title: American attitudes toward nudges

abstract: To successfully select and implement nudges, policy makers need a psychological understanding of who opposes nudges, how they are perceived, and when alternative methods (e.g., forced choice) might work better. Using two representative samples, we examined four factors that influence U.S. attitudes toward nudges â€“ types of nudges, individual dispositions, nudge perceptions, and nudge frames. Most nudges were supported, although opt-out defaults for organ donations were opposed in both samples. â€œSystem 1â€ nudges (e.g., defaults and sequential orderings) were viewed less favorably than â€œSystem 2â€ nudges (e.g., educational opportunities or reminders). System 1 nudges were perceived as more autonomy threatening, whereas System 2 nudges were viewed as more effective for better decision making and more necessary for changing behavior. People with greater empathetic concern tended to support both types of nudges and viewed them as the â€œrightâ€ kind of goals to have. Individualists opposed both types of nudges, and conservatives tended to oppose both types. Reactant people and those with a strong desire for control opposed System 1 nudges. To see whether framing could influence attitudes, we varied the description of the nudge in terms of the target (Personal vs. Societal) and the reference point for the nudge (Costs vs. Benefits). Empathetic people were more supportive when framing highlighted societal costs or benefits, and reactant people were more opposed to nudges when frames highlighted the personal costs of rejection.


title: Empower Sequence Labeling with Task-Aware Neural Language Model

abstract: Linguistic sequence labeling is a general modeling approach that encompasses a variety of problems, such as part-of-speech tagging and named entity recognition. Recent advances in neural networks (NNs) make it possible to build reliable models without handcrafted features. However, in many cases, it is hard to obtain sufficient annotations to train these models. In this study, we develop a novel neural framework to extract abundant knowledge hidden in raw texts to empower the sequence labeling task. Besides word-level knowledge contained in pre-trained word embeddings, character-aware neural language models are incorporated to extract character-level knowledge. Transfer learning techniques are further adopted to mediate different components and guide the language model towards the key knowledge. Comparing to previous methods, these task-specific knowledge allows us to adopt a more concise model and conduct more efficient training. Different from most transfer learning methods, the proposed framework does not rely on any additional supervision. It extracts knowledge from self-contained order information of training sequences. Extensive experiments on benchmark datasets demonstrate the effectiveness of leveraging character-level knowledge and the efficiency of co-training. For example, on the CoNLL03 NER task, model training completes in about 6 hours on a single GPU, reaching F1 score of 91.71$\pm$0.10 without using any extra annotation.


title: A Deep Compositional Framework for Human-like Language Acquisition in Virtual Environment

abstract: We tackle a task where an agent learns to navigate in a 2D maze-like environment called XWORLD. In each session, the agent perceives a sequence of raw-pixel frames, a natural language command issued by a teacher, and a set of rewards. The agent learns the teacher's language from scratch in a grounded and compositional manner, such that after training it is able to correctly execute zero-shot commands: 1) the combination of words in the command never appeared before, and/or 2) the command contains new object concepts that are learned from another task but never learned from navigation. Our deep framework for the agent is trained end to end: it learns simultaneously the visual representations of the environment, the syntax and semantics of the language, and the action module that outputs actions. The zero-shot learning capability of our framework results from its compositionality and modularity with parameter tying. We visualize the intermediate outputs of the framework, demonstrating that the agent truly understands how to solve the problem. We believe that our results provide some preliminary insights on how to train an agent with similar abilities in a 3D environment.


title: Model based control of fiber reinforced elastofluidic enclosures

abstract: Fiber-Reinforced Elastofluidic Enclosures (FREEs), are a subset of pneumatic soft robots with an asymmetric continuously deformable skin that are able to generate a wide range of deformations and forces, including rotation and screw motions. Though these soft robots are able to generate a variety of motions, simultaneously controlling their end effector rotation and position has remained challenging due to the lack of a simple model. This paper presents a model that establishes a relationship between the pressure, torque due to axial loading, and axial rotation to enable a model-driven open-loop control for FREEs. The modeling technique relies on describing force equilibrium between the fiber, fluid, and an elastomer model which is computed via system identification. The model is experimentally tested as these variables are changed, illustrating that it provides good agreement with the real system. To further illustrate the potential of the model, a precision open-loop control experiment of opening a rotational combination lock is presented2.


title: A Visual Framework for Clustering Memes in Social Media

abstract: The spread of "rumours" in Online Social Networks (OSNs) has grown at an alarming rate. Consequently, there is an increasing need to improve understanding of the social and technological processes behind this trend. The first step in detecting rumours is to identify and extract memes, a unit of information that can be spread from person to person in OSNs. This paper proposes four similarity scores and two novel strategies to combine those similarity scores for detecting the spread of memes in OSNs, with the end goal of helping researchers as well as members of various OSNs to study the phenomenon. The two proposed strategies include: (1) automatically computing the similarity score weighting factors for four elements of a submission and (2) allowing users to engage in the clustering process and filter out outlier submissions, modify submission class labels, or assign different similarity score weight factors for various elements of a submission using a visualization prototype. To validate our approach, we collect submissions on Reddit about five controversial topics and demonstrate that the proposed strategies outperform the baseline.


title: Discovery and recognition of motion primitives in human activities

abstract: We present a novel framework for the automatic discovery and recognition of motion primitives in videos of human activities. Given the 3D pose of a human in a video, human motion primitives are discovered by optimizing the `motion flux', a quantity which captures the motion variation of a group of skeletal joints. A normalization of the primitives is proposed in order to make them invariant with respect to a subject anatomical variations and data sampling rate. The discovered primitives are unknown and unlabeled and are unsupervisedly collected into classes via a hierarchical non-parametric Bayes mixture model. Once classes are determined and labeled they are further analyzed for establishing models for recognizing discovered primitives. Each primitive model is defined by a set of learned parameters. ::: Given new video data and given the estimated pose of the subject appearing on the video, the motion is segmented into primitives, which are recognized with a probability given according to the parameters of the learned models. ::: Using our framework we build a publicly available dataset of human motion primitives, using sequences taken from well-known motion capture datasets. We expect that our framework, by providing an objective way for discovering and categorizing human motion, will be a useful tool in numerous research fields including video analysis, human inspired motion generation, learning by demonstration, intuitive human-robot interaction, and human behavior analysis.


title: Separation and identification of the light harvesting proteins contained in grana and stroma thylakoid membrane fractions.

abstract: Abstract This paper presents the results of a study performed to develop a rapid and straightforward method to resolve and simultaneously identify the light-harvesting proteins of photosystem I (LHCI) and photosystem II (LHCII) present in the grana and stroma of the thylakoid membranes of higher plants. These hydrophobic proteins are embedded in the phospholipid membrane, and their extraction usually requires detergent and time consuming manipulations that may introduce artifacts. The method presented here makes use of digitonin, a detergent which causes rapid (within less than 3 min) cleavage of the thylakoid membrane into two subfractions: appressed (grana) and non-appressed (stroma) membranes, the former enriched in photosystem II and the latter containing mainly photosystem I. From these two fractions identification of the protein components was performed by separating them by reversed-phase high-performance liquid chromatography (RP-HPLC) and determining the intact molecular mass by electrospray ionization mass spectrometry (ESI-MS). By this strategy the ion suppression during ESI-MS that normally occurs in the presence of membrane phospholipids was avoided, since RP-HPLC removed most phospholipids from the analytes. Consequently, high quality mass spectra were extracted from the reconstructed ion chromatograms. The specific cleavage of thylakoid membranes by digitonin, as well as the rapid identification and quantification of the antenna composition of the two complexes facilitate future studies of the lateral migration of the chlorophyll-protein complexes along thylakoid membranes, which is well known to be induced by high intensity light or other environmental stresses. Such investigations could not be performed by sodium dodecylsulfate–polyacrylamide gel electrophoresis because of insufficient resolution of the proteins having molecular masses between 22 000 and 25 000.


title: ResQbot: A Mobile Rescue Robot for Casualty Extraction

abstract: Performing search and rescue missions in disaster-struck environments is challenging. Despite the advances in the robotic search phase of the rescue missions, few works have been focused on the physical casualty extraction phase. In this work, we propose a mobile rescue robot that is capable of performing a safe casualty extraction routine. To perform this routine, this robot adopts a loco-manipulation approach. We have designed and built a mobile rescue robot platform called ResQbot as a proof of concept of the proposed system. We have conducted preliminary experiments using a sensorised human-sized dummy as a victim, to confirm that the platform is capable of performing a safe casualty extraction procedure.


Enhanced Visibility of MoS 2 , MoSe 2 , WSe 2 and Black-Phosphorus : Making Optical Identification of 2 D Semiconductors Easier


title: A new steganographic method for color and grayscale image hiding

abstract: In this paper, we present a steganographic method for embedding a color or a grayscale image in a true color image. Three types of secret images can be carried by the proposed method: hiding a color secret image, hiding a palette-based 256-color secret image, and hiding a grayscale image in a true color image. Secret data are protected by the conventional crypto system DES. We compare the image quality and hiding capacity of the proposed method with those of the scheme in Lin et al.'s scheme. According to the experimental results, the image quality of the proposed method is better than that of the Lin et al.'s scheme. In addition, annotation data can be hidden with the secret image in the host image. The hiding capacity of the proposed method is greater than that of other compared schemes. The experimental results show that the proposed method is a secure steganographic method that provides high hiding capacity and good image quality.


title: Internet of Cloud: Security and Privacy issues

abstract: The synergy between Cloud and IoT has emerged largely due to the Cloud having attributes which directly benefit IoT and enable its continued growth. IoT adopting Cloud services has brought new security challenges. In this book chapter, we pursue two main goals: (1) to analyse the different components of Cloud computing and IoT and (2) to present security and privacy problems that these systems face. We thoroughly investigate current security and privacy preservation solutions that exist in this area, with an eye on the Industrial Internet of Things, discuss open issues and propose future directions.


title: Children's Separation Anxiety Scale (CSAS): Psychometric Properties

abstract: This study describes the psychometric properties of the Children's Separation Anxiety Scale (CSAS), which assesses separation anxiety symptoms in childhood. Participants in Study 1 were 1,908 schoolchildren aged between 8 and 11. Exploratory factor analysis identified four factors: worry about separation, distress from separation, opposition to separation, and calm at separation, which explained 46.91% of the variance. In Study 2, 6,016 children aged 8–11 participated. The factor model in Study 1 was validated by confirmatory factor analysis. The internal consistency (α = 0.82) and temporal stability (r = 0.83) of the instrument were good. The convergent and discriminant validity were evaluated by means of correlations with other measures of separation anxiety, childhood anxiety, depression and anger. Sensitivity of the scale was 85% and its specificity, 95%. The results support the reliability and validity of the CSAS.


title: Trajectory based predictive modeling of conversion from mild cognitive impairment to Alzheimer's disease

abstract: Accurate prediction of clinical changes of Mild Cognitive Impairment (MCI) patients at future time points is important for early diagnosis and possible prevention of Alzheimer's disease (AD). In this paper, future clinical changes in Neuropsychological Measures (NM) of MCI patients are estimated via three different predictive models employing linear regression and extrapolation. The completed time domain trajectories are processed in the Euclidean space to extract features encompassing clinical change in the biomarker values. These features are then fed to an optimized SVM classifier to predict conversion of an MCI patient to AD. A wrapper based biomarker subset selection is adopted to analyze the effect of single and combined NM biomarkers. The proposed predictive modelling techniques were validated on 186 MCI subjects with the selected NM readings available at baseline and 3 annual follow-up visits. 1 and 2 year ahead conversion prediction was made using the current and at least 1 year old biomarker reading. Maximum accuracy of 77.87% and 73.24% is achieved for 1 and 2 year ahead conversion prediction respectively.


title: Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities

abstract: This keynote paper: presents a 21st century vision of computing; identifies various computing paradigms promising to deliver the vision of computing utilities; defines Cloud computing and provides the architecture for creating market-oriented Clouds by leveraging technologies such as VMs; provides thoughts on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation; presents some representative Cloud platforms especially those developed in industries along with our current work towards realising market-oriented resource allocation of Clouds by leveraging the 3rd generation Aneka enterprise Grid technology; reveals our early thoughts on interconnecting Clouds for dynamically creating an atmospheric computing environment along with pointers to future community research; and concludes with the need for convergence of competing IT paradigms for delivering our 21st century vision.


title: Docking autonomous robots in passive docks with Infrared sensors and QR codes

abstract: In this paper, we describe an inexpensive and ::: easy to deploy docking solution in passive charging docks for ::: autonomous mobile robots. The objective is to achieve long-term ::: autonomous robots within an experiment test-bed. We propose to ::: combine the use of QR codes as landmarks and Infrared distance ::: sensors. The relative size of the lateral edges of the visual pattern ::: is used to position the robot in relation with the dock. Infrared ::: distance sensors are then used to perform different approaching ::: strategies depending on the distance. Experiments show that the ::: proposed solution is fully operational and robust. Not to rely ::: exclusively on visual pattern recognition avoids potential errors ::: induced by camera calibration. Additionally, as a positive side ::: effect, the use of Infrared sensors allows the robot to avoid ::: obstacles while docking. The finality of such an approach is ::: to integrate these robots into the FIT IoT Lab experimental ::: testbed which allows any experimenter to book wireless resources ::: such as wireless sensors remotely and to test their own code. ::: Wifibots holding wireless sensors will be integrated as additional ::: reservable resources of the platform to enlarge the set of possible ::: experimentations with mobile entities.


title: UnPhishMe: Phishing Attack Detection by Deceptive Login Simulation through an Android Mobile App

abstract: Phishing attacks have been increasing recently. Attackers use clever social engineering techniques to convince their victims into clicking a malware or deceptive login-based webpages. Most solutions for this particular problem focus more on helping desktop computer users than mobile device users. Mobile device users are more vulnerable than their desktop counterparts because they are online most of the time and they have device limitations such as smaller screen size and low computational power. This paper presents UnPhishMe, an effective mobile application prototype that takes advantage of a particular weakness of phishing sites: they accept any kind of input information for authentication. UnPhishMe enables a mobile device user to create fake login account, with fake login credentials, that mimics user login procedure every time the user opens a login webpage and generates an alert to her. UnPhishMe determines whether the current login page shifts to another webpage after an authentication attempt. It does so by monitoring hashcode changes of the URL when the page is loading, listens to HttpURLConnection status code, and then makes a decision on whether the website is fraudulent or not. We measured the effectiveness of UnPhishMe by conducting a user experiment on android platforms and tested its detection accuracy, memory and CPU performance. The results show that UnPhishMe uses a very small amount of computational power and it is effective in assisting users to identify phishing attacks with an accuracy of 96%.


title: An adaptive approach for Malay cheque words recognition using support vector machine

abstract: Support vector machines (SVMs) have played a significant role in the field of pattern recognition. This study utilizes the SVM as a classifier for the analysis of Malay cheque word recognition using Malay lexical database (Ahmad et al., 2007). The SVM system was used for individual character recognition and then lexical verification was applied for word level. Several pre-processing steps were taken such as noise removal, image normalization, and skeletonization prior to feature extraction to improve the dataset perspective and hence the recognition accuracy. Statistical and geometrical extraction techniques have been applied in the approach. The results show that the statistical feature is reliable, accessible and provides more accurate results. The results also show that the new approach passed 97.15% character recognition, and combined with word lexical verification, the recognition rate surpassed 98.2% recognition rate.


title: Flexible Access Management System for Campus VLAN Based on OpenFlow

abstract: Using a lot of VLANs on campus networks has become popular for deploying many logical networks over minimal fibers/cables. A campus-wide Wi-Fi system, for example, requires a lot of VLANs for separating the access networks from other campus networks and for realizing a sophisticated access control such as guest-/home-users separation and security filtering. The requirement is high especially when a network roaming system, such as eduroam, is introduced. The conventional VLAN based on IEEE802.1Q has some limitations, and the system configuration work is laborious. In this paper, we propose a flexible campus VLAN system based on Open Flow to solve the problems. In addition, we introduce a prototype system and show evaluation results. As the results, we confirm that the proposed system can realize flexible network configurations and sophisticated access control with a much simplified network management work.


title: Does interest in language learning affect the non-native phoneme production in elderly learners?

abstract: Second language (L2) learning is widely studied in adult learners. This study was conducted in order to see how auditory training affects elderly learners’ L2 speech production and whether linguistically oriented seniors can benefit more from training compared to seniors not showing special interest in foreign languages. Hence, we studied seniors recruited from language courses and seniors with other than L2 related interests. The two day study included listenand-repeat trainings and production tests. The trained words included vowels from which the three first formants and their standard deviations were measured and analysed. The results showed that formant values changed differently as a function of training in the two groups. Also, the standard deviations developed differently: only the linguistically oriented seniors showed a deviation decrease, indicating non-native learning. This study showed that learning to produce a non-native phoneme can be easier for elderly learners who show a general interest in languages.


title: Solving Big Data Challenges for Enterprise Application Performance Management

abstract: As the complexity of enterprise systems increases, the need for monitoring and analyzing such systems also grows. A number of companies have built sophisticated monitoring tools that go far beyond simple resource utilization reports. For example, based on instrumentation and specialized APIs, it is now possible to monitor single method invocations and trace individual transactions across geographically distributed systems. This high-level of detail enables more precise forms of analysis and prediction but comes at the price of high data rates (i.e., big data). To maximize the benefit of data monitoring, the data has to be stored for an extended period of time for ulterior analysis. This new wave of big data analytics imposes new challenges especially for the application performance monitoring systems. The monitoring data has to be stored in a system that can sustain the high data rates and at the same time enable an up-to-date view of the underlying infrastructure. With the advent of modern key-value stores, a variety of data storage systems have emerged that are built with a focus on scalability and high data rates as predominant in this monitoring use case. ::: ::: In this work, we present our experience and a comprehensive performance evaluation of six modern (open-source) data stores in the context of application performance monitoring as part of CA Technologies initiative. We evaluated these systems with data and workloads that can be found in application performance monitoring, as well as, on-line advertisement, power monitoring, and many other use cases. We present our insights not only as performance results but also as lessons learned and our experience relating to the setup and configuration complexity of these data stores in an industry setting.


title: A hybrid metaheuristic approach for the capacitated p-median problem

abstract: The capacitated p-median problem (CPMP) seeks to obtain the optimal location of p medians considering distances and capacities for the services to be given by each median. This paper presents an efficient hybrid metaheuristic algorithm by combining a proposed cutting-plane neighborhood structure and a tabu search metaheuristic for the CPMP. In the proposed neighborhood structure to move from the current solution to a neighbor solution, an open median is selected and closed. Then, a linear programming (LP) model is generated by relaxing binary constraints and adding new constraints. The generated LP solution is improved using cutting-plane inequalities. The solution of this strong LP is considered as a new neighbor solution. In order to select an open median to be closed, several strategies are proposed. The neighborhood structure is combined with a tabu search algorithm in the proposed approach. The parameters of the proposed hybrid algorithm are tuned using design of experiments approach. The proposed algorithm is tested on several sets of benchmark instances. The statistical analysis shows efficiency and effectiveness of the hybrid algorithm in comparison with the best approach found in the literature.


Railway Network Modelling Using Petri Nets


title: On the Energy Efficiency Prospects of Network Function Virtualization

abstract: Network Function Virtualization (NFV) has recently received significant attention as an innovative way of deploying network services. By decoupling network functions from the physical equipment on which they run, NFV has been proposed as passage towards service agility, better time-to-market, and reduced Capital Expenses (CAPEX) and Operating Expenses (OPEX). One of the main selling points of NFV is its promise for better energy efficiency resulting from consolidation of resources as well as their more dynamic utilization. However, there are currently no studies or implementations which attach values to energy savings that can be expected, which could make it hard for Telecommunication Service Providers (TSPs) to make investment decisions. In this paper, we utilize Bell Labs' GWATT tool to estimate the energy savings that could result from the three main NFV use cases Virtualized Evolved Packet Core (VEPC), Virtualized Customer Premises Equipment (VCPE) and Virtualized Radio Access Network (VRAN). We determine that the part of the mobile network with the highest energy utilization prospects is the Evolved Packet Core (EPC) where virtualization of functions leads to a 22% reduction in energy consumption and a 32% enhancement in energy efficiency.


title: Internally stable matchings and exchanges

abstract: Stability is a central concept in exchange-based mechanism design. It imposes a fundamental requirement that no subset of agents could beneficially deviate from the outcome prescribed by the mechanism. However, deployment of stability in an exchange mechanism presents at least two challenges. First, it reduces social welfare and sometimes prevents the mechanism from producing a solution. Second, it might incur computational cost to clear the mechanism. ::: ::: In this paper, we propose an alternative notion of stability, coined internal stability, under which we analyze the social welfare bounds and computational complexity. Our contributions are as follows: for both pairwise matchings and limited-length exchanges, for both unweighted and weighted graphs, (1) we prove desirable tight social welfare bounds; (2) we analyze the computational complexity for clearing the matchings and exchanges. Extensive experiments on the kidney exchange domain demonstrate that the optimal welfare under internal stability is very close to the unconstrained optimal.


title: Dynamic captioning: video accessibility enhancement for hearing impairment

abstract: There are more than 66 million people su®ering from hearing impairment and this disability brings them di±culty in the video content understanding due to the loss of audio information. If scripts are available, captioning technology can help them in a certain degree by synchronously illustrating the scripts during the playing of videos. However, we show that the existing captioning techniques are far from satisfactory in assisting hearing impaired audience to enjoy videos. In this paper, we introduce a video accessibility enhancement scheme with a Dynamic Captioning approach, which explores a rich set of technologies including face detection and recognition, visual saliency analysis, text-speech alignment, etc. Different from the existing methods that are categorized as static captioning here, dynamic captioning puts scripts at suitable positions to help hearing impaired audience better recognize the speaking characters. In addition, it progressively highlights the scripts word-by-word via aligning them with the speech signal and illustrates the variation of voice volume. In this way, the special audience can better track the scripts and perceive the moods that are conveyed by the variation of volume. We implement the technology on 20 video clips and conduct an in-depth study with 60 real hearing impaired users, and results have demonstrated the effectiveness and usefulness of the video accessibility enhancement scheme.


title: A novel outlier cluster detection algorithm without top-n parameter

abstract: Outlier detection is an important task in data mining with numerous applications, including credit card fraud detection, video surveillance, etc. Outlier detection has been widely focused and studied in recent years. The concept about outlier factor of object is extended to the case of cluster. Although many outlier detection algorithms have been proposed, most of them face the top-n problem, i.e., it is difficult to know how many points in a database are outliers. In this paper we propose a novel outlier cluster detection algorithm called ROCF based on the concept of mutual neighbor graph and on the idea that the size of outlier clusters is usually much smaller than the normal clusters. ROCF can automatically figure out the outlier rate of a database and effectively detect the outliers and outlier clusters without top-n parameter. The formal analysis and experiments show that this method can achieve good performance in outlier detection.


title: Continuous projection for fast L1 reconstruction

abstract: With better and faster acquisition devices comes a demand for fast robust reconstruction algorithms, but no L1-based technique has been fast enough for online use so far. In this paper, we present a novel continuous formulation of the weighted locally optimal projection (WLOP) operator based on a Gaussian mixture describing the input point density. Our method is up to 7 times faster than an optimized GPU implementation of WLOP, and achieves interactive frame rates for moderately sized point clouds. We give a comprehensive quality analysis showing that our continuous operator achieves a generally higher reconstruction quality than its discrete counterpart. Additionally, we show how to apply our continuous formulation to spherical mixtures of normal directions, to also achieve a fast robust normal reconstruction.


title: When Do Morally Motivated Innovators Elicit Inspiration Instead of Irritation?

abstract: Innovators (i.e. consumers who are the first to adopt an innovation) are pivotal for the societal diffusion of sustainable innovations. But when are innovators most influential? Recent work suggests that morally-motivated innovators (i.e., consumers who adopt an innovation out of concern for the welfare of others) can make fellow consumers who have not yet adopted that innovation feel morally inadequate. As a self-defense mechanism, those fellow consumers might dismiss these innovators and their choices. As a result, ironically, morally-motivated innovators might discourage others to adopt sustainable innovations. In an experimental study, we replicate this pattern, but also show that moral innovators can elicit a more positive response as well. Specifically, our results offer initial evidence that morally-motivated innovators may be more inspiring than self-interested innovators, provided that their actions do not directly pose a threat to the moral self-concept of observers. In sum, our research sheds empirical light on the conditions under which innovators are likely to facilitate, rather than slow down the transition to a more sustainable society.


title: Biometrics from Brain Electrical Activity: A Machine Learning Approach

abstract: The potential of brain electrical activity generated as a response to a visual stimulus is examined in the context of the identification of individuals. Specifically, a framework for the visual evoked potential (VEP)-based biometrics is established, whereby energy features of the gamma band within VEP signals were of particular interest. A rigorous analysis is conducted which unifies and extends results from our previous studies, in particular, with respect to 1) increased bandwidth, 2) spatial averaging, 3) more robust power spectrum features, and 4) improved classification accuracy. Simulation results on a large group of subject support the analysis


title: EMI filter design and optimization for both AC and DC side in a DC-fed motor drive system

abstract: This paper proposes an EMI filter design and optimization method for both AC and DC side in a DC-fed motor drive system. Based on the noise generation and propagation mechanism, the analysis of common mode (CM) and differential mode (DM) EMI noise equivalent circuits is provided. Based on those equivalent circuits, this paper investigates the system EMI noise between AC and DC side, which shows the interaction between DM and CM noises and the interaction between adding AC and DC side filters. With these considerations, an optimized EMI filter design procedure is proposed to design CM and DM filters for both AC and DC sides. To minimize the impact on the EMI noise of one side caused by adding filter on the other side, certain order must be followed to design AC and DC CM and DM filters. Moreover, the EMI filter weight optimization method is also discussed to get the minimum weight of EMI filter and improve system power density. Simulation and experimental results verify the interaction between AC and DC filters and show that EMI filters can be designed to suppress both AC and DC EMI noise to meet the standard with the proposed EMI filter design method.


title: Deep Object-Centric Representations for Generalizable Robot Learning

abstract: Robotic manipulation in complex open-world scenarios requires both reliable physical manipulation skills and effective and generalizable perception. In this paper, we propose a method where general purpose pretrained visual models serve as an object-centric prior for the perception system of a learned policy. We devise an object-level attentional mechanism that can be used to determine relevant objects from a few trajectories or demonstrations, and then immediately incorporate those objects into a learned policy. A task-independent meta-attention locates possible objects in the scene, and a task-specific attention identifies which objects are predictive of the trajectories. The scope of the task-specific attention is easily adjusted by showing demonstrations with distractor objects or with diverse relevant objects. Our results indicate that this approach exhibits good generalization across object instances using very few samples, and can be used to learn a variety of manipulation tasks using reinforcement learning.


title: Disease named entity recognition by combining conditional random fields and bidirectional recurrent neural networks

abstract: The recognition of disease and chemical named entities in scientific articles is a very important subtask in information extraction in the biomedical domain. Due to the diversity and complexity of disease names, the recognition of named entities of diseases is rather tougher than those of chemical names. Although there are some remarkable chemical named entity recognition systems available online such as ChemSpot and tmChem, the publicly available recognition systems of disease named entities are rare. This article presents a system for disease named entity recognition (DNER) and normalization. First, two separate DNER models are developed. One is based on conditional random fields model with a rule-based post-processing module. The other one is based on the bidirectional recurrent neural networks. Then the named entities recognized by each of the DNER model are fed into a support vector machine classifier for combining results. Finally, each recognized disease named entity is normalized to a medical subject heading disease name by using a vector space model based method. Experimental results show that using 1000 PubMed abstracts for training, our proposed system achieves an F1-measure of 0.8428 at the mention level and 0.7804 at the concept level, respectively, on the testing data of the chemical-disease relation task in BioCreative V.


title: Pathogenesis of highly virulent African swine fever virus in domestic pigs exposed via intraoropharyngeal, intranasopharyngeal, and intramuscular inoculation, and by direct contact with infected pigs.

abstract: To investigate the pathogenesis of African swine fever virus (ASFV), domestic pigs (n=18) were challenged with a range (10(2)-10(6) 50% hemadsorbing doses (HAD50)) of the highly virulent ASFV-Malawi strain by inoculation via the intraoropharyngeal (IOP), intranasopharyngeal (INP), or intramuscular (IM) routes. A subsequent contact challenge experiment was performed in which six IOP-inoculated donor pigs were allowed to have direct contact (DC) with six naïve pigs for exposure times that varied from 24 to 72 h. All challenge routes resulted in clinical progression and postmortem lesions similar to those previously described in experimental and natural infection. The onset of clinical signs occurred between 1 and 7 days post inoculation (dpi) and included pyrexia with variable progression to obtundation, hematochezia, melena, moribundity and death with a duration of 4-11 days. Viremia was first detected between 4 and 5 dpi in all inoculation groups whereas ASFV shedding from the nasal cavity and tonsil was first detected at 3-9 dpi. IM and DC were the most consistent modes of infection, with 12/12 (100%) of pigs challenged by these routes becoming infected. Several clinical and virological parameters were significantly different between IM and DC groups indicating dissimilarity between these modes of infection. Amongst the simulated natural routes, INP inoculation resulted in the most consistent progression of disease across the widest range of doses whilst preserving simulation of natural exposure and therefore may provide a superior system for pathogenesis and vaccine efficacy investigation.


A Highly Sensitive Capacitive-Based Soft Pressure Sensor Based on a Conductive Fabric and a Microporous Dielectric Layer


title: The double-barreled radial forearm osteocutaneous free flap

abstract: OBJECTIVES/HYPOTHESIS ::: The radial forearm osteocutaneous free flap (RFOCFF) provides a thin pliable skin paddle with up to 11 cm of bone. A limitation of this flap is the thin bone that is obtained and the lack of suitability for dental implants. A minimum depth of 5 mm and height of 10 mm is required for a bony flap to accept osteointegrated implants. We propose that by double barreling the radial bone, it is possible to reconstruct osseous defects with bone of sufficient caliber to accept dental implants. ::: ::: ::: STUDY DESIGN ::: Retrospective review of our experience with double-barreled RFOCFF. ::: ::: ::: METHODS ::: We reviewed all free flaps performed from July 2000 to September 2014 and analyzed patients in whom a radial forearm osteocutaneous flap was used. We then reviewed those who had an osteotomy to form a double-barrel reconstruction. Descriptive data and outcomes were tabulated. ::: ::: ::: RESULTS ::: A total of 458 osteocutaneous free flaps were performed; 75 were RFOCFF and 18 of these were double barreled. All 18 flaps survived. The reconstructed defect size was between 2.6 and 6.8 cm. One patient had dental implants placed at time of surgery that successfully osteointegrated. Postoperative computed tomography scans were available in nine patients and were used to calculate bone dimensions. Bone depth ranged from 7.91 to 13.22 mm, with a mean of 9.77 ± 1.53 mm. Bone height ranged from 8.42 to 17.81 mm, with a mean of 13.82 ± 3.2 mm. ::: ::: ::: CONCLUSIONS ::: The double-barreled RFOCFF provides dependable long-lasting bone with adequate bone dimensions to support osteointegrated dental implants. ::: ::: ::: LEVEL OF EVIDENCE ::: 4.


title: Building an Information System Design Theory for Vigilant EIS

abstract: This paper defines an information system design theory ISDT to be a prescriptive theory which integrates normative and descriptive theories into design paths intended to produce more effective information systems. The nature of ISDTs is articulated using Dubin's concept of theory building and Simon's idea of a science of the artificial. An example of an ISDT is presented in the context of Executive Information Systems EIS. Despite the increasing awareness of the potential of EIS for enhancing executive strategic decision-making effectiveness, there exists little theoretical work which directly guides EIS design. We contend that the underlying theoretical basis of EIS can be addressed through a design theory of vigilant information systems. Vigilance denotes the ability of an information system to help an executive remain alertly watchful for weak signals and discontinuities in the organizational environment relevant to emerging strategic threats and opportunities. Research on managerial information scanning and emerging issue tracking as well as theories of open loop control are synthesized to generate vigilant information system design theory propositions. Transformation of the propositions into testable empirical hypotheses is discussed.


title: An Efficient Ontology Comparison Tool for Semantic Web Applications

abstract: With the growing access to heterogeneous and independent data repositories, determining the semantic difference of two ontologies is critical in information retrieval, information integration and semantic web applications. In this paper, we propose an ontology comparison tool based on a novel senses refinement algorithm, which builds a senses set to accurately represent the semantics of the input ontology. The senses refinement algorithm automatically extracts senses from the electronic lexical database WordNet (locally installed or online), removes unnecessary senses based on the relationship among the entity classes of the ontology, and specifies relations and constraints of the concepts in the refined senses set. The senses refinement converts the measurement of ontology difference into simple set operations based on set theory, thus ensures the efficiency and accuracy of the ontology comparison. Our experimental studies show that the proposed senses refinement algorithm outperforms the naive senses set construction algorithm in terms of efficiency and accuracy.


title: Robobarista: Learning to Manipulate Novel Objects via Deep Multimodal Embedding

abstract: There is a large variety of objects and appliances in human environments, such as stoves, coffee dispensers, juice extractors, and so on. It is challenging for a roboticist to program a robot for each of these object types and for each of their instantiations. In this work, we present a novel approach to manipulation planning based on the idea that many household objects share similarly-operated object parts. We formulate the manipulation planning as a structured prediction problem and learn to transfer manipulation strategy across different objects by embedding point-cloud, natural language, and manipulation trajectory data into a shared embedding space using a deep neural network. In order to learn semantically meaningful spaces throughout our network, we introduce a method for pre-training its lower layers for multimodal feature embedding and a method for fine-tuning this embedding space using a loss-based margin. In order to collect a large number of manipulation demonstrations for different objects, we develop a new crowd-sourcing platform called Robobarista. We test our model on our dataset consisting of 116 objects and appliances with 249 parts along with 250 language instructions, for which there are 1225 crowd-sourced manipulation demonstrations. We further show that our robot with our model can even prepare a cup of a latte with appliances it has never seen before.


Survey on Weak Cache Consistency Mechanisms 1 Shefali,


title: Evolutionary joint selection to improve human action recognition with RGB-D devices

abstract: Interest in RGB-D devices is increasing due to their low price and the wide range of possible applications that come along. These devices provide a marker-less body pose estimation by means of skeletal data consisting of 3D positions of body joints. These can be further used for pose, gesture or action recognition. In this work, an evolutionary algorithm is used to determine the optimal subset of skeleton joints, taking into account the topological structure of the skeleton, in order to improve the final success rate. The proposed method has been validated using a state-of-the-art RGB action recognition approach, and applying it to the MSR-Action3D dataset. Results show that the proposed algorithm is able to significantly improve the initial recognition rate and to yield similar or better success rates than the state-of-the-art methods.


title: AP-initiated Multi-User Transmissions in IEEE 802.11ax WLANs

abstract: Abstract Next-generation IEEE 802.11ax Wireless Local Area Networks (WLANs) will make extensive use of multi-user communications in both Downlink (DL) and Uplink (UL) directions to achieve high and efficient spectrum utilization in scenarios with many user stations per access point. It will become possible with the support of Multiuser (MU) Multiple Input, Multiple output (MIMO) and Orthogonal Frequency Division Multiple Access (OFDMA) transmissions. In this paper, we first overview the novel characteristics introduced by IEEE 802.11ax to implement AP-initiated OFDMA and multiuser MIMO (MU-MIMO) transmissions in both DL and UL directions. Namely, we describe the changes made at the Physical (PHY) layer and at the Medium Access Control (MAC) layer to support OFDMA, the use of trigger frames to schedule uplink multi-user transmissions, and the new multiuser RTS/CTS mechanism to protect large multi-user transmissions from collisions. Then, in order to study the achievable throughput of an IEEE 802.11ax WLAN, we use both mathematical analysis and simulations to numerically quantify the gains of MU transmissions and the impact of IEEE 802.11ax overheads on the WLAN saturation throughput. Results show the advantages of using MU transmissions in scenarios with many user stations. Additionally, we provide novel insights on the conditions in which IEEE 802.11ax WLANs are able to maximize their performance, such as the need to provide strict prioritization to AP-initiated MU transmissions to avoid collisions with transmissions from user stations.


title: Learning to Rank With Bregman Divergences and Monotone Retargeting

abstract: This paper introduces a novel approach for learning to rank (LETOR) based on the notion of monotone retargeting. It involves minimizing a divergence between all monotonic increasing transformations of the training scores and a parameterized prediction function. The minimization is both over the transformations as well as over the parameters. It is applied to Bregman divergences, a large class of "distance like" functions that were recently shown to be the unique class that is statistically consistent with the normalized discounted gain (NDCG) criterion [19]. The algorithm uses alternating projection style updates, in which one set of simultaneous projections can be computed independent of the Bregman divergence and the other reduces to parameter estimation of a generalized linear model. This results in easily implemented, efficiently parallelizable algorithm for the LETOR task that enjoys global optimum guarantees under mild conditions. We present empirical results on benchmark datasets showing that this approach can outperform the state of the art NDCG consistent techniques.


Policy invariance under reward transformations: Theory and application to reward shaping


title: Sparse Principal Component Analysis

abstract: Principal component analysis (PCA) is widely used in data processing and dimensionality reduction. However, PCA suffers from the fact that each principal component is a linear combination of all the original variables, thus it is often difficult to interpret the results. We introduce a new method called sparse principal component analysis (SPCA) using the lasso (elastic net) to produce modified principal components with sparse loadings. We first show that PCA can be formulated as a regression-type optimization problem; sparse loadings are then obtained by imposing the lasso (elastic net) constraint on the regression coefficients. Efficient algorithms are proposed to fit our SPCA models for both regular multivariate data and gene expression arrays. We also give a new formula to compute the total variance of modified principal components. As illustrations, SPCA is applied to real and simulated data with encouraging results.


title: Image-Based Proxy Accumulation for Real-Time Soft Global Illumination

abstract: We present a new, general, and real-time technique for soft global illumination in low-frequency environmental lighting. It accumulates over relatively few spherical proxies which approximate the light blocking and re-radiating effect of dynamic geometry. Soft shadows are computed by accumulating log visibility vectors for each sphere proxy as seen by each receiver point. Inter-reflections are computed by accumulating vectors representing the proxy's unshadowed radiance when illuminated by the environment. Both vectors capture low-frequency directional dependence using the spherical harmonic basis. We also present a new proxy accumulation strategy that splats each proxy to receiver pixels in image space to collect its shadowing and indirect lighting contribution. Our soft GI rendering pipeline unifies direct and indirect soft effects with a simple accumulation strategy that maps entirely to the GPU and outperforms previous vertex-based methods.


title: Milk Run Logistics: Literature Review and Directions

abstract: The purpose of this paper is to review the literature on Milk Run Logistics and to present an overview of its implementation practices adopted by the manufacturing organizations. The paper also discusses milk run logistics in the procurement system with a special emphasis on the automobile industry. Milk run system is all about logistics support for the supply chain. Milk run system results in reduction in cost of transportation, travelling path and fuel consumption. The effects of the direct shipment on the traffic conditions and on the environment have also been studied. By introducing the milk run logistics under heavily congested traffic conditions, the supplier can have full control on the procurement process. Also, the number of trucks on road can be reduced thus resulting in improvement in traffic conditions. The effect of the milk run logistics on the reduction of CO2 is also discussed. The promotion of Milk- R un logistics can be highly evaluated from the viewpoint of environmental policy.


title: Review: A survey of intrusion detection in wireless network applications

abstract: Information systems are becoming more integrated into our lives. As this integration deepens, the importance of securing these systems increases. Because of lower installation and maintenance costs, many of these systems are largely networked by wireless means. In order to identify gaps and propose research directions in wireless network intrusion detection research, we survey the literature of this area. Our approach is to classify existing contemporary wireless intrusion detection system (IDS) techniques based on target wireless network, detection technique, collection process, trust model and analysis technique. We summarize pros and cons of the same or different types of concerns and considerations for wireless intrusion detection with respect to specific attributes of target wireless networks including wireless local area networks (WLANs), wireless personal area networks (WPANs), wireless sensor networks (WSNs), ad hoc networks, mobile telephony, wireless mesh networks (WMNs) and cyber physical systems (CPSs). Next, we summarize the most and least studied wireless IDS techniques in the literature, identify research gaps, and analyze the rationale for the degree of their treatment. Finally, we identify worthy but little explored topics and provide suggestions for ways to conduct research.


title: EBARDenovo : highly accurate de novo assembly of RNA-Seq with efficient chimera-detection

abstract: Motivation: High-accuracy de novo assembly of the short sequencing reads from RNA-Seq technology is very challenging. We introduce a de novo assembly algorithm, EBARDenovo, which stands for Extension, Bridging And Repeat-sensing Denovo. This algorithm uses an efficient chimera-detection function to abrogate the effect of aberrant chimeric reads in RNA-Seq data. Results: EBARDenovo resolves the complications of RNA-Seq assembly arising from sequencing errors, repetitive sequences and aberrant chimeric amplicons. In a series of assembly experiments, our algorithm is the most accurate among the examined programs, including de Bruijn graph assemblers, Trinity and Oases. Availability and implementation: EBARDenovo is available at http:// ebardenovo.sourceforge.net/. This software package (with patent pending) is free of charge for academic use only.


title: Scene text recognition algorithm based on faster RCNN

abstract: Industrial session of the natural scene in the text recognition technology has a great demand. The traditional optical character recognition technology (OCR) requires the text neat layout and neatness and background clean, and industrial production often fail to meet such standards. In this paper, a new text recognition algorithm based on deep learning is proposed for the existing problems of OCR technology. In this paper, a new method based on convolution neural network (Faster RCNN) is proposed to improve the correctness of text recognition. Compared with the conventional detection method, the correct rate of recognition based on Faster RCNN model can reach 90.4%, and the correctness rate is 88.9%. Experiments show that the recognition method in this paper is effective.


title: Neural Networkbased Handwritten Signature Verification

abstract: A fuse inspection device having receiving terminals which are arranged in a longitudinal direction of a fuse box body and with which a plurality of band fuses are held in fit engagement. Windows are provided in side walls of accommodating chambers for the respective receiving terminals, and a slide knob slides along the band fuses. A sensing terminal moves together with the slide knob and has one end electrically connected with the receiving terminals through the window and has the other end brought into contact with a common conductive member. Electric conduction states of the band fuses are successively sensed by electrically connecting the sensing terminal and the receiving terminals through the movement of the slide knob. A plurality of connecting terminals each of which has one end held in contact with the corresponding receiving terminal through the window are arranged in ascendible fashion in the fuse box body and in a moving direction of the slide knob so that when one end of the sensing terminal has come into contact with any of the connecting terminals, the contact pressure between the other end of the sensing terminal and the conductive member rises, thereby to reliably sense the conduction state of the fuse.


title: A 9-mm2 Ultra-Low-Power Highly Integrated 28-nm CMOS SoC for Internet of Things

abstract: This paper gives an overview of the Blackghost 1.0 system-on-chip (SoC) from Qualcomm Research, which was our first test chip that paved the way toward the commercialization of Qualcomm’s most recent ultra-low-power Blackghost SoC family. Specifically designed for battery powered Internet of Things, sensor fusion, wearables, and e-medical applications, this highly integrated SoC delivers high-power efficiency through low-power innovations in architecture and circuit domains. It integrates a small footprint sensor control processor based on ARM Cortex-M0, a vision classifier processor, a streaming DSP hardware accelerator, an ultra-low-power analog-front-end, and an on-die power management unit with direct Li-Ion battery attach capability. The die size of this prototype chip is $3\times 3$ mm2 in a 28LP CMOS process technology. To date, this SoC family has successfully progressed to the mass production of near-threshold computing. The logic computation operates at near-threshold voltages ( $9~\mu $ A/MHz from the directly attached battery.


title: E‐government adoption: architecture and barriers

abstract: Purpose – To provide an integrated architecture framework for e‐government that represents the alignment of IT infrastructure with business process management in public sector organisations and classify the barriers that might complicate the implementation of the proposed architecture framework. The study will help IT practitioners in the public sector learn how to use and manage information technologies to revitalise business processes, improve decision‐making, and gain a competitive advantage from the adoption of e‐government. The proposed architecture framework for e‐government adoption will reduce confusion surrounding e‐government infrastructure in the public sector through understanding the implementation processes, identifying requirements of information and communications technology tools, highlighting the importance of the organisational management resources and the impact of barriers.Design/methodology/approach – A range of earlier studies have been critically examined and analysed to provide an...


title: Compact Dual-Band Printed Quadrifilar Antennas for UHF RFID/GPS Operations

abstract: This letter presents a simple and innovative method for designing and optimizing two circularly polarized printed quadrifilar antennas (PQAs). The PQAs are operated at UHF RFID (902-908 MHz) and GPS (1575 MHz) and intended for use at a portable terminal. This new antenna system comprises two PQAs concentrically arranged, but electrically isolated. Each PQA comprises four inverted-F monopoles arranged orthogonally and excited with a phase difference of 90°.


title: High-gain and high-bandwidth rail-to-rail operational amplifier with slew rate boost circuit

abstract: In this work, a high gain and high bandwidth rail-to-rail operational amplifier with slew rate boost circuit is presented. The constant gm is enhanced through a source-to-bulk bias control of an input pair. A source degeneration scheme is applied to realize an input common-mode range insensitive output stage. Several compensation schemes are applied to maintain the stability. A slew rate boost circuit can improve the output slewing. The op amp performs 100-dB dc gain, 100-MHz bandwidth and 22.5-V/us slew rate at 15-pF output load. The layout area is 593/spl times/519 /spl mu/m/sup 2/ in a 0.18-/spl mu/m CMOS process. It consumes 3.2-mW at 1.8V supply voltage.


title: A Cubesat Payload for Exoplanet Detection

abstract: The search for undiscovered planets outside the solar system is a scientific topic that is rapidly spreading into the astrophysical and engineering communities. In this framework, the design of an innovative payload to detect exoplanets from a nano-sized space platform, like a 3U cubesat, is presented. The selected detection method is photometric transit, and the payload aims to detect flux decrements down to ~0.01% with a precision of 12 ppm. The payload design is also aimed at false positive recognition. The solution consists of a four-facets pyramid on the top of the payload, to allow for measurement redundancy and low-resolution spectral dispersion of the star images. The innovative concept is the use of a small and cheap platform for a relevant astronomical mission. The faintest observable target star has V-magnitude equal to 3.38. Despite missions aimed at ultra-precise photometry from microsatellites (e.g., MOST, BRITE), the transit of exoplanets orbiting very bright stars has not yet been surveyed photometrically from space, since any observation from a small/medium sized (30 cm optical aperture) telescope would saturate the detector. This cubesat mission can provide these missing measurements. This work is set up as a demonstrative project to verify the feasibility of the payload concept.


title: Concept Mining via Embedding

abstract: In this work, we study the problem of concept mining, which serves as the first step in transforming unstructured text into structured information, and supports downstream analytical tasks such as information extraction, organization, recommendation and search. Previous work mainly relies on statistical signals, existing knowledge bases, or predefined linguistic patterns. In this work, we propose a novel approach that mines concepts based on their occurrence contexts, by learning embedding vector representations that summarize the context information for each possible candidates, and use these embeddings to evaluate the concept's global quality and their fitness to each local context. Experiments over several real-world corpora demonstrate the superior performance of our method. A publicly available implementation is provided at https://github.com/kleeeeea/ECON.


title: A direct test of potential roles for beta3 and beta5 integrins in growth and metastasis of murine mammary carcinomas.

abstract: alphavbeta3 or alphavbeta5 integrins are widely expressed on blood and endothelial cells. Inhibition of the functions of these integrins has been reported to suppress neovascularization and tumor growth, suggesting that they may be critical modulators of angiogenesis. However, mice lacking these integrins exhibit extensive angiogenesis. Tumors arising from s.c. injections of tumor cells into mice lacking one or both integrins show enhanced tumor growth compared with growth in control mice due to both increased angiogenesis and to altered innate immune response. Other data suggest additional roles for these integrins, on either platelets or the tumor cells themselves, in enhancing tumor progression and metastasis. Here, we investigate the involvement of beta3 and beta5 integrins in the development and progression of mammary carcinomas. We intercrossed mouse mammary tumor virus (MMTV)-c-neu transgenic mice with beta3 or beta5 or beta3beta5 integrin-deficient mice and observed that multiple, large mammary tumors developed in 100% of mice on all genetic backgrounds. A statistically significant earlier onset of tumor growth was observed in the MMTV-c-neu/beta3beta5 integrin-null females compared with control mice. No major differences were observed in tumor size or number, vessel number or vessel structure and lung metastases were observed with similar frequency and size in all strains. MMTV-c-neu/beta3beta5 integrin-null mice had higher numbers of mammary acini, which may account for the earlier onset of tumors in this strain. These data indicate that alphavbeta3 or alphavbeta5 integrins are not essential for tumor growth and progression, although they might play some role in mammary gland development.


title: ISLES 2015 ‐ A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

abstract: &NA; Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non‐invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub‐challenges: Sub‐Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state‐of‐the‐art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state‐of‐the‐art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub‐acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles‐challenge.org). HighlightsEvaluation framework for automatic stroke lesion segmentation from MRIPublic multi‐center, multi‐vendor, multi‐protocol databases releasedOngoing fair and automated benchmark with expert created ground truth setsComparison of 14+7 groups who responded to an open challenge in MICCAISegmentation feasible in acute and unsolved in sub‐acute cases Graphical abstract Figure. No caption available.


title: NVIDIA cuda software and gpu parallel computing architecture

abstract: In the past, graphics processors were special purpose hardwired application accelerators, suitable only for conventional rasterization-style graphics applications. Modern GPUs are now fully programmable, massively parallel floating point processors. This talk will describe NVIDIA's massively multithreaded computing architecture and CUDA software for GPU computing. The architecture is a scalable, highly parallel architecture that delivers high throughput for data-intensive processing. Although not truly general-purpose processors, GPUs can now be used for a wide variety of compute-intensive applications beyond graphic.


title: A Provenance-Based Trust Model for Delay Tolerant Networks

abstract: Managing trust efficiently and effectively is critical to facilitating cooperation or collaboration and decision making tasks in tactical networks while meeting system goals such as reliability, availability, or scalability. Delay tolerant networks are often encountered in military network environments where end-to-end connectivity is not guaranteed due to frequent disconnection or delay. This work proposes a provenance-based trust framework for efficiency in resource consumption as well as effectiveness in trust evaluation. Provenance refers to the history of ownership of a valued object or information. We adopt the concept of provenance in that trustworthiness of an information provider affects that of information, and vice-versa. The proposed trust framework takes a data-driven approach to reduce resource consumption in the presence of selfish or malicious nodes. This work adopts a model-based method to evaluate the proposed trust framework using Stochastic Petri Nets. The results show that the proposed trust framework achieves desirable accuracy of trust evaluation of nodes compared with an existing scheme while consuming significantly less communication overhead.


title: An Extreme Firm-Specific News Sentiment Asymmetry Based Trading Strategy

abstract: News sentiment has been empirically observed to have impact on financial market returns. In this study, we investigate firm-specific news from the Thomson Reuters News Analytics data from 2003 to 2014 and propose an optimal trading strategy based on a sentiment shock score and a sentiment trend score which measure extreme positive and negative sentiment levels for individual stocks. The intuition behind this approach is that the impact of events that generate extreme investor sentiment changes tends to have long and lasting effects to market movement and hence provides better prediction to market returns. We document that there exists an optimal signal region for both indicators. And we also show extreme positive sentiment provides better a signal than extreme negative sentiment, which presents an asymmetric market behavior in terms of news sentiment impact. The back test results show that extreme positive sentiment generates robust and superior trading signals in all market conditions, and its risk-adjusted returns significantly outperform the S&P 500 index over the same time period.


title: GPLOM: The Generalized Plot Matrix for Visualizing Multidimensional Multivariate Data

abstract: Scatterplot matrices (SPLOMs), parallel coordinates, and glyphs can all be used to visualize the multiple continuous variables (i.e., dependent variables or measures) in multidimensional multivariate data. However, these techniques are not well suited to visualizing many categorical variables (i.e., independent variables or dimensions). To visualize multiple categorical variables, 'hierarchical axes' that 'stack dimensions' have been used in systems like Polaris and Tableau. However, this approach does not scale well beyond a small number of categorical variables. Emerson et al. [8] extend the matrix paradigm of the SPLOM to simultaneously visualize several categorical and continuous variables, displaying many kinds of charts in the matrix depending on the kinds of variables involved. We propose a variant of their technique, called the Generalized Plot Matrix (GPLOM). The GPLOM restricts Emerson et al.'s technique to only three kinds of charts (scatterplots for pairs of continuous variables, heatmaps for pairs of categorical variables, and barcharts for pairings of categorical and continuous variable), in an effort to make it easier to understand. At the same time, the GPLOM extends Emerson et al.'s work by demonstrating interactive techniques suited to the matrix of charts. We discuss the visual design and interactive features of our GPLOM prototype, including a textual search feature allowing users to quickly locate values or variables by name. We also present a user study that compared performance with Tableau and our GPLOM prototype, that found that GPLOM is significantly faster in certain cases, and not significantly slower in other cases.


title: Improving Awareness of Social Engineering Attacks

abstract: Social engineering is a method of attack involving the exploitation of human weakness, gullibility and ignorance. Although related techniques have existed for some time, current awareness of social engineering and its many guises is relatively low and efforts are therefore required to improve the protection of the user community. This paper begins by examining the problems posed by social engineering, and outlining some of the previous efforts that have been made to address the threat. This leads toward the discussion of a new awareness-raising website that has been specifically designed to aid users in understanding and avoiding the risks. Findings from an experimental trial involving 46 participants are used to illustrate that the system served to increase users’ understanding of threat concepts, as well as providing an engaging environment in which they would be likely to persevere with their learning.


title: Fanning the Flames of Hate: Social Media and Hate Crime

abstract: This paper investigates the link between social media and hate crime using Facebook data. We study the case of Germany, where the recently emerged right-wing party Alternative fur Deutschland (AfD) has developed a major social media presence. We show that right-wing anti-refugee sentiment on Facebook predicts violent crimes against refugees in otherwise similar municipalities with higher social media usage. To further establish causality, we exploit exogenous variation in major internet and Facebook outages, which fully undo the correlation between social media and hate crime. We further find that the effect decreases with distracting news events; increases with user network interactions; and does not hold for posts unrelated to refugees. Our results suggest that social media can act as a propagation mechanism between online hate speech and real-life violent crime.Keywords: social media, hate crime, minorities, Germany, AfD JEL Classification: D74, J15, Z10, D72, O35, N32, N34.


title: Brief Communication Reward without Dopamine

abstract: Dopamine (DA) is believed to play a fundamental role in reward processes. Virtually all drugs of abuse activate dopaminergic systems, as do "natural" rewards such as sexual interaction and food. Sweet-tasting solutions, for example, are a well characterized natural reward. In the present experiments, we used mice that cannot make DA (DD mice) to test the hypothesis that DA is necessary for reward. Sucrose preference, assessed with a computerized "lickometer," was used to determine whether DD mice respond preferentially for rewarding stimuli. DD mice preferentially chose sucrose over water, and also preferred the noncaloric sweetener saccharin. Furthermore, the rate of licking, bout size, and length were greater in DD mice drinking sweets than in controls. These data refute the necessity of DA for the reward processes manifested by sucrose preference. However, DD mice initiated licking less frequently than control mice and had fewer total licks. We suggest that DD mice have a deficit of goal-directed behavior that is not specific to reward processes. Lastly, juvenile DD mice demonstrate robust sucrose preference before experience with food in the presence of DA. Thus, DA is not required for mice to learn to consume sweet solutions preferentially. We conclude that DA is not required to find the sweet tastes of sucrose or saccharin rewarding.


title: Measuring the potential of augmented reality in civil engineering

abstract: Recently building information models have substantially improved the explicit semantic content of design information. Information models are used to integrate the initial phases of project development. On the construction site, however, the designs are still mostly represented as line-based paper drawings or projections on portable displays. A generic technology that can integrate information and situate it in time, place and context is augmented reality. The specific research issues addressed are (1) does augmented reality have a potential use in civil engineering, (2) how big - in comparison to other technologies - is this potential and (3) what are the main barriers to its adoption. The generic research issue was to develop a methodology for evaluation of potentials of technology. A prototype was built. It was tested on a real construction site to evaluate the potential of its use using the action-research method. A set of structured interviews with potential users was then conducted to compare the prototype to conventional presentation methods. Using this methodology it has been found out that augmented reality is expected to be as big a step as the transition from 2D line drawings to photorealistic 3D projections. The main barrier to the adoption is immature core virtual reality technology, conservative nature of construction businesses and size of building information models. Well-formed digital models, such as BIM, are a prerequisite for construction AR.Augmented reality can contribute to the understanding of project documentation.Construction AR needs to be developed in parallel with conventional methods.Construction AR can help better understand construction 3D models.


title: Elastic energy storage in leaf springs for a lever-arm based Variable Stiffness Actuator

abstract: The increasing use of Variable Stiffness Actuators (VSAs) in robotic joints is helping robots to meet the demands of human-robot interaction, requiring high safety and adaptability. The key feature of a VSA is the ability to exploit internal elastic elements to obtain a variable output stiffness. These allow the joints to store mechanical energy supplied through interaction with the environment and make the system more robust, efficient, and safe. This paper discusses the design of leaf springs for a sub-class of VSAs that use variable lever arm ratios as means to change their output stiffness. Given the trade-off between compactness and the maximum energy storage capacity, the internal springs' dimensions and material choice are assessed through a theoretical analysis and practical experiments.


title: Learning Independently-Obtainable Reward Functions

abstract: We present a novel method for learning a set of disentangled reward functions that sum to the original environment reward and are constrained to be independently obtainable. We define independent obtainability in terms of value functions with respect to obtaining one learned reward while pursuing another learned reward. Empirically, we illustrate that our method can learn meaningful reward decompositions in a variety of domains and that these decompositions exhibit some form of generalization performance when the environment's reward is modified. Theoretically, we derive results about the effect of maximizing our method's objective on the resulting reward functions and their corresponding optimal policies.


title: Online Learning for Effort Reduction in Interactive Neural Machine Translation

abstract: Abstract Neural machine translation systems require large amounts of training data and resources. Even with this, the quality of the translations may be insufficient for some users or domains. In such cases, the output of the system must be revised by a human agent. This can be done in a post-editing stage or following an interactive machine translation protocol. We explore the incremental update of neural machine translation systems during the post-editing or interactive translation processes. Such modifications aim to incorporate the new knowledge, from the edited sentences, into the translation system. Updates to the model are performed on-the-fly, as sentences are corrected, via online learning techniques. In addition, we implement a novel interactive, adaptive system, able to react to single-character interactions. This system greatly reduces the human effort required for obtaining high-quality translations. In order to stress our proposals, we conduct exhaustive experiments varying the amount and type of data available for training. Results show that online learning effectively achieves the objective of reducing the human effort required during the post-editing or the interactive machine translation stages. Moreover, these adaptive systems also perform well in scenarios with scarce resources. We show that a neural machine translation system can be rapidly adapted to a specific domain, exclusively by means of online learning techniques.


title: Compliant locomotion using whole-body control and Divergent Component of Motion tracking

abstract: This paper presents a compliant locomotion framework for torque-controlled humanoids using model-based whole-body control. In order to stabilize the centroidal dynamics during locomotion, we compute linear momentum rate of change objectives using a novel time-varying controller for the Divergent Component of Motion (DCM). Task-space objectives, including the desired momentum rate of change, are tracked using an efficient quadratic program formulation that computes optimal joint torque setpoints given frictional contact constraints and joint position / torque limits. In order to validate the effectiveness of the proposed approach, we demonstrate push recovery and compliant walking using THOR, a 34 DOF humanoid with series elastic actuation. We discuss details leading to the successful implementation of optimization-based whole-body control on our hardware platform, including the design of a “simple” joint impedance controller that introduces inner-loop velocity feedback into the actuator force controller.


Extracting Syntactic Trees from Transformer Encoder Self-Attentions


title: FILIAL SOCIAL BOND FORMATION IN FRY OF THE MATERNAL MOUTHBROODING TILAPIA (PISCES: CICHLIDAE): A COMPARATIVE STUDY

abstract: The mother - fry relationship in the maternal mouthbrooding species of tilapia has become a model of social bond formation in fish because of the relatively extensive care given to the young. This relationship has been extensively studied in Oreochromis mossambicus. In order to determine if the response pattern observed in O. mossambicus fry has broader applications, the critical experiments of these studies were replicated in two closely related species of maternal mouthbrooding tilapia, O. niloticus and O. esculentus. All fry used in the study were removed from their mother's mouth as eggs and hatched artificially in groups. The fry were also exposed to maternal models in groups, but all fry in the study were tested for their responsiveness or preferential behaviour to maternal models individually. Experiment I determined the responsiveness of fry naive to maternal models in order to establish a baseline for future comparisons. O. niloticus fry exhibited a significant decline in responsiveness to models between days 11 and 12 post-hatching while O. esculentus fry exhibited a significant decline between days 16 and 18, suggesting the possible existence of a sensitive period in these two species. In order to obtain evidence for the existence of a sensitive period, naive fry of both species in Experiment II were exposed to maternal models at their peak of responsiveness and then tested at a later age at which responsiveness in naive fry had fallen significantly. In 15 of the 18 comparisons involving the two species, exposure to a maternal model at the peak of responsiveness for naive fry prevented the later decline in responsiveness. Experiment III examined whether experience with maternal models effected how exclusively fry responded to such models in the future. It was predicted that, like O. mossambicus fry, experienced fry of both species would exhibit a decline in responsiveness to models that formed at least a partial mismatch with the fry's initial schema for maternal stimuli. This prediction was not supported. Experiment IV examined preferential behaviour. It was predicted that fry exposed to a maternal model would later behave preferentially toward whichever model of a pair formed a closer match with their schema, and not necessarily toward the model to which they had been previously exposed. Maternally naive fry were not expected to behave preferentially. These predictions were generally supported, although the effect was less vigorous or consistent than in O. mossambicus. Filial social bond formation in these species of maternal mouthbrooding tilapia appears to be characterized by strong predispositions for maternally relevant visual stimuli which require appropriate experience for their maintenance and for the induction of preferences. Since a similar developmental pattern in seen in (e.g.) song learning in passerine birds, imprinting in precocial birds and filial following in substrate spawning cichlid fish, the phenomenon appears to be of broad significance.


title: Identification of Refused Bequest Code Smells

abstract: Accumulated technical debt can be alleviated by means of refactoring application aiming at architectural improvement. A prerequisite for wide scale refactoring application is the automated identification of the corresponding refactoring opportunities, or code smells. One of the major architectural problems that has received limited attention is the so called 'Refused Bequest' which refers to inappropriate use of inheritance in object-oriented systems. This code smell occurs when subclasses do not take advantage of the inherited behavior, implying that replacement by delegation should be used instead. In this paper we propose a technique for the identification of Refused Bequest code smells whose major novelty lies in the intentional introduction of errors in the inherited methods. The essence of inheritance is evaluated by exercising the system's functionality through the corresponding unit tests in order to reveal whether inherited methods are actually employed by clients. Based on the results of this approach and other structural information, an indication of the smell strength on a 'thermometer' is obtained. The proposed approach has been implemented as an Eclipse plug in.


title: UMTS-WiMAX Vertical Handover in Next Generation Wireless Networks

abstract: The vision of next generation wireless network (NGWN) is to integrate different wireless access technologies, each with its own characteristics, into a common IP-based core network to provide mobile user with service continuity and seamless roaming. One of the major issues for the converged heterogeneous networks is providing a seamless vertical handover (VHO) with QoS support. In this paper we have reviewed the various interworking architectures and handover scenarios between UMTS and WiMAX. Also, we have compared the proposed solutions based on different criteria and revealed the pros and cons of each scheme. The comparison aids to adopt a better interworking and handover mechanism in NGWN.


Agile Human-Centered Software Engineering


title: Decision making via semi-supervised machine learning techniques

abstract: Semi-supervised learning (SSL) is a class of supervised learning tasks and techniques that also exploits the unlabeled data for training. SSL significantly reduces labeling related costs and is able to handle large data sets. The primary objective is the extraction of robust inference rules. Decision support systems (DSSs) who utilize SSL have significant advantages. Only a small amount of labelled data is required for the initialization. Then, new (unlabeled) data can be utilized and improve system's performance. Thus, the DSS is continuously adopted to new conditions, with minimum effort. Techniques which are cost effective and easily adopted to dynamic systems, can be beneficial for many practical applications. Such applications fields are: (a) industrial assembly lines monitoring, (b) sea border surveillance, (c) elders' falls detection, (d) transportation tunnels inspection, (e) concrete foundation piles defect recognition, (f) commercial sector companies financial assessment and (g) image advanced filtering for cultural heritage applications.


title: A critical analysis of racial difference with mycophenolate mofetil (MMF) dosing, clinical outcomes and adverse effects in pediatric kidney transplant patients.

abstract: There is paucity in the data examining the differences in mycophenolate mofetil (MMF) dosing and outcomes among pediatric kidney transplant recipients (PKTX) between races. The aims of this study were as follows (i) to assess whether higher doses of MMF are being utilized in African American (AA) PKTX (ii) to determine whether there is a correlation between MMF dose and outcomes between races, and (iii) to assess the adverse effects of MMF between races. This study analyzed 109 PKTX who received MMF between 7/99 and 5/08. Demographics were similar between groups. Fewer AAs received kidneys from living donors (18% vs. 44%), spent more time on dialysis (1.0 vs. 0.5 yr), and had more human leukocyte antigen mismatches (4 vs. 3). MMF doses among AA patients were higher throughout the study, with statistical differences at week 4, month 3, and month 18. AA patients had significantly higher acute rejection rates and trended toward poorer graft survival; infections, adverse events from MMF and post-transplant lymphoproliferative disease tended to be lower in the AA patients. AA PKTX received higher MMF doses within the first three yr post-transplant compared to their non-AA counterparts, yet demonstrate significantly more acute rejection episodes. Importantly, MMF caused fewer adverse events in AA patients, despite these patients receiving higher doses.


title: A Cybersecurity Detection Framework for Supervisory Control and Data Acquisition Systems

abstract: This paper presents a distributed intrusion detection system (DIDS) for supervisory control and data acquisition (SCADA) industrial control systems, which was developed for the CockpitCI project. Its architecture was designed to address the specific characteristics and requirements for SCADA cybersecurity that cannot be adequately fulfilled by techniques from the information technology world, thus requiring a domain-specific approach. DIDS components are described in terms of their functionality, operation, integration, and management. Moreover, system evaluation and validation are undertaken within an especially designed hybrid testbed emulating the SCADA system for an electrical distribution grid.


title: Learning to Compose Skills

abstract: We present a differentiable framework capable of learning a wide variety of compositions of simple policies that we call skills. By recursively composing skills with themselves, we can create hierarchies that display complex behavior. Skill networks are trained to generate skill-state embeddings that are provided as inputs to a trainable composition function, which in turn outputs a policy for the overall task. Our experiments on an environment consisting of multiple collect and evade tasks show that this architecture is able to quickly build complex skills from simpler ones. Furthermore, the learned composition function displays some transfer to unseen combinations of skills, allowing for zero-shot generalizations.


title: DeepRoadMapper: Extracting Road Topology from Aerial Images

abstract: Creating road maps is essential for applications such as autonomous driving and city planning. Most approaches in industry focus on leveraging expensive sensors mounted on top of a fleet of cars. This results in very accurate estimates when exploiting a user in the loop. However, these solutions are very expensive and have small coverage. In contrast, in this paper we propose an approach that directly estimates road topology from aerial images. This provides us with an affordable solution with large coverage. Towards this goal, we take advantage of the latest developments in deep learning to have an initial segmentation of the aerial images. We then propose an algorithm that reasons about missing connections in the extracted road topology as a shortest path problem that can be solved efficiently. We demonstrate the effectiveness of our approach in the challenging TorontoCity dataset [23] and show very significant improvements over the state-of-the-art.


title: Battery health management system for electric UAVs

abstract: This paper presents a novel battery health management system for electric UAVs (unmanned aerial vehicles) based on a Bayesian inference driven prognostic framework. The aim is to be able to predict the end-of-discharge (EOD) event that indicates that the battery pack has run out of charge for any given flight of an electric UAV platform. The amount of usable charge of a battery for a given discharge profile is not only dependent on the starting state-of-charge (SOC), but also other factors like battery health and the discharge or load profile imposed. This problem is more pronounced in battery powered electric UAVs since different flight regimes like takeoff/landing and cruise have different power requirements and a dead stick condition (battery shut off in flight) can have catastrophic consequences. Since UAVs deployments are relatively new, there is a lack of statistically significant flight data to motivate data-driven approaches. Consequently, we have developed a detailed discharge model for the batteries used and used it in a Bayesian inference based filtering (Particle Filtering) technique to generate remaining useful life (RUL) distributions for a given discharge. The results section presents the validation of this approach in hardware-in-the-loop tests.12


title: Simulation and analysis of an ultra-wideband TEM horn antenna with ridge

abstract: An ultra-wideband TEM horn antenna with ridge is simulated and designed. The feed mode of the antenna adopts the form that the coaxial line is parallel to the radiation direction. By modeling and simulating in CST Microwave Studio® software, it can realize that the impedance bandwidth of |S 11 |< −10dB is 1‱30 GHz or more. Within this operating bandwidth, the antenna has directional radiation pattern with the gain 6.70–19.40dBi. The simulation results of the antenna are analyzed, and it shows the principle of ultra-wideband operation and directional radiation. The antenna's bandwidth covers L/S/C/X/Ku/K band and it can be widely used in communication and radar system of the band mentioned above.


Culture, threat, and error: lessons from aviation


title: Deconstructing the Ladder Network Architecture

abstract: The Ladder Network is a recent new approach to semi-supervised learning that turned out to be very successful. While showing impressive performance, the Ladder Network has many components intertwined, whose contributions are not obvious in such a complex architecture. This paper presents an extensive experimental investigation of variants of the Ladder Network in which we replaced or removed individual components to learn about their relative importance. For semi-supervised tasks, we conclude that the most important contribution is made by the lateral connections, followed by the application of noise, and the choice of what we refer to as the 'combinator function'. As the number of labeled training examples increases, the lateral connections and the reconstruction criterion become less important, with most of the generalization improvement coming from the injection of noise in each layer. Finally, we introduce a combinator function that reduces test error rates on Permutation-Invariant MNIST to 0.57% for the supervised setting, and to 0.97% and 1.0% for semi-supervised settings with 1000 and 100 labeled examples, respectively.


title: Testes de rastreamento x testes de diagnóstico: atualidades no contexto da atuação fonoaudiológica

abstract: BACKGROND: evaluation instruments, properties, selection indicators, application and validation of screening and diagnostic tests. AIM: to present some concepts concerning screening and diagnostic tests and their application according to a specific purpose. To present a few practical examples of the application of these instruments related to human communication, as well as to present validation criteria of tests in the population and criteria used for the rational selection of screening or diagnostic instruments in health programs and health services based on epidemiological concepts indexed in Scielo, Lilacs or Medline up to January 2007. CONCLUSION: diagnostic instruments differ from screening instruments in their objectives and eligibility criteria. Sensibility and specificity are two important indicators to be considered when choosing an instrument for screening or diagnosis. Reproducibility, time required to complete the evaluation and previous preparation of patients, if needed, are also indicators to be considered when choosing an instrument. Publication and information exchange regarding the properties of evaluation instruments, used for diagnosis or screening, related to the Speech, Language and Hearing Sciences must be systematically stimulated. Besides that, improving the knowledge about methodologies and evaluation instruments under different perspectives contribute to the better use of human and financial resources. Furthermore, the elaboration of studies that promote the correct validation of screening and diagnostic instruments used in human communication disorders contributes to the increase in knowledge in the field of Speech, Language and Hearing Sciences and, indirectly, to the acknowledgement of this science, based on technical-scientific evidence, in health promotion.


title: Intelligent Lighting Control for Vision-Based Robotic Manipulation

abstract: The ability of a robot vision system to capture informative images is greatly affected by the condition of lighting in the scene. This paper reveals the importance of active lighting control for robotic manipulation and proposes novel strategies for good visual interpretation of objects in the workspace. Good illumination means that it helps to get images with large signal-to-noise ratio, wide range of linearity, high image contrast, and true color rendering of the object's natural properties. It should also avoid occurrences of highlight and extreme intensity unbalance. If only passive illumination is used, the robot often gets poor images where no appropriate algorithms can be used to extract useful information. A fuzzy controller is further developed to maintain the lighting level suitable for robotic manipulation and guidance in dynamic environments. As carried out in this paper, with both examples of numerical simulations and practical experiments, it promises satisfactory results with the proposed idea of active lighting control.


title: Ka-band MEMS TTD passive electronically scanned array (ESA)

abstract: In this paper, design, fabrication and measurement results of the first MEMS based, passive subarray is discussed. The system is based on distributed MEMS transmission lines (DMTL) by Barker and Rebeiz, (2003), which are the lowest loss analog time delay units (TDUs) currently available, outperforming contemporary MMIC, ferroelectric and photonic true time delay (TTD) units in terms of loss and outperforming digital beamforming (DBF) technology in terms of cost and feasibility. Also, in contrast to switched beamforming networks (BFN) based on SPNT switches and constrained lenses, such as the bootlace, Rotman and Archer lens, the noise figure of the passive line array scales readily with the amount of antenna elements and the transmit power is distributed and not limited by the SPNT, making it the subarray of choice for future integration in a hybrid ESA.