title: Assessing and moving on from the dominant project management discourse in the light of project overruns

abstract: There has been much prescriptive work in project management, exemplified in various "Bodies of Knowledge". However, experience shows some projects overspending considerably. Recently, systemic modeling research into the behavior of large projects explains project oversponds by "systemic" effects and the (sometimes counterintuitive) effect of management actions. However, while this work is becoming more widely known, embedding the lessons in project-management practice is not straightforward. The current prescriptive dominant discourse of project management contains implicit underlying assumptions with which the systemic modeling work clashes, indeed showing how conventional methods can exacerbate rather than alleviate project problems. Exploration of this modeling suggests that for projects that are complex, uncertain, and time-limited, conventional methods might be inappropriate, and aspects of newer methodologies in which the project "emerges" rather than being fully preplanned might be more appropriate. Some of the current literature on project-classification schemes also suggests similar parameters, without the rationale that the systemic modeling provides, thus providing useful backup to this analysis. The eventual aim of this line of work is to enable project managers to choose effective ways to manage projects based on understanding and model-based theory.


title: Millennial medical anthropology: from there to here and beyond, or the problem of global health.

abstract: While much of Medical Anthropology was and is what we can call “Normal” (following Kuhn) Medical Anthropology, I coined the term Millennial Medical Anthropology for that branch of the discipline that, in the 1990s, was departing from the Normal research paradigms and was deserving of a distinct sobriquet. This paper considers the Strong Program in Medical Anthropology’s Millennial Medical Anthropology and its key subdivisions, the Cultural Studies of Science and Cultural Bioethics. Specifically it considers Medical Anthropology’s movement from the past into an ethical future wherein Normal Biomedicine, Bioethics and Global Health are problematized. This provides the basis for the construction of a truly anthropological global health (i.e., Global, Global Health or Global Health 2.0).


title: Using unmanned aerial vehicle for remote sensing application

abstract: With the availability and development of multisensor, multitemporal, multiresolution, and multifrequency image data from operational Earth observation satellites, more limitations have also appeared in remote sensing image applications, and the most important one is the cost limit. To overcome the limitations, unmanned aerial vehicle (UAV) technology is developped for remote sensing application. It forms a rapidly developing area of research in remote sensing. This review paper describes and explains data acquisition and processing technologies for imageries from UAV, as well as summarizes the current application research with UAV. Then, it presents some problems and prospective for UAV development. It is our hope that this survey will provide guidelines for future applications of UAV and possible areas.


Detecting Duplicate Questions with Deep Learning


title: AOT-controlled dual-mode AVP buck regulator with AEAF mechanism

abstract: A novel adaptive voltage positioning (AVP) buck regulator using adaptive on-time (AOT) control targeted for applications with low-ESR output capacitors is proposed. In this work, AOT control is adapted to keep the system's switching frequency quasi-fixed or independent of the input supply voltage and the AVP mechanism is realized without the need to use conventional error amplifier compensator or extra current-sensing circuit. For ensuring the system's switching frequency not entering the range of acoustic frequency at light load, an AEAF (avoid entering acoustic frequency) circuit is also proposed. For comparison purpose, the implemented buck regulator can be set to operate under AVP or non-AVP mode. This work has been fabricated and verified with a standard 0.18μm CMOS technology. Experimental results show excellent transient recovery time of 4μs (under AVP mode), ±0.11% switching frequency variation (for the specified input voltage range), and 91% peak conversion efficiency.


title: Cuckoo Search Algorithm using Lèvy Flight: A Review

abstract: Cuckoo Search (CS) is a new met heuristic algorithm. It is being used for solving optimization problem. It was developed in 2009 by Xin- She Yang and Susah Deb. Uniqueness of this algorithm is the obligatory brood parasitism behavior of some cuckoo species along with the Levy Flight behavior of some birds and fruit flies. Cuckoo Hashing to Modified CS have also been discussed in this paper. CS is also validated using some test functions. After that CS performance is compared with those of GAs and PSO. It has been shown that CS is superior with respect to GAs and PSO. At last, the effect of the experimental results are discussed and proposed for future research.


title: Continuous Video to Simple Signals for Swimming Stroke Detection with Convolutional Neural Networks

abstract: In many sports, it is useful to analyse video of an athlete in competition for training purposes. In swimming, stroke rate is a common metric used by coaches; requiring a laborious labelling of each individual stroke. We show that using a Convolutional Neural Network (CNN) we can automatically detect discrete events in continuous video (in this case, swimming strokes). We create a CNN that learns a mapping from a window of frames to a point on a smooth 1D target signal, with peaks denoting the location of a stroke, evaluated as a sliding window. To our knowledge this process of training and utilizing a CNN has not been investigated before; either in sports or fundamental computer vision research. Most research has been focused on action recognition and using it to classify many clips in continuous video for action localisation. ::: In this paper we demonstrate our process works well on the task of detecting swimming strokes in the wild. However, without modifying the model architecture or training method, the process is also shown to work equally well on detecting tennis strokes, implying that this is a general process. ::: The outputs of our system are surprisingly smooth signals that predict an arbitrary event at least as accurately as humans (manually evaluated from a sample of negative results). A number of different architectures are evaluated, pertaining to slightly different problem formulations and signal targets.


title: A Wideband, Wide Scanning Tightly Coupled Dipole Array With Integrated Balun (TCDA-IB)

abstract: A key challenge in the design of wideband dipole phased arrays is the design of equally wideband baluns which are sufficiently compact to fit within the unit cell (typically in the linear dimension at low frequencies). In this paper, we exploit the reactance of a compact Marchand balun as an impedance matching network for each array element. The elimination of bulky external baluns results in a significant reduction of size, weight and cost, while the bandwidth is simultaneously improved by over 30%, compared to standard feeding techniques. In this manner, a tightly coupled dipole array with an integrated balun (TCDA-IB) is developed which achieves 7.35:1 bandwidth (0.68 - 5.0 GHz) while scanning to ±45° in all directions, subject to . In a dual-polarization configuration, the TCDA-IB has low cross polarization of over the majority of the band. Measured results are presented for a prototype 8 × 8 element TCDA-IB, showing good agreement with simulation.


title: MEMS technology in seismology: A short review

abstract: Great advancements in the broad field of the seismic monitoring have been achieved in the last decades mainly because of the technical advancement of the instrumentation. Also the seismic monitoring applications greatly developed, mainly since the 1990s owing to the Micro Electro-Mechanical Systems (MEMS) technology. In this short review we outline the applications of the MEMS technology in various sectors of broad field of seismology, distinguishing three main areas of interest and summarizing the state of the art and the most recent progresses.


Architecture and Implementation of a Java Package for Multiple Input Devices (MID


title: Using Deep Q-Learning to Control Optimization Hyperparameters

abstract: We present a novel definition of the reinforcement learning state, actions and reward function that allows a deep Q-network (DQN) to learn to control an optimization hyperparameter. Using Q-learning with experience replay, we train two DQNs to accept a state representation of an objective function as input and output the expected discounted return of rewards, or q-values, connected to the actions of either adjusting the learning rate or leaving it unchanged. The two DQNs learn a policy similar to a line search, but differ in the number of allowed actions. The trained DQNs in combination with a gradient-based update routine form the basis of the Q-gradient descent algorithms. To demonstrate the viability of this framework, we show that the DQN's q-values associated with optimal action converge and that the Q-gradient descent algorithms outperform gradient descent with an Armijo or nonmonotone line search. Unlike traditional optimization methods, Q-gradient descent can incorporate any objective statistic and by varying the actions we gain insight into the type of learning rate adjustment strategies that are successful for neural network optimization.


title: Deep Symbolic Representation Learning for Heterogeneous Time-series Classification

abstract: In this paper, we consider the problem of event classification with multi-variate time series data consisting of heterogeneous (continuous and categorical) variables. The complex temporal dependencies between the variables combined with sparsity of the data makes the event classification problem particularly challenging. Most state-of-art approaches address this either by designing hand-engineered features or breaking up the problem over homogeneous variates. In this work, we propose and compare three representation learning algorithms over symbolized sequences which enables classification of heterogeneous time-series data using a deep architecture. The proposed representations are trained jointly along with the rest of the network architecture in an end-to-end fashion that makes the learned features discriminative for the given task. Experiments on three real-world datasets demonstrate the effectiveness of the proposed approaches.


title: Docking analysis of hexanoic acid and quercetin with seven domains of polyketide synthase A provided insight into quercetin-mediated aflatoxin biosynthesis inhibition in Aspergillus flavus

abstract: Studies on phytochemicals as anti-aflatoxigenic agents have gained importance including quercetin. Thus, to understand the molecular mechanism behind inhibition of aflatoxin biosynthesis by quercetin, interaction study with polyketide synthase A (PksA) of Aspergillus flavus was undertaken. The 3D structure of seven domains of PksA was modeled using SWISS-MODEL server and docking studies were performed by Autodock tools-1.5.6. Docking energies of both the ligands (quercetin and hexanoic acid) were compared with each of the domains of PksA enzyme. Binding energy for quercetin was lesser that ranged from − 7.1 to − 5.25 kcal/mol in comparison to hexanoic acid (− 4.74 to − 3.54 kcal/mol). LigPlot analysis showed the formation of 12 H bonds in case of quercetin and 8 H bonds in hexanoic acid. During an interaction with acyltransferase domain, both ligands showed H bond formation at Arg63 position. Also, in product template domain, quercetin creates four H bonds in comparison to one in hexanoic acid. Our quantitative RT-PCR analysis of genes from aflatoxin biosynthesis showed downregulation of pksA, aflD, aflR, aflP and aflS at 24 h time point in comparison to 7 h in quercetin-treated A. flavus. Overall results revealed that quercetin exhibited the highest level of binding potential (more number of H bonds) with PksA domain in comparison to hexanoic acid; thus, quercetin possibly inhibits via competitively binding to the domains of polyketide synthase, a key enzyme of aflatoxin biosynthetic pathway. Further, we propose that key enzymes from aflatoxin biosynthetic pathway in aflatoxin-producing Aspergilli could be explored further using other phytochemicals as inhibitors.


title: The hunt for the CIA: factors which demonstrate corticotrophin-inhibitory activity

abstract: Cette revue fait la synthese des travaux consacres a l'investigation des facteurs ayant une activite inhibitrice de la corticotrophine


title: Corpus specificity in LSA and Word2vec: the role of out-of-domain documents

abstract: Latent Semantic Analysis (LSA) and Word2vec are some of the most widely used word embeddings. Despite the popularity of these techniques, the precise mechanisms by which they acquire new semantic relations between words remain unclear. In the present article we investigate whether LSA and Word2vec capacity to identify relevant semantic dimensions increases with size of corpus. One intuitive hypothesis is that the capacity to identify relevant dimensions should increase as the amount of data increases. However, if corpus size grow in topics which are not specific to the domain of interest, signal to noise ratio may weaken. Here we set to examine and distinguish these alternative hypothesis. To investigate the effect of corpus specificity and size in word-embeddings we study two ways for progressive elimination of documents: the elimination of random documents vs. the elimination of documents unrelated to a specific task. We show that Word2vec can take advantage of all the documents, obtaining its best performance when it is trained with the whole corpus. On the contrary, the specialization (removal of out-of-domain documents) of the training corpus, accompanied by a decrease of dimensionality, can increase LSA word-representation quality while speeding up the processing time. Furthermore, we show that the specialization without the decrease in LSA dimensionality can produce a strong performance reduction in specific tasks. From a cognitive-modeling point of view, we point out that LSA's word-knowledge acquisitions may not be efficiently exploiting higher-order co-occurrences and global relations, whereas Word2vec does.


title: Balancing Circuit New Control for Supercapacitor Storage System Lifetime Maximization

abstract: Energy storage elements such as supercapacitors are widely used in high-power applications. However, due to single cell voltage limitation, an energy storage system with a large number of supercapacitors is often employed. Energy management systems are associated to energy storage systems in order to assure user and equipment safety. Balancing circuits, which enable the equalization of the voltage of each element in series, are a part of the energy management system device. The work presented in this paper aims to enhance the lifetime of energy storage systems. It relies on better controlling balancing circuits on the terminals of the storage system elements. With the conventional function of balancing circuit, the energy storage system is limited by its weakest element that may fail prematurely. Thus, a new balancing approach is presented, discussed, and analyzed. It is based on the elements degradation level prediction. The model predictive control used with the new approach aims to equalize aging speed between elements of a module and ensures a maximum lifetime to the energy storage system. A comparison with the conventional control shows that adopting this new approach, with the same equipment, can enhance the storage system's lifetime by dozens percent.


title: From ML to Ada: Strongly-Typed Language Interoperability via Source Translation

abstract: We describe a system that supports source-level integration of ML-like functional language code with ANSI C or Ada83 code. The system works by translating the functional code into type-correct, ‘vanilla’ C or Ada; it offers simple, efficient, type-safe inter-operation between new functional code components and ‘legacy’ third-generation-language components. Our translator represents a novel synthesis of techniques including user-parameterized specification of primitive types and operators; removal of polymorphism by code specialization; removal of higher-order functions using closure datatypes and interpretation; and aggressive optimization of the resulting first-order code, which can be viewed as encoding the result of a closure analysis. Programs remain fully typed at every stage of the translation process, using only simple, standard type systems. Target code runs at speeds comparable to the output of current optimizing ML compilers, even though handicapped by a conservative garbage collector.


title: The geometry of nesting problems: A tutorial

abstract: Cutting and packing problems involving irregular shapes is an important problem variant with a wide variety of industrial applications. Despite its relevance to industry, research publications are relatively low when compared to other cutting and packing problems. One explanation offered is the perceived difficulty and substantial time investment of developing a geometric tool box to assess computer generated solutions. In this paper we set out to provide a tutorial covering the core geometric methodologies currently employed by researchers in cutting and packing of irregular shapes. The paper is not designed to be an exhaustive survey of the literature but instead will draw on the literature to illustrate the theory and implementation of the approaches. We aim to provide a sufficiently instructive description to equip new and current researchers in the area to select the most appropriate methodology for their needs.


title: Unsupervised Entailment Detection between Dependency Graph Fragments

abstract: Entailment detection systems are generally designed to work either on single words, relations or full sentences. We propose a new task -- detecting entailment between dependency graph fragments of any type -- which relaxes these restrictions and leads to much wider entailment discovery. An unsupervised framework is described that uses intrinsic similarity, multi-level extrinsic similarity and the detection of negation and hedged language to assign a confidence score to entailment relations between two fragments. The final system achieves 84.1% average precision on a data set of entailment examples from the biomedical domain.


title: NOMA in 5G Systems: Exciting Possibilities for Enhancing Spectral Efficiency

abstract: This article provides an overview of power-domain non-orthogonal multiple access for 5G systems. The basic concepts and benefits are briefly presented, along with current solutions and standardization activities. In addition, limitations and research challenges are discussed.


title: The Monte Carlo Method

abstract: A description of the many facets of the Monte Carlo Method is presented. The subject is traversed from the most elementary to the more difficult techniques, and from the least practical to the most fruitful applications. The generation of random numbers in the modern electronic computing machine is dealt with. Most of the material is of an expository nature, but the section on the solution of elliptic partial differential equations with certain boundary conditions is new. Some general remarks on evaluating the method are given. (W.D.M.)


title: Electrokinetic remediation: challenging and optimization of electrolyte for sulfate removal in textile effluent-contaminated farming soil

abstract: In the current situation, soil salinity has increased due to the uncontrolled discharge of industrial effluent. In the present investigation, an electrokinetic (EK) technique was used for the inclusion and removal of sulfate in farming soil. Initially, the influence of the electrode distance was evaluated by the use of two cell configurations. The nature of the catholyte was assessed for the inclusion of sulfate in the soil. Decolorized textile effluent and 5% Na2SO4 were tested as the catholyte and double-distilled water as the anolyte for the inclusion process. Subsequently, selection of the electrolyte concentration and pH was investigated for the removal of sulfate concentrations in contaminated farming soil. Optimization of the pH and electrolyte for the process of sulfate removal in farming soil was performed and a mechanism of the removal process was proposed. A total of 96% sulfate removal was achieved using 0.01 M hydrochloric acid at a pH of 4.5 for a 96 h EK experiment. The initial conductivity of the soil was 5.32 dS m−1, and after sulfate removal the conductivity was about 0.714 dS m−1 and the energy consumption was 119 kW h m−3 for the farming soil.


title: The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration

abstract: The value of any kind of data is greatly enhanced when it exists in a form that allows it to be integrated with other data. One approach to integration is through the annotation of multiple bodies of data using common controlled vocabularies or 'ontologies'. Unfortunately, the very success of this approach has led to a proliferation of ontologies, which itself creates obstacles to integration. The Open Biomedical Ontologies (OBO) consortium is pursuing a strategy to overcome this problem. Existing OBO ontologies, including the Gene Ontology, are undergoing coordinated reform, and new ontologies are being created on the basis of an evolving set of shared principles governing ontology development. The result is an expanding family of ontologies designed to be interoperable and logically well formed and to incorporate accurate representations of biological reality. We describe this OBO Foundry initiative and provide guidelines for those who might wish to become involved.


title: Breaking the Closed-World Assumption in Stylometric Authorship Attribution

abstract: Stylometry is a form of authorship attribution that relies on the linguistic information found in a document. While there has been significant work in stylometry, most research focuses on the closed-world problem where the author of the document is in a known suspect set. For open-world problems where the author may not be in the suspect set, traditional classification methods are ineffective. This paper proposes the “classify-verify” method that augments classification with a binary verification step evaluated on stylometric datasets. This method, which can be generalized to any domain, significantly outperforms traditional classifiers in open-world settings and yields an F1-score of 0.87, comparable to traditional classifiers in closed-world settings. Moreover, the method successfully detects adversarial documents where authors deliberately change their styles, a problem for which closed-world classifiers fail.


title: Full-arch implant fixed prostheses: a comparative study on the effect of connection type and impression technique on accuracy of fit.

abstract: PURPOSE ::: The aim of this study was to assess the effect of connection type and impression technique on the accuracy of fit of implant-supported fixed complete-arch dental prostheses (IFCDPs). ::: ::: ::: MATERIALS AND METHODS ::: An edentulous mandibular cast with five implants was fabricated to serve as master cast (control) for both implant- and abutment-level baselines. A titanium one-piece framework for an IFCDP was milled at abutment level and used for accuracy of fit measurements. Polyether impressions were made using a splinted and non-splinted technique at the implant and abutment level leading to four test groups, n = 10 each. Hence, four groups of test casts were generated. The impression accuracy was evaluated indirectly by assessing the fit of the IFCDP framework on the generated casts of the test groups, clinically and radiographically. Additionally, the control and all test casts were digitized with a high-resolution reference scanner (IScan D103i, Imetric, Courgenay, Switzerland) and standard tessellation language datasets were generated and superimposed. Potential correlations between the clinical accuracy of fit data and the data from the digital scanning were investigated. To compare the accuracy of casts of the test groups versus the control at the implant and abutment level, Fisher's exact test was used. ::: ::: ::: RESULTS ::: Of the 10 casts of test group I (implant-level splint), all 10 presented with accurate clinical fit when the framework was seated on its respective cast, while only five of 10 casts of test group II (implant-level non-splint) showed adequate fit. All casts of group III (abutment-level splint) presented with accurate fit, whereas nine of 10 of the casts of test group IV (abutment-level non-splint) were accurate. Significant 3D deviations (P < 0.05) were found between group II and the control. No statistically significant differences were found between groups I, III, and IV compared with the control. Implant connection type (implant level vs. abutment level) and impression technique did affect the 3D accuracy of implant impressions only with the non-splint technique (P < 0.05). ::: ::: ::: CONCLUSION ::: For one-piece IFCDPs, the implant-level splinted impression technique showed to be more accurate than the non-splinted approach, whereas at the abutment-level, no difference in the accuracy was found.


title: Idiopathic Scoliosis in Adolescents

abstract: Idiopathic scoliosis affects 2% of adolescents. In adolescents with an immature skeleton, bracing is commonly recommended if there is curve progression to 25 to 45 degrees. A randomized trial comparing bracing with watchful waiting is under way.


title: Survey Design News - Edition 4 (March 2004)

abstract: A vital portion of conceptual analysis is the identification and definition of the semantic structure of a domain. This paper discusses semantic structure, contrasting it with semantic content, and describes the role of both in formal descriptions of domain concepts. The ER model offers the scope and expressive capacity appropriate for representing semantic structure (but is inadequate for modeling semantic content). Use of the ER technique for analysis of semantic structure in survey design is presented. The ER diagram for a survey explicitly depicts how a population is defined, and how data are obtained. The paper closes with a discussion of the contribution of the ER model to the design and documentation of a survey.


title: Kaldi+PDNN: Building DNN-based ASR Systems with Kaldi and PDNN

abstract: The Kaldi toolkit is becoming popular for constructing automated speech recognition (ASR) systems. Meanwhile, in recent years, deep neural networks (DNNs) have shown state-of-the-art performance on various ASR tasks. This document describes our open-source recipes to implement fully-fledged DNN acoustic modeling using Kaldi and PDNN. PDNN is a lightweight deep learning toolkit developed under the Theano environment. Using these recipes, we can build up multiple systems including DNN hybrid systems, convolutional neural network (CNN) systems and bottleneck feature systems. These recipes are directly based on the Kaldi Switchboard 110-hour setup. However, adapting them to new datasets is easy to achieve.


title: Depth from Monocular Images using a Semi-Parallel Deep Neural Network (SPDNN) Hybrid Architecture

abstract: Computing pixel depth values provide a basis for understanding the 3D geometrical structure of an image. As it has been presented in recent research, using stereo images provides an accurate depth due to the advantage of having local correspondences; however, the processing time of these methods are still an open issue. To solve this problem, it has been suggested to use single images to compute the depth values but extracting depth from monocular images requires extracting a large number of cues from the global and local information in the image. This challenge has been studied for a decade and it is still an open problem. Recently the idea of using neural networks to solve this problem has attracted attention. In this paper, we tackle this challenge by employing a Deep Neural Network (DNN) equipped with semantic pixel-wise segmentation utilizing our recently published disparity post-processing method. Four models are trained in this study and they have been evaluated at 2 stages on KITTI dataset. The ground truth images in the first part of the experiment come from the benchmark and for the second part, the ground truth images are considered to be the disparity results from applying a state-of-art stereo matching method. The results of this evaluation demonstrate that using post-processing techniques to refine the target of the network increases the accuracy of depth estimation on individual mono images. The second evaluation shows that using segmentation data as the input can improve the depth estimation results to a point where performance is comparable with stereo depth matching.


title: Underground structure monitoring with wireless sensor networks

abstract: Environment monitoring in coal mines is an important application of wireless sensor networks (WSNs) that has commercial potential. We discuss the design of a structure-aware self-adaptive WSN system, SASA. By regulating the mesh sensor network deployment and formulating a collaborative mechanism based on a regular beacon strategy, SASA is able to rapidly detect structure variations caused by underground collapses. A prototype is deployed with 27 Mica2 motes. We present our implementation experiences as well as the experimental results. To better evaluate the scalability and reliability of SASA, we also conduct a large-scale trace-driven simulation based on real data collected from the experiments.


title: ABLE, an innovative transparent exoskeleton for the upper-limb

abstract: This paper presents ABLE, an innovative exoskeleton for the upper limb currently under development at CEA-LIST Interactive Robotics Unit. Its distinctive high performance mechanical transmissions - screw and cable patented arrangement - and its integrated architecture makes it the very first of its category. The first 4 axis model is described here but more complete models are already planned: a 7 axis model as well as portable versions. Its back-drivable, high efficiency, low inertia actuators provide a high capacity (around 40 N continuous effort at the hand) and allow hybrid force-position control without requiring any force sensor. Its first application is currently a research program in rehabilitation (BRAHMA project) and professional use is already in view. Assistance tasks for disabled persons (carrying a bottle) are also typical tasks to be performed by ABLE as well as intuitive telerobotics, haptic device for VR, and sport training, etc. Furthermore, its versatility and simple design allow industrial versions to become soon available.


title: Heterologous expression of anti-apoptotic human 14-3-3β/α enhances iron-mediated programmed cell death in yeast

abstract: The induction of Programmed Cell Death (PCD) requires the activation of complex responses involving the interplay of a variety of different cellular proteins, pathways, and processes. Uncovering the mechanisms regulating PCD requires an understanding of the different processes that both positively and negatively regulate cell death. Here we have examined the response of normal as well as PCD resistant yeast cells to different PCD inducing stresses. As expected cells expressing the pro-survival human 14-3-3β/α sequence show increased resistance to numerous stresses including copper and rapamycin. In contrast, other stresses including iron were more lethal in PCD resistant 14-3-3β/α expressing cells. The increased sensitivity to PCD was not iron and 14-3-3β/α specific since it was also observed with other stresses (hydroxyurea and zinc) and other pro-survival sequences (human TC-1 and H-ferritin). Although microscopical examination revealed little differences in morphology with iron or copper stresses, cells undergoing PCD in response to high levels of prolonged copper treatment were reduced in size. This supports the interaction some forms of PCD have with the mechanisms regulating cell growth. Analysis of iron-mediated effects in yeast mutant strains lacking key regulators suggests that a functional vacuole is required to mediate the synergistic effects of iron and 14-3-3β/α on yeast PCD. Finally, mild sub-lethal levels of copper were found to attenuate the observed inhibitory effects of iron. Taken together, we propose a model in which a subset of stresses like iron induces a complex process that requires the cross-talk of two different PCD inducing pathways.


title: Resampling Forgery Detection Using Deep Learning and A-Contrario Analysis

abstract: The amount of digital imagery recorded has recently grown exponentially, and with the advancement of software, such as Photoshop or Gimp, it has become easier to manipulate images. However, most images on the internet have not been manipulated and any automated manipulation detection algorithm must carefully control the false alarm rate. In this paper we discuss a method to automatically detect local resampling using deep learning while controlling the false alarm rate using a-contrario analysis. The automated procedure consists of three primary steps. First, resampling features are calculated for image blocks. A deep learning classifier is then used to generate a heatmap that indicates if the image block has been resampled. We expect some of these blocks to be falsely identified as resampled. We use a-contrario hypothesis testing to both identify if the patterns of the manipulated blocks indicate if the image has been tampered with and to localize the manipulation. We demonstrate that this strategy is effective in indicating if an image has been manipulated and localizing the manipulations.


title: BiSet: Semantic Edge Bundling with Biclusters for Sensemaking

abstract: Identifying coordinated relationships is an important task in data analytics. For example, an intelligence analyst might want to discover three suspicious people who all visited the same four cities. Existing techniques that display individual relationships, such as between lists of entities, require repetitious manual selection and significant mental aggregation in cluttered visualizations to find coordinated relationships. In this paper, we present BiSet, a visual analytics technique to support interactive exploration of coordinated relationships. In BiSet, we model coordinated relationships as biclusters and algorithmically mine them from a dataset. Then, we visualize the biclusters in context as bundled edges between sets of related entities. Thus, bundles enable analysts to infer task-oriented semantic insights about potentially coordinated activities. We make bundles as first class objects and add a new layer, “in-between”, to contain these bundle objects. Based on this, bundles serve to organize entities represented in lists and visually reveal their membership. Users can interact with edge bundles to organize related entities, and vice versa, for sensemaking purposes. With a usage scenario, we demonstrate how BiSet supports the exploration of coordinated relationships in text analytics.


title: Real-Time Ethernet - Industry Prospective

abstract: After more than ten years of experience with applications of fieldbus in automation technology, the industry has started to develop and adopt Real-Time Ethernet (RTE) solutions. There already exists now more than ten proposed solutions. International Electrotechnical Commission standards are trying to give a guideline and selection criteria based on recognized indicators for the user.


title: Network-based analysis of diagnosis progression patterns using claims data

abstract: In recent years, several network models have been introduced to elucidate the relationships between diseases. However, important risk factors that contribute to many human diseases, such as age, gender and prior diagnoses, have not been considered in most networks. Here, we construct a diagnosis progression network of human diseases using large-scale claims data and analyze the associations between diagnoses. Our network is a scale-free network, which means that a small number of diagnoses share a large number of links, while most diagnoses show limited associations. Moreover, we provide strong evidence that gender, age and disease class are major factors in determining the structure of the disease network. Practically, our network represents a methodology not only for identifying new connectivity that is not found in genome-based disease networks but also for estimating directionality, strength, and progression time to transition between diseases considering gender, age and incidence. Thus, our network provides a guide for investigators for future research and contributes to achieving precision medicine.


title: TriResNet: A Deep Triple-stream Residual Network for Histopathology Grading

abstract: While microscopic analysis of histopathological slides is generally considered as the gold standard method for performing cancer diagnosis and grading, the current method for analysis is extremely time consuming and labour intensive as it requires pathologists to visually inspect tissue samples in a detailed fashion for the presence of cancer. As such, there has been significant recent interest in computer aided diagnosis systems for analysing histopathological slides for cancer grading to aid pathologists to perform cancer diagnosis and grading in a more efficient, accurate, and consistent manner. In this work, we investigate and explore a deep triple-stream residual network (TriResNet) architecture for the purpose of tile-level histopathology grading, which is the critical first step to computer-aided whole-slide histopathology grading. In particular, the design mentality behind the proposed TriResNet network architecture is to facilitate for the learning of a more diverse set of quantitative features to better characterize the complex tissue characteristics found in histopathology samples. Experimental results on two widely-used computer-aided histopathology benchmark datasets (CAMELYON16 dataset and Invasive Ductal Carcinoma (IDC) dataset) demonstrated that the proposed TriResNet network architecture was able to achieve noticeably improved accuracies when compared with two other state-of-the-art deep convolutional neural network architectures for histopathology grading. Based on these promising results, the hope is that the proposed TriResNet network architecture could become a useful tool to aiding pathologists increase the consistency, speed, and accuracy of the histopathology grading process.


title: Generative Adversarial Networks for Electronic Health Records: A Framework for Exploring and Evaluating Methods for Predicting Drug-Induced Laboratory Test Trajectories

abstract: Generative Adversarial Networks (GANs) represent a promising class of generative networks that combine neural networks with game theory. From generating realistic images and videos to assisting musical creation, GANs are transforming many fields of arts and sciences. However, their application to healthcare has not been fully realized, more specifically in generating electronic health records (EHR) data. In this paper, we propose a framework for exploring the value of GANs in the context of continuous laboratory time series data. We devise an unsupervised evaluation method that measures the predictive power of synthetic laboratory test time series. Further, we show that when it comes to predicting the impact of drug exposure on laboratory test data, incorporating representation learning of the training cohorts prior to training GAN models is beneficial.


title: Dopamine release in the basal ganglia

abstract: Dopamine (DA) is a key transmitter in the basal ganglia, yet DA transmission does not conform to several aspects of the classic synaptic doctrine. Axonal DA release occurs through vesicular exocytosis and is action potential- and Ca2+ -dependent. However, in addition to axonal release, DA neurons in midbrain exhibit somatodendritic release by an incompletely understood, but apparently exocytotic, mechanism. Even in striatum, axonal release sites are controversial, with evidence for DA varicosities that lack postsynaptic specialization, and largely extrasynaptic DA receptors and transporters. Moreover, DA release is often assumed to reflect a global response to a population of activities in midbrain DA neurons, whether tonic or phasic, with precise timing and specificity of action governed by other basal ganglia circuits. This view has been reinforced by anatomical evidence showing dense axonal DA arbors throughout striatum, and a lattice network formed by DA axons and glutamatergic input from cortex and thalamus. Nonetheless, localized DA transients are seen in vivo using voltammetric methods with high spatial and temporal resolution. Mechanistic studies using similar methods in vitro have revealed local regulation of DA release by other transmitters and modulators, as well as by proteins known to be disrupted in Parkinson's disease and other movement disorders. Notably, the actions of most other striatal transmitters on DA release also do not conform to the synaptic doctrine, with the absence of direct synaptic contacts for glutamate, GABA, and acetylcholine (ACh) on striatal DA axons. Overall, the findings reviewed here indicate that DA signaling in the basal ganglia is sculpted by cooperation between the timing and pattern of DA input and those of local regulatory factors. ::: ::: This article is part of a Special Issue entitled: Function and Dysfunction of the Basal Ganglia.


A development pathway to optimise sustainability of water investment and minimise social cost


title: Epidemiology and clinical significance of hepatotropic infections in dialysis patients. Recent evidence.

abstract: Hepatitis C virus (HCV) infection is frequent among patients receiving long-term dialysis in developed and developing countries. It is difficult to assess the natural history of HCV in the dialysis population; however, recent studies have demonstrated that positive anti-HCV status is a significant and independent risk factor for mortality among dialysis patients. Recent meta-analyses have shown that interferon (IFN) initial monotherapy is effective in the treatment of chronic hepatitis C among dialysis patients, but tolerance to IFN mono-therapy was rather poor. Large, multicenter and prospective trials based on pegylated IFN for the treatment of chronic hepatitis C are planned. The frequency of HBV infection in patients undergoing maintenance dialysis in the industrialized world is low but not negligible; persistent HBsAg seropositivity is much higher in less-developed countries. Recent surveys have shown that detectable HBsAg/ HBV DNA status in serum is an independent and significant predictive factor for hepatocellular dysfunction in dialysis patients. No significant difference in morbidity and mortality between dialysis patients according to hepatitis B surface antigen status has been consistently shown. Preliminary reports suggest that lamivudine appears to be safe and effective in patients receiving long-term dialysis.


A Paradox of Unity


title: Closed-loop IGBT gate drive featuring highly dynamic di/dt and dv/dt control

abstract: In this paper, a closed-loop active IGBT gate drive providing highly dynamic di C /dt and dv CE /dt control is proposed. By means of using only simple passive measurement circuits for the generation of the feedback signals and a single operational amplifier as PI-controller, high analog control bandwidth is achieved enabling the application even for switching times in the sub-microsecond range.


title: Learning feature engineering for classification

abstract: Feature engineering is the task of improving predictive modelling performance on a dataset by transforming its feature space. Existing approaches to automate this process rely on either transformed feature space exploration through evaluation-guided search, or explicit expansion of datasets with all transformed features followed by feature selection. Such approaches incur high computational costs in runtime and/or memory. We present a novel technique, called Learning Feature Engineering (LFE), for automating feature engineering in classification tasks. LFE is based on learning the effectiveness of applying a transformation (e.g., arithmetic or aggregate operators) on numerical features, from past feature engineering experiences. Given a new dataset, LFE recommends a set of useful transformations to be applied on features without relying on model evaluation or explicit feature expansion and selection. Using a collection of datasets, we train a set of neural networks, which aim at predicting the transformation that impacts classification performance positively. Our empirical results show that LFE outperforms other feature engineering approaches for an overwhelming majority (89%) of the datasets from various sources while incurring a substantially lower computational cost.


SoilJ: An ImageJ Plugin for the Semiautomatic Processing of Three-Dimensional X-ray Images of Soils


title: Effects of Cartilage Scoring in Correction of Prominent Ear with Incisionless Otoplasty Technique in Pediatric Patients

abstract: OBJECTIVE ::: The aim of this study was to investigate the efficacy, complication rates, patient satisfaction, and recurrence risks of the incisionless otoplasty technique performed with or without cartilage scoring for correcting the prominent ear in pediatric patients. ::: ::: ::: MATERIAL AND METHODS ::: A total of 49 patients with prominent ears were operated with incisionless otoplasty. In Group 1, 44 ears of 24 patients were operated with incisionless otoplasty without cartilage scoring. In Group 2, 46 ears of 25 patients were operated with incisionless otoplasty with cartilage scoring. For comparison, auriculocephalic distances were measured at three different levels: preoperatively, at the end of surgery, and at 1th and 6th month post-operatively. Patient satisfaction was evaluated using a visual analog scale (VAS). The global esthetic improvement scale (GAIS) was applied by an independent, non-participating plastic surgeon at 6 months after surgery. ::: ::: ::: RESULTS ::: Prior to surgery and at the end of surgery, no statistically significant difference was observed between the groups in terms of auriculocephalic distances at the three levels. At the and 6th month after surgery, auriculocephalic distances were significantly higher in Group 1. There were no significant differences in VAS results and GAIS values between the groups. The recurrence rate was 9.1% in Group 1 and 4.3% in Group 2. The suture extrusion rate was 18.2% in Group 1 and 13% in Group 2. ::: ::: ::: CONCLUSION ::: Although there was a significant difference of 1-2 mm in auriculocephalic distances, our study showed that cartilage scoring is not mandatory to correct the prominent ear in pediatric patients with soft cartilages and to achieve patient and surgeon satisfaction.


title: PV-OWL — Pharmacovigilance surveillance through semantic web-based platform for continuous and integrated monitoring of drug-related adverse effects in open data sources and social media

abstract: The recent EU regulation on Pharmacovigilance [Regulation (EU) 1235/2010, Directive 2010/84/EU] imposes both to Pharmaceutical companies and Public health agencies to maintain updated safety information of drugs, monitoring all available data sources. Here, we present our project aiming to develop a web platform for continuous monitoring of adverse effects of medicines (pharmacovigilance), by integrating information from public databases, scientific literature and social media. The project will start by scanning all available data sources concerning drug adverse events, both open (e.g., FAERS — FDA Adverse Event Reporting Systems, medical literature, social media, etc.) and proprietary data (e.g., discharge hospital records, drug prescription archives, electronic health records), that require agreement with respective data owners. Subsequent, pharmacovigilance experts will perform a semi-automatic mapping of codes identifying drugs and adverse events, to build the thesaurus of the web based platform. After these preliminary activities, signal generation and prioritization will be the core of the project. This task will result in risk confidence scores for each included data source and a comprehensive global score, indicating the possible association between a specific drug and an adverse event. The software framework MOMIS, an open source data integration system, will allow semi-automatic virtual integration of heterogeneous and distributed data sources. A web platform, based on MOMIS, able to merge many heterogeneous data sets concerning adverse events will be developed. The platform will be tested by external specialized subjects (clinical researchers, public or private employees in pharmacovigilance field). The project will provide a) an innovative way to link, for the first time in Italy, different databases to obtain novel safety indicators; b) a web platform for a fast and easy integration of all available data, useful to verify and validate hypothesis generated in signal detection. Finally, the development of the unified safety indicator (global risk score) will result in a compelling, easy-to-understand, visual format for a broad range of professional and not professional users like patients, regulatory authorities, clinicians, lawyers, human scientists.


A Novel Projection based Electro-Stereolithography ( PES ) Process for Composite Printing


title: The ties that lead: A social network approach to leadership

abstract: This article investigates, for leadership research, the implications of new directions in social network theory that emphasize networks as both cognitive structures in the minds of organizational members and opportunity structures that facilitate and constrain action. We introduce the four core ideas at the heart of the network research program: the importance of relations, actors' embeddedness, the social utility of connections, and the structural patterning of social life. Then we present a theoretical model of how network cognitions in the minds of leaders affect three types of networks: the direct ties surrounding leaders, the pattern of direct and indirect ties within which leaders are embedded in the whole organization and the interorganizational linkages formed by leaders as representatives of organizations. We suggest that these patterns of ties can contribute to leader effectiveness. © 2006 Elsevier Inc. All rights reserved.


title: Mining Knowledge Graphs From Text

abstract: Knowledge graphs have become an increasingly crucial component in machine intelligence systems, powering ubiquitous digital assistants and inspiring several large scale academic projects across the globe. Our tutorial explains why knowledge graphs are important, how knowledge graphs are constructed, and where new research opportunities exist for improving the state-of-the-art. In this tutorial, we cover the many sophisticated approaches that complete and correct knowledge graphs. We organize this exploration into two main classes of models. The first include probabilistic logical frameworks that use graphical models, random walks, or statistical rule mining to construct knowledge graphs. The second class of models includes latent space models such as matrix and tensor factorization and neural networks. We conclude the tutorial with a critical comparison of techniques and results. We will offer practical advice for novices to identify common empirical challenges and concrete data sets for initial experimentation. Finally, we will highlight promising areas of current and future work.


title: Deep learning of fMRI big data: a novel approach to subject-transfer decoding

abstract: As a technology to read brain states from measurable brain activities, brain decoding are widely applied in industries and medical sciences. In spite of high demands in these applications for a universal decoder that can be applied to all individuals simultaneously, large variation in brain activities across individuals has limited the scope of many studies to the development of individual-specific decoders. In this study, we used deep neural network (DNN), a nonlinear hierarchical model, to construct a subject-transfer decoder. Our decoder is the first successful DNN-based subject-transfer decoder. When applied to a large-scale functional magnetic resonance imaging (fMRI) database, our DNN-based decoder achieved higher decoding accuracy than other baseline methods, including support vector machine (SVM). In order to analyze the knowledge acquired by this decoder, we applied principal sensitivity analysis (PSA) to the decoder and visualized the discriminative features that are common to all subjects in the dataset. Our PSA successfully visualized the subject-independent features contributing to the subject-transferability of the trained decoder.


On the equivalence of nonnegative matrix factorization and spectral clustering


title: An introduction to biometric recognition

abstract: A wide variety of systems requires reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user and no one else. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. In the absence of robust personal recognition schemes, these systems are vulnerable to the wiles of an impostor. Biometric recognition, or, simply, biometrics, refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics. By using biometrics, it is possible to confirm or establish an individual's identity based on "who she is", rather than by "what she possesses" (e.g., an ID card) or "what she remembers" (e.g., a password). We give a brief overview of the field of biometrics and summarize some of its advantages, disadvantages, strengths, limitations, and related privacy concerns.


title: Radiation Performance Analysis of 28 GHz Antennas Integrated in 5G Mobile Terminal Housing

abstract: Radiation from printed millimeter-wave antennas integrated in mobile terminals is affected by surface currents on chassis, guided waves trapped in dielectric layers, superstrates, and the user’s hand, making mobile antenna design for 5G communication challenging. In this paper, four canonical types of printed 28-GHz antenna elements are integrated in a 5G mobile terminal mock-up. Different kinds of terminal housing effects are examined separately, and the terminal housing effects are also diagnosed through equivalent currents by using the inverse source technique. To account for the terminal housing effects on a beam-scanning antenna subarray, we propose the effective beam-scanning efficiency to evaluate its coverage performance. This paper presents the detailed analysis, results, and new concepts regarding the terminal housing effects, and thereby provides valuable insight into the practical 5G mobile antenna design and radiation performance characterization.


title: Intrusion detection systems vulnerability on adversarial examples

abstract: Intrusion detection systems define an important and dynamic research area for cybersecurity. The role of Intrusion Detection System within security architecture is to improve a security level by identification of all malicious and also suspicious events that could be observed in computer or network system. One of the more specific research areas related to intrusion detection is anomaly detection. Anomaly-based intrusion detection in networks refers to the problem of finding untypical events in the observed network traffic that do not conform to the expected normal patterns. It is assumed that everything that is untypical/anomalous could be dangerous and related to some security events. To detect anomalies many security systems implements a classification or clustering algorithms. However, recent research proved that machine learning models might misclassify adversarial events, e.g. observations which were created by applying intentionally non-random perturbations to the dataset. Such weakness could increase of false negative rate which implies undetected attacks. This fact can lead to one of the most dangerous vulnerabilities of intrusion detection systems. The goal of the research performed was verification of the anomaly detection systems ability to resist this type of attack. This paper presents the preliminary results of tests taken to investigate existence of attack vector, which can use adversarial examples to conceal a real attack from being detected by intrusion detection systems.


title: Sparsified SGD with Memory

abstract: Nowadays machine learning applications require stochastic optimization algorithms that can be implemented on distributed systems. The communication overhead of the algorithms is a key bottleneck that hinders perfect scalability. Various recent works proposed to use quantization or sparsification techniques to reduce the amount of data that needs to be communicated, for instance by only sending the most significant entries of the stochastic gradient (top-k sparsification). Whilst this scheme shows good performance in practice it eluded theoretical analysis so far. ::: In this work we analyze a variant of Stochastic Gradient Descent (SGD) with k-sparsification (for instance top-k or random-k) and show that this scheme converges at the same rate as vanilla SGD. That is, the communication can be reduced by a factor of the dimension of the problem (sometimes even more) whilst still converging at the same rate. We present numerical experiments to illustrate the theoretical findings and especially the better scalability for distributed applications.


title: Actively controlled lateral gait assistance in a lower limb exoskeleton

abstract: Various powered wearable lower limb exoskeletons are designed for paraplegics to make them walk again. Control methods are developed and implemented in these exoskeletons to provide active gait assistance in the sagittal plane while active control in the frontal plane is still missing. This paper proposed a control method that provided gait assistance in both lateral and sagittal plane. First, in the lateral plane, the exoskeleton was controlled to support the weight shift during stepping by providing assisting hip ab/adduction torques when the subject initiated a small amount of weight shift to the stance side to trigger a step. Second, the exoskeleton's hip ab/adduction during stepping was controlled to improve lateral stability. This was achieved by altering the amount of hip ab/adduction to change step width at heel strike. Using these controls, an able-bodied subject could walk in the exoskeleton without any external balance aids, i.e. crutches or a walker, where his hip and knee joints were controlled by the exoskeleton and his ankle joints were constrained by the exoskeleton. The next step is to test whether the proposed method improves balance in spinal cord injured subjects.


title: Flow-level Traffic Analysis of the Blaster and Sobig Worm Outbreaks in an Internet Backbone

abstract: We present an extensive flow-level traffic analysis of the network worm Blaster.A and of the e-mail worm Sobig.F. Based on packet-level measurements with these worms in a testbed we defined flow-level filters. We then extracted the flows that carried malicious worm traffic from AS559 (SWITCH) border router backbone traffic that we had captured in the DDoSVax project. We discuss characteristics and anomalies detected during the outbreak phases, and present an in-depth analysis of partially and completely successful Blaster infections. Detailed flow-level traffic plots of the outbreaks are given. We found a short network test of a Blaster pre-release, significant changes of various traffic parameters, backscatter effects due to non-existent hosts, ineffectiveness of certain temporary port blocking countermeasures, and a surprisingly low frequency of successful worm code transmissions due to Blaster‘s multi-stage nature. Finally, we detected many TCP packet retransmissions due to Sobig.F‘s far too greedy spreading algorithm.


title: Pectin and acrylamide based hydrogels for environment management technologies: Synthesis, characterization, and metal ions sorption

abstract: The present article describes various aspects of preparation and characterization of hydrogels based on pectin and three different amide monomers viz: acrylamide (AAm), N-isopropyl acrylamide (N-i-PAAm), and 2-acrylamido-2-methyl-1-propane sulfonic acid (AAmPSA). Hydrogels have been prepared by graft copolymerization as well as in the presence of crosslinker N,N-Methylene bisacrylamide ((N,N-MBAAm) and initiated by redox system comprising of ammonium peroxysulphate–ferrous ammonium sulfate (APS: FAS) at two temperatures. Hydrogels thus synthesized have been characterized by SEM, FTIR, and water uptake studies. The later has been carried as a function of time, temperature, pH, crosslinker concentration, and temperature at which the hydrogels were prepared. Candidate hydrogels have been used for the sorption of some common metal ions pollutants found in soil, industrial, and mining water bodies. Biodegradability studies have been carried by soil burial method to investigate the effect of chemical modification on biodegradability of pectin and to understand the possibility of eco-friendly nature and to explore the scope of reusability of the hydrogels and waste minimization. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci, 2007


title: Accelerated telomere shortening in response to life stress

abstract: Numerous studies demonstrate links between chronic stress and indices of poor health, including risk factors for cardiovascular disease and poorer immune function. Nevertheless, the exact mechanisms of how stress gets “under the skin” remain elusive. We investigated the hypothesis that stress impacts health by modulating the rate of cellular aging. Here we provide evidence that psychological stress— both perceived stress and chronicity of stress—is significantly associated with higher oxidative stress, lower telomerase activity, and shorter telomere length, which are known determinants of cell senescence and longevity, in peripheral blood mononuclear cells from healthy premenopausal women. Women with the highest levels of perceived stress have telomeres shorter on average by the equivalent of at least one decade of additional aging compared to low stress women. These findings have implications for understanding how, at the cellular level, stress may promote earlier onset of age-related diseases.


title: Learning from time-changing data with adaptive windowing

abstract: We present a new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time. We use sliding windows whose size, instead of being fixed a priori, is recomputed online according to the rate of change observed from the data in the window itself: The window will grow automatically when the data is stationary, for greater accuracy, and will shrink automatically when change is taking place, to discard stale data. This delivers the user or programmer from having to guess a time-scale for change. Contrary to many related works, we provide rigorous guarantees of performance, as bounds on the rates of false positives and false negatives. In fact, for some change structures, we can formally show that the algorithm automatically adjusts the window to a statistically optimal length. Using ideas from data stream algorithmics, we develop a time- and memory-ecient version of this algorithm, called ADWIN2. We show how to incorporate this strategy easily into


title: A systematic review of empirical research on self-reported racism and health

abstract: This paper reviews 138 empirical quantitative population-based studies of self-reported racism and health. These studies show an association between self-reported racism and ill health for oppressed racial groups after adjustment for a range of confounders. The strongest and most consistent findings are for negative mental health outcomes and health-related behaviours, with weaker associations existing for positive mental health outcomes, self-assessed health status, and physical health outcomes. Most studies in this emerging field have been published in the past 5 years and have been limited by a dearth of cohort studies, a lack of psychometrically validated exposure instruments, poor conceptualization and definition of racism, conflation of racism with stress, and debate about the aetiologically relevant period for self-reported racism. Future research should examine the psychometric validity of racism instruments and include these instruments, along with objectively measured health outcomes, in existing large-scale survey vehicles as well as longitudinal studies and studies involving children. There is also a need to gain a better understanding of the perception, attribution, and reporting of racism, to investigate the pathways via which self-reported racism affects health, the interplay between mental and physical health outcomes, and exposure to intra-racial, internalized, and systemic racism. Ensuring the quality of studies in this field will allow future research to reveal the complex role that racism plays as a determinant of population health.


title: A Broadband Planar Magic-T Using Microstrip–Slotline Transitions

abstract: The improved version of a broadband planar magic-T using microstrip-slotline transitions is presented. The design implements a small microstrip-slotline tee junction with minimum size slotline terminations to reduce radiation loss. A multisection impedance transformation network is used to increase the operating bandwidth and minimize the parasitic coupling around the microstrip-slotline tee junction. As a result, the improved magic-T has greater bandwidth and lower phase imbalance at the sum and difference ports than the earlier magic-T design. The experimental results show that the 10-GHz magic-T provides more than 70% of 1-dB operating bandwidth with the average in-band insertion loss of less than 0.6 dB. It also has phase and amplitude imbalance of less than plusmn1deg and plusmn0.25 dB, respectively.


title: $${\mathrm {RRT^{X}}}$$ RRT X : Real-Time Motion Planning/Replanning for Environments with Unpredictable Obstacles

abstract: We present \({\mathrm {RRT^{X}}}\), the first asymptotically optimal sampling-based motion planning algorithm for real-time navigation in dynamic environments (containing obstacles that unpredictably appear, disappear, and move). Whenever obstacle changes are observed, e.g., by onboard sensors, a graph rewiring cascade quickly updates the search-graph and repairs its shortest-path -to-goal subtree. Both graph and tree are built directly in the robot’s state space, respect the kinematics of the robot, and continue to improve during navigation. \({\mathrm {RRT^{X}}}\) is also competitive in static environments—where it has the same amortized per iteration runtime as RRT and RRT* \(\varTheta \left( \log {n}\right) \) and is faster than RRT# \(\omega \left( \log ^2{n}\right) \). In order to achieve \(O\left( \log {n}\right) \) iteration time, each node maintains a set of \(O\left( \log {n}\right) \) expected neighbors, and the search graph maintains \(\epsilon \)-consistency for a predefined \(\epsilon \).


title: Coherent TDOA/FDOA estimation method for frequency-hopping signal

abstract: Considering the limited FDOA estimation performance for frequency-hopping signal, a coherent accumulation method is developed in this paper. The method normalizes the FDOA of every hopping pulse and then accumulates all these modified CAFs coherently. As a result, it can enlarge the accumulative time length and improve FDOA resolution and accuracy distinctly. Theoretical analysis and simulation results finally show the superior performance of the proposed method in FDOA resolution and accuracy.


title: The Use of Fractional Laser Photothermolysis for the Treatment of Atrophic Scars

abstract: BACKGROUND Patients with atrophic scars commonly seek treatment for their removal but are often concerned about the prolonged recovery, short-lived results, and/or ineffectiveness of available therapies. A novel treatment using a 1,550-nm erbium-doped fiber laser to induce fractional photothermolysis of treated skin has been used to resurface photodamaged skin but has not been studied previously in patients with atrophic scars to determine its effectiveness for this condition. ::: ::: ::: ::: PURPOSE To determine the effectiveness and safety of 1,550-nm erbium-doped fiber laser treatment on atrophic scars. ::: ::: ::: ::: METHODS Fifty-three patients (skin phototypes I–V) with mild to moderate atrophic facial acne scars received monthly treatment with a 1,550-nm erbium-doped fiber laser (Fraxel, Reliant Technologies Inc., San Diego, CA). Clinical response to treatment was determined at each treatment visit and 6 months after the final treatment session by two independent assessors using a quartile grading scale. Side effects and patient satisfaction were monitored at each follow-up visit. ::: ::: ::: ::: RESULTS Clinical improvement averaged 51% to 75% in nearly 90% of patients after three monthly laser treatments. Mean improvement scores increased proportionately with each successive laser session. Clinical response rates were independent of age, gender, or skin phototype. Side effects included transient erythema and edema in most patients, but no dyspigmentation, ulceration, or scarring. ::: ::: ::: ::: CONCLUSIONS Atrophic scars can be effectively and safely reduced with 1,550-nm erbium-doped fiber laser treatment.


title: A framework to explore innovation at SAP through bibliometric analysis of patent applications

abstract: We provide an analysis of innovation at SAP using bibliometric analysis of patents.A rotation and sort procedure was applied to the text.A frequent itemset analysis was performed using the word occurrence matrix.A blockmodeling algorithm was applied to explore relatedness between terms.We integrate text and network analysis tools to perform bibliometric analysis. Easily accessible patent databases and advances in technology have enabled the exploration of organizational innovation through the analysis of patent records. However, the textual content of patents presents obstacles to gleaning useful information. In this study, we develop an expert system framework that utilizes text and data mining procedures for analyzing innovation through textual patent data. Specifically, we use patent titles representing the innovation activity at one company (SAP) and perform a bibliometric analysis using our proposed framework. Enterprise software, of which SAP is a pioneering developer, must serve a wide assortment of functions for companies in many different industries. In addition, SAP's sole focus is on enterprise software and it is a market leader in the category with substantial patent activity over the last decade. Using our framework to analyze SAP's patent activity provides a demonstration of how our bibliometric analysis can summarize and identify trends in innovation in a large software company. Our results illustrate that SAP has a breadth of innovative activity spread over the three-tier software engineering architecture and a lack of topical repetition indicative of limited depth. SAP's innovation is also seen to emphasize data management and quickly integrate emerging technologies. Results of an analysis on any company following our framework could be used for a variety of purposes, including: to examine the scope and scale of innovation of an organization, to examine the influence of technological trends on businesses, or to gain insight into corporate strategy that could be used to aid planning, investment, and purchasing decisions.


title: A Component-Based Simplex Architecture for High-Assurance Cyber-Physical Systems

abstract: We present Component-Based Simplex Architecture (CBSA), a new framework for assuring the runtime safety of component-based cyber-physical systems (CPSs). CBSA integrates Assume-Guarantee (A-G) reasoning with the core principles of the Simplex control architecture to allow component-based CPSs to run advanced, uncertified controllers while still providing runtime assurance that A-G contracts and global properties are satisfied. In CBSA, multiple Simplex instances, which can be composed in a nested, serial or parallel manner, coordinate to assure system-wide properties. Combining A-G reasoning and the Simplex architecture is a challenging problem that yields significant benefits. By utilizing A-G contracts, we are able to compositionally determine the switching logic for CBSAs, thereby alleviating the state explosion encountered by other approaches. Another benefit is that we can use A-G proof rules to decompose the proof of system-wide safety assurance into sub-proofs corresponding to the component-based structure of the system architecture. We also introduce the notion of coordinated switching between Simplex instances, a key component of our compositional approach to reasoning about CBSA switching logic. We illustrate our framework with a component-based control system for a ground rover. We formally prove that the CBSA for this system guarantees energy safety (the rover never runs out of power), and collision freedom (the rover never collides with a stationary obstacle). We also consider a CBSA for the rover that guarantees mission completion: all target destinations visited within a prescribed amount of time.


title: Integrating Association Rule Mining with Relational Database Systems: Alternatives and Implications

abstract: Data mining on large data warehouses is becoming increasingly important. In support of this trend, we consider a spectrum of architectural alternatives for coupling mining with database systems. These alternatives include: loose-coupling through a SQL cursor interface; encapsulation of a mining algorithm in a stored procedure; caching the data to a file system on-the-fly and mining; tight-coupling using primarily user-defined functions; and SQL implementations for processing in the DBMS. We comprehensively study the option of expressing the mining algorithm in the form of SQL queries using Association rule mining as a case in point. We consider four options in SQL-92 and six options in SQL enhanced with object-relational extensions (SQL-OR). Our evaluation of the different architectural alternatives shows that from a performance perspective, the Cache option is superior, although the performance of the SQL-OR option is within a factor of two. Both the Cache and the SQL-OR approaches incur a higher storage penalty than the loose-coupling approach which performance-wise is a factor of 3 to 4 worse than Cache. The SQL-92 implementations were too slow to qualify as a competitive option. We also compare these alternatives on the basis of qualitative factors like automatic parallelization, development ease, portability and inter-operability. As a byproduct of this study, we identify some primitives for native support in database systems for decision-support applications.


title: Stacking-based Deep Neural Network: Deep Analytic Network on Convolutional Spectral Histogram Features

abstract: Stacking-based deep neural network (S-DNN), in general, denotes a deep neural network (DNN) resemblance in terms of its very deep, feedforward network architecture. The typical S-DNN aggregates a variable number of individually learnable modules in series to assemble a DNN-alike alternative to the targeted object recognition tasks. This work likewise devises an S-DNN instantiation, dubbed deep analytic network (DAN), on top of the spectral histogram (SH) features. The DAN learning principle relies on ridge regression, and some key DNN constituents, specifically, rectified linear unit, fine-tuning, and normalization. The DAN aptitude is scrutinized on three repositories of varying domains, including FERET (faces), MNIST (handwritten digits), and CIFAR10 (natural objects). The empirical results unveil that DAN escalates the SH baseline performance over a sufficiently deep layer.


title: When Companies Don't Make the Ad: A Multi-Method Inquiry into the Differential Effectiveness of Consumer-Generated Advertising

abstract: This four-part multi-method investigation into the under-researched yet increasingly prevalent phenomenon of consumer-generated advertising (CGA) confirms a performance advantage over traditional advertising and suggests a rationale for this differential. CGAs benefit from heightened consumer engagement and increased trustworthiness. CGAs also garner perceived quality advantages that are linked to consumers lowering their expectations and using different evaluation criteria to judge the ad. The ad creator - a personalized, identifiable and relatable entity in the case of CGAs - plays a central role in anchoring and shaping ad reactions. The “consumer-made” characteristic - the fact that CGAs are not made by companies but by independent people - is powerful and stands strong in the face of commercial motives, and presents paradigmatic implications for advertising practice and research.


title: The costs and benefits of pair programming

abstract: Pair or collaborative programming is where two programmers develop software side by side at one computer. Using interviews and controlled experiments, the authors investigated the costs and benefits of pair programming. They found that for a development-time cost of about 15%, pair programming improves design quality, reduces defects, reduces staffing risk, enhances technical skills, improves team communications and is considered more enjoyable at statistically significant levels.


title: Strabismus surgery on the nonamblyopic eye

abstract: To date most surgeons operate only on the nonamblyopic eye in patients with strabismus and amblyopia with unpredictable, initial, surgical results. We present a series of 13 patients with amblyopia and strabismus plus anomalous head positions who underwent surgery on their nonamblyopic eye. The good, initial, surgical results in 77% of these patients suggest that operating on the good eye is important to dictate the final surgical results in correcting the deviation and normalizing the head position.


title: The linked data benchmark council: a graph and RDF industry benchmarking effort

abstract: The Linked Data Benchmark Council (LDBC) is an EU project that aims to develop industry-strength benchmarks for graph and RDF data management systems. It includes the creation of a non-profit LDBC organization, where industry players and academia come together for managing the development of benchmarks as well as auditing and publishing official results. We present an overview of the project including its goals and organization, and describe its process and design methodology for benchmark development. We introduce so-called "choke-point" based benchmark development through which experts identify key technical challenges, and introduce them in the benchmark workload. Finally, we present the status of two benchmarks currently in development, one targeting graph data management systems using a social network data case, and the other targeting RDF systems using a data publishing case.


title: An empirical study of the effects of test-suite reduction on fault localization

abstract: Fault-localization techniques that utilize information about all test cases in a test suite have been presented. These techniques use various approaches to identify the likely faulty part(s) of a program, based on information about the execution of the program with the test suite. Researchers have begun to investigate the impact that the composition of the test suite has on the effectiveness of these fault-localization techniques. In this paper, we present the first experiment on one aspect of test-suite composition--test-suite reduction. Our experiment studies the impact of the test-suite reduction on the effectiveness of fault-localization techniques. In our experiment, we apply 10 test-suite reduction strategies to test suites for eight subject programs. We then measure the differences between the effectiveness of four existing fault-localization techniques on the unreduced and reduced test suites. We also measure the reduction in test-suite size of the 10 test-suite reduction strategies. Our experiment shows that fault-localization effectiveness varies depending on the test-suite reduction strategy used, and it demonstrates the trade-offs between test-suite reduction and fault-localization effectiveness.


title: A modified Artificial Bee Colony algorithm for real-parameter optimization

abstract: Swarm intelligence is a research field that models the collective intelligence in swarms of insects or animals. Many algorithms that simulates these models have been proposed in order to solve a wide range of problems. The Artificial Bee Colony algorithm is one of the most recent swarm intelligence based algorithms which simulates the foraging behaviour of honey bee colonies. In this work, modified versions of the Artificial Bee Colony algorithm are introduced and applied for efficiently solving real-parameter optimization problems.


The Author-Topic Model and the author prediction


title: A Class-G Headphone Amplifier in 65 nm CMOS Technology

abstract: This paper presents a class G amplifier based on a low distortion switching principle technique called switching currents injection. The switching circuit enables a very smooth handover between the voltage supply rails obtaining both high efficiency and low distortion. An approach for the evaluation of the switching distortion in a class G amplifier (and the ability of the loop to reject it) is proposed and the results obtained are used to optimize the overall distortion after compression by the feedback loop. The integrated 65 nm CMOS class G headphone driver based on the above concept operates from ±1.4 V and ±0.35 V supplies. At low power level it uses almost exclusively the low voltage supply reducing the dissipation to 1.63 mW @ Pout = 0.5 mW into 32 Ω. At higher power level, where both supplies are used, the smooth transition between the rails allows a THD + N better than -80 dB for Pout ≤ 16 mW into 32 Ω. The SNR is 101 dB, quiescent power is 0.41 m W and active die area is 0.14 mm2.


title: Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

abstract: We explore deep reinforcement learning methods for multi-agent domains. We begin by analyzing the difficulty of traditional algorithms in the multi-agent case: Q-learning is challenged by an inherent non-stationarity of the environment, while policy gradient suffers from a variance that increases as the number of agents grows. We then present an adaptation of actor-critic methods that considers action policies of other agents and is able to successfully learn policies that require complex multi-agent coordination. Additionally, we introduce a training regimen utilizing an ensemble of policies for each agent that leads to more robust multi-agent policies. We show the strength of our approach compared to existing methods in cooperative as well as competitive scenarios, where agent populations are able to discover various physical and informational coordination strategies.


Intellect, mental illness, and survival in the aged: a longitudinal investigation.


title: Spike-and-Slab Sparse Coding for Unsupervised Feature Discovery

abstract: We consider the problem of using a factor model we call {\em spike-and-slab sparse coding} (S3C) to learn features for a classification task. The S3C model resembles both the spike-and-slab RBM and sparse coding. Since exact inference in this model is intractable, we derive a structured variational inference procedure and employ a variational EM training algorithm. Prior work on approximate inference for this model has not prioritized the ability to exploit parallel architectures and scale to enormous problem sizes. We present an inference procedure appropriate for use with GPUs which allows us to dramatically increase both the training set size and the amount of latent factors. ::: We demonstrate that this approach improves upon the supervised learning capabilities of both sparse coding and the ssRBM on the CIFAR-10 dataset. We evaluate our approach's potential for semi-supervised learning on subsets of CIFAR-10. We demonstrate state-of-the art self-taught learning performance on the STL-10 dataset and use our method to win the NIPS 2011 Workshop on Challenges In Learning Hierarchical Models' Transfer Learning Challenge.


title: Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks

abstract: Recurrent Neural Networks (RNNs) continue to show outstanding performance in sequence modeling tasks. However, training RNNs on long sequences often face challenges like slow inference, vanishing gradients and difficulty in capturing long term dependencies. In backpropagation through time settings, these issues are tightly coupled with the large, sequential computational graph resulting from unfolding the RNN in time. We introduce the Skip RNN model which extends existing RNN models by learning to skip state updates and shortens the effective size of the computational graph. This model can also be encouraged to perform fewer state updates through a budget constraint. We evaluate the proposed model on various tasks and show how it can reduce the number of required RNN updates while preserving, and sometimes even improving, the performance of the baseline RNN models. Source code is publicly available at this https URL .


International Studies of Educational Achievement: Methodological Issues


title: Learning to Reason: End-to-End Module Networks for Visual Question Answering

abstract: Natural language questions are inherently compositional, and many are most easily answered by reasoning about their decomposition into modular sub-problems. For example, to answer "is there an equal number of balls and boxes?" we can look for balls, look for boxes, count them, and compare the results. The recently proposed Neural Module Network (NMN) architecture implements this approach to question answering by parsing questions into linguistic substructures and assembling question-specific deep networks from smaller modules that each solve one subtask. However, existing NMN implementations rely on brittle off-the-shelf parsers, and are restricted to the module configurations proposed by these parsers rather than learning them from data. In this paper, we propose End-to-End Module Networks (N2NMNs), which learn to reason by directly predicting instance-specific network layouts without the aid of a parser. Our model learns to generate network structures (by imitating expert demonstrations) while simultaneously learning network parameters (using the downstream task loss). Experimental results on the new CLEVR dataset targeted at compositional question answering show that N2NMNs achieve an error reduction of nearly 50% relative to state-of-the-art attentional approaches, while discovering interpretable network architectures specialized for each question.


title: Automated production system simulations using commercial off-the-shelf simulation tools

abstract: A multi-year research project focused on a global aerospace company's design-to-production transition, and in particular how to answer production-related questions much earlier in a program's design cycle than is possible today. A fundamental difficulty is that the time and expertise required to formulate appropriate analysis models prevents their routine use, especially in new program development. The project's goal was to reduce these requirements, and by late 2014 a methodology had been developed for on-demand analysis generation to answer routine questions about production systems. A pilot project was conducted in 2015 to demonstrate efficacy, that an implementation of the methodology could in fact reduce by at least an order of magnitude the time required to answer a frequently-asked question, in a repeatable way while specification of the products, their process plans, planned facilities, and available resources were frequently changing. This paper summarizes the methodology, its pilot project implementation, and preliminary results.


title: Simple, Accurate, and Robust Nonparametric Blind Super-Resolution

abstract: This paper proposes a simple, accurate, and robust approach to single image blind super-resolution (SR). This task is formulated as a functional to be minimized with respect to both an intermediate super-resolved image and a non- parametric blur-kernel. The proposed method includes a convolution consistency constraint which uses a non-blind learning-based SR result to better guide the estimation process. Another key component is the bi-l 0-l 2-norm regularization placed on the super-resolved, sharp image and the blur-kernel, which is shown to be quite beneficial for accurate blur-kernel estimation. The numerical optimization is implemented by coupling the splitting augmented Lagrangian and the conjugate gradient. With the pre-estimated blur-kernel, the final SR image is reconstructed using a simple TV-based non-blind SR method. The new method is demonstrated to achieve better performance than Michaeli and Irani [2] in both terms of the kernel estimation accuracy and image SR quality.


Data mining applications in civil aviation sector : State-of-art review


title: Analytic PCA Construction for Theoretical Analysis of Lighting Variability in Images of a Lambertian Object

abstract: We analyze theoretically the subspace best approximating images of a convex Lambertian object taken from the same viewpoint, but under different distant illumination conditions. We analytically construct the principal component analysis for images of a convex Lambertian object, explicitly taking attached shadows into account, and find the principal eigenmodes and eigenvalues with respect to lighting variability. Our analysis makes use of an analytic formula for the irradiance in terms of spherical-harmonic coefficients of the illumination and shows, under appropriate assumptions, that the principal components or eigenvectors are identical to the spherical harmonic basis functions evaluated at the surface normal vectors. Our main contribution is in extending these results to the single-viewpoint case, showing how the principal eigenmodes and eigenvalues are affected when only a limited subset (the upper hemisphere) of normals is available and the spherical harmonics are no longer orthonormal over the restricted domain. Our results are very close, both qualitatively and quantitatively, to previous empirical observations and represent the first essentially complete theoretical explanation of these observations.


Balancing within the Margin : Causal Effect Estimation with Support Vector Machines ∗


title: Impact of reducing STI-induced stress on layout dependence of MOSFET characteristics

abstract: Active-area layout dependence of MOSFET parametric characteristics and its reduction by reducing shallow trench isolation (STI)-induced mechanical stress were investigated. Threshold voltages (V/sub th/) and saturation drain currents (I/sub ds/) become sensitive to the active-area layout of MOSFET in scaled-down technology. This phenomenon is the effect of mechanical stress from STI edge, which reduces impurity diffusion in channel region and enhances carrier mobility. To reduce the STI-induced stress, we examined STI-wall-oxide nitridation and STI gap-fill-oxide densifying in pure N/sub 2/ ambient. These processes reduced the reoxidation of the STI wall oxide, therefore reduced the STI-induced stress. According to the new STI process, the active-area layout dependence of V/sub th/ and I/sub ds/ were reduced successfully.


title: A New Wideband Elliptic Bandstop Filter With Cross Coupling

abstract: A novel fifth-order elliptic cross-coupled wideband bandstop filter (BSF) is proposed and designed in this letter. It is devised by introducing additional electric coupling into two nonadjacent quarter-wavelength shunt stubs of a conventional commensurate line BSF. The selectivity and stopband rejection can be effectively enhanced by two extra transmission zeros generated from the coupling. The proposed BSF is designed directly from the normalized lowpass prototype by using the design formulas, which are derived based on the rigorous correspondence between the BSF and its lumped prototype in the $S$ -plane. In order to demonstrate our design, a BSF example is developed, with a measured 27.2 dB stopband over a fractional bandwidth of 141.7%.


title: A loan application fraud detection method based on knowledge graph and neural network

abstract: Dianrong, a tech-driven internet finance company, provides loans to a large number of people and small business users. The ability to predict fraud from loan applications is key to the company's business. Based on published literature on fraud detection techniques, features have to be extracted manually for further rule design or machine learning. But as fraudulent behaviors change over time to avoid detection, simple features or rules become obsolete quickly. Normally, we have to extract hundreds of features, which is a time and resource consuming process. This paper proposes a new way to extract features automatically from a borrower's phone network graph using neural networks, which not only overcomes the above issue, but also captures features that are hard to fake. This method has yielded strong results in reality.


title: Overview of image-based 3D vision systems for agricultural applications

abstract: Various agricultural robots exist which are intended to help farmers to make decisions. Vision systems are an integral and essential part of these agricultural robots to perceive and evaluate the work space, inspect crops and detect objects. The use of 3D vision systems can provide a 3D reconstructed model which will supply detailed information including depth estimation about a crop or an object or environmental structures. However, these vision systems have some drawbacks which are limiting their use in an agricultural area. The limiting factors are dealing with lighting conditions, navigating unstructured environment, harvesting speed, occlusion and overlapping of fruits, accuracy of the system etc. This paper investigates the limitations and challenges of the state-of-the-art of 3D image reconstruction techniques in agricultural applications. Active and passive approaches have been reviewed. Factors to be considered while designing the vision system have been identified.


title: Quantum computing: a survey

abstract: Computers today become faster by becoming smaller. By reducing the size of transistors, engineers are able to fit more of them on a given size microprocessor, thus increasing the processor's computational power. This process cannot continue forever. It has been estimated that sometime within the next two decades, at the current rate, engineers will be faced with the problem of building something that is smaller than an atom. This is as far as our current "classical" computing paradigm will take us. Quantum computing is a potential solution to this problem. This paradigm of computing seeks to directly exploit quantum mechanical phenomena to perform calculations or in some way boost computational efficiency. Some problems can theoretically be solved on a quantum computer exponentially faster than on a classical computer. An overview of the subject is provided here with emphasis on quantum information processing and physical realizations of a quantum computer. Included is a brief history of the subject, a discussion of quantum computing notions such as parallelism and entanglement and their use in quantum algorithms, and conjecture on the prospects and pitfalls of this theory.


title: Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data

abstract: In this paper we introduce a new underlying probabilistic model for principal component analysis (PCA). Our formulation interprets PCA as a particular Gaussian process prior on a mapping from a latent space to the observed data-space. We show that if the prior's covariance function constrains the mappings to be linear the model is equivalent to PCA, we then extend the model by considering less restrictive covariance functions which allow non-linear mappings. This more general Gaussian process latent variable model (GPLVM) is then evaluated as an approach to the visualisation of high dimensional data for three different data-sets. Additionally our non-linear algorithm can be further kernelised leading to 'twin kernel PCA' in which a mapping between feature spaces occurs.


title: Reinventing the Wheel: Transforming Steering Wheel Systems for Autonomous Vehicles

abstract: In this paper, we introduce two different transforming steering wheel systems that can be utilized to augment user experience for future partially autonomous and fully autonomous vehicles. The first one is a robotic steering wheel that can mechanically transform by using its actuators to move the various components into different positions. The second system is a LED steering wheel that can visually transform by using LEDs embedded along the rim of wheel to change colors. Both steering wheel systems contain onboard microcontrollers developed to interface with our driving simulator. The main function of these two systems is to provide emergency warnings to drivers in a variety of safety critical scenarios, although the design space that we propose for these steering wheel systems also includes the use as interactive user interfaces. To evaluate the effectiveness of the emergency alerts, we conducted a driving simulator study examining the performance of participants (N=56) after an abrupt loss of autonomous vehicle control. Drivers who experienced the robotic steering wheel performed significantly better than those who experienced the LED steering wheel. The results of this study suggest that alerts utilizing mechanical movement are more effective than purely visual warnings.