title: Maxillary anterior teeth dimensions and proportions in a central mainland chinese population.

abstract: The Chinese Journal of Dental Research including dental aesthetics, gingival aesthetics, and also the more subjective aesthetic integration into the framework of smile, face, and, more generally, the individual. Within the aesthetic checklist, the relative dimensions of the teeth seem to be among the most objective dental criteria. Actual tooth dimensions have been addressed in many dental anatomy textbooks and journals4,5. In these textbooks, the dimensions presented were derived from the skull measurements. Measurements were taken from the cementoenamel junction (CEJ) to the incisal tip, and hence clinical crown height was not measured. Moreover, their reports lacked information about skull age, gender, and overall sample size. Several mathematical formulas, concepts and theories have been advocated for artificial teeth selection but with little scientific foundation. The “theory of correspondence and harmony”, put forward by J. W. White in 1872, was probably the first aesthetic concept regard1 Dental Centre, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China 2 National Academy of Medical Sciences, Bir Hospital, Kathmandu, Nepal.


title: Green Biosynthesis and Characterization of Magnetic Iron Oxide (Fe3O4) Nanoparticles Using Seaweed (Sargassum muticum) Aqueous Extract

abstract: The synthesis of nanoparticles has become a matter of great interest in recent times due to their various advantageous properties and applications in a variety of fields. The exploitation of different plant materials for the biosynthesis of nanoparticles is considered a green technology because it does not involve any harmful chemicals. In this study, iron oxide nanoparticles (Fe3O4-NPs) were synthesized using a rapid, single step and completely green biosynthetic method by reduction of ferric chloride solution with brown seaweed (BS, Sargassum muticum) water extract containing sulphated polysaccharides as a main factor which acts as reducing agent and efficient stabilizer. The structural and properties of the Fe3O4-NPs were investigated by X-ray diffraction, Fourier transform infrared spectroscopy, field emission scanning electron microscopy (FESEM), energy dispersive X-ray fluorescence spectrometry (EDXRF), vibrating sample magnetometry (VSM) and transmission electron microscopy. The average particle diameter as determined by TEM was found to be 18 ± 4 nm. X-ray diffraction showed that the nanoparticles are crystalline in nature, with a cubic shape. The nanoparticles synthesized through this biosynthesis method can potentially useful in various applications.


title: ScaffCC: Scalable Compilation and Analysis of Quantum Programs

abstract: Abstract We present ScaffCC, a scalable compilation and analysis framework based on LLVM (Lattner and Adve, 2004), which can be used for compiling quantum computing applications at the logical level. Drawing upon mature compiler technologies, we discuss similarities and differences between compilation of classical and quantum programs, and adapt our methods to optimizing the compilation time and output for the quantum case. Our work also integrates a reversible-logic synthesis tool in the compiler to facilitate coding of quantum circuits. Lastly, we present some useful quantum program analysis scenarios and discuss their implications, specifically with an elaborate discussion of timing analysis for critical path estimation. Our work focuses on bridging the gap between high-level quantum algorithm specifications and low-level physical implementations, while providing good scalability to larger and more interesting problems.


title: The OMG-Emotion Behavior Dataset

abstract: This paper is the basis paper for the accepted IJCNN challenge One-Minute Gradual-Emotion Recognition (OMG-Emotion) by which we hope to foster long-emotion classification using neural models for the benefit of the IJCNN community. The proposed corpus has as the novelty the data collection and annotation strategy based on emotion expressions which evolve over time into a specific context. Different from other corpora, we propose a novel multimodal corpus for emotion expression recognition, which uses gradual annotations with a focus on contextual emotion expressions. Our dataset was collected from Youtube videos using a specific search strategy based on restricted keywords and filtering which guaranteed that the data follow a gradual emotion expression transition, i.e. emotion expressions evolve over time in a natural and continuous fashion. We also provide an experimental protocol and a series of unimodal baseline experiments which can be used to evaluate deep and recurrent neural models in a fair and standard manner.


title: Adaptive encrypted cloud storage model

abstract: In this paper, we propose an adaptive model of data storage in a heterogeneous distributed cloud environment. Our system utilizes the methods of secret sharing schemes and error correction codes based on Redundant Residue Number System (RRNS). We consider data uploading, storing and downloading. To minimize data access, we use data transfer mechanism between cloud providers. We provide theoretical analysis and experimental evaluation of our scheme with six real data storage providers. We show how dynamic adaptive strategies not only increase security, reliability, and reduction of data redundancy but allow processing encrypted data. We also discuss potentials of this approach, and address methods for mitigating the risks of confidentiality, integrity, and availability associated with the loss of information, denial of access for a long time, and information leakage.


Room-temperature ferromagnetism in Ti1？xVxO2 nanocrystals synthesized from an organic-free and water-soluble precursor


title: Kasami Code-Shift-Keying Modulation for Ultra-Wideband Communication Systems

abstract: Ultra-wideband (UWB) communication systems are generally applied to short-range wireless communications. In order to achieve higher rates or to support multiple access capabilities, direct sequence spread spectrum (DSSS) techniques have been introduced to UWB systems, and multiple pulses corresponding to a certain pseudo-noise (PN) code are transmitted to represent a symbol. In addition, the concept of M-ary code shift keying (M-CSK) was introduced into DSSS systems to achieve higher rates. In this work, we propose an M-CSK modulation technique based on the large set of Kasami sequences since it possesses good code properties, including a large code set size and low cross correlations. The modulation and demodulation schemes are developed, and the system performance in additive white Gaussian noise (AWGN) and UWB channels exposed to multiple user interference is investigated thoroughly. It was found that the Kasami M-CSK modulation is superior to other M-CSK modulation schemes in the bandwidth efficiency, and therefore a higher data rate can be achieved. Furthermore, based on our proposed demodulation scheme, the hardware complexity of receivers can be greatly reduced to O(M1/3), and the implementation of receivers for a very large M becomes feasible.


title: Study of Proton Binding Sites at the Silicon Dioxide‐Electrolyte Interface with Conductance Spectroscopy

abstract: The kinetics of trapping and detrapping of protons at surface sites on thermally grown SiO 2 have been studied in detail. A Shockley-Read-Hall-type model has been used to model trapping and detrapping of protons at these sites which behave as traps with two levels. A small signal equivalent circuit is obtained. The model has been validated with extensive imittance measurements on electrolyte-oxide-semiconductor structures. from the conductance frequency measurements, the response time of these traps is of the order of a few milliseconds which would also be the intrinsic response time of ion sensitive field effect transistor. The approach is quite general and could be used for studying trapping-detrapping of ions or charged molecules at other electrolyte-insulator interfaces


title: Design of Alcohol Detection System for Car Users thru Iris Recognition Pattern Using Wavelet Transform

abstract: The purpose of this paper is to develop a system that captures the Iris image of the driver by detecting if the person is drunk and likewise to develop a reliable algorithm for Iris Recognition. This paper is composed of hardware and software system which focuses on the implementation of an algorithm based on Gabor Filter. The system consists of CCD Camera and Analog-to-Digital Converter, which is linked into a MATLAB program to simulate the captured image which then provides a signal going to the microcontroller and a relay circuit to manipulate the car ignition. If the MATLAB program detects that the driver is under the influence of alcohol, a bypass system follows through a password which is recognized by the MATLAB program then the car/vehicle starts.


title: Flourishing on facebook: virtue friendship & new social media

abstract: The widespread and growing use of new social media, especially social networking sites such as Facebook and Twitter, invites sustained ethical reflection on emerging forms of online friendship. Social scientists and psychologists are gathering a wealth of empirical data on these trends, yet philosophical analysis of their ethical implications remains comparatively impoverished. In particular, there have been few attempts to explore how traditional ethical theories might be brought to bear upon these developments, or what insights they might offer, if any. In attempting to address this lacuna in applied ethical research, this paper investigates the ethical significance of online friendship by means of an Aristotelian theory of the good life, which holds that human flourishing is chiefly realized through ‘complete’ friendships of virtue. Here, four key dimensions of ‘virtue friendship’ are examined in relation to online social media: reciprocity, empathy, self-knowledge and the shared life. Online social media support and strengthen friendship in ways that mirror these four dimensions, particularly when used to supplement rather than substitute for face-to-face interactions. However, deeper reflection on the meaning of the shared life (suzen) for Aristotle raises important and troubling questions about the capacity of online social media to support complete friendships of virtue in the contemporary world, along with significant concerns about the enduring relevance of this Aristotelian ideal for the good life in the 21st century.


title: Analysis of Consumer Repurchase Intention towards Online Shopping in Indonesia's Online Retail Business Market

abstract: Factors affecting repurchase intention have been investigated by many prior studies. However, the study towards online shopping repurchase intention and specifically in the Indonesia region has not been studied. An empirical study was conducted to investigate and identify the key factors that make customers willing to repurchase through online shopping. We evaluated some theories that have been used to investigate repurchase intentions and proposed a modified framework and develop our hypotheses. Our results pointed that Performance Expectancy and Facilitating Conditions to be the two most important factors that will influence the repurchase intention of customers. The implications of the findings were also discussed.


title: Optical interconnection networks for high-performance computing systems

abstract: Enabled by silicon photonic technology, optical interconnection networks have the potential to be a key disruptive technology in computing and communication industries. The enduring pursuit of performance gains in computing, combined with stringent power constraints, has fostered the ever-growing computational parallelism associated with chip multiprocessors, memory systems, high-performance computing systems and data centers. Sustaining these parallelism growths introduces unique challenges for on- and off-chip communications, shifting the focus toward novel and fundamentally different communication approaches. Chip-scale photonic interconnection networks, enabled by high-performance silicon photonic devices, offer unprecedented bandwidth scalability with reduced power consumption. We demonstrate that the silicon photonic platforms have already produced all the high-performance photonic devices required to realize these types of networks. Through extensive empirical characterization in much of our work, we demonstrate such feasibility of waveguides, modulators, switches and photodetectors. We also demonstrate systems that simultaneously combine many functionalities to achieve more complex building blocks. We propose novel silicon photonic devices, subsystems, network topologies and architectures to enable unprecedented performance of these photonic interconnection networks. Furthermore, the advantages of photonic interconnection networks extend far beyond the chip, offering advanced communication environments for memory systems, high-performance computing systems, and data centers.


title: Investigating Output Accuracy for a Discrete Event Simulation Model and an Agent Based Simulation Model

abstract: In this paper, we investigate output accuracy for a Discrete Event Simulation (DES) model and Agent Based Simulation (ABS) model. The purpose of this investigation is to find out which of these simulation techniques is the best one for modelling human reactive behaviour in the retail sector. In order to study the output accuracy in both models, we have carried out a validation experiment in which we compared the results from our simulation models to the performance of a real system. Our experiment was carried out using a large UK department store as a case study. We had to determine an efficient implementation of management policy in the store’s fitting room using DES and ABS. Overall, we have found that both simulation models were a good representation of the real system when modelling human reactive behaviour.


title: Mood States and Consumer Behavior: A Critical Review

abstract: A conceptual framework is presented that depicts both the mediating role of mood states and their potential importance in consumer behavior. Reviewing findings from the psychological literature indicates that mood states have direct and indirect effects on behavior, evaluation, and recall. The scope and limitations of these effects are addressed, and the implications for consumer behavior in three areas—service encounters, point-of-purchase stimuli, and communications (context and content)—are examined. Finally, the potential feasibility and viability of mood-related approaches to marketing research and practice are discussed.


title: The frontal aslant tract underlies speech fluency in persistent developmental stuttering

abstract: The frontal aslant tract (FAT) is a pathway that connects the inferior frontal gyrus with the supplementary motor area (SMA) and pre-SMA. The FAT was recently identified and introduced as part of a “motor stream” that plays an important role in speech production. In this study, we use diffusion imaging to examine the hypothesis that the FAT underlies speech fluency, by studying its properties in individuals with persistent developmental stuttering, a speech disorder that disrupts the production of fluent speech. We use tractography to quantify the volume and diffusion properties of the FAT in a group of adults who stutter (AWS) and fluent controls. Additionally, we use tractography to extract these measures from the corticospinal tract (CST), a well-known component of the motor system. We compute diffusion measures in multiple points along the tracts, and examine the correlation between these diffusion measures and behavioral measures of speech fluency. Our data show increased mean diffusivity in bilateral FAT of AWS compared with controls. In addition, the results show regions within the left FAT and the left CST where diffusivity values are increased in AWS compared with controls. Last, we report that in AWS, diffusivity values measured within sub-regions of the left FAT negatively correlate with speech fluency. Our findings are the first to relate the FAT with fluent speech production in stuttering, thus adding to the current knowledge of the functional role that this tract plays in speech production and to the literature of the etiology of persistent developmental stuttering.


title: Combining information extraction and human computing for crowdsourced knowledge acquisition

abstract: Automatic information extraction (IE) enables the construction of very large knowledge bases (KBs), with relational facts on millions of entities from text corpora and Web sources. However, such KBs contain errors and they are far from being complete. This motivates the need for exploiting human intelligence and knowledge using crowd-based human computing (HC) for assessing the validity of facts and for gathering additional knowledge. This paper presents a novel system architecture, called Higgins, which shows how to effectively integrate an IE engine and a HC engine. Higgins generates game questions where players choose or fill in missing relations for subject-relation-object triples. For generating multiple-choice answer candidates, we have constructed a large dictionary of entity names and relational phrases, and have developed specifically designed statistical language models for phrase relatedness. To this end, we combine semantic resources like WordNet, ConceptNet, and others with statistics derived from a largeWeb corpus. We demonstrate the effectiveness of Higgins for knowledge acquisition by crowdsourced gathering of relationships between characters in narrative descriptions of movies and books.


title: Comparative analysis of UFMC technology in 5G networks

abstract: The article dwells upon a new promising technology Universal Filtered Multi-Carrier (UFMC) for future 5G mobile networks. The technology is compared with OFDM technology used in 4G networks, and its effectiveness is analyzed. A disadvantage of OFDM technology is a necessity to use a cyclic prefix, which reduces data transmission rate. Due to UFMC technology, the cyclic prefix can be rejected, and thereby a spectral efficiency of mobile network can be increased in comparison with OFDM technology. Also, UFMC technology is less sensitive to estimation errors of frequency and time shifts.


title: Evaluation of the Potential of Dipole Field Navigation for the Targeted Delivery of Therapeutic Agents in a Human Vascular Network

abstract: Magnetically guided agents in the vascular network are expected to enable the targeted delivery of therapeutics to localized regions while avoiding their systemic circulation. Due to the small size of the medically applicable superparamagnetic microscale agents required to reach the smaller arteries, high magnetic fields and gradients are required to reach saturation magnetization and generate sufficient directional forces, respectively, for their effective navigation in the vascular environment. Currently, the only method that provides both a high field and high magnetic gradient strengths in deep tissues at the human scale is known as dipole field navigation (DFN). This method relies on the controlled distortion of the field inside a magnetic resonance imaging scanner by precisely positioning ferromagnetic cores around the patient. This paper builds on previous works that have experimentally demonstrated the feasibility of the method and proposed optimization algorithms for placing the cores. The maximum gradient strengths that can be generated for single and multibifurcation vascular routes are investigated while considering the major constraints on core positions (limited space in the scanner, magnetic interactions). Using disc cores, which were previously shown particularly effective for the DFN, results show that gradient strengths exceeding 400 mT/m (a tenfold increase with respect to typical gradients generated by clinical MRI scanners) can be achieved at 10 cm inside the patient, but decrease as the complexity of the vascular route increases. The potential of the method is evaluated for targeting regions of a vascular model of a human liver, segmented from clinical data, with encouraging results showing strengths up to 150 mT/m for generating gradients at three consecutive bifurcations within 20° of average gradient direction error.


Long QT Syndrome and Potentially Pathogenic Genetic Variants


Auditory cortical deactivation during speech production and following speech perception: an EEG investigation of the temporal dynamics of the auditory alpha rhythm


title: Support Vector Machine Application for Multiphase Flow Pattern Prediction

abstract: In this paper a data analytical approach featuring support vector machines (SVM) is employed to train a predictive model over an experimentaldataset, which consists of the most relevant studies for two-phase flow pattern prediction. The database for this study consists of flow patterns or flow regimes in gas-liquid two-phase flow. The term flow pattern refers to the geometrical configuration of the gas and liquid phases in the pipe. When gas and liquid flow simultaneously in a pipe, the two phases can distribute themselves in a variety of flow configurations. Gas-liquid two-phase flow occurs ubiquitously in various major industrial fields: petroleum, chemical, nuclear, and geothermal industries. The flow configurations differ from each other in the spatial distribution of the interface, resulting in different flow characteristics. Experimental results obtained by applying the presented methodology to different combinations of flow patterns demonstrate that the proposed approach is state-of-the-art alternatives by achieving 97% correct classification. The results suggest machine learning could be used as an effective tool for automatic detection and classification of gas-liquid flow patterns.


title: Domal Stabilization Suture in Tip Rhinoplasty

abstract: Objective To investigate use of the domal stabilization suture as a complementary suture modification technique for refining and securing the nasal tip. Methods A single permanent or absorbable suture is placed via an open or cartilage delivery approach. The suture is placed along the cephalic borders of the domes at the medial third of the lateral crura bilaterally just posterior to the junction of the intermediate and lateral crura as a final step in tip rhinoplasty. Results The domal stabilization suture provided a means to help maintain dome symmetry in the setting of variable healing and scarring forces with no complications and no effect on tip rotation or projection. Conclusion Use of the domal stabilization suture enables correction of subtle changes in mild tip asymmetry and irregularities in domal height and provides subtle narrowing of the interdomal distance.


title: An integrated e‐recruitment system for automated personality mining and applicant ranking

abstract: Purpose – The purpose of this paper is to present a novel approach for recruiting and ranking job applicants in online recruitment systems, with the objective to automate applicant pre‐screening. An integrated, company‐oriented, e‐recruitment system was implemented based on the proposed scheme and its functionality was showcased and evaluated in a real‐world recruitment scenario.Design/methodology/approach – The proposed system implements automated candidate ranking, based on objective criteria that can be extracted from the applicant's LinkedIn profile. What is more, candidate personality traits are automatically extracted from his/her social presence using linguistic analysis. The applicant's rank is derived from individual selection criteria using analytical hierarchy process (AHP), while their relative significance (weight) is controlled by the recruiter.Findings – The proposed e‐recruitment system was deployed in a real‐world recruitment scenario, and its output was validated by expert recruiters. It...


title: Fragmentation or cohesion? Visualizing the process and consequences of information system diversity, 1993–2012

abstract: In information systems (IS) literature, there is ongoing debate as to whether the field has become fragmented and lost its identity in response to the rapid changes of the field. The paper contributes to this discussion by providing quantitative measurement of the fragmentation or cohesiveness level of the field. A co-word analysis approach aiding in visualization of the intellectual map of IS is applied through application of clustering analysis, network maps, strategic diagram techniques, and graph theory for a collection of 47,467 keywords from 9551 articles, published in 10 major IS journals and the proceedings of two leading IS conferences over a span of 20 years, 1993 through 2012. The study identified the popular, core, and bridging topics of IS research for the periods 1993–2002 and 2003–2012. Its results show that research topics and subfields underwent substantial change between those two periods and the field became more concrete and cohesive, increasing in density. Findings from this study suggest that the evolution of the research topics and themes in the IS field should be seen as part of the natural metabolism of the field, rather than a process of fragmentation or disintegration.


title: Discovering Finance Keywords via Continuous-Space Language Models

abstract: The growing amount of public financial data makes it increasingly important to learn how to discover valuable information for financial decision making. This article proposes an approach to discovering financial keywords from a large number of financial reports. In particular, we apply the continuous bag-of-words (CBOW) model, a well-known continuous-space language model, to the textual information in 10-K financial reports to discover new finance keywords. In order to capture word meanings to better locate financial terms, we also present a novel technique to incorporate syntactic information into the CBOW model. Experimental results on four prediction tasks using the discovered keywords demonstrate that our approach is effective for discovering predictability keywords for post-event volatility, stock volatility, abnormal trading volume, and excess return predictions. We also analyze the discovered keywords that attest to the ability of the proposed method to capture both syntactic and contextual information between words. This shows the success of this method when applied to the field of finance.


title: Autonomous Battery Exchange of UAVs with a Mobile Ground Base

abstract: This paper presents the autonomous battery exchange operation for small scale UAVs, using a mobile ground base that carries a robotic arm and a service station containing the battery exchange mechanism. The goal of this work is to demonstrate the means to increase the autonomy and persistence of robotic systems without requiring human intervention. The design and control of the system and its components are presented in detail, as well as the collaborative software framework used to plan and execute complex missions. Finally, the results of autonomous outdoor experiments are presented, in which the ground rover successfully localizes, retrieves, services, and deploys the landed UAV, proving its capacity to extend and enhance autonomous operations.


title: Learning Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection

abstract: The performance of object detection has recently been significantly improved due to the powerful features learnt through convolutional neural networks (CNNs). Despite the remarkable success, there are still several major challenges in object detection, including object rotation, within-class diversity, and between-class similarity, which generally degenerate object detection performance. To address these issues, we build up the existing state-of-the-art object detection systems and propose a simple but effective method to train rotation-invariant and Fisher discriminative CNN models to further boost object detection performance. This is achieved by optimizing a new objective function that explicitly imposes a rotation-invariant regularizer and a Fisher discrimination regularizer on the CNN features. Specifically, the first regularizer enforces the CNN feature representations of the training samples before and after rotation to be mapped closely to each other in order to achieve rotation-invariance. The second regularizer constrains the CNN features to have small within-class scatter but large between-class separation. We implement our proposed method under four popular object detection frameworks, including region-CNN (R-CNN), Fast R- CNN, Faster R- CNN, and R- FCN. In the experiments, we comprehensively evaluate the proposed method on the PASCAL VOC 2007 and 2012 data sets and a publicly available aerial image data set. Our proposed methods outperform the existing baseline methods and achieve the state-of-the-art results.


title: A Parallel Softmax Classification Algorithm Based on MapReduce

abstract: Softmax Classification algorithm is the generalization of Logistic classification algorithm on multi-classification problems. The traditional stand-alone training algorithm's efficiency is extremely low in the running condition of large amount of data. When substantially increasing the amount of data, the algorithm above will take a lot of time to update parameters. Although the Mahout in Hadoop has realized the Logistic regression, naive Bayesian classifier and classification algorithms, the Softmax classification algorithms has not. The core part of the implementation of parallel Softmax classification algorithm is to use Mapreduce to read the training data partially and use Map tasks and Reduce tasks to realize Parallel gradient descent algorithm, which could finally iteratively update algorithm parameters. Via correlative experiment, it can be proved that the Parallelized Softmax algorithm based on Mapreduce can shorten the process of iteration and the running time and improve training efficiency and precision.


title: Unsupervised Extraction of Diagnosis Codes from EMRs Using Knowledge-Based and Extractive Text Summarization Techniques

abstract: Diagnosis codes are extracted from medical records for billing and reimbursement and for secondary uses such as quality control and cohort identification. In the US, these codes come from the standard terminology ICD-9-CM derived from the international classification of diseases (ICD). ICD-9 codes are generally extracted by trained human coders by reading all artifacts available in a patient’s medical record following specific coding guidelines. To assist coders in this manual process, this paper proposes an unsupervised ensemble approach to automatically extract ICD-9 diagnosis codes from textual narratives included in electronic medical records (EMRs). Earlier attempts on automatic extraction focused on individual documents such as radiology reports and discharge summaries. Here we use a more realistic dataset and extract ICD-9 codes from EMRs of 1000 inpatient visits at the University of Kentucky Medical Center. Using named entity recognition (NER), graph-based concept-mapping of medical concepts, and extractive text summarization techniques, we achieve an example based average recall of 0.42 with average precision 0.47; compared with a baseline of using only NER, we notice a 12% improvement in recall with the graph-based approach and a 7% improvement in precision using the extractive text summarization approach. Although diagnosis codes are complex concepts often expressed in text with significant long range non-local dependencies, our present work shows the potential of unsupervised methods in extracting a portion of codes. As such, our findings are especially relevant for code extraction tasks where obtaining large amounts of training data is difficult.


title: Why Aren't Operating Systems Getting Faster As Fast as Hardware

abstract: This note evaluates several hardware platforms and operating systems using a set of benchmarks that test memory bandwidth and various operating system features such as kernel entry/exit and file systems. The overall conclusion is that operating system performance does not seem to be improving at the same rate as the base speed of the underlying hardware. Copyright  1989 Digital Equipment Corporation d i g i t a l Western Research Laboratory 100 Hamilton Avenue Palo Alto, California 94301 USA


title: ArcTrees: Visualizing Relations in Hierarchical Data

abstract: In this paper we present, ARCTREES, a novel way of visualizing hierarchical and non-hierarchical relations within one interactive visualization. Such a visualization is challenging because it must display hierarchical information in a way that the user can keep his or her mental map of the data set and include relational information without causing misinterpretation. We propose a hierarchical view derived from traditional Treemaps and augment this view with an arc diagram to depict relations. In addition, we present interaction methods that allow the exploration of the data set using Focus+Context techniques for navigation. The development was motivated by a need for understanding relations in structured documents but it is also useful in many other application domains such as project management and calendars.


title: DAG-aware AIG rewriting a fresh look at combinational logic synthesis

abstract: This paper presents a technique for preprocessing combinational logic before technology mapping. The technique is based on the representation of combinational logic using and-inverter graphs (AIGs), a networks of two-input ANDs and inverters. The optimization works by alternating DAG-aware AIG rewriting, which reduces area by sharing common logic without increasing delay, and algebraic AIG balancing, which minimizes delay without increasing area. The new technology-independent flow is implemented in a public-domain tool ABC. Experiments on large industrial benchmarks show that the proposed methodology scales to very large designs and is several orders of magnitude faster than SIS and MVSIS while offering comparable or better quality when measured by the quality of the network after mapping.


title: Proximity relevance model for query expansion

abstract: Query expansion (QE) aims at improving information retrieval effectiveness by enhancing the query formulation. Because users' queries are generally short and because of the language ambiguity, some information needs are difficult to satisfy. Query reformulation and QE methods have been developed to face this issue. Pseudo relevance feedback (PRF) considers the top retrieved documents as relevant and uses their content in order to expand the initial query. Rather than considering feedback documents as a bag of words, it is possible to exploit term proximity information. Although there are some researches in this direction, the majority of them is empirical. The lack of theoretical works in this area motivated us to introduce a novel method integrated into the language model formalism that takes advantage of the remoteness of candidate terms for QE from query terms within feedback documents. In contrast to previous works, our approach captures the proximity directly and in terms of sentences rather than tokens. We show that the method significantly improves the retrieval performance on TREC collections especially for difficult queries.


title: Corporate Responsibility and Hypercompetition. The Ikea Case

abstract: The rapid changes caused by growing complexity, accelerating technological innovation and globalisation, have driven companies to play a precise economicsocial role. Hypercompetition implies a substantial change in a company’s strategic objectives: every advantage gained over the rivals is rapidly imitated and surpassed. Hypercompetition needs from the company the ability to control simultaneously: costs and quality; timing and know-how; strengths and weaknesses; financial resources. Following the expansion of its target market which has become global, a company often adopts a network structure. The network requires the activation of relations with local interlocutors and presupposes a coordinated system of values inside the organisation.


title: Securing the exocortex: A twenty-first century cybernetics challenge

abstract: An exocortex is a wearable (or implanted) computer used to augment a brain?s biological high-level cognitive processes and inform a user?s decisions and actions. Brain-computer interfaces (BCIs) are a special type of exocortex used to interact with the environment via neural signals. Uses for BCI range from medical applications and rehabilitation to operation of assistive devices. They can also be used for marketing, gaming, and for entertainment, where BCIs are used to provide users with a more personalized experience.


title: DISCUSSING ACCURACY IN AN AUTOMATIC MEASUREMENT SYSTEM USING COMPUTER VISION TECHNIQUES

abstract: A Computer Vision system extracts and analyzes image features in order to recognize and/or measure it. A Computer Vision system for inspection has received considerable industry attention, in the last years. This paper uses some Computer Vision techniques to measure parts and discusses common difficulties of automated inspection. The parts conformity analysis using a non-contact measurement system has been adopted specially to small objects, where accurate instruments are not so simple to be used. In these cases Coordinate Measuring Machine (CMM) can be used. However, in many times it is impossible to move the object to the CMM. In this case an image automated inspection should be a good alternative. The proposed system consists in a CCD camera positioned in the upper viewer of the object. Some considerations must be pointed out: (1) the object should be thin, to reduce the height influence in the measurement; (2) a standard gage must be used to convert pixels units to millimeters units. We present the algorithms developed to make dimensional measurements in industrial parts. A rectangle object with three holes is analyzed. The obtained results are compared with convencional instruments measurements. We highlight the difficults concerned to image automated inspection, discussing accuracy and the relationships among images and measurements.


title: Anomaly pattern detection in categorical datasets

abstract: We propose a new method for detecting patterns of anomalies in categorical datasets. We assume that anomalies are generated by some underlying process which affects only a particular subset of the data. Our method consists of two steps: we first use a "local anomaly detector" to identify individual records with anomalous attribute values, and then detect patterns where the number of anomalous records is higher than expected. Given the set of anomalies flagged by the local anomaly detector, we search over all subsets of the data defined by any set of fixed values of a subset of the attributes, in order to detect self-similar patterns of anomalies. We wish to detect any such subset of the test data which displays a significant increase in anomalous activity as compared to the normal behavior of the system (as indicated by the training data). We perform significance testing to determine if the number of anomalies in any subset of the test data is significantly higher than expected, and propose an efficient algorithm to perform this test over all such subsets of the data. We show that this algorithm is able to accurately detect anomalous patterns in real-world hospital, container shipping and network intrusion data.


title: Deep Deconvolutional Neural Network for Target Segmentation of Nasopharyngeal Cancer in Planning Computed Tomography Images

abstract: Background: Radiotherapy is one of the main treatment methods for nasopharyngeal carcinoma (NPC). It requires exact delineation of the nasopharynx gross tumor volume (GTVnx), the metastatic lymph node gross tumor volume (GTVnd), the clinical target volume (CTV), and organs at risk (OARs) in the planning computed tomography (CT) images. However, this task is time-consuming and operator-dependent. In the present study, we developed an end-to-end deep deconvolutional neural network (DDNN) for segmentation of these targets. Methods: The proposed DDNN is an end-to-end architecture enabling fast training and testing. It consists of two important components: an encoder network and a decoder network. The encoder network was used to extract the visual features of a medical image and the decoder network was used to recover the original resolution by deploying deconvolution. A total of 230 patients diagnosed with NPC stage I or stage II were included in this study. Data from 184 patients were chosen randomly as a training set to adjust the parameters of DDNN, and the remaining 46 patients were the test set to assess the performance of the model. The Dice similarity coefficient (DSC) was used to quantify the segmentation results of the GTVnx, GTVnd, and CTV. In addition, the performance of DDNN was compared with the VGG-16 model. Results: The proposed DDNN method outperformed the VGG-16 in all the segmentation. The mean DSC values of DDNN were 80.9% for GTVnx, 62.3% for the GTVnd, and 82.6% for CTV, whereas VGG-16 obtained 72.3%, 33.7%, and 73.7% for the DSC values, respectively. Conclusions: DDNN can be used to segment the GTVnx and CTV accurately. The accuracy for the GTVnd segmentation was relatively low due to the considerable differences in its shape, volume, and location among patients. The accuracy is expected to increase with more training data and combination of MR images. In conclusion, DDNN has the potential to improve the consistency of contouring and streamline radiotherapy workflows, but careful human review and a considerable amount of editing will be required.


title: Practical regression test selection with dynamic file dependencies

abstract: Regression testing is important but can be time-intensive. One approach to speed it up is regression test selection (RTS), which runs only a subset of tests. RTS was proposed over three decades ago but has not been widely adopted in practice. Meanwhile, testing frameworks, such as JUnit, are widely adopted and well integrated with many popular build systems. Hence, integrating RTS in a testing framework already used by many projects would increase the likelihood that RTS is adopted. We propose a new, lightweight RTS technique, called Ekstazi, that can integrate well with testing frameworks. Ekstazi tracks dynamic dependencies of tests on files, and unlike most prior RTS techniques, Ekstazi requires no integration with version-control systems. We implemented Ekstazi for Java and JUnit, and evaluated it on 615 revisions of 32 open-source projects (totaling almost 5M LOC) with shorter- and longer-running test suites. The results show that Ekstazi reduced the end-to-end testing time 32% on average, and 54% for longer-running test suites, compared to executing all tests. Ekstazi also has lower end-to-end time than the existing techniques, despite the fact that it selects more tests. Ekstazi has been adopted by several popular open source projects, including Apache Camel, Apache Commons Math, and Apache CXF.


title: Artificial Intelligence and Ethics: An Exercise in the Moral Imagination

abstract: The possibility of constructing a personal AI raises many ethical and religious questions that have been dealt with seriously only by imaginative works of fiction; they have largely been ignored by technical experts and by philosophical and theological ethicists. Arguing that a personal AI is possible in principle, and that its accomplishments could be adjudicated by the Turing Test, the article suggests some of the moral issues involved in AI experimentation by comparing them to issues in medical experimentation. Finally, the article asks questions about the capacities and possibilities of such an artifact for making moral decisions. It is suggested that much a priori ethical thinking is necessary and that, that such a project cannot only stimulate our moral imaginations, but can also tell us much about our moral thinking and pedagogy, whether or not it is ever accomplished in fact.


title: Bitcoin Concepts, Threats, and Machine-Learning Security Solutions

abstract: The concept of Bitcoin was first introduced by an unknown individual (or a group of people) named Satoshi Nakamoto before it was released as open-source software in 2009. Bitcoin is a peer-to-peer cryptocurrency and a decentralized worldwide payment system for digital currency where transactions take place among users without any intermediary. Bitcoin transactions are performed and verified by network nodes and then registered in a public ledger called blockchain, which is maintained by network entities running Bitcoin software. To date, this cryptocurrency is worth close to U.S. $150 billion and widely traded across the world. However, as Bitcoin’s popularity grows, many security concerns are coming to the forefront. Overall, Bitcoin security inevitably depends upon the distributed protocols-based stimulant-compatible proof-of-work that is being run by network entities called miners, who are anticipated to primarily maintain the blockchain (ledger). As a result, many researchers are exploring new threats to the entire system, introducing new countermeasures, and therefore anticipating new security trends. In this survey paper, we conduct an intensive study that explores key security concerns. We first start by presenting a global overview of the Bitcoin protocol as well as its major components. Next, we detail the existing threats and weaknesses of the Bitcoin system and its main technologies including the blockchain protocol. Last, we discuss current existing security studies and solutions and summarize open research challenges and trends for future research in Bitcoin security.


title: Design Considerations of Coils Used in Wireless Charger for EV

abstract: This paper presents the design method of coils for a 10kW wireless charger for electric vehicle (EV). Design procedures are elaborated based on the specifications such as power ratings, gap distance, space limitation. Experimental results validate the design methods.


title: Collaborative interdisciplinary astrobiology research: a bibliometric study of the NASA Astrobiology Institute

abstract: This study aims to undertake a bibliometric investigation of the NASA Astrobiology Institute (NAI) funded research that was published between 2008 and 2012 (by teams of Cooperative Agreement Notice Four and Five). For this purpose, the study creates an inventory of publications co-authored through NAI funding and investigates journal preferences, international and institutional collaboration, and citation behaviors of researchers to reach a better understanding of interdisciplinary and collaborative astrobiology research funded by the NAI. Using the NAI annual reports, 1210 peer-reviewed publications are analyzed. The following conclusions are drawn: (1) NAI researchers prefer publishing in high-impact multidisciplinary journals. (2) Astronomy and astrophysics are the most preferred categories to publish based on Web of Science subject categories. (3) NAI is indeed a virtual institution; researchers collaborate with other researchers outside their organization and in some cases outside the U.S. (4) There are prominent scholars in the NAI co-author network but none of them dominates astrobiology.


title: Any “Body” There? Avatar Visibility Effects in a Virtual Reality Game

abstract: This article presents an experiment exploring the possible impact of avatar's body part visibility on players' experience and performance using current Virtual Reality (VR) gaming platform capacities. In an action-based VR game, a player sees an avatar in first person perspective which is replicating his/her hand, head and body motion. In contrast to the expected outcome from non-game VR contexts, our results did not reveal significant differences with an avatar presenting an increasing number of visible body parts. The body ownership, immersion, emotional and cognitive involvements as well as the perceived control and difficulty were not improved with a more coherent virtual body. This tends to confirm the strong performance aspect of action-based games, whereby control efficiency and enemy awareness is paramount, and could overcome the perceptual, behavioural or emotional effects of avatar embodiment. Digital games are indeed prone to create an intense flow state typically reducing self-awareness, and focusing on the game completion and high performance achievements. However, further experiments with full-body tracking and different game types are necessary to confirm this trend. This research outcome motivates further analysis of the mutual influence of bottom-up and top-down factors of avatar embodiment causing psychophysical effects. In addition, it provides useful indications for VR game developers and researchers on possible effects and evaluation methods.


title: Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization

abstract: In this paper, we propose a novel sufficient decrease technique for stochastic variance reduced gradient descent methods such as SVRG and SAGA. In order to make sufficient decrease for stochastic optimization, we design a new sufficient decrease criterion, which yields sufficient decrease versions of stochastic variance reduction algorithms such as SVRG-SD and SAGA-SD as a byproduct. We introduce a coefficient to scale current iterate and to satisfy the sufficient decrease property, which takes the decisions to shrink, expand or even move in the opposite direction, and then give two specific update rules of the coefficient for Lasso and ridge regression. Moreover, we analyze the convergence properties of our algorithms for strongly convex problems, which show that our algorithms attain linear convergence rates. We also provide the convergence guarantees of our algorithms for non-strongly convex problems. Our experimental results further verify that our algorithms achieve significantly better performance than their counterparts.


title: A Review of African Swine Fever and the Potential for Introduction into the United States and the Possibility of Subsequent Establishment in Feral Swine and Native Ticks

abstract: African swine fever (ASF) is caused by African swine fever virus (ASFV) which can cause substantial morbidity and mortality events in swine. The virus can be transmitted via direct and indirect contact with infected swine, their products or via competent vector species, especially Ornithodoros ticks. Africa and much of Eastern Europe are endemic for ASF; a viral introduction to countries that are currently ASF-free could have severe economic consequences due to the loss of production from infected animals and the trade restrictions that would likely be imposed as a result of an outbreak. We identified vulnerabilities that could lead to ASFV introduction or persistence in the United States or other ASF-free regions. Both legal and illegal movement of live animals, as well as the importation of animal products, byproducts, and animal feed, pose a risk of virus introduction. Each route is described and current regulations designed to prevent ASFV and other pathogens from entering the United States are outlined. Furthermore, existing ASFV research gaps are highlighted. Laboratory experiments to evaluate multiple species of Ornithodoros ticks that have yet to be characterized would be useful to understand vector competence, host preferences, and distribution of competent soft tick vectors in relation to high pig production areas as well as regions with high feral swine (wild boar or similar) densities. Knowledge relative to antigenic viral proteins that contribute to host response and determination of immune mechanisms that lead to protection are foundational in the quest for a vaccine. Finally, sampling of illegally imported and confiscated wild suid products for ASFV could shed light on the types of products being imported and provide a more informed perspective relative to the risk of ASFV importation.


title: Image Segmentation Based on Statistical Confidence Intervals

abstract: Image segmentation is defined as a partition realized to an image into homogeneous regions to modify it into something that is more meaningful and softer to examine. Although several segmentation approaches have been proposed recently, in this paper, we develop a new image segmentation method based on the statistical confidence interval tool along with the well-known Otsu algorithm. According to our numerical experiments, our method has a dissimilar performance in comparison to the standard Otsu algorithm to specially process images with speckle noise perturbation. Actually, the effect of the speckle noise entropy is almost filtered out by our algorithm. Furthermore, our approach is validated by employing some image samples.


title: Pricing mechanisms for crowdsourcing markets

abstract: Every day millions of crowdsourcing tasks are performed in exchange for payments. Despite the important role pricing plays in crowdsourcing campaigns and the complexity of the market, most platforms do not provide requesters appropriate tools for effective pricing and allocation of tasks. In this paper, we introduce a framework for designing mechanisms with provable guarantees in crowdsourcing markets. The framework enables automating the process of pricing and allocation of tasks for requesters in complex markets like Amazon's Mechanical Turk where workers arrive in an online fashion and requesters face budget constraints and task completion deadlines. We present constant-competitive incentive compatible mechanisms for maximizing the number of tasks under a budget, and for minimizing payments given a fixed number of tasks to complete. To demonstrate the effectiveness of this framework we created a platform that enables applying pricing mechanisms in markets like Mechanical Turk. The platform allows us to show that the mechanisms we present here work well in practice, as well as to give experimental evidence to workers' strategic behavior in absence of appropriate incentive schemes.


title: Pathologic Aneurysmal Dilation of the Ascending Aorta and Dilation of the Main Pulmonary Artery in Patients with Kabuki Syndrome: Valve-sparing Aortic Root Replacement

abstract: We report the aneurysmal dilation of the ascending aorta and the main pulmonary artery in 2 children with Kabuki syndrome. In 1 patient, there was progressive aneurysmal dilation of the ascending aorta necessitating aortoplasty. Histologic examination of the resected aorta revealed disrupted and fragmented elastic fibers in the medial layer, along with mucinous degeneration of the aortic wall. This is the first recognition and report of these findings as part of the Kabuki syndrome.


Safety and Effectiveness of Anti-Retroviral Drug Regimens Zln and Tle in Tertiary Care Teaching Hospital : A Prospective Observational Study


title: MobiSens: A Versatile Mobile Sensing Platform for Real-World Applications

abstract: We present the design, implementation and evaluation of MobiSens, a versatile mobile sensing platform for a variety of real-life mobile sensing applications. MobiSens addresses common requirements of mobile sensing applications on power optimization, activity segmentation, recognition and annotation, interaction between mobile client and server, motivating users to provide activity labels with convenience and privacy concerns. After releasing three versions of MobiSens to the Android Market with evolving UI and increased functionalities, we have collected 13,993 h of data from 310 users over five months. We evaluate and compare the user experience and the sensing efficiency in each release. We show that the average number of activities annotated by a user increases from 0.6 to 6. This result indicates the activity auto-segmentation/recognition feature and certain UI design changes significantly improve the user experience and motivate users to use MobiSens more actively. Based on the MobiSens platform, we have developed a range of mobile sensing applications including Mobile Lifelogger, SensCare for assisted living, Ground Reporting for soldiers to share their positions and actions horizontally and vertically, and CMU SenSec, a behavior-driven mobile Security system.


title: Targeted bulk-loading of fluorescent indicators for two-photon brain imaging in vivo

abstract: One of the challenges for modern neuroscience is to understand the rules of concerted neuronal function in vivo. This question can be addressed using noninvasive high-resolution imaging techniques like two-photon microscopy. This protocol describes a versatile approach for in vivo two-photon calcium imaging of neural networks, stained with membrane-permeant fluorescent-indicator dyes. It is based on a targeted pressure ejection of the dye into the tissue of interest and can be used for a large spectrum of indicator dyes, including Oregon Green 488 BAPTA-1 acetoxymethyl ester and Fura-2 acetoxymethyl ester. Through the use of dye mixtures and multicolor imaging, this technique allows the visualization of distinct neurons and glial cells up to 500 μm below the brain surface. It is suitable for staining the brain tissue of various different species (e.g., mouse, rat, cat and zebrafish) at all developmental stages. When combined with brain microendoscopy, it allows the monitoring of intracellular calcium signals in awake, behaving animals. The total time required to carry out the protocol, including dissection and cell staining, is ∼2 h. Thereafter, imaging experiments might be performed for at least 6 h.


title: Model predictive control for DC-DC buck power converter-DC motor system with uncertainties using a GPI observer

abstract: In this paper, angular velocity control of permanent magnet dc motor is addressed by using an output feedback discrete-time model predictive control (MPC) algorithm. The operational constraints are imposed on the control input. The control system is designed based on a topology of the dc-dc buck power converter-dc motor system in the presence of unknown, exogenous, time-varying load torque disturbances. The generalized proportional integral (GPI) observer is used to estimate the time-varying disturbances. Finally, the effectiveness of the proposed model predictive controller based on the generalized proportional integral (GPI) observer algorithm is demonstrated by simulation results.


Internet of Things


title: Apoptosis: role in myeloid cell development

abstract: Hematopoiesis is the process that generates blood cells in an organism from the pluripotent stem cells. Hematopoietic stem cells are characterized by their ability to undergo self-renewal and differentiation. The self-renewing ability ensures that these pluripotent cells are not depleted from the bone marrow niche. A proper balance between cell death and cell survival is necessary to maintain a homeostatic condition, hence, apoptosis, or programmed cell death, is an essential step in hematopoiesis. Recent studies, however, have introduced a new aspect to this process, citing the significance of the apoptosis mediator, caspase, in cell development and differentiation. Extensive research has been carried out to study the possible role of caspases and other apoptosis related factors in the developmental processes. This review focuses on the various apoptotic factors involved in the development and differentiation of myeloid lineage cells: erythrocytes, megakaryocytes, and macrophages.


title: Collaborative Filtering on the Blockchain: A Secure Recommender System for e-Commerce

abstract: In collaborative filtering approaches, recommendations are inferred from user data. A large volume and a high data quality is essential for an accurate and precise recommender system. As consequence, companies are collecting large amounts of personal user data. Such data is often highly sensitive and ignoring users’ privacy concerns is no option. Companies address these concerns with several risk reduction strategies, but none of them is able to guarantee cryptographic secureness. To close that gap, the present paper proposes a novel recommender system using the advantages of blockchain-supported secure multiparty computation. A potential costumer is able to allow a company to apply a recommendation algorithm without disclosing her personal data. Expected benefits are a reduction of fraud and misuse and a higher willingness to share personal data. An outlined experiment will compare users’ privacy-related behavior in the proposed recommender system with existent solutions.


title: Design Methodology of Bidirectional CLLC Resonant Converter for High-Frequency Isolation of DC Distribution Systems

abstract: A bidirectional full-bridge CLLC resonant converter using a new symmetric LLC-type resonant network is proposed for a low-voltage direct current power distribution system. This converter can operate under high power conversion efficiency because the symmetric LLC resonant network has zero-voltage switching capability for primary power switches and soft commutation capability for output rectifiers. In addition, the proposed topology does not require any snubber circuits to reduce the voltage stress of the switching devices because the switch voltage of the primary and secondary power stage is confined by the input and output voltage, respectively. In addition, the power conversion efficiency of any directions is exactly same as each other. Using digital control schemes, a 5-kW prototype converter designed for a high-frequency galvanic isolation of 380-V dc buses was developed with a commercial digital signal processor. Intelligent digital control algorithms are also proposed to regulate output voltage and to control bidirectional power conversions. Using the prototype converter, experimental results were obtained to verify the performance of the proposed topology and control algorithms. The converter could softly change the power flow directions and its maximum power conversion efficiency was 97.8% during the bidirectional operation.


title: Smart gloves for hand gesture recognition: Sign language to speech conversion system

abstract: People with speech impairment find it difficult to communicate in a society where most of the people do not understand sign language. The idea proposed in this paper is a smart glove which can convert sign language to speech output. The glove is embedded with flex sensors and an Inertial Measurement Unit (IMU) to recognize the gesture. A novel method of State Estimation has been developed to track the motion of hand in three dimensional spaces. The prototype was tested for its feasibility in converting Indian Sign Language to voice output. Though the glove is intended for sign language to speech conversion, it is a multipurpose glove and finds its applications in gaming, robotics and medical field.


title: Assessing stochastic algorithms for large scale nonlinear least squares problems using extremal probabilities of linear combinations of gamma random variables

abstract: This paper considers stochastic algorithms for efficiently solving a class of large scale nonlinear least squares (NLS) problems which frequently arise in applications. We propose eight variants of a practical randomized algorithm where the uncertainties in the major stochastic steps are quantified. Such stochastic steps involve approximating the NLS objective function using Monte Carlo methods, and this is equivalent to the estimation of the trace of corresponding symmetric positive semidefinite matrices. For the latter, we prove tight necessary and \sufficient conditions on the sample size (which translates to cost) to satisfy the prescribed probabilistic accuracy. We show that these conditions are practically computable and yield small sample sizes. They are then incorporated in our stochastic algorithm to quantify the uncertainty in each randomized step. The bounds we use are applications of more general results regarding extremal tail probabilities of linear combinations of gamma distributed random v...


title: Improving live debugging of concurrent threads through thread histories

abstract: Abstract Concurrency issues are inherently harder to identify and fix than issues in sequential programs, due to aspects like indeterminate order of access to shared resources and thread synchronisation. Live debuggers are often used by developers to gain insights into the behaviour of concurrent programs by exploring the call stacks of threads. Nevertheless, contemporary live debuggers for concurrent programs are usually sequential debuggers augmented with the ability to display different threads in isolation. To these debuggers every thread call stack begins with a designated start routine and the calls that led to the creation of the thread are not visible, as they are part of a different thread. This requires developers to manually link stack traces belonging to related but distinct threads, adding another burden to the already difficult act of debugging concurrent programs. To improve debugging of concurrent programs we address the problem of incomplete call stacks in debuggers through a thread and debugger model that enables live debugging of child threads within the context of their parent threads. The proposed debugger operates on a virtual thread that merges together multiple relevant threads. To better understand the features of debuggers for concurrent programs we present an in-depth discussion of the concurrency related features in current live debuggers. We test the applicability of the proposed model by instantiating it for simple threads, local and remote promises, and a remote object-oriented database. Starting from these use cases we further discuss implementation details ensuring a practical approach.


Deep Asymmetric Transfer Network for Unbalanced Domain Adaptation


title: Neuropsychology 3.0: evidence-based science and practice.

abstract: Neuropsychology is poised for transformations of its concepts and methods, leveraging advances in neuroimaging, the human genome project, psychometric theory, and information technologies. It is argued that a paradigm shift toward evidence-based science and practice can be enabled by innovations, including (1) formal definition of neuropsychological concepts and tasks in cognitive ontologies; (2) creation of collaborative neuropsychological knowledgebases; and (3) design of Web-based assessment methods that permit free development, large-sample implementation, and dynamic refinement of neuropsychological tests and the constructs these aim to assess. This article considers these opportunities, highlights selected obstacles, and offers suggestions for stepwise progress toward these goals. (JINS, 2011, 17, 000–000)


title: The brain basis of language processing: From structure to function

abstract: Language processing is a trait of human species. The knowledge about its neurobiological basis has been increased considerably over the past decades. Different brain regions in the left and right h...


title: A combined Bayesian network method for predicting drive failure times from SMART attributes

abstract: Statistical and machine learning methods have been proposed to predict hard drive failure based on SMART attributes, and many achieve good performance. However, these models do not give a good indication as to when a drive will fail, only predicting that it will fail. To this end, we propose a new notion of a drive's health degree based on the remaining working time of hard drive before actual failure occurs. An ensemble learning method is implemented to predict these health degrees: four popular individual classifiers are individually trained and used in a Combined Bayesian Network (CBN). Experiments show that the CBN model can give a health assessment under the proposed definition where drives are predicted to fail no later than their actual failure time 70% or more of the time, while maintaining prediction performance standards at least approximately as good as the individual classifiers.


title: A review on global sensitivity analysis methods

abstract: This chapter makes a review, in a complete methodological framework, of various global sensitivity analysis methods of model output. Numerous statistical and probabilistic tools (regression, smoothing, tests, statistical learning, Monte Carlo, …) aim at determining the model input variables which mostly contribute to an interest quantity depending on model output. This quantity can be for instance the variance of an output variable. Three kinds of methods are distinguished: the screening (coarse sorting of the most influential inputs among a large number), the measures of importance (quantitative sensitivity indices) and the deep exploration of the model behaviour (measuring the effects of inputs on their all variation range). A progressive application methodology is illustrated on a scholar application. A synthesis is given to place every method according to several axes, mainly the cost in number of model evaluations, the model complexity and the nature of brought information.


title: Neural Font Style Transfer

abstract: In this paper, we chose an approach to generate fonts by using neural style transfer. Neural style transfer uses Convolution Neural Networks(CNN) to transfer the style of one image to another. By modifying neural style transfer, we can achieve neural font style transfer. We also demonstrate the effects of using different weighted factors, character placements, and orientations. In addition, we show the results of using non-Latin alphabets, non-text patterns, and non-text images as style images. Finally, we provide insight into the characteristics of style transfer with fonts.


title: Investigating on Requirements for Business Model Representations: The Case of Information Technology in Healthcare

abstract: Business Models (BMs) are in focus of research and practice for many years. These are representations of an organisation's business activities that intercede between business processes and strategy. Typically, industries that are supported by or based on information technology are in the focus for setting up BMs as dynamic business environments require such anchor points. Business Model Representations (BMRs) are especially important for Information Systems Research. These are conceptual models that depict or represent BMs. Both for BMs and BMRs, there are no common understanding or generally accepted core concepts. Existing BMRs are quite generic and hamper the capturing of the specifics of concrete business cases. The present article structures and assesses the field of BMRs. This cannot be done without a focus on the distinct domain and its specific issues and circumstances. The focus is on Health Information Technology. This is a highly relevant field for the modernisation and the increase of the efficiency of the healthcare systems that are under pressure all in the (western) world. For this, existing BMR approaches will be assessed regarding their suitability for that domain. The revealed deficits form the basis for more suitable BMRs. This allows to assess the value of possible system elements or functionalities and to guide the development and use of respective IT solutions.


title: EnGarde: Mutually-Trusted Inspection of SGX Enclaves

abstract: Intel's SGX architecture allows cloud clients to create enclaves, whose contents are cryptographically protected by the hardware even from the cloud provider. While this feature protects the confidentiality and integrity of the client's enclave content, it also means that enclave content is completely opaque to the cloud provider. Thus, the cloud provider is unable to enforce policy compliance on enclaves. In this paper, we introduce EnGarde, a system that allows cloud providers to ensure SLA compliance on enclave content. In EnGarde, cloud providers and clients mutually agree upon a set of policies that the client's enclave content must satisfy. EnGarde executes when the client provisions the enclave, ensuring that only policy-compliant content is loaded into the enclave. EnGarde is able to achieve its goals without compromising the security guarantees offered by the SGX, and imposes no runtime overhead on the execution of enclave code. We have demonstrated the utility of EnGarde by using it to enforce a variety of security policies on enclave content.


Securing Big Data Hadoop : A Review of Security Issues , Threats and Solution


title: Toward improving graftability on automated program repair

abstract: In software evolution, many bugs occur and developers spend a long time to fix them. Program debugging is a costly and difficult task. Automated program repair is a promising way to reduce costs on program debugging dramatically. Several repair techniques reusing existing code lines have been proposed in the past. They reuse code lines already existing in the source code to generate variant source code of a given source code (if an inserted code line to fix a given bug is identical to any of the code lines in existing source code, we call the code line graftable). However, there are many bugs that such techniques cannot automatically repair. One of the reasons is that many bugs require code lines not existing in the source code of the software. In order to mitigate this issue, we are conducting our research with two ideas. The first idea is using a large dataset of source code to reuse code lines. The second idea is reusing only structures of code lines. Vocabularies are obtained from faulty code regions. In this paper, we report the feasibilities of the two ideas. More concretely, we found that the first and second ideas improved graftability of code lines to 43–59% and 56–64% from 34–54%, respectively. If we combine both the ideas, graftability was improved to 64–69%. In cases where we used the second idea, 24–49% variables used in reused code lines were able to be retrieved from the surrounding code of given faulty code regions.


Credibility assessment and inference for fusion of hard and soft information


title: INCONCO: interpretable clustering of numerical and categorical objects

abstract: The integrative mining of heterogeneous data and the interpretability of the data mining result are two of the most important challenges of today's data mining. It is commonly agreed in the community that, particularly in the research area of clustering, both challenges have not yet received the due attention. Only few approaches for clustering of objects with mixed-type attributes exist and those few approaches do not consider cluster-specific dependencies between numerical and categorical attributes. Likewise, only a few clustering papers address the problem of interpretability: to explain why a certain set of objects have been grouped into a cluster and what a particular cluster distinguishes from another. In this paper, we approach both challenges by constructing a relationship to the concept of data compression using the Minimum Description Length principle: a detected cluster structure is the better the more efficient it can be exploited for data compression. Following this idea, we can learn, during the run of a clustering algorithm, the optimal trade-off for attribute weights and distinguish relevant attribute dependencies from coincidental ones. We extend the efficient Cholesky decomposition to model dependencies in heterogeneous data and to ensure interpretability. Our proposed algorithm, INCONCO, successfully finds clusters in mixed type data sets, identifies the relevant attribute dependencies, and explains them using linear models and case-by-case analysis. Thereby, it outperforms existing approaches in effectiveness, as our extensive experimental evaluation demonstrates.


title: Whiplash injury and oculomotor dysfunctions: clinical–posturographic correlations

abstract: Oculomotor dysfunctions are hidden causes of invalidity following whiplash injury. Many patients with whiplash injury grade II present oculomotor dysfunctions related to input disturbances of cervical or vestibular afferents. We used static posturography to investigate 40 consecutive patients with whiplash injury grade II and oculomotor dysfunctions. We demonstrated a relation between length and surface of body sway: the surface value (A) was higher than the length value (L) and this led to an open graph of body sway in the statokinesigram. Oculomotor rehabilitation can resolve the impairment of vestibular function but if therapy is delayed or the patient has been wearing an orthopaedic neck collar, more therapeutic sessions are required. In conclusion, without rehabilitation of the oculomotor muscles other therapies are not sufficient to recover the impairment caused by whiplash injury.


title: On the Philosophy of Bitcoin/Blockchain Technology: Is it a Chaotic, Complex System?

abstract: The philosophy of blockchain technology is concerned, among other things, with blockchain ontology, how it might be characterised, how it is being created, implemented, and adopted, how it operates in the world, and how it evolves over time. This paper concentrates on whether Bitcoin/blockchain can be considered a complex system and, if so, whether it is a chaotic one. Beyond mere academic curiosity, a positive response would raise concerns about the likelihood of Bitcoin/blockchain entering a 2010-Flash-Crash-type of chaotic regime, with catastrophic consequences for financial systems based on it. The paper starts by highlighting the relevant details of the Bitcoin/blockchain ecosystem formed by the blockchain itself, bitcoin end users (payers and payees), capital gains seekers, miners, full nodes maintainers, and developers, and their interactions. Then the Information Theory of Complex Systems is briefly discussed for later use. Finally, the blockchain is investigated with the help of Crutchfield's Statistical Complexity measure. The low non-null statistical complexity value obtained suggests that the blockchain may be considered algorithmically complicated but hardly a complex system and unlikely to enter a chaotic regime.


title: Organization of CA1 projections to the subiculum: A PHA-L analysis in the rat

abstract: The organization of CA1 projections to the rat subiculum was investigated with the anterograde tracer, Phaseolus vulgaris leucoagglutinin (PHA-L). Discrete iontophoretic injections of PHA-L were placed into various transverse positions of the CA1 field at different septotemporal levels of the hippocampus. The distribution of CA1 projections was observed in dissected and extended hippocampal preparations. CA1 cells located proximally in the field, i.e., close to the CA2 field, gave rise to projections that terminated in the distal third of the subiculum, i.e., close to the presubiculum. CA1 cells located distally in the field, i.e., close to the subiculum, gave rise to projections that terminated proximally in the subiculum, i.e., just across the CA1/subiculum border. CA1 cells in the middle of the field projected to a midtransverse portion of the subiculum. The same general pattern of projections was observed at all septotemporal levels of the hippocampus. Varicose fibers from the CA1 neurons terminated among the basal dendrites of the subicular pyramidal cells, within the pyramidal cell layer, and in the deep portion of the molecular layer. In addition to the CA1 to subiculum projections, the discrete PHA-L injections provided the opportunity of examining the extent of local and associational connections within CA1. In general, associational connections in CA1 are far less extensive than in CA3. CA1 is not entirely without local connections, however. CA1 cells located close to the subicular border, for example, originated axons that first innervated the proximal subiculum and then reentered the CA1 field at the interface between stratum radiatum and stratum lacunosum-moleculare. In most of the experimental cases, there were collaterals located in stratum oriens of CA1 that branched from the fibers directed toward the subiculum. Thus, the basal dendrites of CA1 cells may receive associational inputs. The organization of the CA1 projections to the subiculum is discussed in relation to the organization of CA3 projections to CA1 and the differential output of transverse regions of the subiculum. The possibility is raised that information may be “channeled” through the hippocampal formation via the transverse organization of these connections and ultimately distributed to different recipients of hippocampal efferent projections.


title: Data Mining for Internet of Things: A Survey

abstract: It sounds like mission impossible to connect everything on the Earth together via Internet, but Internet of Things (IoT) will dramatically change our life in the foreseeable future, by making many "impossibles" possible. To many, the massive data generated or captured by IoT are considered having highly useful and valuable information. Data mining will no doubt play a critical role in making this kind of system smart enough to provide more convenient services and environments. This paper begins with a discussion of the IoT. Then, a brief review of the features of "data from IoT" and "data mining for IoT' is given. Finally, changes, potentials, open issues, and future trends of this field are addressed.


title: An Effective DRAM Address Remapping for Mitigating Rowhammer Errors

abstract: A rowhammer error represents a loss of data stored in a DRAM cell caused by electromagnetic interference due to repetitive access to the same and/or adjacent rows. Due to the concentrated occurrence of rowhammer errors in specific rows and columns, these errors cannot be corrected by the conventional error correcting code (ECC) commonly used in DRAM devices. Previous techniques avoid these errors by having additional refresh operations that require additional hardware resources and/or power consumption. This paper proposes a different approach to handle rowhammer errors by distributing them across different DRAM rows and columns so that the attack cells are not concentrated on specific rows and columns. To this end, the distribution of rowhammer errors is observed with experiments using several commercial DRAM devices by employing state-of-the-art rowhammer attack techniques. The observation of the rowhammer errors concentrated in specific rows and columns underlies the proposal of an effective DRAM address remapping scheme for re-distribution of rowhammer errors. By using different address mappings to different chips and arrays in a DIMM, the proposed remapping effectively distributes errors over different rows and columns. As a result, the proposed remapping scheme decreases the possibility of multiple errors in a single word, and consequently, reduces uncorrectable errors under single error or single symbol correcting ECC. Experimental results with commercial DIMMs show that the proposed scheme reduces uncorrectable errors by about 95 percent while incurring a small additional hardware cost.


title: Meerkat: A framework for developing presence monitoring software based on face recognition

abstract: Presence monitoring is the feature that recognizes presence of a person or persons automatically. It improves upon both manual and automatic presence verification process by allowing unobtrusive and continuous monitoring rather than performing a discrete check at the beginning and/or at the end of the participation period. The software with presence monitoring capability is particularly useful in today's higher education settings where various soft skills must be continuously developed, monitored and assessed. This paper proposes a framework-called Meerkat-for developing presence monitoring software. The framework relies on the face recognition technology from Microsoft Cognitive Services as a convenient tool to produce web-based APIs that can easily be used to develop web applications for presence monitoring. In a case study, an application has been developed as a proof of concept to confirm the integration between the presence monitoring feature and the Face API of Microsoft Cognitive Services. Furthermore, to evaluate the performance of the application, an error analysis on this application has been carried out that shows a satisfactory performance. As Meerkat is based on face recognition which extends the Microsoft Cognitive Services, the results confirm that most of the errors highly correlate with the image quality and the posture of the faces.


title: Broadband Elliptical Slotted Patch Antenna for 5G Communications

abstract: In this paper, a broadband elliptical slotted patch antenna has been proposed for 5G wireless communications. The proposed antenna apparatus has a minimal size of 0.308λ 0 × 0.308λ 0 × 0.009λ 0 at 22 GHz. The antenna bandwidth is enhanced for an elliptical ground plane. Microstrip feed line has been connected by a stub, to achieve better impedance. Array design has been designed for better gain, therefore array design has been made. The array has a high gain of about 18 dBi at 28 GHz and 21dBi at 38 GHz. The proposed antenna covers a wide band area from 22 GHz to 48 GHz. The proposed antenna covers 28/38 GHz. The antenna additionally gives omnidirectional to required 5G bands.


title: Automatic Modulation Classification of Overlapped Sources Using Multi-Gene Genetic Programming With Structural Risk Minimization Principle

abstract: As the spectrum environment becomes increasingly crowded and complicated, primary users may be interfered by secondary users and other illegal users. Automatic modulation classification (AMC) of a single source cannot recognize the overlapped sources. Consequently, the AMC of overlapped sources attracts much attention. In this paper, we propose a genetic programming-based modulation classification method for overlapped sources (GPOS). The proposed GPOS consists of two stages, the training stage, and the classification stage. In the training stage, multi-gene genetic programming (MGP)-based feature engineering transforms sample estimates of cumulants into highly discriminative MGP-features iteratively, until optimal MGP-features (OMGP-features) are obtained, where the structural risk minimization principle (SRMP) is employed to evaluate the classification performance of MGP-features and train the classifier. Moreover, a self-adaptive genetic operation is designed to accelerate the feature engineering process. In the classification stage, the classification decision is made by the trained classifier using the OMGP-features. Through simulation results, we demonstrate that the proposed scheme outperforms other existing methods in terms of classification performance and robustness in case of varying power ratios and fading channel.


title: Smart False Data Injection attacks against State Estimation in Power Grid

abstract: In this paper a new class of cyber attacks against state estimation in the electric power grid is considered. This class of attacks is named false data injection attacks. We show that with the knowledge of the system configuration an attacker could successfully inject false data into certain state variable while bypassing existing techniques for bad data detection. In the preliminary section we consider the feasibility of such an attack and the necessary condition to successfully avoid detection. After that we show that with the knowledge of the system configuration, certain line flow measurements could be manipulated to lead to profitable misconduct. By controlling Regional Transmission Organizations (RTOs) view of system power flow and congestion, an attacker could manipulate the LMPs of targeted buses according to prior biddings. Also, in this paper we show the implementation of the false data injection attacks. The numerical example considered was applied to a malicious data detection algorithm that was designed on a microcontroller. The results demonstrated the effectiveness of injecting false data measurements into the state estimation of electric power grids.


title: An intelligent system to detect, avoid and maintain potholes: A graph theoretic approach

abstract: In this paper, we propose a conceptual framework where a centralized system, classifies the road based upon the level of damage. The centralized system also identifies the traffic intensity thereby prioritizing the roads that need quick action to be taken upon. Moreover, the system helps the driver to detect the level of damage to the road stretch and route the vehicle from an alternative path to its destination. The system sends a feedback to the concerned authorities for a quick response to the condition of the roads. The system we use comprises a laser sensor and pressure sensors in shock absorbers to detect and quantify the intensity of the pothole, a centralized server which maintains a database of locations of all the potholes which can be accessed by another unit inside the vehicle. A point to point connection device is also installed in vehicles so that, when a vehicle detects a pothole which is not in the database, all the vehicles within a range of 20 meters are warned about the pothole. The system computes a route with least number of potholes which is nearest to the desired destination . If the destination is unknown, then the system will check for potholes in the current road and displays the level of damage. The system is flexible enough that the destination can be added, removed or changed any time during the travel. The best possible route is suggested by the system upon the alteration. We prove that the algorithm returns an efficient path with least number of potholes.


title: A classified and comparative study of edge detection algorithms

abstract: Since edge detection is in the forefront of image processing for object detection, it is crucial to have a good understanding of edge detection algorithms. This paper introduces a new classification of most important and commonly used edge detection algorithms, namely ISEF, Canny, Marr-Hildreth, Sobel, Kirsch, Lapla1 and Lapla2. Five categories are included in our classification, and then advantages and disadvantages of some available algorithms within this category are discussed. A representative group containing the above seven algorithms are the implemented in C++ and compared subjectively, using 30 images out of 100 images. Two sets of images resulting from the application of those algorithms are then presented. It is shown that under noisy conditions, ISEF, Canny, Marr-Hildreth, Kirsch, Sobel, Lapla2, Lapla1 exhibit better performance, respectively.


title: The Situational Eight DIAMONDS: A Taxonomy of Major Dimensions of Situation Characteristics

abstract: Taxonomies of person characteristics are well developed, whereas taxonomies of psychologically important situation characteristics are underdeveloped. A working model of situation perception implies the existence of taxonomizable dimensions of psychologically meaningful, important, and consequential situation characteristics tied to situation cues, goal affordances, and behavior. Such dimensions are developed and demonstrated in a multi-method set of 6 studies. First, the �Situational Eight DIAMONDS� dimensions Duty, Intellect, Adversity, Mating, pOsitivity, Negativity, Deception, and Sociality (Study 1) are established from the Riverside Situational Q-Sort (Sherman, Nave, & Funder, 2010, 2012, 2013; Wagerman & Funder, 2009). Second, their rater agreement (Study 2) and associations with situation cues and goal/trait affordances (Studies 3 and 4) are examined. Finally, the usefulness of these dimensions is demonstrated by examining their predictive power of behavior (Study 5), particularly vis-a-vis measures of personality and situations (Study 6). Together, we provide extensive and compelling evidence that the DIAMONDS taxonomy is useful for organizing major dimensions of situation characteristics. We discuss the DIAMONDS taxonomy in the context of previous taxonomic approaches and sketch future research directions. (PsycINFO Database Record (c) 2014 APA, all rights reserved)


title: Implicit feedback for inferring user preference: a bibliography

abstract: Relevance feedback has a history in information retrieval that dates back well over thirty years (c.f [SL96]). Relevance feedback is typically used for query expansion during short-term modeling of a user's immediate information need and for user profiling during long-term modeling of a user's persistent interests and preferences. Traditional relevance feedback methods require that users explicitly give feedback by, for example, specifying keywords, selecting and marking documents, or answering questions about their interests . Such relevance feedback methods force users to engage in additional activities beyond their normal searching behavior . Since the cost to the user is high and the benefits are not always apparent, it can be difficult to collect the necessary data and the effectiveness of explicit techniques can be limited.


title: Magnetic plethysmograph transducers for local blood pulse wave velocity measurement

abstract: We present the design of magnetic plethysmograph (MPG) transducers for detection of blood pulse waveform and evaluation of local pulse wave velocity (PWV), for potential use in cuffless blood pressure (BP) monitoring. The sensors utilize a Hall effect magnetic field sensor to capture the blood pulse waveform. A strap based design is performed to enable reliable capture of large number of cardiac cycles with relative ease. The ability of the transducer to consistently detect the blood pulse is verified by in-vivo trials on few volunteers. A duality of such transducers is utilized to capture the local PWV at the carotid artery. The pulse transit time (PTT) between the two detected pulse waveforms, measured along a small section of the carotid artery, was evaluated using automated algorithms to ensure consistency of measurements. The correlation between the measured values of local PWV and BP was also investigated. The developed transducers provide a reliable, easy modality for detecting pulse waveform on superficial arteries. Such transducers, used for measurement of local PWV, could potentially be utilized for cuffless, continuous evaluation of BP at various superficial arterial sites.


title: Tracking drone orientation with multiple GPS receivers

abstract: Inertial sensors continuously track the 3D orientation of a flying drone, serving as the bedrock for maneuvers and stabilization. However, even the best inertial measurement units (IMU) are prone to various types of correlated failures. We consider using multiple GPS receivers on the drone as a fail-safe mechanism for IMU failures. The core challenge is in accurately computing the relative locations between each receiver pair, and translating these measurements into the drone's 3D orientation. Achieving IMU-like orientation requires the relative GPS distances to be accurate to a few centimeters -- a difficult task given that GPS today is only accurate to around 1-4 meters. Moreover, GPS-based orientation needs to be precise even under sharp drone maneuvers, GPS signal blockage, and sudden bouts of missing data. This paper designs SafetyNet, an off-the-shelf GPS-only system that addresses these challenges through a series of techniques, culminating in a novel particle filter framework running over multi-GNSS systems (GPS, GLONASS, and SBAS). Results from 11 sessions of 5-7 minute flights report median orientation accuracies of 2° even under overcast weather conditions. Of course, these improvements arise from an increase in cost due to the multiple GPS receivers, however, when safety is of interest, we believe that tradeoff is worthwhile.


title: Local and global sparse Gaussian process approximations

abstract: Gaussian process (GP) models are flexible probabilistic nonparametric models for regression, classification and other tasks. Unfortunately they suffer from computational intractability for large data sets. Over the past decade there have been many different approximations developed to reduce this cost. Most of these can be termed global approximations, in that they try to summarize all the training data via a small set of support points. A different approach is that of local regression, where many local experts account for their own part of space. In this paper we start by investigating the regimes in which these different approaches work well or fail. We then proceed to develop a new sparse GP approximation which is a combination of both the global and local approaches. Theoretically we show that it is derived as a natural extension of the framework developed by Quinonero Candela and Rasmussen [2005] for sparse GP approximations. We demonstrate the benefits of the combined approximation on some 1D examples for illustration, and on some large real-world data sets.


title: Diffusion and Phase Transitions Accelerated by Severe Plastic Deformation

abstract: Severe plastic deformation (SPD) can lead to the phase transformations in the materials. Even the SPD-treatment at ambient temperature TSPD = 300 K is frequently equivalent to the heat treatment at a certain elevated temperature (effective temperature) Teff > 300 K. However, if the real annealing at effective temperature leads to the grain growth, SPD leads to strong grain refinement. SPD also accelerates the mass transfer in the materials. In this review the methods of determination for effective temperature after high-pressure torsion of metallic alloys are discussed as well as SPD-driven acceleration of diffusion.


title: Psilocybin induces schizophrenia-like psychosis in humans via a serotonin-2 agonist action

abstract: Psilocybin, an indoleamine hallucinogen, produces a psychosis-like syndrome in humans that resembles first episodes of schizophrenia. In healthy human volunteers, the psychotomimetic effects of psilocybin were blocked dose-dependently by the serotonin-2A antagonist ketanserin or the atypical antipsychotic risperidone, but were increased by the dopamine antagonist and typical antipsychotic haloperidol. These data are consistent with animal studies and provide the first evidence in humans that psilocybin-induced psychosis is due to serotonin-2A receptor activation, independently of dopamine stimulation. Thus, serotonin-2A overactivity may be involved in the pathophysiology of schizophrenia and serotonin-2A antagonism may contribute to therapeutic effects of antipsychotics. Language: en