tensile testing and can move easily. The interval between the transmitter and the receiver is 10mm and can be moved along the parallel direction or the vertical direction of the tensile load. The transit time was measured by a cross-correlation method and converted into the stress on the test specimen using the acoustoelastic method. We confirmed that the stress measurement using an SH0-plate wave was superior to that with an S0-Lamb wave.


title: Multiple kernel learning for emotion recognition in the wild

abstract: We propose a method to automatically detect emotions in unconstrained settings as part of the 2013 Emotion Recognition in the Wild Challenge [16], organized in conjunction with the ACM International Conference on Multimodal Interaction (ICMI 2013). Our method combines multiple visual descriptors with paralinguistic audio features for multimodal classification of video clips. Extracted features are combined using Multiple Kernel Learning and the clips are classified using an SVM into one of the seven emotion categories: Anger, Disgust, Fear, Happiness, Neutral, Sadness and Surprise. The proposed method achieves competitive results, with an accuracy gain of approximately 10% above the challenge baseline.


title: Analyzing the Network for AWS Distributed Cloud Computing

abstract: Cloud computing is a global service. Cloud Service Providers, such as AWS, allow users to launch VM instances on multiple data centers (regions) around the world. However, the network connectivity and bandwidth between these different geographically distributed regions varies significantly depending on the user's location. In this paper, we analyze the network performance between pairs of AWS instances hosted on all available regions. We leverage our analysis to derive the optimal hosting region for web service providers depending on the customer locations.


title: A mobile application of augmented reality for aerospace maintenance training

abstract: Aircraft maintenance technicians (AMTs) must obtain new levels of job task skill and knowledge to effectively work with modem computer-based avionics and advanced composite materials. Traditional methods of training, such as on-the-job training (OJT), may not have potential to fulfill the training requirements to meet future trends in aviation maintenance. A new instruction delivery system could assist AMTs with job task training and job tasks. The purpose of this research is to analyze the use of an augmented reality (AR) system as a training medium for novice AMTs. An AR system has the potential to enable job task training and job task guidance for the novice technician in a real world environment. An AR system could reduce the cost for training and retraining of AMTs by complementing human information processing and assisting with performance of job tasks. An AR system could eliminate the need to leave the aircraft for the retrieval of information from maintenance manuals for inspection and repair procedures. AR has the potential to supply rapid and accurate feedback to an AMT with any information that he/she needs to successfully complete a job task. New technologies that promote smaller computer-based systems make the application of a mobile AR system possible in the near future.


title: BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions

abstract: DNA methylation is an important epigenetic modification involved in gene regulation, which can now be measured using whole-genome bisulfite sequencing. However, cost, complexity of the data, and lack of comprehensive analytical tools are major challenges that keep this technology from becoming widely applied. Here we present BSmooth, an alignment, quality control and analysis pipeline that provides accurate and precise results even with low coverage data, appropriately handling biological replicates. BSmooth is open source software, and can be downloaded from http://rafalab.jhsph.edu/bsmooth.


title: Differentiation of Level Process Values forDiverse PID Controller Techniques

abstract: The main purport of this paper is to ascertain the best control action for a cylindrical tank level process using PID controllers. A PID controller is the combination of proportional, Integral and derivative control action which possess the ability to reduce the present, past and anticipation of errors. It has the optimal control dynamics of Zero steady state, quick response, no oscillation and higher stability. Initially, the FOPTD with dead time is identified from a real time process using the process modelling of SK method. Various controller tuning methods such as MZN (Modified Ziegler Nichol’s), IMC (Internal Model Control), CHR (Chien Hrones Reswick), ZN (Ziegler Nichol’s) and TL (Tyreus-Luyben) are implemented for this process and based on the examination of time domain specifications and performance error criteria ,the best control action is determined.


title: Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders

abstract: Lesion detection in brain Magnetic Resonance Images (MRI) remains a challenging task. State-of-the-art approaches are mostly based on supervised learning making use of large annotated datasets. Human beings, on the other hand, even non-experts, can detect most abnormal lesions after seeing a handful of healthy brain images. Replicating this capability of using prior information on the appearance of healthy brain structure to detect lesions can help computers achieve human level abnormality detection, specifically reducing the need for numerous labeled examples and bettering generalization of previously unseen lesions. To this end, we study detection of lesion regions in an unsupervised manner by learning data distribution of brain MRI of healthy subjects using auto-encoder based methods. We hypothesize that one of the main limitations of the current models is the lack of consistency in latent representation. We propose a simple yet effective constraint that helps mapping of an image bearing lesion close to its corresponding healthy image in the latent space. We use the Human Connectome Project dataset to learn distribution of healthy-appearing brain MRI and report improved detection, in terms of AUC, of the lesions in the BRATS challenge dataset.


title: FP-DNN: An Automated Framework for Mapping Deep Neural Networks onto FPGAs with RTL-HLS Hybrid Templates

abstract: DNNs (Deep Neural Networks) have demonstrated great success in numerous applications such as image classification, speech recognition, video analysis, etc. However, DNNs are much more computation-intensive and memory-intensive than previous shallow models. Thus, it is challenging to deploy DNNs in both large-scale data centers and real-time embedded systems. Considering performance, flexibility, and energy efficiency, FPGA-based accelerator for DNNs is a promising solution. Unfortunately, conventional accelerator design flows make it difficult for FPGA developers to keep up with the fast pace of innovations in DNNs. To overcome this problem, we propose FP-DNN (Field Programmable DNN), an end-to-end framework that takes TensorFlow-described DNNs as input, and automatically generates the hardware implementations on FPGA boards with RTL-HLS hybrid templates. FP-DNN performs model inference of DNNs with our high-performance computation engine and carefully-designed communication optimization strategies. We implement CNNs, LSTM-RNNs, and Residual Nets with FPDNN, and experimental results show the great performance and flexibility provided by our proposed FP-DNN framework.


title: The impact of operating system scheduling policies and synchronization methods of performance of parallel applications

abstract: Shared-memory multiprocessors are frequently used as compute servers with multiple parallel applications executing at the same time. In such environments, the efficiency of a parallel application can be significantly affected by the operating system scheduling policy. In this paper, we use detailed simulation studies to evaluate the performance of several different scheduling strategies, These include regular priority scheduling, coscheduling or gang scheduling, process control with processor partitioning, handoff scheduling, and affinity-based scheduling. We also explore tradeoffs between the use of busy-waiting and blocking synchronization primitives and their interactions with the scheduling strategies. Since effective use of caches is essential to achieving high performance, a key focus is on the impact of the scheduling strategies on the caching behavior of the applications.Our results show that in situations where the number of processes exceeds the number of processors, regular priority-based scheduling in conjunction with busy-waiting synchronization primitives results in extremely poor processor utilization. In such situations, use of blocking synchronization primitives can significantly improve performance. Process control and gang scheduling strategies are shown to offer the highest performance, and their performance is relatively independent of the synchronization method used. However, for applications that have sizable working sets that fit into the cache, process control performs better than gang scheduling. For the applications considered, the performance gains due to handoff scheduling and processor affinity are shown to be small.


title: Analysis of Different Clustering Techniques in Data and Text Mining

abstract: In recent days clustering becomes important in pattern detection, unsupervised learning process, data concept construction, information retrieval, text mining, web analysis, marketing and medical diagnostic. The purpose of this paper is an attempt to reconnoiter some of the important clustering techniques in the data mining literature and to compare some aspects of clustering algorithms which contains performance, order of input, accuracy, scalability, shapes discovered, dimensionality and dealing with noisy data. The algorithms are Partitional approach, hierarchical approach, seeded approach, ontology approach, concept based approach.


title: Thermal Modeling of Directly Cooled Electric Machines Using Lumped Parameter and Limited CFD Analysis

abstract: This paper presents a practical approach to model thermal effects in directly cooled electric machines. The main focus is put on modeling the heat transfer in the stator winding and to the cooling system, which are the two critical parts of the studied machines from a thermal point of view. A multisegment structure is proposed that divides the stator, winding, and cooling system into a number of angular segments. Thereby, the circumferential temperature variation due to the nonuniform distribution of the coolant in the cooling channels can be predicted. Additionally, partial computational fluid dynamics (CFD) simulations are carried out to model the coolant flow in the cooling channels and also on the outer surface of the end winding bodies. The CFD simulation results are used as input to the analytical models describing the convective heat transfer to the coolant. The modeling approach is attractive due to its simplicity since CFD simulations of the complete machine are avoided. The proposed thermal model is evaluated experimentally on two directly cooled induction machines where the stator winding is impregnated using varnish and epoxy, respectively. A good correspondence between the predicted and measured temperatures under different cooling conditions and loss levels is obtained.


title: Writing Qualitative IS Literature Reviews—Guidelines for Synthesis, Interpretation, and Guidance of Research

abstract: The literature review is an established research genre in many academic disciplines, including the IS discipline. Although many scholars agree that systematic literature reviews should be rigorous, few instructional texts for compiling a solid literature review, at least with regard to the IS discipline, exist. In response to this shortage, in this tutorial, I provide practical guidance for both students and researchers in the IS community who want to methodologically conduct qualitative literature reviews. The tutorial differs from other instructional texts in two regards. First, in contrast to most textbooks, I cover not only searching and synthesizing the literature but also the challenging tasks of framing the literature review, interpreting research findings, and proposing research paths. Second, I draw on other texts that provide guidelines for writing literature reviews in the IS discipline but use many examples of published literature reviews. I use an integrated example of a literature review, which guides the reader through the overall process of compiling a literature review.


title: Evaluation of Interest Point Detectors

abstract: Many different low-level feature detectors exist and it is widely agreed that the evaluation of detectors is important. In this paper we introduce two evaluation criteria for interest points' repeatability rate and information content. Repeatability rate evaluates the geometric stability under different transformations. Information content measures the distinctiveness of features. Different interest point detectors are compared using these two criteria. We determine which detector gives the best results and show that it satisfies the criteria well.


title: Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

abstract: Deep learning tools have gained tremendous attention in applied machine learning. However such tools for regression and classification do not capture model uncertainty. In comparison, Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computational cost. In this paper we develop a new theoretical framework casting dropout training in deep neural networks (NNs) as approximate Bayesian inference in deep Gaussian processes. A direct result of this theory gives us tools to model uncertainty with dropout NNs -- extracting information from existing models that has been thrown away so far. This mitigates the problem of representing uncertainty in deep learning without sacrificing either computational complexity or test accuracy. We perform an extensive study of the properties of dropout's uncertainty. Various network architectures and non-linearities are assessed on tasks of regression and classification, using MNIST as an example. We show a considerable improvement in predictive log-likelihood and RMSE compared to existing state-of-the-art methods, and finish by using dropout's uncertainty in deep reinforcement learning.


title: Multi-source transfer ELM-based Q learning

abstract: Abstract Extreme learning machine (ELM) has advantages of good generalization property, simple structure and convenient calculation. Therefore, an ELM-based Q learning is proposed by using an ELM as a Q-value function approximator, which is suitable for large-scale or continuous space problems. This is the first contribution of this paper. Because the number of ELM hidden layer nodes is equal to that of training samples, large sample size will seriously affect the learning speed. Therefore, a rolling time-window mechanism is introduced into the ELM-based Q learning to reduce the size of training samples of the ELM. In addition, in order to reduce the learning difficulty of new tasks, transfer learning technology is introduced into the ELM-based Q learning. The transfer learning technology can reuse past experience and knowledge to solve current issues. Thus the second contribution is to propose a multi-source transfer ELM-based Q learning (MST-ELMQ), which can take full advantage of valuable information from multiple source tasks and avoid negative transfer resulted from irrelevant information. According to the Bayesian theory, each source task is assigned with a task transfer weight and each source sample is assigned with a sample transfer weight. The task and sample transfer weights determine the number and the manner of transfer samples. Samples with large sample transfer weights are selected from each source task, and assist Q learning agent in quick decision-making for the target task. Simulations results concerning on a boat problem show that MST-ELMQ has better performance than that of Q learning algorithms without or with a single source task, i.e., it can effectively reduce learning difficulty and find an optimal solution with fewer number of training.


title: What does dopamine mean?

abstract: Dopamine is a critical modulator of both learning and motivation. This presents a problem: how can target cells know whether increased dopamine is a signal to learn or to move? It is often presumed that motivation involves slow ('tonic') dopamine changes, while fast ('phasic') dopamine fluctuations convey reward prediction errors for learning. Yet recent studies have shown that dopamine conveys motivational value and promotes movement even on subsecond timescales. Here I describe an alternative account of how dopamine regulates ongoing behavior. Dopamine release related to motivation is rapidly and locally sculpted by receptors on dopamine terminals, independently from dopamine cell firing. Target neurons abruptly switch between learning and performance modes, with striatal cholinergic interneurons providing one candidate switch mechanism. The behavioral impact of dopamine varies by subregion, but in each case dopamine provides a dynamic estimate of whether it is worth expending a limited internal resource, such as energy, attention, or time.


title: Human-in-the-Loop Interpretability Prior

abstract: We often desire our models to be interpretable as well as accurate. Prior work on optimizing models for interpretability has relied on easy-to-quantify proxies for interpretability, such as sparsity or the number of operations required. In this work, we optimize for interpretability by directly including humans in the optimization loop. We develop an algorithm that minimizes the number of user studies to find models that are both predictive and interpretable and demonstrate our approach on several data sets. Our human subjects results show trends towards different proxy notions of interpretability on different datasets, which suggests that different proxies are preferred on different tasks.


title: Capacitive Touch Systems With Styli for Touch Sensors: A Review

abstract: This paper presents the latest progress in development and applications of the capacitive touch systems (CTSs) with styli. The CTSs, which include the touch sensor, analog front-end (AFE) integrated circuit (IC), and micro-controller unit, are reviewed along with the passive and active styli. The architecture of the CTS is explained first, followed by an exploration of the touch sensors: (1) types of touch sensors, such as in-cell, on-cell, and add-on types according to the size and material, (2) AFE IC with the driving and sensing methods, and (3) passive and active styli for the CTS. Finally, the future perspectives of the CTS are given from the viewpoint of the technical developments.


title: Geo-location Based Augmented Reality Application For Cultural Heritage Using Drones

abstract: Augmented Reality (AR) already is very interesting topic for cultural heritage since it can be very easy to reconstruct the ancient building on the site without any physical intervention. With the AR technology, 3D models of ancient buildings can be overimposed to remains of the site. In this way historical sites become more interesting and appealing places. In this study, we developed a geo-location based AR application for Roman Baths of Ankara which is placed in Turkey. Novel part of this study is using drones for video source which model of the bath will be overlaid to it.


title: Dynamic Pricing Without Knowing the Demand Function: Risk Bounds and Near-Optimal Algorithms

abstract: We consider a single-product revenue management problem where, given an initial inventory, the objective is to dynamically adjust prices over a finite sales horizon to maximize expected revenues. Realized demand is observed over time, but the underlying functional relationship between price and mean demand rate that governs these observations (otherwise known as the demand function or demand curve) is not known. We consider two instances of this problem: (i) a setting where the demand function is assumed to belong to a known parametric family with unknown parameter values; and (ii) a setting where the demand function is assumed to belong to a broad class of functions that need not admit any parametric representation. In each case we develop policies that learn the demand function “on the fly,” and optimize prices based on that. The performance of these algorithms is measured in terms of the regret: the revenue loss relative to the maximal revenues that can be extracted when the demand function is known prior to the start of the selling season. We derive lower bounds on the regret that hold for any admissible pricing policy, and then show that our proposed algorithms achieve a regret that is “close” to this lower bound. The magnitude of the regret can be interpreted as the economic value of prior knowledge on the demand function, manifested as the revenue loss due to model uncertainty.


title: SynopSys: large graph analytics in the SAP HANA database through summarization

abstract: Graph-structured data is ubiquitous and with the advent of social networking platforms has recently seen a significant increase in popularity amongst researchers. However, also many business applications deal with this kind of data and can therefore benefit greatly from graph processing functionality offered directly by the underlying database. This paper summarizes the current state of graph data processing capabilities in the SAP HANA database and describes our efforts to enable large graph analytics in the context of our research project SynopSys. With powerful graph pattern matching support at the core, we envision OLAP-like evaluation functionality exposed to the user in the form of easy-to-apply graph summarization templates. By combining them, the user is able to produce concise summaries of large graph-structured datasets. We also point out open questions and challenges that we plan to tackle in the future developments on our way towards large graph analytics.


title: Babelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling

abstract: Babelomics is a response to the growing necessity of integrating and analyzing different types of genomic data in an environment that allows an easy functional interpretation of the results. Babelomics includes a complete suite of methods for the analysis of gene expression data that include normalization (covering most commercial platforms), pre-processing, differential gene expression (case-controls, multiclass, survival or continuous values), predictors, clustering; large-scale genotyping assays (case controls and TDTs, and allows population stratification analysis and correction). All these genomic data analysis facilities are integrated and connected to multiple options for the functional interpretation of the experiments. Different methods of functional enrichment or gene set enrichment can be used to understand the functional basis of the experiment analyzed. Many sources of biological information, which include functional (GO, KEGG, Biocarta, Reactome, etc.), regulatory (Transfac, Jaspar, ORegAnno, miRNAs, etc.), text-mining or protein–protein interaction modules can be used for this purpose. Finally a tool for the de novo functional annotation of sequences has been included in the system. This provides support for the functional analysis of non-model species. Mirrors of Babelomics or command line execution of their individual components are now possible. Babelomics is available at http://www.babelomics.org.


title: Validating Formative Partial Least Squares (PLS) Models: Methodological Review and Empirical Illustration

abstract: The issue of formative constructs, as opposed to th e more frequently used reflective ones, has recently gained momentum among IS and Management re searchers. Most researchers maintain that formative constructs have been understudied, a nd that there is paucity in methodological literature to guide researchers on how such constru cts should be developed and estimated. A survey of IS research has revealed that about 29% o f constructs were misspecified as reflective rather than formative constructs. Furthermore, guid elines about how models containing formative constructs should be indentified and estimated are fragmented and inconsistent. Thus, this paper aims to present a methodological review of formativ e model identification and evaluation. We bring a brief theoretical overview of formative con structs, and put together a guideline for estimating formative measurement and structural mod els. We then present a simplified model composed of three formative constructs and illustra te how it is assessed and estimated using SmartPLS.


title: Cartesian contact force estimation for robotic manipulators using Kalman filters and the generalized momentum

abstract: Estimating contact forces and torques in Cartesian space enables force-controlled robotic applications as well as collision detection without costly additional sensing. A new approach towards online estimation of contact forces and torques at the tool center point from motor torques as well as joint angles and speeds is presented, which is based on analyzing the generalized momentum of the manipulator. Existing generalized momentum based methods are extended by designing a Kalman filter to estimate the generalized momentum as well as contact forces and torques simultaneously. This approach introduces additional degrees of freedom in the design of the estimator that are exploited to increase robustness with respect to disturbances such as uncertainty in joint friction. The method is verified by simulation results obtained from a dynamic model of an ABB YuMi, a dual-arm collaborative robot with 7DOF each arm.


title: Overview of candidate device technologies for storage-class memory

abstract: Storage-class memory (SCM) combines the benefits of a solid-state memory, such as high performance and robustness, with the archival capabilities and low cost of conventional hard-disk magnetic storage. Such a device would require a solid-state nonvolatile memory technology that could be manufactured at an extremely high effective areal density using some combination of sublithographic patterning techniques, multiple bits per cell, and multiple layers of devices. We review the candidate solid-state nonvolatile memory technologies that potentially could be used to construct such an SCM. We discuss evolutionary extensions of conventional flash memory, such as SONOS (silicon-oxide-nitride-oxide-silicon) and nanotraps, as well as a number of revolutionary new memory technologies. We review the capabilities of ferroelectric, magnetic, phase-change, and resistive random-access memories, including perovskites and solid electrolytes, and finally organic and polymeric memory. The potential for practical scaling to ultrahigh effective areal density for each of these candidate technologies is then compared.


title: Biological Aspects of Mountain Pine Beetle in Lodgepole Pine Stands of Different Densities in Colorado, USA

abstract: Research Highlights: The biology of mountain pine beetle (MPB), Dendroctonus ponderosae Hopkins, in Colorado’s lodgepole pine forests exhibits similarities and differences to other parts of its range. Brood emergence was not influenced by stand density nor related to tree diameter. The probability of individual tree attack is influenced by stocking and tree size. Findings have implications for understanding MPB as a disturbance agent and for developing management strategies. Background and Objectives: MPB causes extensive tree mortality of lodgepole pine, Pinus contorta Douglas ex Loudon, across the western US and Canada and is probably the most studied bark beetle in North America. However, most of the current knowledge on the biology and ecology of MPB in lodgepole pine comes from the Intermountain Region of the US and western Canada. Little information is available from Colorado. This is the first study addressing effects of stand stocking levels on the biology of MPB and quantifying phloem consumption. In addition, although data are available on the conditions that foster stand infestation, this is the first study estimating the probability of individual tree attack among stands of known different stocking. Materials and Methods: Studies were conducted in managed lodgepole pine stands in Colorado. Unbaited traps were used to monitor MPB flight across stands of different densities. Cages were used to monitor emergence and bark samples to determine attack densities, and phloem consumption in trees growing under different stocking. Beetle collections were used to determine emergence across the growing season. Tree mortality data from plots of different densities were used to examine the probability of individual tree infestation. Results: More beetles were caught flying through higher density stands. More attacks were observed in lower stocking stands but there were no differences in the number of insects emerging nor phloem consumption. There was no relationship between tree size and beetle emergence. Peak flight occurred in early to mid-August and only one peak of beetle emergence occurred. The probability of tree attack was influenced by stand stocking and tree diameter. Conclusions: In general, aspects of the biology of MPB in Colorado exhibit similarities and differences with other regions. The data suggest the need to more closely examine how MPB functions in stands of different stocking and how the distribution of tree sizes influence the probability of infestation and extent of mortality in stands. Biological characteristics of MPB in Colorado need further examination, particularly as climate change continues to manifest. Baseline information will be critical to refine management approaches, and extend the understanding of how MPB contributes to shape forest composition and structure in Colorado.


title: Conservative voxelization

abstract: We propose a novel hardware-accelerated voxelization algorithm for polygonal models. Compared with previous approaches, our algorithm has a major advantage that it guarantees the conservative correctness in voxelization: every voxel intersecting the input model is correctly recognized. This property is crucial for applications like collision detection, occlusion culling and visibility processing. We also present an efficient and robust implementation of the algorithm in the GPU. Experiments show that our algorithm has a lower memory consumption than previous approaches and is more efficient when the volume resolution is high. In addition, our algorithm requires no preprocessing and is suitable for voxelizing deformable models.


title: GenDocSum+MCLR: Generic document summarization based on maximum coverage and less redundancy

abstract: With the rapid growth of information on the Internet and electronic government recently, automatic multi-document summarization has become an important task. Multi-document summarization is an optimization problem requiring simultaneous optimization of more than one objective function. In this study, when building summaries from multiple documents, we attempt to balance two objectives, content coverage and redundancy. Our goal is to investigate three fundamental aspects of the problem, i.e. designing an optimization model, solving the optimization problem and finding the solution to the best summary. We model multi-document summarization as a Quadratic Boolean Programing (QBP) problem where the objective function is a weighted combination of the content coverage and redundancy objectives. The objective function measures the possible summaries based on the identified salient sentences and overlap information between selected sentences. An innovative aspect of our model lies in its ability to remove redundancy while selecting representative sentences. The QBP problem has been solved by using a binary differential evolution algorithm. Evaluation of the model has been performed on the DUC2002, DUC2004 and DUC2006 data sets. We have evaluated our model automatically using ROUGE toolkit and reported the significance of our results through 95% confidence intervals. The experimental results show that the optimization-based approach for document summarization is truly a promising research direction.


title: MITOS-RCNN: A Novel Approach to Mitotic Figure Detection in Breast Cancer Histopathology Images using Region Based Convolutional Neural Networks

abstract: Studies estimate that there will be 266,120 new cases of invasive breast cancer and 40,920 breast cancer induced deaths in the year of 2018 alone. Despite the pervasiveness of this affliction, the current process to obtain an accurate breast cancer prognosis is tedious and time consuming, requiring a trained pathologist to manually examine histopathological images in order to identify the features that characterize various cancer severity levels. We propose MITOS-RCNN: a novel region based convolutional neural network (RCNN) geared for small object detection to accurately grade one of the three factors that characterize tumor belligerence described by the Nottingham Grading System: mitotic count. Other computational approaches to mitotic figure counting and detection do not demonstrate ample recall or precision to be clinically viable. Our models outperformed all previous participants in the ICPR 2012 challenge, the AMIDA 2013 challenge and the MITOS-ATYPIA-14 challenge along with recently published works. Our model achieved an F-measure score of 0.955, a 6.11% improvement in accuracy from the most accurate of the previously proposed models.


title: Multibody System Dynamics: Roots and Perspectives

abstract: The paper reviews the roots, the state-of-the-art and perspectives of multibody system dynamics. Some historical remarks show that multibody system dynamics is based on classical mechanics and its engineering applications ranging from mechanisms, gyroscopes, satellites and robots to biomechanics. The state-of-the-art in rigid multibody systems is presented with reference to textbooks and proceedings. Multibody system dynamics is characterized by algorithms or formalisms, respectively, ready for computer implementation. As a result simulation and animation are most important. The state-of-the-art in flexible multibody systems is considered in a companion review by Shabana.


title: Cryptographic Processors-A Survey

abstract: Tamper-resistant cryptographic processors are becoming the standard way to enforce data-usage policies. Their origins lie with military cipher machines and PIN processing in banking payment networks, expanding in the 1990s into embedded applications: token vending machines for prepayment electricity and mobile phone credit. Major applications such as GSM mobile phone identification and pay TV set-top boxes have pushed low-cost cryptoprocessors toward ubiquity. In the last five years, dedicated crypto chips have been embedded in devices such as game console accessories and printer ink cartridges, to control product and accessory after markets. The "Trusted Computing" initiative will soon embed cryptoprocessors in PCs so they can identify each other remotely. This paper surveys the range of applications of tamper-resistant hardware and the array of attack and defense mechanisms which have evolved in the tamper-resistance arms race.


title: Information revelation and internet privacy concerns on social network sites: a case study of facebook

abstract: Despite concerns raised about the disclosure of personal information on social network sites, research has demonstrated that users continue to disclose personal information. The present study employs surveys and interviews to examine the factors that influence university students to disclose personal information on Facebook. Moreover, we study the strategies students have developed to protect themselves against privacy threats. The results show that personal network size was positively associated with information revelation, no association was found between concern about unwanted audiences and information revelation and finally, students' Internet privacy concerns and information revelation were negatively associated. The privacy protection strategies employed most often were the exclusion of personal information, the use of private email messages, and altering the default privacy settings. Based on our findings, we propose a model of information revelation and draw conclusions for theories of identity expression.


A novel image alignment and a fast efficient localized euclidean distance minutia matching algorithm for fingerprint recognition system.


title: Multi-Class Segmentation with Relative Location Prior

abstract: Multi-class image segmentation has made significant advances in recent years through the combination of local and global features. One important type of global feature is that of inter-class spatial relationships. For example, identifying "tree" pixels indicates that pixels above and to the sides are more likely to be "sky" whereas pixels below are more likely to be "grass." Incorporating such global information across the entire image and between all classes is a computational challenge as it is image-dependent, and hence, cannot be precomputed. ::: ::: In this work we propose a method for capturing global information from inter-class spatial relationships and encoding it as a local feature. We employ a two-stage classification process to label all image pixels. First, we generate predictions which are used to compute a local relative location feature from learned relative location maps. In the second stage, we combine this with appearance-based features to provide a final segmentation. We compare our results to recent published results on several multi-class image segmentation databases and show that the incorporation of relative location information allows us to significantly outperform the current state-of-the-art.


title: The Risk of Alar Necrosis Associated with Dermal Filler Injection

abstract: BACKGROUNDInjection of dermal fillers is one of the most commonly performed cosmetic procedures. Serious complications from fillers are rare but potentially devastating to patients and physicians. Skin necrosis, such as nasal alar necrosis, is one of the most feared serious complications of dermal f


title: Analysis of Feature Selection Algorithms on Classification: A Survey

abstract: 1. INTRODUCTIONmining is a process of knowledge discovery. The KDD is an automated process of knowledge discovery from the original data. The KDD consists of many steps like data cleaning, data integration, data selection, data transformation, data mining, pattern evaluation and knowledge representation. Among the steps the data selection is very much important to select the relevant feature and remove the irrelevant attributes. Classification is one of the datamining techniques used to discover the unknown class. The different classification methods in data mining are Bayesian classification (Statistical classifier), Decision tree induction, and Rule based classification (IF THEN Rule), Classification using Back propagation (Neural network algorithm), Support vector machine, Classification using Association Rule, k-nearest neighbor classifiers, casebased reasoning classifiers, Rough set approach, Genetic algorithm, Fuzzy set approach.


title: Algorithms for range-skyline queries

abstract: Let S be a set of n points in Rd where each point has t ≥ 1 real-valued attributes called features. A range-skyline query on S takes as input a query box q e Rd and returns the skyline of the points of q ∩ S, computed w.r.t. their features (not their coordinates in Rd). Efficient algorithms are given for computing range-skylines and a related hardness result is established.


CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers


title: The Role of Social Media in Dental Education

abstract: Social media, also known as Web 2.0, includes a set of web-based technologies in which users actively share and create content through open collaboration. The current students in dental school are Millennial learners who are comfortable using social media, such as Facebook and Twitter, for both socialization and learning. This article defines and explores the range of Web 2.0 technologies available for use in dental education, addresses their underlying pedagogy, and discusses potential problems and barriers to their implementation.


Deep Imitation Learning for Playing Real Time Strategy Games


title: FFT implementation and optimization on FPGA

abstract: Nowadays, the development of the Fast Fourier Transform (FFT) remains of a great importance due to its substantial role in the field of signal processing and imagery. This latter still attracts the attention of several researchers around the globe. In this paper, an optimized design of the FFT using the radix-2 algorithm, 32 point is proposed. The developed architecture was implemented using an FPGA regarding its flexibility as well as its parallelism and its computational speed. Though, the material resources of the FPGA are limited, particularly the integrated DSP blocks, a new calculation approach was introduced during the VHDL description with the aim to reduce the necessary number of multiplication operation. The experimental validation of the adopted architecture was realized using a Virtex 6, where the numerical synthesis and the post and route described in VHDL was realized using ISE Design Suite 14.7.


title: Web usage mining for Web site evaluation

abstract: An electrical connector having means providing a signal when the connector members are fully interengaged. The signal means includes a pair of contacts in the two connector members which are axially spaced apart a distance greater than that of the power contacts in the two members so that the power contacts will become electrically coupled prior to the signal contacts when the connector members are mated. The signal contacts are preferably fixedly mounted in the connector insulators and conductors are releasably coupled to the rear portions of such contacts.


title: A Full Soft-Switching ZVZCS Flyback Converter Using an Active Auxiliary Cell

abstract: In this paper, a new soft switching flyback dc–dc converter with a simple zero-voltage and zero-current switching (ZVZCS) cell is proposed. The auxiliary resonant cell consists of one switch, one diode, one inductor, and two capacitors. It provides ZVZCS not only for the main switch but also for the auxiliary switch at both turn-on and turn-off times . In addition, all the auxiliary diodes and body diodes of the switches operate under full ZVZCS except the output diode which turns off only with zero current switching. This soft switching technique makes the proposed converter have less switching losses which leads the converter to operate in higher frequencies and efficiency. Another outstanding feature of the proposed converter is using the auxiliary resonant cell out of the main power path to have lower voltage stress on the switches. In this paper, pulse width modulation (PWM) is employed to control the main switch. A detailed mode analysis of the proposed converter operation is presented along with design procedure. Finally, the accuracy performance of the converter is verified through a 60 W experimental prototype results.


title: Conditioning of three-dimensional generative adversarial networks for pore and reservoir-scale models

abstract: Geostatistical modeling of petrophysical properties is a key step in modern integrated oil and gas reservoir studies. Recently, generative adversarial networks (GAN) have been shown to be a successful method for generating unconditional simulations of pore- and reservoir-scale models. This contribution leverages the differentiable nature of neural networks to extend GANs to the conditional simulation of three-dimensional pore- and reservoir-scale models. Based on the previous work of Yeh et al. (2016), we use a content loss to constrain to the conditioning data and a perceptual loss obtained from the evaluation of the GAN discriminator network. The technique is tested on the generation of three-dimensional micro-CT images of a Ketton limestone constrained by two-dimensional cross-sections, and on the simulation of the Maules Creek alluvial aquifer constrained by one-dimensional sections. Our results show that GANs represent a powerful method for sampling conditioned pore and reservoir samples for stochastic reservoir evaluation workflows.


title: Analyzing and mitigating the impact of permanent faults on a systolic array based neural network accelerator

abstract: Due to their growing popularity and computational cost, deep neural networks (DNNs) are being targeted for hardware acceleration. A popular architecture for DNN acceleration, adopted by the Google Tensor Processing Unit (TPU), utilizes a systolic array based matrix multiplication unit at its core. This paper deals with the design of fault-tolerant, systolic array based DNN accelerators for high defect rate technologies. To this end, we empirically show that the classification accuracy of a baseline TPU drops significantly even at extremely low fault rates (as low as $0.006\%$). We then propose two novel strategies, fault-aware pruning (FAP) and fault-aware pruning+retraining (FAP+T), that enable the TPU to operate at fault rates of up to $50\%$, with negligible drop in classification accuracy (as low as $0.1\%$) and no run-time performance overhead. The FAP+T does introduce a one-time retraining penalty per TPU chip before it is deployed, but we propose optimizations that reduce this one-time penalty to under 12 minutes. The penalty is then amortized over the entire lifetime of the TPU's operation.


title: Empirical support for higher-order theories of conscious awareness

abstract: Higher-order theories of consciousness argue that conscious awareness crucially depends on higher-order mental representations that represent oneself as being in particular mental states. These theories have featured prominently in recent debates on conscious awareness. We provide new leverage on these debates by reviewing the empirical evidence in support of the higher-order view. We focus on evidence that distinguishes the higher-order view from its alternatives, such as the first-order, global workspace and recurrent visual processing theories. We defend the higher-order view against several major criticisms, such as prefrontal activity reflects attention but not awareness, and prefrontal lesion does not abolish awareness. Although the higher-order approach originated in philosophical discussions, we show that it is testable and has received substantial empirical support.


title: Long-term follow-up study of osseointegrated implants in the treatment of totally edentulous jaws.

abstract: This study reviews the long-term outcome of prostheses and fixtures (implants) in 759 totally edentulous jaws of 700 patients. A total of 4,636 standard fixtures were placed and followed according to the osseointegration method for a maximum of 24 years by the original team at the University of Göteborg. Standardized annual clinical and radiographic examinations were conducted as far as possible. A lifetable approach was applied for statistical analysis. Sufficient numbers of fixtures and prostheses for a detailed statistical analysis were present for observation times up to 15 years. More than 95% of maxillae had continuous prosthesis stability at 5 and 10 years, and at least 92% at 15 years. The figure for mandibles was 99% at all time intervals. Calculated from the time of fixture placement, the estimated survival rates for individual fixtures in the maxilla were 84%, 89%, and 92% at 5 years; 81% and 82% at 10 years; and 78% at 15 years. In the mandible they were 91%, 98%, and 99% at 5 years; 89% and 98% at 10 years; and 86% at 15 years. (The different percentages at 5 and 10 years refer to results for different routine groups of fixtures with 5 to 10, 10 to 15, and 1 to 5 years of observation time, respectively.) The results of this study concur with multicenter and earlier results for the osseointegration method.


title: Reciprocal developmental pathways for the generation of pathogenic effector TH17 and regulatory T cells

abstract: On activation, T cells undergo distinct developmental pathways, attaining specialized properties and effector functions. T-helper (TH) cells are traditionally thought to differentiate into TH1 and TH2 cell subsets. TH1 cells are necessary to clear intracellular pathogens and TH2 cells are important for clearing extracellular organisms1,2. Recently, a subset of interleukin (IL)-17-producing T (TH17) cells distinct from TH1 or TH2 cells has been described and shown to have a crucial role in the induction of autoimmune tissue injury3,4,5. In contrast, CD4+CD25+Foxp3+ regulatory T (Treg) cells inhibit autoimmunity and protect against tissue injury6. Transforming growth factor-β (TGF-β) is a critical differentiation factor for the generation of Treg cells7. Here we show, using mice with a reporter introduced into the endogenous Foxp3 locus, that IL-6, an acute phase protein induced during inflammation8,9, completely inhibits the generation of Foxp3+ Treg cells induced by TGF-β. We also demonstrate that IL-23 is not the differentiation factor for the generation of TH17 cells. Instead, IL-6 and TGF-β together induce the differentiation of pathogenic TH17 cells from naive T cells. Our data demonstrate a dichotomy in the generation of pathogenic (TH17) T cells that induce autoimmunity and regulatory (Foxp3+) T cells that inhibit autoimmune tissue injury.


Implantable Sensors and Systems


Pleistocene seedbeds of western Pacific maritime cultures and the importance of chronology


title: AutoBrief: an experimental system for the automatic generation of briefings in integrated text and information graphics

abstract: This paper describes AutoBrief. an experimental intelligent multimedia presentation system that generates presentations in text and information graphics in the domain of transportation scheduling. Acting as an intelligent assistant, AutoBrief creates a presentation to communicate its analysis of alternative schedules. In addition, the multimedia presentation facilitates data exploration through its complex information visualizations and support for direct manipulation of presentation elements. AutoBrief's research contributions include (1) a design enabling a new human-computer interaction style in which intelligent multimedia presentation objects (textual or graphic) can be used by the audience in direct manipulation operations for data exploration. (2) an application-independent approach to multimedia generation based on the representation of communicative goals suitable for both generation of text and of complex information graphics, and (3) an application-independent approach to intelligent graphic design based upon communicative goals. This retrospective overview paper, aimed at a multidisciplinary audience from the fields of human computer interaction and natural language generation, presents AutoBrief's design and design rationale.


title: Ruggedness analysis of 3.3 kV high voltage diodes considering various buffer structures and edge terminations

abstract: Buffer structures and edge termination have a decisive influence on the static and dynamic characteristics of free-wheeling diodes. In this paper the influence of buffer structures at the cathode side, the influence of the design of the edge termination and of a resistive zone at the anode side are analysed with respect to the ruggedness of free-wheeling diodes. Therefore, we investigated the device behaviour by means of numerical device simulation concerning the formation of current filamentation and the correlated shape of the electrical field distribution. The considered edge termination of the diodes was planar junction termination extensions and a beveled edge. Various buffer structures, a Gaussian buffer and a buried n-doped layer of increased doping called epitaxy level buffer are compared with a reference diode without any buffer structure.


title: Epidemic Spread in Dynamic Human Networks

abstract: The emergence of Delay Tolerant Networks (DTNs) has culminated in ::: a new generation of wireless networking. New communication paradigms, which ::: use dynamic interconnectedness as people encounter each other opportunistically, ::: lead towards a world where digital traffic flows more easily. We focus on humanto- ::: human communication in environments that exhibit the characteristics of social ::: networks. This paper describes our study of information flow during epidemic ::: spread in such dynamic human networks, a topic which shares many issues with ::: network-based epidemiology. We explore hub nodes extracted from real world ::: connectivity traces and show their influence on the epidemic to demonstrate the ::: characteristics of information propagation.


title: Recent developments in clustering algorithms

abstract: In this paper, we give a short review of recent developments in clustering. We shortly summarize important clustering paradigms before addressing important topics including metric adaptation in clustering, dealing with non-Euclidean data or large data sets, clustering evaluation, and learning theoretical foundations.


title: Induced Lightning Stroke Identification on Protection Relays

abstract: It is well known that lightning strokes produce mal-operation of transmission line protection relays. Where, protection devices cannot identify correctly between lightning stroke that generates or not permanent faults. Thus, when a flash produces a permanent fault, the protection relay sends a trip order, and the Transmission Line (TL) is disconnected correctly. However, when a lightning stroke does not produce a permanent fault, the protection devices also send the trip order, and the TL is disconnected unnecessary. In this context, these phenomena can produce unnecessary electric power outages, producing damage for the society and economic. Therefore, it is necessary to correctly identify between these lightning stroke signals. Besides that, direct lightning strokes, which hit directly on TLs or transmission towers, have usually been analyzed. However, induced lightning strokes, which hit on ground, have not been considered in previous analyzed. In this paper, in order to identify these induced lightning strokes, an algorithm based on a deterministic focus is proposed.


title: Self confidence and personal motivation

abstract: We analyze the value placed by rational agents on self-confidence, and the strategies employed in its pursuit. Confidence in one's abilities generally enhances motivation, making it a valuable asset for individuals with imperfect willpower. This demand for self-serving beliefs (which can also arise from hedonic or signaling motives) must be weighed against the risks of overconfidence. On the supply side, we develop a model of self-deception through endogenous memory that reconciles the motivated and rational features of human cognition. The resulting intrapersonal game of strategic communication typically leads to multiple equilibria. While "positive thinking" can improve welfare, it can also be self-defeating (and nonetheless pursued).


title: One-Shot Session Recommendation Systems with Combinatorial Items

abstract: In recent years, content recommendation systems in large websites (or \emph{content providers}) capture an increased focus. While the type of content varies, e.g.\ movies, articles, music, advertisements, etc., the high level problem remains the same. Based on knowledge obtained so far on the user, recommend the most desired content. In this paper we present a method to handle the well known user-cold-start problem in recommendation systems. In this scenario, a recommendation system encounters a new user and the objective is to present items as relevant as possible with the hope of keeping the user's session as long as possible. We formulate an optimization problem aimed to maximize the length of this initial session, as this is believed to be the key to have the user come back and perhaps register to the system. In particular, our model captures the fact that a single round with low quality recommendation is likely to terminate the session. In such a case, we do not proceed to the next round as the user leaves the system, possibly never to seen again. We denote this phenomenon a \emph{One-Shot Session}. Our optimization problem is formulated as an MDP where the action space is of a combinatorial nature as we recommend in each round, multiple items. This huge action space presents a computational challenge making the straightforward solution intractable. We analyze the structure of the MDP to prove monotone and submodular like properties that allow a computationally efficient solution via a method denoted by \emph{Greedy Value Iteration} (G-VI).


title: Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms

abstract: Many different machine learning algorithms exist; taking into account each algorithm's hyperparameters, there is a staggeringly large number of possible alternatives overall. We consider the problem of simultaneously selecting a learning algorithm and setting its hyperparameters, going beyond previous work that attacks these issues separately. We show that this problem can be addressed by a fully automated approach, leveraging recent innovations in Bayesian optimization. Specifically, we consider a wide range of feature selection techniques (combining 3 search and 8 evaluator methods) and all classification approaches implemented in WEKA's standard distribution, spanning 2 ensemble methods, 10 meta-methods, 27 base classifiers, and hyperparameter settings for each classifier. On each of 21 popular datasets from the UCI repository, the KDD Cup 09, variants of the MNIST dataset and CIFAR-10, we show classification performance often much better than using standard selection and hyperparameter optimization methods. We hope that our approach will help non-expert users to more effectively identify machine learning algorithms and hyperparameter settings appropriate to their applications, and hence to achieve improved performance.


title: Brain basis of early parent–infant interactions: psychology, physiology, and in vivo functional neuroimaging studies

abstract: Parenting behavior critically shapes human infants’ current and future behavior. The parent–infant relationship provides infants with their first social experiences, forming templates of what they can expect from others and how to best meet others’ expectations. In this review, we focus on the neurobiology of parenting behavior, including our own functional magnetic resonance imaging (fMRI) brain imaging experiments of parents. We begin with a discussion of background, perspectives and caveats for considering the neurobiology of parent–infant relationships. Then, we discuss aspects of the psychology of parenting that are significantly motivating some of the more basic neuroscience research. Following that, we discuss some of the neurohormones that are important for the regulation of social bonding, and the dysregulation of parenting with cocaine abuse. Then, we review the brain circuitry underlying parenting, proceeding from relevant rodent and nonhuman primate research to human work. Finally, we focus on a study-by-study review of functional neuroimaging studies in humans. Taken together, this research suggests that networks of highly conserved hypothalamic–midbrain–limbic–paralimbic–cortical circuits act in concert to support aspects of parent response to infants, including the emotion, attention, motivation, empathy, decision-making and other thinking that are required to navigate the complexities of parenting. Specifically, infant stimuli activate basal forebrain regions, which regulate brain circuits that handle specific nurturing and caregiving responses and activate the brain's more general circuitry for handling emotions, motivation, attention, and empathy – all of which are crucial for effective parenting. We argue that an integrated understanding of the brain basis of parenting has profound implications for mental health.