title: Cost-driven build orientation and bin packing of parts in Selective Laser Melting (SLM)

abstract: Abstract Selective Laser Melting (SLM) is an additive manufacturing process capable of producing mixed batches of parts simultaneously within a single build. The build orientation of a part in SLM is a key process parameter, affecting the build cost, time and quality, as well as batch size. Choosing an optimal arrangement of multiple heterogeneous parts inside the SLM machine also presents a challenging irregular bin packing problem. Since the two problems are interdependent, this paper addresses the combined problem of finding an optimal build orientation and two-dimensional irregular bin packing solution of a mixed batch of parts across identical SLM machines. We address this problem specifically in the context of low-volume high-variety (LVHV) production in the aerospace sector, using total build cost as the objective function. To solve this problem, we present an Iterative Tabu Search Procedure (ITSP), which consists of six distinct stages. We test each stage in the ITSP on 27 manually generated instances, based on 68 unique geometries ranging in convexity and size, including six real-life components from the aerospace industry. Two of the six stages, which are driven by support structure volume, returned the highest improvement in cost. Overall, the results showed an average cost improvement of 16.2% over the initial solution. The initial solution of the procedure was benchmarked against a commercial software, showing comparable results.


of Aperture Coupled Microstrip Antennas : History , Operation , Development , and Applications


Cloud and the Law Blockchain : A Panacea for Healthcare Cloud-Based Data Security and Privacy ?


title: Approaches for Enriching and Improving Textual Knowledge Bases

abstract: Verifiability is one of the core editing principles in Wikipedia, where editors are encouraged to provide citations for the added statements. Statements can be any arbitrary piece of text, ranging from a sentence up to a paragraph. However, in many cases, citations are either outdated, missing, or link to non-existing references (e.g. dead URL, moved content etc.). In total, 20\% of the cases such citations refer to news articles and represent the second most cited source. Even in cases where citations are provided, there are no explicit indicators for the span of a citation for a given piece of text. In addition to issues related with the verifiability principle, many Wikipedia entity pages are incomplete, with relevant information that is already available in online news sources missing. Even for the already existing citations, there is often a delay between the news publication time and the reference time. In this thesis, we address the aforementioned issues and propose automated approaches that enforce the verifiability principle in Wikipedia, and suggest relevant and missing news references for further enriching Wikipedia entity pages.


title: Study of electrical properties and petrography for carbonate rocks in the Jurassic Formations: Sinai Peninsula, Egypt

abstract: Electrical methods are used in a wide range of applications including groundwater, geothermal, mineral discovery, oil and gas exploration, and deep tectonic processes. The Jurassic sedimentary section in North Sinai is of a special interest in particular for its economic potentiality and environmental conditions such as continental, marine, fluviomarine. The understanding petrographical and electrical properties of these rocks are essential for investigating minerals, oil, and water exploration. This paper presents a study of the petrographical and AC electrical properties of the Jurassic carbonates. Electrical measurements have been conducted for twenty nine (29) carbonate samples. The slight changes between samples in electrical properties were attributed to the changes in mineral composition, texture pore spaces, and pore throat distribution of the samples. Electrical properties generally change with many factors (grain size, mineral composition, grain shape, and facies). The dielectric constant decreases with frequency and increases with the presence of conductive solids (silt and clay) and its composition. The conductivity increases with the increase of conductor paths between electrodes. The main goal of this paper is to shed more light on interrelations between electrical properties (conductivity, dielectric constant and impedance as a function of frequency), petrography, and mineral composition (carbonates that contain clays and quartz grains). This is the first time to make some complex electrical properties on the carbonate rocks at this area as a prelude to make forward and inverse modeling.


title: Connected component-based technique for automatic extraction of road centerline in high resolution satellite images

abstract: In remote sensing analysis, automatic extraction of road network from satellite or aerial images can be a most needed approach for efficient road database creation, refinement, and updating. In this paper, a method for automatic extraction of road centerline from high resolution satellite image is proposed. The proposed work consists of three stages: segmentation of road region, connected component-based operations used to extract the connected road component from segmented road region, and removal of unwanted non-road pixels using morphological operation. The proposed algorithm is implemented on various satellite images, and the results are given in this work. The performance of the proposed method is evaluated by comparing the results with ground truth road map as reference data, and performance measures such as completeness, correctness, and quality are calculated. The average value of completeness, correctness, and quality of various images are 90%, 96%, and 87%, respectively. These measures prove that the proposed work yields road network very closer to reference road map. The proposed method yields very good result for noisy image also, and it proved that the proposed method is insensitive for noise. Performance measures of the proposed work are compared with other methods, and this comparison proves that the proposed method yields very good results than other methods.


title: Flux-Charge Description of Circuits With Non-Volatile Switching Memristor Devices

abstract: This brief aims at defining a class ${\mathcal{ D}}$ of extended memristors by using a combination of fundamental algebraic circuit elements corresponding to ideal memristors and nonlinear resistors. By massaging the characteristics of the constitutive elements, such class ${\mathcal{ D}}$ of extended memristors is able to approximate rectifying effects and asymmetric pinched hysteresis loops of real non-volatile switching memristor devices. Finally, it is shown that if the nonlinear resistors are described in terms of piecewise linear characteristics, then the flux-charge analysis method permits to analyze the dynamics of nonlinear circuits with extended memristors in ${\mathcal{ D}}$ .


title: Sex differences in the association between perceived stress and adiposity in a nationally representative sample

abstract: Abstract Purpose Prior studies examining the association between perceived stress and adiposity have reported mixed findings, and sex differences have largely not been examined. Methods We examined the relationship between perceived stress and body mass index (BMI) and waist circumference in young adults in the National Longitudinal Study of Adolescent to Adult Health. Participants (mean age 29 years; N = 14,044) completed the short form of Cohen's Perceived Stress Scale during a home visit. Height, weight, and waist circumference were assessed during the same visit. BMI was calculated based on measured height and weight. Results In the sample, 52% were male and 65% were identified as white. In adjusted linear regression analyses, a sex by Perceived Stress Scale interaction was noted ( P Conclusions In this nationally representative sample of young adults, perceived stress was associated with lower levels of adiposity among men. Noted differences could be attributed to different behavioral and coping strategies in response to stress between men and women as well as biological mechanisms which should be explored further.


title: Effect of the Synchronization-Based Control of a Wearable Robot Having a Non-Exoskeletal Structure on the Hemiplegic Gait of Stroke Patients

abstract: We have been developing the robotic wear curara as both a welfare device and rehabilitation robot that assists the elderly and disabled. curara is aimed at user friendliness. We have, thus, chosen a non-exoskeleton structure made of a plastic so that the robot is as light in weight as possible and to minimize the restraining stress against natural human movement. We verified the assistance effect of curara on 15 hemiplegic patients with stroke by comparing gait parameters (i.e., velocity, step length, cadence, and symmetry of joint angles) among three conditions. The conditions were "without assistance" (i.e., a control mode that cancels frictional resistances in actuators), Condition A (where joint angles are enlarged but there is no change in gait cycle), and Condition B (where there is no change in joint angles but the gait cycle is shortened). curara improved the walking velocity by 19% and 27% under Conditions A and B, respectively. Improvements in step length and cadence were, respectively, 11% and 7% under Condition A and 14% and 11% under Condition B. Moreover, the two assistance conditions reduced the difference in joint angles between unaffected and paralyzed legs. We consider that curara will facilitate the rehabilitation of stroke patients.


title: Full-Swing Local Bitline SRAM Architecture Based on the 22-nm FinFET Technology for Low-Voltage Operation

abstract: The previously proposed average-8T static random access memory (SRAM) has a competitive area and does not require a write-back scheme. In the case of an average-8T SRAM architecture, a full-swing local bitline (BL) that is connected to the gate of the read buffer can be achieved with a boosted wordline (WL) voltage. However, in the case of an average-8T SRAM based on an advanced technology, such as a 22-nm FinFET technology, where the variation in threshold voltage is large, the boosted WL voltage cannot be used, because it degrades the read stability of the SRAM. Thus, a full-swing local BL cannot be achieved, and the gate of the read buffer cannot be driven by the full supply voltage ( $V_{\textrm {DD}}$ ), resulting in a considerably large read delay. To overcome the above disadvantage, in this paper, a differential SRAM architecture with a full-swing local BL is proposed. In the proposed SRAM architecture, full swing of the local BL is ensured by the use of cross-coupled pMOSs, and the gate of the read buffer is driven by a full $V_{\textrm {DD}}$ , without the need for the boosted WL voltage. Various configurations of the proposed SRAM architecture, which stores multiple bits, are analyzed in terms of the minimum operating voltage and area per bit. The proposed SRAM that stores four bits in one block can achieve a minimum voltage of 0.42 V and a read delay that is 62.6 times lesser than that of the average-8T SRAM based on the 22-nm FinFET technology.


title: Design and Implementation of Intelligent Community System Based on Thin Client and Cloud Computing

abstract: With the continuous development of science and technology, the intelligent development of community system becomes a trend. Meanwhile, smart mobile devices and cloud computing technology are increasingly used in intelligent information systems; however, smart mobile devices such as smartphone and smart pad, also known as thin clients, limited by either their capacities (CPU, memory or battery) or their network resources, do not always meet users' satisfaction in using mobile services. Mobile cloud computing, in which resource-rich virtual machines of smart mobile device are provided to a customer as a service, can be terrific solution for expanding the limitation of real smart mobile device, but the resources utilization rate is low and the information cannot be shared easily. To address the problems above, this paper proposes an information system for intelligent community, which is composed of thin clients, wide band network and cloud computing servers. On one hand, the thin clients with the characteristics of energy efficiency, high robustness and high computing capacity can efficiently avoid the problems encountered in the PC architecture and mobile devices. On the other hand, the cloud computing servers in the proposed information system solve the problems of resource sharing barriers. Finally, the system is built in real environments to evaluate the performance. We deploy the proposed system in a community with more than 2000 residents, and it is demonstrated that the proposed system is robust and efficient.


Design of an Adaptive PID Neural Controller for Continuous Stirred Tank Reactor based on Particle Swarm Optimization


title: Switch size control circuit in wide-load PWM/PFM DC-DC buck converters

abstract: A switch size control circuit for voltage-mode DC-DC buck converter is presented. By detecting the average load current, the circuit not only achieves hybrid mode of PWM and PFM operation, but also changes the power switch size according to the load conditions. Both the high-side and low-side switch currents are fully sensed by detecting the filtered smooth voltage of the switch node. The generated digital signal maintains a substantially constant mode and switch size changing point. A monolithic DC-DC buck converter using the proposed circuit has been fabricated with a 0.5µm CMOS process for validation. The switch size is 1/3 of the maximum value in PFM operation and changes to full size in PWM operation. High efficiency over a wide load current from 0.001 to 5A is achieved. The measurement result shows that the switch size control circuit has good performance and agrees well with the theoretical analysis.


title: Identification of appropriate responsibilities and positions for business process management success

abstract: Purpose – Identifying appropriate responsibilities and positions needed for successful business process management (BPM) initiatives has become a challenge. Past research efforts have been based on homogenous small samples. The purpose of this paper is to validate a BPM position and responsibility framework among a larger BPM community, by measuring the framework's validity, reliability, and level of agreement among BPM professionals.Design/methodology/approach – A survey of 111 BPM practitioners (19 percent outside of the USA), representing 98 different organizations (primarily in business services, manufacturing, public administration, and finance), was conducted to measure the construct validity and reliability of an existing BPM position and responsibility framework.Findings – The results indicate that the BPM responsibilities possess adequate reliability, and that the framework demonstrates excellent convergent and discriminant validity. BPM professionals indicated significant agreement with the four...


title: Low Standby Power and Robust FinFET Based SRAM Design

abstract: In this paper, we propose low power and robust 6T SRAM cells. The cells are based on the Vt-control of the cross-coupled inverters of the SRAM cell to reduce leakage power when SRAM is in the idle mode. Using the Vt-control method along with the built-in feedback leads to increasing the SNM. In comparison to a previous work, our schemes have a higher static noise margin (SNM) and lower standby power consumption. To assess the efficiency of the approach, HSPICE simulations in 45 nm and 32 nm FinFET technologies are used. The results show considerable improvements in terms of the standby power as well as the hold and read SNM. This suggests that the Vt-control method may be used for realizing low-standby power and robust SRAM.


ON SETS OF DISTANCES OF n POINTS IN EUCLIDEAN SPACE


title: A Review of Internet of Things Architecture, Technologies and Analysis Smartphone-based Attacks Against 3D printers

abstract: Human beings cannot be happy with any kind of tiredness based work, so they focused on machines to work on behalf of humans. The Internet-based latest technology provides the platforms for human beings to relax and unburden feeling. The Internet of Things (IoT) field efficiently helps human beings with smart decisions through Machine-to-Machine (M2M) communication all over the world. It has been difficult to ignore the importance of the IoT field with the new development of applications such as a smartphone in the present era. The IoT field sensor plays a vital role in sensing the intelligent object/things and making an intelligent decision after sensing the objects. The rapid development of new applications using smartphones in the world caused all users of the IoT community to be faced with one major challenge of security in the form of side channel attacks against highly intensive 3D printing systems. The smartphone formulated Intellectual property (IP) of side channel attacks investigate against 3D printer in the physical domain through reconstructed G-code file through primitive operations. The smartphone (Nexus 5) solved the main problems such as orientation fixing, model accuracy of frame size and validate the feasibility and effectiveness in real case studies against the 3D printer. The 3D printing estimated value reached 20.2 billion of dollars in 2021. The thermal camera is used for exploring the side channel attacks after reconstructing the objects against 3D printers. The researcher analyzed IoT security relevant issues which were avoided in future by enhanced strong security mechanism strategy, encryption, and machine learning-based algorithms, latest technologies, schemes and protocols utilized in an efficient way. Keywords: - Internet of Things (IoT), Machine-to-Machine (M2M), Security, 3D printer, smartphone


title: CIM and IEC 61850 integration issues: Application to power systems

abstract: Common Information Model (CIM) is emerging as a standard for information modelling for power control centers. While, IEC 61850 by International Electrotechnical Commission (IEC) is emerging as a standard for achieving interoperability and automation at the substation level. In future, once these two standards are well adopted, the issue of integration of these standards becomes imminent. Some efforts reported towards the integration of these standards have been surveyed. This paper describes a possible approach for the integration of IEC 61850 and CIM standards based on mapping between the representation of elements of these two standards. This enables seamless data transfer from one standard to the other. Mapping between the objects of IEC 61850 and CIM standards both in the static and dynamic models is discussed. A CIM based topology processing application is used to demonstrate the design of the data transfer between the standards. The scope and status of implementation of CIM in the Indian power sector is briefed.


title: Densely Connected Convolutional Neural Network for Multi-purpose Image Forensics under Anti-forensic Attacks

abstract: Multiple-purpose forensics has been attracting increasing attention worldwide. However, most of the existing methods based on hand-crafted features often require domain knowledge and expensive human labour and their performances can be affected by factors such as image size and JPEG compression. Furthermore, many anti-forensic techniques have been applied in practice, making image authentication more difficult. Therefore, it is of great importance to develop methods that can automatically learn general and robust features for image operation detectors with the capability of countering anti-forensics. In this paper, we propose a new convolutional neural network (CNN) approach for multi-purpose detection of image manipulations under anti-forensic attacks. The dense connectivity pattern, which has better parameter efficiency than the traditional pattern, is explored to strengthen the propagation of general features related to image manipulation detection. When compared with three state-of-the-art methods, experiments demonstrate that the proposed CNN architecture can achieve a better performance (i.e., with a 11% improvement in terms of detection accuracy under anti-forensic attacks). The proposed method can also achieve better robustness against JPEG compression with maximum improvement of 13% on accuracy under low-quality JPEG compression.


title: Spatio-Temporal Recurrent Convolutional Networks for Citywide Short-term Crowd Flows Prediction

abstract: With the rapid development of urban traffic, forecasting the flows of crowd plays an increasingly important role in traffic management and public safety. However, it is very challenging as it is affected by many complex factors, including spatio-temporal dependencies of regions and other external factors such as weather and holiday. In this paper, we proposed a deep-learning-based approach, named STRCNs, to forecast both inflow and outflow of crowds in every region of a city. STRCNs combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network structures to capture spatio-temporal dependencies, simultaneously. More particularly, our model can be decomposed into four components: Closeness captures the changes of instantaneous flows; Daily influence detects the changes of daily influence flows regularly; Weekly influence reacts weekly patterns of influence flows and External influence gets the influence of external factors. For the first three properties (Closeness, Daily influence and Weekly influence), we give a branch of recurrent convolutional network units to learn both spatial and temporal dependencies in crowd flows. External factors are fed into a two-layers fully connected neural network. STRCNs assigns different weights to different branches, and then merges the outputs of the four parts together. Experimental results on two data sets (MobileBJ and TaxiBJ) demonstrate that STRCNs outperforms classical time series and other deep-learning-based prediction methods.


title: Reviewing the Blue Ocean Strategy. Is the Blue Ocean Strategy valid and reliable

abstract: Many companies are familiar with the red oceans and feel accustomed to competition. However the main challenge companies face thereby is their incapacity to create new demand or expand their market share. The Blue Ocean Strategy offers users a framework for creating uncontested market space and change the focus from the current competition to creation of innovative value and demand. ::: The objective of this paper is to research the validity and reliability of the Blue Ocean Strategy with a two way approach. First testing the framework on a theoretical basis via a literature review, and then examining its practical adaptability. ::: The theoretical analysis reviews the core basics of the Blue Ocean Strategy, distinguishing between red and blue oceans. Here the focus lies on the opinion of the authors, the framework tools used, the advantages and critiques on the theory. In the practical part of the research the Blue Ocean Strategy is tested on its reliability using a three step cross-case analysis. After the formation collection and data evaluation, a cross-case overview is conducted that illustrates similarities and differences of the cases according to the Blue Ocean Strategy. ::: The conclusion describes that the Blue Ocean Strategy could be assessed to a set amount as valid and reliable. Also several shortcomings for the theoretical and practical part are mentioned. ::: In the final chapter recommendations for improvement of the Blue Ocean Strategy are provided for creating a theory that is even more valid and reliable.


title: SlowFast Networks for Video Recognition

abstract: We present SlowFast networks for video recognition. Our model involves (i) a Slow pathway, operating at low frame rate, to capture spatial semantics, and (ii) a Fast pathway, operating at high frame rate, to capture motion at fine temporal resolution. The Fast pathway can be made very lightweight by reducing its channel capacity, yet can learn useful temporal information for video recognition. Our models achieve strong performance for both action classification and detection in video, and large improvements are pin-pointed as contributions by our SlowFast concept. We report state-of-the-art accuracy on major video recognition benchmarks, Kinetics, Charades and AVA. Code has been made available at: https://github.com/facebookresearch/SlowFast.


title: Information security strategies: towards an organizational multi-strategy perspective

abstract: There considerable advice in both research and practice oriented literature on the topic of information security. Most of the discussion in literature focuses on how to prevent security attacks using technical countermeasures even though there are a number of other viable strategies such as deterrence, deception, detection and response. This paper reports on a qualitative study, conducted in Korea, to determine how organizations implement security strategies to protect their information systems. The findings reveal a deeply entrenched preventive mindset, driven by the desire to ensure availability of technology and services, and a comparative ignorance of exposure to business security risks. Whilst there was some evidence of usage of other strategies, they were also deployed in a preventive capacity. The paper presents a research agenda that calls for research on enterprise-wide multiple strategy deployment with a focus on how to combine, balance and optimize strategies.


Pure Sinusoidal PWM Signal Generation Technique for Three Phase Voltage Source Inverter with Simulation of PWM Inverter


title: Review of Microstrip Patch Antenna for WLAN and WiMAX Application

abstract: In this rapid changing world in wireless communication, dual or multiband antenna has been playing a key role for wireless service requirements. Wireless local area network (WLAN) and Worldwide Interoperability for Microwave Access (WiMAX) have been widely applied in mobile devices such as handheld computers and smart phones. These two techniques have been widely considered as a cost-effective, flexible, reliable and highspeed data connectivity solution, enabling user mobility. This paper presents a literature survey of dual band rectangular patch antenna for WLAN and WiMAX application with variety of substrate, feed techniques and slots. In this paper we also discuss the basics of microstrip antenna, various feeding techniques, design model and antenna parameters with their advantage and disadvantages.


title: Pilot-symbol-aided channel estimation for OFDM in wireless systems

abstract: In this paper, we investigate pilot-symbol-aided parameter estimation for orthogonal frequency division multiplexing (OFDM) systems. We first derive a minimum mean-square error (MMSE) pilot-symbol-aided parameter estimator. Then, we discuss a robust implementation of the pilot-symbol-aided estimator that is insensitive to channel statistics. From the simulation results, the required signal-to-noise ratios (SNRs) for a 10% word error rate (WER) are 6.8 dB and 7.3 dB for the typical urban (TU) channels with 40 Hz and 200 Hz Doppler frequencies, respectively, and they are 8 dB and 8.3 dB for the hilly-terrain (HT) channels with 40 Hz and 200 Hz Doppler frequencies, respectively. Compared with the decision-directed parameter estimator, the pilot-symbol-aided estimator is highly robust to Doppler frequency for dispersive fading channels with noise impairment even though it has some performance degradation for systems with lower Doppler frequencies.


title: DeepCruiser: Automated Guided Testing for Stateful Deep Learning Systems

abstract: Deep learning (DL) defines a data-driven programming paradigm that automatically composes the system decision logic from the training data. In company with the data explosion and hardware acceleration during the past decade, DL achieves tremendous success in many cutting-edge applications. However, even the state-of-the-art DL systems still suffer from quality and reliability issues. It was only until recently that some preliminary progress was made in testing feed-forward DL systems. In contrast to feed-forward DL systems, recurrent neural networks (RNN) follow a very different architectural design, implementing temporal behaviors and memory with loops and internal states. Such stateful nature of RNN contributes to its success in handling sequential inputs such as audio, natural languages and video processing, but also poses new challenges for quality assurance. ::: In this paper, we initiate the very first step towards testing RNN-based stateful DL systems. We model RNN as an abstract state transition system, based on which we define a set of test coverage criteria specialized for stateful DL systems. Moreover, we propose an automated testing framework, DeepCruiser, which systematically generates tests in large scale to uncover defects of stateful DL systems with coverage guidance. Our in-depth evaluation on a state-of-the-art speech-to-text DL system demonstrates the effectiveness of our technique in improving quality and reliability of stateful DL systems.


title: A Scalable Dual-Polarized 256-Element Ku-Band Phased-Array SATCOM Receiver with ±70° Beam Scanning

abstract: This paper presents a scalable 256-element Ku-band dual-polarized phased-Array SATCOM receiver. The array elements are spaced $\pmb{\lambda/2}$ apart at 12.25 GHz in the x and y directions. This phased array uses 64 silicon quad-core receive chips with 8 channels to feed a dual-polarized 2×2 quad antennas. Those silicon core-chip are QFN packaged and soldered directly on a 12-layer low-cost printed circuit board with integrated stacked patch antennas and Wilkinson combiners. The measured patterns of the 256-element phased-array show near-ideal patterns with a scan range of $\pmb{\pm 70^{\circ}}$ in E- and H-planes and low sidelobes. This array has a 3-dB instantaneous scanning bandwidth of 10.6-12.5 GHz. The design achieves a low cross polarization $\pmb{ over a wide scan range of $\pmb{\pm 70^{\circ}}$ and it is scalable to allow large-scale phased-arrays construction of 1024 elements or larger. To our knowledge, this represents state-of-the art in Ku-band receive phased-arrays in terms of integration level. The array compact size of (19.7 cm x 22 cm) makes it suitable for a low-cost mobile Ku-band SATCOM terminal.


title: Automated Dental Image Analysis by Deep Learning on Small Dataset

abstract: Dental radiography provides important evidence for clinical diagnosis, treatment and quality evaluation. Much effort has been spent on developing digitalized dental X-ray image analysis systems for clinical quality improvement. In this paper, we present the datasets, procedures, and results conducted to evaluate dental treatment qualities using periapical dental X-ray images taken before and after the operations. In order to support dentists to make clinical decisions, we propose a tool pipeline for automated clinical quality evaluation. We build a dataset with 196 patients' periapical dental radiography images before and after the treatments. Radiography images are labelled as cases that are `getting better', `getting worse' and `have no explicit change' by designated dental experts. Our proposal includes an automatic method with the medical knowledge to crop the ROIs for clinical evaluation – the apical adjacent regions, and then pairs of ROIs are fed into a CNN to train the model for automated clinical quality evaluation. Our approach achieves the F1 score of 0.749, which is comparable to the performance of expert dentists and radiologists.


title: A 15nV/√Hz noise 0.2μV offset chopper conditioning amplifier for monolithic infrared sensing systems

abstract: Conventional chopper conditioning amplifiers with a differential architecture are largely inappropriate for the monolithic infrared sensing systems with the requirements of single-ended architecture, low power, and yet low noise and low offset. In this paper, a novel chopper amplifier is proposed to satisfy all the aforesaid requirements. This is achieved by means of a novel chopper demodulator that not only incurs no hardware overhead but also inherently suppresses noise and offset. On the basis of computer simulations, the proposed chopper amplifier features very low input referred noise (15nV/√Hz), very low input referred offset (0.2μV), high Signal-to-Noise Ratio (∼87dB) and high Common Mode Rejection Ratio (98dB), and dissipates low quiescent current (57.6μA). When compared to reported differential chopper amplifiers, the proposed amplifier depicts very competitive Figure-Of-Merit.


title: The customer relationship management process: its measurement and impact on performance

abstract: Customer relationship management (CRM) systems track and measure marketing campaigns over different networks. There are literally many studies associated with the implementation of CRM in different industries. This paper presents an empirical investigation to determine effective factors influencing on the success of CRM implementation. The study designs a questionnaire in Likert scale on measuring various factors and distributes it among selected employees who work for the biggest software maker firm in city of Tehran, Iran. Using principal component analysis, the study has detected five factors influencing on the succes of CRM including customer relationship technologies, customer oriented, enterprise development strategies, customer services and business plan.


abstract: Hydrothermal processing of lignocellulosic materials (wood or agricultural residues) causes a variety of effects including extractive removal, hemicellulose hydrolysis and alteration of the properties of both cellulose and lignin. This article reviews the chemical composition of raw materials potentially useful for hydrothermal processing as well as the results reported on the kinetics of hemicellulose hydrolysis by water treatments. The fundamentals of the mathematical modelling of hemicellulose degradation reactions by pseudohomogeneous kinetics and by severity factors are reviewed. Additional information on the effects caused by hydrothermal treatments on lignin and cellulose is also provided.


title: Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation

abstract: Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acquisition. The highly constrained nature of anatomical objects can be well captured with learning-based techniques. However, in most recent and promising techniques such as CNN-based segmentation it is not obvious how to incorporate such prior knowledge. State-of-the-art methods operate as pixel-wise classifiers where the training objectives do not incorporate the structure and inter-dependencies of the output. To overcome this limitation, we propose a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularisation model, which is trained end-to-end. The new framework encourages models to follow the global anatomical properties of the underlying anatomy ( e.g. shape, label structure) via learnt non-linear representations of the shape. We show that the proposed approach can be easily adapted to different analysis tasks ( e.g. image enhancement, segmentation) and improve the prediction accuracy of the state-of-the-art models. The applicability of our approach is shown on multi-modal cardiac data sets and public benchmarks. In addition, we demonstrate how the learnt deep models of 3-D shapes can be interpreted and used as biomarkers for classification of cardiac pathologies.


title: Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection

abstract: Intrusion detection is a fundamental part of security tools, such as adaptive security appliances, intrusion detection systems, intrusion prevention systems, and firewalls. Various intrusion detection techniques are used, but their performance is an issue. Intrusion detection performance depends on accuracy, which needs to improve to decrease false alarms and to increase the detection rate. To resolve concerns on performance, multilayer perceptron, support vector machine (SVM), and other techniques have been used in recent work. Such techniques indicate limitations and are not efficient for use in large data sets, such as system and network data. The intrusion detection system is used in analyzing huge traffic data; thus, an efficient classification technique is necessary to overcome the issue. This problem is considered in this paper. Well-known machine learning techniques, namely, SVM, random forest, and extreme learning machine (ELM) are applied. These techniques are well-known because of their capability in classification. The NSL–knowledge discovery and data mining data set is used, which is considered a benchmark in the evaluation of intrusion detection mechanisms. The results indicate that ELM outperforms other approaches.


title: Stacked nanosheet gate-all-around transistor to enable scaling beyond FinFET

abstract: In this paper, for the first time we demonstrate that horizontally stacked gate-all-around (GAA) Nanosheet structure is a good candidate for the replacement of FinFET at the 5nm technology node and beyond. It offers increased W eff per active footprint and better performance compared to FinFET, and with a less complex patterning strategy, leveraging EUV lithography. Good electrostatics are reported at L g =12nm and aggressive 44/48nm CPP (Contacted Poly Pitch) ground rules. We demonstrate work function metal (WFM) replacement and multiple threshold voltages, compatible with aggressive sheet to sheet spacing for wide stacked sheets. Stiction of sheets in long-channel devices is eliminated. Dielectric isolation is shown on standard bulk substrate for sub-sheet leakage control. Wrap-around contact (WAC) is evaluated for extrinsic resistance reduction.


title: QuME: a mechanism to support expertise finding in online help-seeking communities

abstract: Help-seeking communities have been playing an increasingly critical role in the way people seek and share information. However, traditional help-seeking mechanisms of these online communities have some limitations. In this paper, we describe an expertise-finding mechanism that attempts to alleviate the limitations caused by not knowing users' expertise levels. As a result of using social network data from the online community, this mechanism can automatically infer expertise level. This allows, for example, a question list to be personalized to the user's expertise level as well as to keyword similarity. We believe this expertise location mechanism will facilitate the development of next generation help-seeking communities.


title: DeepTrack: Learning Discriminative Feature Representations by Convolutional Neural Networks for Visual Tracking

abstract: Defining hand-crafted feature representations needs expert knowledge, requires timeconsuming manual adjustments, and besides, it is arguably one of the limiting factors of object tracking. In this paper, we propose a novel solution to automatically relearn the most useful feature representations during the tracking process in order to accurately adapt appearance changes, pose and scale variations while preventing from drift and tracking failures. We employ a candidate pool of multiple Convolutional Neural Networks (CNNs) as a data-driven model of different instances of the target object. Individually, each CNN maintains a specific set of kernels that favourably discriminate object patches from their surrounding background using all available low-level cues. These kernels are updated in an online manner at each frame after being trained with just one instance at the initialization of the corresponding CNN. Given a frame, the most promising CNNs in the pool are selected to evaluate the hypothesises for the target object. The hypothesis with the highest score is assigned as the current detection window and the selected models are retrained using a warm-start back-propagation which optimizes a structural loss function. In addition to the model-free tracker, we introduce a class-specific version of the proposed method that is tailored for tracking of a particular object class such as human faces. Our experiments on a large selection of videos from the recent benchmarks demonstrate that our method outperforms the existing state-of-the-art algorithms and rarely loses the track of the target object.


title: Understanding Data Sharing in Private Personal Clouds

abstract: Data sharing in Personal Clouds blurs the lines between on-line storage and content distribution with a strong social component. Such social information may be exploited by researchers to devise optimized data management techniques for Personal Clouds. Unfortunately, due their proprietary nature, data sharing is one of the least studied facets of these systems. In this work, we present the first study of data sharing in a private Personal Cloud. Concretely, we contribute a dataset collected at the metadata back-end of NEC: an enterprise oriented Personal Cloud. First, our analysis provides a deep inspection of the storage layer of NEC, comparing it with a well-known public vendor (UbuntuOne). Second, we study the social structure of NEC user communities, as well as the storage characteristics of user sharing links via multiplex network techniques. Finally, we discuss a battery of data management optimizations for NEC derived from our findings, which may be of independent interest for other similar systems. Our proposals include content distribution, caching and data placement. We believe that both our study and dataset will foster further research in this field.


title: Low power 3–2 and 4–2 adder compressors implemented using ASTRAN

abstract: This paper presents two adder compressors architectures addressing high-speed and low power. Adder compressors are used to implement arithmetic circuits such as multipliers and digital signal processing units like the Fast Fourier Transform (FTT). To address the objective of high-speed and low power, it is well known that optimization efforts should be applied in all abstraction levels. In this paper are combined optimizations at logic, electrical and physical level. At the logic level, the circuit is optimized by using multiplexers instead of XOR gates to reduce delay, power and area. At the electrical level, this work presents an architecture that generate the XOR and XNOR signals simultaneously, this reduce internal glitches hence dynamic power as well. And finally at the physical level, and automatic layout generation tool (ASTRAN) is used to make the adder compressors layouts. This tool has proved to reduce power consumption and delay due to the smaller input capacitances of the complex gates generated compared to manual-designed layouts.


title: How many clusters? Which clustering method? Answers via model-based cluster analysis

abstract: We consider the problem of determining the structure of clustered data, without prior knowledge of the number of clusters or any other information about their composition. Data are represented by a mixture model in which each component corresponds to a different cluster. Models with varying geometric properties are obtained through Gaussian components with different parametrizations and cross-cluster constraints. Noise and outliers can be modelled by adding a Poisson process component. Partitions are determined by the expectation-maximization (EM) algorithm for maximum likelihood, with initial values from agglomerative hierarchical clustering. Models are compared using an approximation to the Bayes factor based on the Bayesian information criterion (BIC); unlike significance tests, this allows comparison of more than two models at the same time, and removes the restriction that the models compared be nested. The problems of determining the number of clusters and the clustering method are solved simultaneously by choosing the best model. Moreover, the EM result provides a measure of uncertainty about the associated classification of each data point. Examples are given, showing that this approach can give performance that is much better than standard procedures, which often fail to identify groups that are either overlapping or of varying sizes and shapes.


title: Toward Accurate and Fast Iris Segmentation for Iris Biometrics

abstract: Iris segmentation is an essential module in iris recognition because it defines the effective image region used for subsequent processing such as feature extraction. Traditional iris segmentation methods often involve an exhaustive search of a large parameter space, which is time consuming and sensitive to noise. To address these problems, this paper presents a novel algorithm for accurate and fast iris segmentation. After efficient reflection removal, an Adaboost-cascade iris detector is first built to extract a rough position of the iris center. Edge points of iris boundaries are then detected, and an elastic model named pulling and pushing is established. Under this model, the center and radius of the circular iris boundaries are iteratively refined in a way driven by the restoring forces of Hooke's law. Furthermore, a smoothing spline-based edge fitting scheme is presented to deal with noncircular iris boundaries. After that, eyelids are localized via edge detection followed by curve fitting. The novelty here is the adoption of a rank filter for noise elimination and a histogram filter for tackling the shape irregularity of eyelids. Finally, eyelashes and shadows are detected via a learned prediction model. This model provides an adaptive threshold for eyelash and shadow detection by analyzing the intensity distributions of different iris regions. Experimental results on three challenging iris image databases demonstrate that the proposed algorithm outperforms state-of-the-art methods in both accuracy and speed.


title: Mapping Neural Network Back-Propagation onto Parallel Computers with Computation/Communication Overlapping

abstract: It is shown in [6] that mapping neural networks onto existing parallel computers leads to an unsatisfactory efficiency, except for irregularly connected networks, with several distinguishable highly connected regions. This paper shows how a four step decomposition of the back-propagation algorithm allows to introduce computation/communication overlapping so as to improve any parallel mapping of differentiable feed-forward neural networks. This solution can adapt to both irregular and regular feedforward neural structures. Its computational complexity is estimated for multilayered networks. Unlike most network partitioning schemes, it can deal with multilayer networks using non-standard neurons, such as wavelet networks. Unlike a pattern partitioning algorithm, it is able to implement the stochastic gradient learning algorithm. Numerical results show that this solution should be considered as soon as communication overlapping is available.


title: Internet addiction: definition, assessment, epidemiology and clinical management.

abstract: cupations, urges or behaviours regarding computer use and internet access that lead to impairment or distress. The condition has attracted increasing attention in the popular media and among researchers, and this attention has paralleled the growth in computer (and Internet) access. Prevalence estimates vary widely, although a recent random telephone survey of the general US population reported an estimate of 0.3–0.7%. The disorder occurs worldwide, but mainly in countries where computer access and technology are widespread. Clinical samples and a majority of relevant surveys report a male preponderance. Onset is reported to occur in the late 20s or early 30s age group, and there is often a lag of a decade or more from initial to problematic computer usage. Internet addiction has been associated with dimensionally measured depression and indicators of social isolation. Psychiatric co-morbidity is common, particularly mood, anxiety, impulse control and substance use disorders. Aetiology is unknown, but probably involves psychological, neurobiological and cultural factors. There are no evidence-based treatments for internet addiction. Cognitive behavioural approaches may be helpful. There is no proven role for psychotropic medication. Marital and family therapy may help in selected cases, and online self-help books and tapes are available. Lastly, a self-imposed ban on computer use and Internet access may be necessary in some cases.


A Comprehensive Review of Swarm Optimization Algorithms


title: A Hierarchical Approach for Generating Descriptive Image Paragraphs

abstract: Recent progress on image captioning has made it possible to generate novel sentences describing images in natural language, but compressing an image into a single sentence can describe visual content in only coarse detail. While one new captioning approach, dense captioning, can potentially describe images in finer levels of detail by captioning many regions within an image, it in turn is unable to produce a coherent story for an image. In this paper we overcome these limitations by generating entire paragraphs for describing images, which can tell detailed, unified stories. We develop a model that decomposes both images and paragraphs into their constituent parts, detecting semantic regions in images and using a hierarchical recurrent neural network to reason about language. Linguistic analysis confirms the complexity of the paragraph generation task, and thorough experiments on a new dataset of image and paragraph pairs demonstrate the effectiveness of our approach.


title: Optimising frequency band selection with forward-addition and backward-elimination algorithms in EEG-based brain-computer interfaces

abstract: A major problem in a brain-computer interface (BCI) based on electroencephalogram (EEG) recordings is the varying statistical properties of the signals during inter- or intra-session transfers that often lead to deteriorated BCI performances. A filter bank CSP (FBCSP) algorithm typically uses all the features from all the bands to extract and select robust features. In this paper, we evaluate the performance of four methods for frequency band selection applied to binary motor imagery classification: forward-addition (FA), backward-elimination (BE), the intersection and the union of the FA and BE. These methods automatically select and learn the best discriminative sets of frequency bands, and their corresponding CSP features. The performances of the proposed methods are evaluated on binary motor imagery classification using a publicly available real-world dataset (BCI competition 2008 dataset 2A). It is found that the BE method provides the best improvement resulting in an average classification accuracy increase of the BCI system over the FBCSP algorithm, from 77.06% to 79.09%.


title: Condition monitoring of a complex hydraulic system using multivariate statistics

abstract: In this paper, a systematic approach for the automated training of condition monitoring systems for complex hydraulic systems is developed and evaluated. We analyzed different fault scenarios using a test rig that allows simulating a reversible degradation of component's conditions. By analyzing the correlation of features extracted from raw sensor data and the known fault characteristics of experimental obtained data, the most significant features specific to a fault case can be identified. These feature values are transferred to a lower-dimensional discriminant space using linear discriminant analysis (LDA) which allows the classification of fault condition and grade of severity. We successfully implemented and tested the system for a fixed working cycle of the hydraulic system. Furthermore, the classification rate for random load cycles was enhanced by a distribution analysis of feature trends.


title: OpenChirp: A Low-Power Wide-Area Networking architecture

abstract: Infrastructure monitoring applications currently lack a cost-effective and reliable solution for supporting the last communication hop for low-power devices. The use of cellular infrastructure requires contracts and complex radios that are often too power hungry and cost prohibitive for sensing applications that require just a few bits of data each day. New low-power, sub-GHz, long-range radios are an ideal technology to help fill this communication void by providing access points that are able to cover multiple kilometers of urban space with thousands of end-point devices. These new Low-Power Wide-Area Networking (LPWAN) platforms provide a cost-effective and highly deployable option that could piggyback off of existing public and private wireless networks (WiFi, Cellular, etc). In this paper, we present OpenChirp, a prototype end-to-end LPWAN architecture built using LoRa Wide-Area Network (LoRaWAN) with the goal of simplifying the design and deployment of Internet-of-Things (IoT) devices across wide areas like campuses and cities. We present a software architecture that exposes an application layer allowing users to register devices, describe transducer properties, transfer data and retrieve historical values. We define a service model on top of LoRaWAN that acts as a session layer to provide basic encoding and syntax to raw data streams. At the device-level, we introduce and benchmark an open-source hardware platform that uses Bluetooth Low-Energy (BLE) to help provision LoRa clients that can be extended with custom transducers. We evaluate the system in terms of end-node energy consumption, radio penetration into buildings as well as coverage provided by a network currently deployed at Carnegie Mellon University.


title: Fast Two-Dimensional CFAR Procedure

abstract: An FMCW radar system, which transmits a sequence of rapid chirp signals, is considered to measure target range and radial velocity simultaneously even in multitarget situations. The main outcome of the coherent processing procedure applied to the received echo signal is the two-dimensional range-Doppler-matrix (RDM), which is the basis for an adaptive constant false alarm rate (CFAR) target detection technique. The well-known one-dimensional cell averaging (CA) CFAR procedure suffers from masking effects in all multitarget situations and is additionally very limited in the proper choice of the reference window length. In contrast the ordered statistic (OS) CFAR is very robust in multitarget situations but requires a high computation power. Therefore two-dimensional CFAR procedure based on a combination of OS and CA CFAR is proposed. In this case any masking effects in multitarget situations can be avoided without increasing the computation complexity. In addition the size of the reference window can be adjusted in order to optimize the detection performance.


title: Cross-language transfer of semantic annotation via targeted crowdsourcing: task design and evaluation

abstract: Modern data-driven spoken language systems (SLS) require manual semantic annotation for training spoken language understanding parsers. Multilingual porting of SLS demands significant manual effort and language resources, as this manual annotation has to be replicated. Crowdsourcing is an accessible and cost-effective alternative to traditional methods of collecting and annotating data. The application of crowdsourcing to simple tasks has been well investigated. However, complex tasks, like cross-language semantic annotation transfer, may generate low judgment agreement and/or poor performance. The most serious issue in cross-language porting is the absence of reference annotations in the target language; thus, crowd quality control and the evaluation of the collected annotations is difficult. In this paper we investigate targeted crowdsourcing for semantic annotation transfer that delegates to crowds a complex task such as segmenting and labeling of concepts taken from a domain ontology; and evaluation using source language annotation. To test the applicability and effectiveness of the crowdsourced annotation transfer we have considered the case of close and distant language pairs: Italian–Spanish and Italian–Greek. The corpora annotated via crowdsourcing are evaluated against source and target language expert annotations. We demonstrate that the two evaluation references (source and target) highly correlate with each other; thus, drastically reduce the need for the target language reference annotations.


title: Internet of multimedia things

abstract: Internet of Things (IoT) systems cannot successfully realize the notion of ubiquitous connectivity of everything if they are not capable to truly include 'multimedia things'. However, the current research and development activities in the field do not mandate the features of multimedia objects, thus leaving a gap to benefit from multimedia content based services and applications. In this paper, we analyze this issue by contemplating the concept of IoT and drawing an inspiration towards the perspective vision of 'Internet of Multimedia Things' (IoMT). Therein, we introduce IoMT as a novel paradigm in which smart heterogeneous multimedia things can interact and cooperate with one another and with other things connected to the Internet to facilitate multimedia based services and applications that are globally available to the users. Some applications and use-cases for IoMT are presented to reflect the possibilities enabled by this new paradigm. An IoMT architecture is then presented which is segregated into four distinct stages; (i) multimedia sensing, (ii) reporting and addressability, (iii) multimedia-aware cloud, and (iv) multi-agent systems. Instead of proposing specific technical solutions for each individual stage of the presented architecture, we survey the already existing technologies, providing a synthesis for the realization of the vision of IoMT. Subsequently, various requirements and challenges as well as the feasibility of existing solutions for each stage of proposed IoMT architecture are comprehensively discussed.


Overview of TAC-KBP 2016 Event Nugget Track


title: Complete Penile Duplication with Structurally Normal Penises: A Case Report

abstract: Background: Diphallia is a very rare anomaly and seen once in every 5.5 million live births. True diphallia with normal penile structures is extremely rare. Surgical management for patients with complete penile duplication without any penile or urethral pathology is challenging. Case Report: A 4-year-old boy presented with diphallia. Initial physical examination revealed first physical examination revealed complete penile duplication, urine flow from both penises, meconium flow from right urethra, and anal atresia. Further evaluations showed double colon and rectum, double bladder, and large recto-vesical fistula. Two cavernous bodies and one spongious body were detected in each penile body. Surgical treatment plan consisted of right total penectomy and end-to-side urethra-urethrostomy. No postoperative complications and no voiding dysfunction were detected during the 18 months follow-up. Conclusion: Penile duplication is a rare anomaly, which presents differently in each patient. Because of this, the treatment should be individualized and end-to-side urethra-urethrostomy may be an alternative to removing posterior urethra. This approach eliminates the risk of damaging prostate gland and sphincter


title: XISL: A Modality-Independent MMI Description Language

abstract: In this chapter we outline a multimodal interaction description language XISL (eXtensible Interaction Scenario Language) that has been developed to describe MMI scenarios. The main feature of XISL is that it allows modalities to be described flexibly, which makes it easy to add new modalities or to modify existing modalities on MMI systems. Moreover, XISL is separately described from XML or HTML contents, thus making both the XISL and XML (HTML) documents more reusable. We constructed three types of XISL execution systems, namely a PC terminal, a PDA terminal, and a mobile phone terminal, and show the descriptive power of XISL by implementing an online shopping application on these terminals.


ENTERPRISE ARCHITECTURE INSTITUTIONALIZATION: A TALE OF TWO CASES


title: Mirror, mirror on the wall: viewing a mirror reflection of unilateral hand movements facilitates ipsilateral M1 excitability

abstract: Primary motor cortex (M1) excitability is modulated by both ipsilateral limb movement and passive observation of movement of the contralateral limb. An interaction of these effects within M1 may account for recent research suggesting improved functional recovery of the impaired arm following stroke by viewing a mirror reflection of movements of the unimpaired arm superimposed over the (unseen) impaired arm. This hypothesis was tested in the present study using single-pulse transcranial magnetic stimulation (TMS) in eight neurologically healthy subjects. Excitability of M1 ipsilateral to a phasic, unilateral hand movement was measured while subjects performed paced (1 Hz), unilateral index finger-thumb opposition movements. Motor evoked potentials (MEPs) were obtained from the inactive first dorsal interosseous (FDI) in each of four viewing conditions: Active (viewing the active hand), Central (viewing a mark positioned between hands), Inactive (viewing the inactive hand) and Mirror (viewing a mirror-reflection of the active hand in a mirror oriented in the mid-sagittal plane) and with both hands at rest (Rest). MEPs were significantly enhanced during ipsilateral hand movement compared with the Rest condition (P<0.05). Largest MEPs were obtained in the Mirror condition, and this was significant compared with both the Inactive and Central viewing conditions (P<0.05). There was no difference between the dominant and non-dominant hand. Excitability of M1 ipsilateral to a unilateral hand movement is facilitated by viewing a mirror reflection of the moving hand. This finding provides neurophysiological evidence supporting the application of mirror therapy in stroke rehabilitation.


title: Assessing the effects of different multimedia materials on emotions and learning performance for visual and verbal style learners

abstract: Multimedia materials are now increasingly used in curricula. However, individual preferences for multimedia materials based on visual and verbal cognitive styles may affect learners' emotions and performance. Therefore, in-depth studies that investigate how different multimedia materials affect learning performance and the emotions of learners with visual and verbal cognitive styles are needed. Additionally, many education scholars have argued that emotions directly affect learning performance. Therefore, a further study that confirms the relationships between learners' emotions and performance for learners with visual and verbal cognitive styles will provide useful knowledge in terms of designing an emotion-based adaptive multimedia learning system for supporting personalized learning. To investigate these issues, the study applies the Style of Processing (SOP) scale to identify verbalizers and visualizers. Moreover, the emotion assessment instrument emWave, which was developed by HeartMath, is applied to assess variations in emotional states for verbalizers and visualizers during learning processes. Three different multimedia materials, static text and image-based multimedia material, video-based multimedia material, and animated interactive multimedia material, were presented to verbalizers and visualizers to investigate how different multimedia materials affect individual learning performance and emotion, and to identify relationships between learning performance and emotion. Experimental results show that video-based multimedia material generates the best learning performance and most positive emotion for verbalizers. Moreover, dynamic multimedia materials containing video and animation are more appropriate for visualizers than static multimedia materials containing text and image. Finally, a partial correlation exists between negative emotion and learning performance; that is, negative emotion and pretest scores considered together and negative emotion alone can predict learning performance of visualizers who use video-based multimedia material for learning.


title: Sub-band Common Spatial Pattern (SBCSP) for Brain-Computer Interface

abstract: Brain-computer interface (BCI) is a system to translate humans thoughts into commands. For electroencephalography (EEG) based BCI, motor imagery is considered as one of the most effective ways. Different imagery activities can be classified based on the changes in mu and/or beta rhythms and their spatial distributions. However, the change in these rhythmic patterns varies from one subject to another. This causes an unavoidable time-consuming fine-tuning process in building a BCI for every subject. To address this issue, we propose a new method called sub-band common spatial pattern (SBCSP) to solve the problem. First, we decompose the EEG signals into sub-bands using a filter bank. Subsequently, we apply a discriminative analysis to extract SBCSP features. The SBCSP features are then fed into linear discriminant analyzers (LDA) to obtain scores which reflect the classification capability of each frequency band. Finally, the scores are fused to make decision. We evaluate two fusion methods: recursive band elimination (RBE) and meta-classifier (MC). We assess our approaches on a standard database from BCI Competition III. We also compare our method with two other approaches that address the same issue. The results show that our method outperforms the other two approaches and achieves similar result as compared to the best one in the literature which was obtained by a time-consuming fine-tuning process.


Review and Fin Structure Design for Robotic Manta Ray (RoMan IV)


title: Analytic displacement mapping using hardware tessellation

abstract: Displacement mapping is ideal for modern GPUs since it enables high-frequency geometric surface detail on models with low memory I/O. However, problems such as texture seams, normal recomputation, and undersampling artifacts have limited its adoption. We provide a comprehensive solution to these problems by introducing a smooth analytic displacement function. Coefficients are stored in a GPU-friendly tile-based texture format, and a multiresolution mip hierarchy of this function is formed. We propose a novel level-of-detail scheme by computing per-vertex adaptive tessellation factors and select the appropriate prefiltered mip levels of the displacement function. Our method obviates the need for a precomputed normal map since normals are directly derived from the displacements. Thus, we are able to perform authoring and rendering simultaneously without typical displacement map extraction from a dense triangle mesh. This not only is more flexible than the traditional combination of discrete displacements and normal maps, but also provides faster runtime due to reduced memory I/O.


Mental Imagery, Impact, and Affect: A Mediation Model for Charitable Giving


title: GAN2C: Information Completion GAN with Dual Consistency Constraints

abstract: This paper proposes an information completion technique, GAN2C, by imposing dual consistency constraints (2C) to a closed loop encoder-decoder architecture based on the generative adversarial nets (GAN). When adopting deep neural networks as function approximators, GAN2C enables highly effective multi-modality image conversion with sparse observation in the target modes. For empirical demonstration and model evaluation, we show that trained deep neural networks in GAN2C can infer colors for grayscale images, as well as estimate rich 3D information of a scene by densely predicting the depths. The results of the experiments show that in both tasks GAN2C as a generic framework has been comparable to or advanced the state-of-the-art performance which are achieved by highly specialized systems. Code is available at https://github.com/AdalinZhang/GAN2C.


title: AC2: A Policy Gradient Actor with Primary and Secondary Critics

abstract: We propose AC2, a policy gradient algorithm that employs a primary and a secondary critic to manage both bias and variance in policy gradients. We present through analyses and experiments that performance becomes more stable if a secondary critic concentrates on few problematic states (upper 95-percentile) that cause extreme changes in value estimates. This scheme can keep biases tolerable while lowering variances. We relate our algorithm with critic ensembles that have more components and show that ensemble averaging may not significantly reduce gradient variances in more difficult environments. We test our algorithm in a series of high-dimensional experiments and report better performance than ensembles with more critic components especially in harder environments. In addition, performance is more stable if the secondary critic trains on a few problematic states than by random sampling. Our algorithm reports better reward performance than single critic and other RL models.


title: Shared Learning : Enhancing Reinforcement in $Q$-Ensembles

abstract: Deep Reinforcement Learning has been able to achieve amazing successes in a variety of domains from video games to continuous control by trying to maximize the cumulative reward. However, most of these successes rely on algorithms that require a large amount of data to train in order to obtain results on par with human-level performance. This is not feasible if we are to deploy these systems on real world tasks and hence there has been an increased thrust in exploring data efficient algorithms. To this end, we propose the Shared Learning framework aimed at making $Q$-ensemble algorithms data-efficient. For achieving this, we look into some principles of transfer learning which aim to study the benefits of information exchange across tasks in reinforcement learning and adapt transfer to learning our value function estimates in a novel manner. In this paper, we consider the special case of transfer between the value function estimates in the $Q$-ensemble architecture of BootstrappedDQN. We further empirically demonstrate how our proposed framework can help in speeding up the learning process in $Q$-ensembles with minimum computational overhead on a suite of Atari 2600 Games.


title: Interactive Visual Profiling of Musicians

abstract: Determining similar objects based upon the features of an object of interest is a common task for visual analytics systems. This process is called profiling , if the object of interest is a person with individual attributes. The profiling of musicians similar to a musician of interest with the aid of visual means became an interesting research question for musicologists working with the Bavarian Musicians Encyclopedia Online. This paper illustrates the development of a visual analytics profiling system that is used to address such research questions. Taking musicological knowledge into account, we outline various steps of our collaborative digital humanities project, priority (1) the definition of various measures to determine the similarity of musicians' attributes, and (2) the design of an interactive profiling system that supports musicologists in iteratively determining similar musicians. The utility of the profiling system is emphasized by various usage scenarios illustrating current research questions in musicology.


title: Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks

abstract: Real world data, especially in the domain of robotics, is notoriously costly to collect. One way to circumvent this can be to leverage the power of simulation to produce large amounts of labelled data. However, training models on simulated images does not readily transfer to real-world ones. Using domain adaptation methods to cross this "reality gap" requires a large amount of unlabelled real-world data, whilst domain randomization alone can waste modeling power. In this paper, we present Randomized-to-Canonical Adaptation Networks (RCANs), a novel approach to crossing the visual reality gap that uses no real-world data. Our method learns to translate randomized rendered images into their equivalent non-randomized, canonical versions. This in turn allows for real images to also be translated into canonical sim images. We demonstrate the effectiveness of this sim-to-real approach by training a vision-based closed-loop grasping reinforcement learning agent in simulation, and then transferring it to the real world to attain 70% zero-shot grasp success on unseen objects, a result that almost doubles the success of learning the same task directly on domain randomization alone. Additionally, by joint finetuning in the real-world with only 5,000 real-world grasps, our method achieves 91%, attaining comparable performance to a state-of-the-art system trained with 580,000 real-world grasps, resulting in a reduction of real-world data by more than 99%.


title: A conceptual green-ICT implementation model based-on ZEN and G-readiness framework

abstract: ICT is no longer simply be a supporting tool, but as an integral part of the business itself, a strategic role. As well as for the education area, higher education institution (HEI) is one-of that use fairly large of the ICT solution. Since there is a positive correlation between ICT usage and economic growth potential, arising new issues to the environment. There is an urge to balance between the usage of ICT and environmental awareness. Indonesia as one of the emerging economic powers want to participate in the greening movement, but the problem arises when there is still no formal regulation / framework yet. Furthermore, ICT also has not been seen as one of the aspects that need to be given a special attention in greening movement. There are several frameworks available for green-IT, but the question is how can we adopt it for HEI case in Indonesia? This paper as a preliminary study, try to correlate one of the green-IT framework, named G-readiness framework with ZEN framework, an Indonesia national framework for ICT adoption in HEI. The study found that both the frameworks are mutually relevant. The derived model use 5 domains of G-readiness that represent critical success factors for green-IT initiatives correlate with ZEN framework attributes as practical implementation checklist. From the correlation process, we add one new domain, named ‘Value’, so the derived model has total six domains (Attitude, Policy, Practice, Technology, Governance and Value).


title: Brain Tumor Classification via Statistical Features and Back-Propagation Neural Network

abstract: Classification of brain tumor is the heart of the computer-aided diagnosis (CAD) system designed to aid the radiologist in the diagnosis of such tumors using Magnetic Resonance Image (MRI). In this paper, we present a framework for classification of brain tumors in MRI images that combines statistical features and neural network algorithms. This algorithm uses region of interest (ROI), i.e. the tumor segment that is identified either manually by the technician/radiologist or by using any of the ROI segmentation techniques. We focus on feature selection by using a combination of the 2D Discrete Wavelet Transform (DWT) and 2D Gabor filter techniques. We create the features set using a complete set of the transform domain statistical features. For classification, back propagation neural network classifier has been selected to test the features selection impact. To do so, we used a large dataset consisting of 3,064 slices of T1-weighted MRI images with three types of brain tumors, Meningioma, Glioma, and Pituitary tumor. We obtained a total accuracy of 91.9%, and specificity of 96%, 96.29%, and 95.66% for Meningioma, Glioma, and Pituitary tumor respectively. Experimental results validate the effectiveness of the features selection method and indicate that it can compose an effective feature set to be used as a framework that can be combined with other classifications technique to enhance the performance.


title: A Comparative Analysis of Registration Tools: Traditional vs Deep Learning Approach on High Resolution Tissue Cleared Data

abstract: Image registration plays an important role in comparing images. It is particularly important in analyzing medical images like CT, MRI, PET, etc. to quantify different biological samples, to monitor disease progression and to fuse different modalities to support better diagnosis. The recent emergence of tissue clearing protocols enable us to take images at cellular level resolution. Image registration tools developed for other modalities are currently unable to manage images of such extreme high resolution. The recent popularity of deep learning based methods in the computer vision community justifies a rigorous investigation of deep-learning based methods on tissue cleared images along with their traditional counterparts. In this paper, we investigate and compare the performance of a deep learning based registration method with traditional optimization based methods on samples from tissue-clearing methods. From the comparative results it is found that a deep-learning based method outperforms all traditional registration tools in terms of registration time and has achieved promising registration accuracy.


title: Optimizing segmentation granularity for neural machine translation

abstract: In neural machine translation (NMT), it has become standard to translate using subword units to allow for an open vocabulary and improve accuracy on infrequent words. Byte-pair encoding (BPE) and its variants are the predominant approach to generating these subwords, as they are unsupervised, resource-free, and empirically effective. However, the granularity of these subword units is a hyperparameter to be tuned for each language and task, using methods such as grid search. Tuning may be done inexhaustively or skipped entirely due to resource constraints, leading to sub-optimal performance. In this paper, we propose a method to automatically tune this parameter using only one training pass. We incrementally introduce new BPE vocabulary online based on the held-out validation loss, beginning with smaller, general subwords and adding larger, more specific units over the course of training. Our method matches the results found with grid search, optimizing segmentation granularity while significantly reducing overall training time. We also show benefits in training efficiency and performance improvements for rare words due to the way embeddings for larger units are incrementally constructed by combining those from smaller units.


title: Comparing order picking assisted by head-up display versus pick-by-light with explicit pick confirmation

abstract: Manual order picking is an important part of distribution. Many techniques have been proposed to improve pick efficiency and accuracy. Previous studies compared pick-by-HUD (Head-Up Display) with pick-by-light but without the explicit pick confirmation that is typical in industrial environments. We compare a pick-by-light system designed to emulate deployed systems with a pick-by-HUD system using Google Glass. The pick-by-light system tested 50% slower than pick-by-HUD and required a higher workload. The number of errors committed and picker preference showed no statistically significant difference.


title: Design of Majority Logic (ML) Based Approximate Full Adders

abstract: As a new paradigm in the nanoscale technologies, approximate computing enables error tolerance in the computational process; it has also emerged as a low power design methodology for arithmetic circuits. Majority logic (ML) is applicable to many emerging technologies and its basic building block (the 3-input majority voter) has been extensively used in digital circuit design. In this paper, we propose the design of a one-bit approximate full adder based on majority logic. Furthermore, multi-bit approximate full adders are also proposed and studied; the application of these designs to quantum-dot cellular automata (QCA) is also presented as an example. The designs are evaluated using hardware metrics (including delay and area) as well as error metrics. Compared with other circuits found in the technical literature, the optimal designs are found to offer superior performance.


title: An Analysis of the Application of Simplified Silhouette to the Evaluation of k-means Clustering Validity

abstract: This paper analyses the application of Simplified Silhouette to the evaluation of k-means clustering validity and compares it with the k-means Cost Function and the original Silhouette. We conclude that for a given dataset the k-means Cost Function is the most valid and efficient measure in the evaluation of the validity of k-means clustering with the same k value, but that Simplified Silhouette is more suitable than the original Silhouette in the selection of the best result from k-means clustering with different k values.


title: Expletives: neurolinguistic and neurobehavioral perspectives on swearing

abstract: Abstract Severe aphasia, adult left hemispherectomy, Gilles de la Tourette syndrome (GTS), and other neurological disorders have in common an increased use of swearwords. There are shared linguistic features in common across these language behaviors, as well as important differences. We explore the nature of swearing in normal human communication, and then compare the clinical presentations of selectively preserved, impaired and augmented swearing. These neurolinguistic observations, considered along with related neuroanatomical and neurochemical information, provide the basis for considering the neurobiological foundation of various types of swearing behaviors.


title: NLRP6 Inflammasome Orchestrates the Colonic Host-Microbial Interface by Regulating Goblet Cell Mucus Secretion

abstract: Mucus production by goblet cells of the large intestine serves as a crucial antimicrobial protective mechanism at the interface between the eukaryotic and prokaryotic cells of the mammalian intestinal ecosystem. However, the regulatory pathways involved in goblet cell-induced mucus secretion remain largely unknown. Here, we demonstrate that the NLRP6 inflammasome, a recently described regulator of colonic microbiota composition and biogeographical distribution, is a critical orchestrator of goblet cell mucin granule exocytosis. NLRP6 deficiency leads to defective autophagy in goblet cells and abrogated mucus secretion into the large intestinal lumen. Consequently, NLRP6 inflammasome-deficient mice are unable to clear enteric pathogens from the mucosal surface, rendering them highly susceptible to persistent infection. This study identifies an innate immune regulatory pathway governing goblet cell mucus secretion, linking nonhematopoietic inflammasome signaling to autophagy and highlighting the goblet cell as a critical innate immune player in the control of intestinal host-microbial mutualism. PAPERCLIP:


title: Imaginative Recall with Story Intention Graphs.

abstract: Intelligent storytelling systems either formalize specific narrative structures proposed by narratologists (such as Propp and Bremond), or are founded on formal representations from artificial intelligence (such as plan structures from classical planning). This disparity in underlying knowledge representations leads to a lack of common evaluation metrics across story generation systems, particularly around the creativity aspect of generators. This paper takes Skald, a reconstruction of the Minstrel creative story generation system, and maps the representation to a formal narrative representation of Story Intention Graphs (SIG) proposed by Elson et al. This mapping facilitates the opportunity to expand the creative space of stories generated through imaginative recall in Minstrel while maintaining narrative complexity. We show that there is promise in using the SIG as an intermediate representation that is useful for evaluation of story generation systems.


title: Efficacy of the projection onto convex sets (POCS) algorithm at Gd-EOB-DTPA-enhanced hepatobiliary-phase hepatic MRI

abstract: PurposeTo investigate the efficacy of the projection onto convex sets (POCS) algorithm at Gd-EOB-DTPA-enhanced hepatobiliary-phase MRI.MethodsIn phantom study, we scanned a phantom and obtained images by conventional means (P1 images), by partial-Fourier image reconstruction (PF, P2 images) and by PF with the POCS algorithm (P3 images). Then we acquired and compared subtraction images (P2–P1 images and P3–P1 images). In clinical study, 55 consecutive patients underwent Gd-EOB-DTPA (EOB)-enhanced 3D hepatobiliary-phase MRI on a 1.5T scanner. Images were obtained using conventional method (C1 images), PF (C2 images), and PF with POCS (C3 images). The acquisition time was 17-, 14-, and 14 s for protocols C1, C2 and C3, respectively. Two radiologists assigned grades for hepatic vessel sharpness and we compared the visual grading among the 3 protocols. And one radiologist compared signal-to-noise-ratio (SNR) of the hepatic parenchyma.ResultsIn phantom study, there was no difference in signal intensity on a peripheral phantom column on P3–P1 images. In clinical study, there was no significant difference between C1 and C3 images (2.62 ± 0.49 vs. 2.58 ± 0.49, p = 0.70) in the score assigned for vessel sharpness nor in SNR (13.3 ± 2.67 vs. 13.1 ± 2.51, p = 0.18).ConclusionThe POCS algorithm makes it possible to reduce the scan time of hepatobiliary phase (from 17 to 14 s) without reducing SNR and without increasing artifacts.


Anonymity of Bitcoin Transactions An Analysis of Mixing Services


title: Impact of communication on consumers' food choices: Plenary Lecture

abstract: Consumers' food choices and dietary behaviour can be markedly affected by communication and information. Whether the provided information is processed by the receiver, and thus becomes likely to be effective, depends on numerous factors. The role of selected determinants such as uncertainty, knowledge, involvement, health-related motives and trust, as well as message content variables, are discussed in the present paper based on previous empirical studies. The different studies indicate that: uncertainty about meat quality and safety does not automatically result in more active information search; subjective knowledge about fish is a better predictor of fish consumption than objective knowledge; high subjective knowledge about functional foods as a result of a low trusted information source such as mass media advertising leads to a lower probability of adopting these foods in the diet. Also, evidence of the stronger impact of negative news as compared with messages promoting positive outcomes of food choices is discussed. Finally, three audience-segmentation studies based on consumers' involvement with fresh meat, individuals' health-related-motive orientations and their use of and trust in fish information sources are presented. A clear message from these studies is that communication and information provision strategies targeted to a specific audience's needs, interests or motives stand a higher likelihood of being attended to and processed by the receiving audience, and therefore also stand a higher chance of yielding their envisaged impact in terms of food choice and dietary behaviour.


title: A Broadband Passive Monopulse Comparator MMIC

abstract: A broadband monopulse comparator MMIC (Monolithic Microwave Integrated Circuit) based on GaAs process is presented in this Letter. The comparator network constructed by three magic tees and one lumped power divider is proposed for one sum channel and two delta channels. The measurement results show that very wide frequency band from 15 to 30 GHz (66.7% relatively frequency bandwidth) with less than 2.5-dB loss can be achieved for the sum channel. And the null depth is more than 22 dB in 15–27GHz and 17 dB in 27–30 GHz for two delta channels. The total chip size is 3.4 mm 3.4 mm ( $0.26\lambda _{0}~0.26\lambda _{0}$ at the center frequency of 22.5 GHz).


title: Pharmaceutical research and development: what do we get for all that money?

abstract: Data indicate that the widely touted “innovation crisis” in pharmaceuticals is a myth. The real innovation crisis, say Donald Light and Joel Lexchin , stems from current incentives that reward companies for developing large numbers of new drugs with few clinical advantages over existing ones


title: Assessing Students’ Use of Evidence and Organization in Response-to-Text Writing: Using Natural Language Processing for Rubric-Based Automated Scoring

abstract: This paper presents an investigation of score prediction based on natural language processing for two targeted constructs within analytic text-based writing: 1) students’ effective use of evidence and, 2) their organization of ideas and evidence in support of their claim. With the long-term goal of producing feedback for students and teachers, we designed a task-dependent model, for each dimension, that aligns with the scoring rubric and makes use of the source material. We believe the model will be meaningful and easy to interpret given the writing task. We used two datasets of essays written by students in grades 5–6 and 6–8. Our experimental results show that our task-dependent model (consistent with the rubric) performs as well as if not outperforms competitive baselines. We also show the potential generalizability of the rubric-based model by performing cross-corpus experiments. Finally, we show that the predictive utility of different feature groups in our rubric-based modeling approach is related to how much each feature group covers a rubric’s criteria.


title: Security Analysis of Emerging Smart Home Applications

abstract: Recently, several competing smart home programming frameworks that support third party app development have emerged. These frameworks provide tangible benefits to users, but can also expose users to significant security risks. This paper presents the first in-depth empirical security analysis of one such emerging smart home programming platform. We analyzed Samsung-owned SmartThings, which has the largest number of apps among currently available smart home platforms, and supports a broad range of devices including motion sensors, fire alarms, and door locks. SmartThings hosts the application runtime on a proprietary, closed-source cloud backend, making scrutiny challenging. We overcame the challenge with a static source code analysis of 499 SmartThings apps (called SmartApps) and 132 device handlers, and carefully crafted test cases that revealed many undocumented features of the platform. Our key findings are twofold. First, although SmartThings implements a privilege separation model, we discovered two intrinsic design flaws that lead to significant overprivilege in SmartApps. Our analysis reveals that over 55% of SmartApps in the store are overprivileged due to the capabilities being too coarse-grained. Moreover, once installed, a SmartApp is granted full access to a device even if it specifies needing only limited access to the device. Second, the SmartThings event subsystem, which devices use to communicate asynchronously with SmartApps via events, does not sufficiently protect events that carry sensitive information such as lock codes. We exploited framework design flaws to construct four proof-of-concept attacks that: (1) secretly planted door lock codes, (2) stole existing door lock codes, (3) disabled vacation mode of the home, and (4) induced a fake fire alarm. We conclude the paper with security lessons for the design of emerging smart home programming frameworks.


title: Improved LSB based Steganography Techniques for Color Images in Spatial Domain

abstract: This research paper aims to propose a new improved approach for Information Security in RGB Color Images using a Hybrid Feature detection technique; Two Component based Least Significant Bit (LSB) Substitution Technique and Adaptive LSB substitution technique for data hiding. Advanced Encryption Standard (AES) is used to provide Two Tier Security; Random Pixel Embedding imparts resistant to attacks and Hybrid Filtering makes it immune to various disturbances like noise. An image is combination of edge and smooth areas which gives an ample opportunity to hide information in it. The proposed work is direct implementation of the principle that edge areas being high in contrast, color, density and frequency can tolerate more changes in their pixel values than smooth areas, so can be embedded with a large number of secret data while retaining the original characteristics of image. The proposed approach achieved Improved Imperceptibility, Capacity than the various existing techniques along with Better Resistance to various Steganalysis attacks like Histogram Analysis, Chi-Square and RS Analysis as proven experimentally.


title: Depth and Appearance for Mobile Scene Analysis

abstract: In this paper, we address the challenging problem of simultaneous pedestrian detection and ground-plane estimation from video while walking through a busy pedestrian zone. Our proposed system integrates robust stereo depth cues, ground-plane estimation, and appearance-based object detection in a principled fashion using a graphical model. Object-object occlusions lead to complex interactions in this model that make an exact solution computationally intractable. We therefore propose a novel iterative approach that first infers scene geometry using belief propagation and then resolves interactions between objects using a global optimization procedure. This approach leads to a robust solution in few iterations, while allowing object detection to benefit from geometry estimation and vice versa. We quantitatively evaluate the performance of our proposed approach on several challenging test sequences showing strolls through busy shopping streets. Comparisons to various baseline systems show that it outperforms both a system using no scene geometry and one just relying on structure-from-motion without dense stereo.


title: Taming the 800 Pound Gorilla: The Rise and Decline of NTP DDoS Attacks

abstract: Distributed Denial of Service (DDoS) attacks based on Network Time Protocol (NTP) amplification, which became prominent in December 2013, have received significant global attention. We chronicle how this attack rapidly rose from obscurity to become the dominant large DDoS vector. Via the lens of five distinct datasets, we characterize the advent and evolution of these attacks. Through a dataset that measures a large fraction of global Internet traffic, we show a three order of magnitude rise in NTP. Using a large darknet, we observe a similar rise in global scanning activity, both malicious and research. We then dissect an active probing dataset, which reveals that the pool of amplifiers totaled 2.2M unique IPs and includes a small number of "mega amplifiers," servers that replied to a single tiny probe packet with gigabytes of data. This dataset also allows us, for the first time, to analyze global DDoS attack victims (including ports attacked) and incidents, where we show 437K unique IPs targeted with at least 3 trillion packets, totaling more than a petabyte. Finally, ISP datasets shed light on the local impact of these attacks. In aggregate, we show the magnitude of this major Internet threat, the community's response, and the effect of that response.


title: Predicting speed at traffic lights—the problem with static assessments of frailty

abstract: For some time, considerable research has focused on developing screening tools effective in identifying frail elderly patients at risk of adverse outcomes. The comprehensive geriatric assessment (CGA) remains the gold standard for identifying modifiable risk factors within this group. However, resources are stretched. A good CGA takes time and can involve several members of the multidisciplinary team. An instrument that can effectively guide implementation of the CGA in a discriminatory way is therefore much sought after. ISAR [1], Barthel [2], Katz [3], SHARE [4] to name but a few have been adopted for this purpose. This profusion of studies unfortunately only re-enforces the lack of consensus on an instrument that satisfactorily fulfils this role. In order for this screening tool to transcend from an academic exercise into real world practice, it must fulfil certain characteristics. Perhaps most importantly, it should be quick and easy to administer; ideally utilising data that are readily available to clinicians. The sensitivity should be high enough to make the exercise worthwhile while retaining the specificity to prevent indiscriminate use of resources. The paper by Heim et al … Language: en


title: Focal Lesions in Acute Mild Traumatic Brain Injury and Neurocognitive Outcome: CT versus 3T MRI

abstract: Abstract Mild traumatic brain injury (mTBI) is associated with long-term cognitive deficits. This study compared the detection rate of acute post-traumatic focal lesions on computed tomography (CT) and 3T (Tesla) magnetic resonance (MR) imaging with neurocognitive outcomes. Adults (n = 36; age range, 19—52 years) with a single episode of mTBI (Glasgow Coma Scale 13—15, as well as loss of consciousness and post-traumatic amnesia) were prospectively enrolled and had CT within 24 h of injury and 3T MR within 2 weeks of injury. The CT and MR scans were reviewed by two neuroradiologists who were blinded to clinical information. Twenty-eight of these mTBI subjects and 18 matched healthy volunteers also underwent serial neurocognitive testing. Of the 36 mTBI cases, intraparenchymal lesions were detected in 18 CT and 27 acute MR exams, consisting of hemorrhagic traumatic axonal injury (TAI) (eight CT, 17 MR), non-hemorrhagic TAI (zero CT, four MR), and cerebral contusions (13 CT, 21 MR). Mild TBI patients had sig...