title: Disentangled Sequential Autoencoder

abstract: We present a VAE architecture for encoding and generating high dimensional sequential data, such as video or audio. Our deep generative model learns a latent representation of the data which is split into a static and dynamic part, allowing us to approximately disentangle latent time-dependent features (dynamics) from features which are preserved over time (content). This architecture gives us partial control over generating content and dynamics by conditioning on either one of these sets of features. In our experiments on artificially generated cartoon video clips and voice recordings, we show that we can convert the content of a given sequence into another one by such content swapping. For audio, this allows us to convert a male speaker into a female speaker and vice versa, while for video we can separately manipulate shapes and dynamics. Furthermore, we give empirical evidence for the hypothesis that stochastic RNNs as latent state models are more efficient at compressing and generating long sequences than deterministic ones, which may be relevant for applications in video compression.


title: CMRadar: A Personal Assistant Agent for Calendar Management

abstract: Personal assistant agents have long promised to automate routine everyday tasks in order to reduce the cognitive load on humans. One such routine task is the management of a user's calendar. In this paper, we describe CMRadar, a calendar management system that is a significant step towards achieving the enduring vision of assistant agents. CMRadar is an implemented system with wide-ranging capabilities for supporting email exchange, multiagent negotiations and schedule optimization based on user preferences. The motivation is to develop an end-to-end system for use by real users to obtain data to facilitate learning. Having now completed an initial prototype which we believe is the first end-to-end agent for calendar management, we present as contributions our architecture design, the communication language used to tie system components together, and initial simulation experiments that isolate negotiation cost a key factor to be logged and predicted in order to improve performance.


title: Visualization and Measurement of Source Code

abstract: The GRASP (Graphical Representations of Algorithms, Structures, and Processes) project,which has successfully prototyped a new algorithmic-level graphical representation forsoftware—the control structure diagram (CSD)—is currently focused on the generation of anew fine-grained complexity metric called the complexity profile graph (CPG). The primaryimpetus for creation and refinement of the CSD and the CPG is to improve thecomprehension efficiency of software and, as a result, improve reliability and reduce costs.The current GRASP release provides automatic CSD generation for Ada 95, C, C++, Java,and Very High-Speed Integrated Circuit Hardware Description Language (VHDL) sourcecode, and CPG generation for Ada 95 source code. The examples and discussion in thisarticle are based on using GRASP with Ada 95.


Thymolipomas in association with myasthenia gravis


title: The Evolution of Base Station Antennas for Mobile Communications

abstract: This paper gives a general overview of the design of base station antennas for mobile communications. It explains underlying theoretical and practical implementation aspects in mobile communication networks of today and the future. In the first part the fundamental parameters of a base station antenna are discussed in the context of radio network design. In particular we discuss parameters such as gain, radiation patterns, frequency bands and power handling and put them in the context of cell planning, propagation and capacity. In the final parts of the paper we give an overview of the underlying theory of diversity and MIMO systems.


title: Reach on Sobel Operator for Vehicle Recognition

abstract: Vehicle type recognition is an active subject in the area of computer pattern recognition, which has been a focus in reach for the last couple of decades because of its wide potential application. And Edge detection is an important step for vehicle type recognition, a vehicle type recognition method based on Sobel was proposed. Also discussed are the vehicle vector and the vehicle recognize process. Experiments have been conducted for vehicle video. The result shows that the vehicle edge could be detected easily by the Sobel operator, and the vehicle feature can be extracted in short time. So, the Sobel operator can be good fit for vehicle type recognition.


title: Energy theft in the advanced metering infrastructure

abstract: Global energy generation and delivery systems are transitioning to a new computerized "smart grid". One of the principle components of the smart grid is an advanced metering infrastructure (AMI). AMI replaces the analog meters with computerized systems that report usage over digital communication interfaces, e.g., phone lines. However, with this infrastructure comes new risk. In this paper, we consider adversary means of defrauding the electrical grid by manipulating AMI systems. We document the methods adversaries will use to attempt to manipulate energy usage data, and validate the viability of these attacks by performing penetration testing on commodity devices. Through these activities, we demonstrate that not only is theft still possible in AMI systems, but that current AMI devices introduce a myriad of new vectors for achieving it.


title: Accurate Cervical Cell Segmentation from Overlapping Clumps in Pap Smear Images

abstract: Accurate segmentation of cervical cells in Pap smear images is an important step in automatic pre-cancer identification in the uterine cervix. One of the major segmentation challenges is overlapping of cytoplasm, which has not been well-addressed in previous studies. To tackle the overlapping issue, this paper proposes a learning-based method with robust shape priors to segment individual cell in Pap smear images to support automatic monitoring of changes in cells, which is a vital prerequisite of early detection of cervical cancer. We define this splitting problem as a discrete labeling task for multiple cells with a suitable cost function. The labeling results are then fed into our dynamic multi-template deformation model for further boundary refinement. Multi-scale deep convolutional networks are adopted to learn the diverse cell appearance features. We also incorporated high-level shape information to guide segmentation where cell boundary might be weak or lost due to cell overlapping. An evaluation carried out using two different datasets demonstrates the superiority of our proposed method over the state-of-the-art methods in terms of segmentation accuracy.


title: A ball tracking framework for broadcast soccer video

abstract: It is challenging to detect and track the ball from the broadcast soccer video. The feature-based tracking method to judge if a sole object is a target are inadequate because the features of the balls change fast over frames and we cannot differ the ball from other objects by them. This paper proposes a new framework to find the ball position by creating and analyzing the trajectory. The ball trajectory is obtained from the candidate collection by use of the heuristic false candidate reduction, the Kalman filter-based trajectory mining, and the trajectory evaluation. The ball trajectory is extended via a localized Kalman filter-based model matching procedure. The experimental results on two consecutive 1000-frame sequences illustrate that the proposed framework is very effective and can obtain a very high accuracy that is much better than existing methods.


title: A deep symmetry convnet for stroke lesion segmentation

abstract: Stroke is one of the leading causes of death and disability. Clinically, to establish stroke patient prognosis, an accurate delineation of brain lesion is essential, which is time consuming and prone to subjective errors. In this paper, we propose a novel method call Deep Lesion Symmetry ConvNet to automatically segment chronic stroke lesions using MRI. An 8-layer 3D convolutional neural network is constructed to handle the MRI voxels. An additional CNN stream using the corresponding symmetric MRI voxels is combined, leading to a significant improvement in system performance. The high average dice coefficient achieved on our dataset based on data collected from three research labs demonstrates the effectiveness of our method.


Social Network Analysis For Organizations 1


title: Morphological Modularity Can Enable the Evolution of Robot Behavior to Scale Linearly with the Number of Environmental Features

abstract: In evolutionary robotics, populations of robots are typically trained in simulation before one or more of them are instantiated as physical robots. However, in order to evolve robust behavior, each robot must be evaluated in multiple environments. If an environment is characterized by $f$ free parameters, each of which can take one of $n_p$ features, each robot must be evaluated in all $n_p^f$ environments to ensure robustness. Here we show that, if the robots are constrained to have modular morphologies and controllers, they only need to be evaluated in $n_p$ environments to reach the same level of robustness. This becomes possible because the robots evolve such that each module of the morphology allows the controller to independently recognize a familiar percept in the environment, and each percept corresponds to one of the environmental free parameters. When exposed to a new environment, the robot perceives it as a novel combination of familiar percepts which it can solve without requiring further training. A non-modular morphology and controller however perceives the same environment as a completely novel environment, requiring further training. This acceleration in evolvability -- the rate of the evolution of adaptive and robust behavior -- suggests that evolutionary robotics may become a scalable approach for automatically creating complex autonomous machines, if the evolution of neural and morphological modularity is taken into account.


title: Mining location-aware discriminative blocklets for recognizing landmark architectures

abstract: Building recognition is an important and still challenging problem in computer vision and pattern recognition field. In this work, we propose a location-aware building recognition model by introducing blocklets, i.e., spatial adjacent blocks associated with their relative positions. First, by evenly partitioning each building image into blocks, we construct a spatial pyramid to describe the spatial relations of blocks. Second, we obtain blocklets by extracting spatial adjacent blocks. And we can cast the building recognition as matching between blocklets from different buildings. Third, towards an efficient matching, a hierarchical sparse coding method is proposed to represent each blocklet by a linear combination of basis blocklets. Furthermore, towards an effective matching, an LDA Jieping (In: Proceedings of ICML 1087---1093 2007)-like scheme is adopted to select the blocklets with high discrimination. Finally, we carry out the architecture recognition based on the selected highly discriminative blocklets. Experimental results on four datasets demonstrate our model is robust to occlusions and large change in backgrounds.


title: Multi-factor authentication based on multimodal biometrics (MFA-MB) for Cloud Computing

abstract: Cloud Computing (CC) was introduced recently as a new paradigm to host and deliver Information Technology Services. Despite its advantages and maturity, security and privacy issues in CC remain an open challenge. Usually, cloud-based systems use login and password combination, PINs, smart cards, or unimodal biometrics for users authentication; Multimodal biometrics can be considered as an alternative solution and additional factor to increase CC authentication security level. First, the paper deals with the authentication security in CC and proposes a new approach to implement a multimodal biometric systems for authentication and identity management using user's physiological and/or behavioral traits. Second, combining the advantages of multi-factor and multimodal biometric techniques we develop a hybrid scheme called Multi-factor Authentication based on Multimodal Biometrics (MFA-MB) in order to authenticate and allow access for cloud consumers. Further, a classification of different practical multiple biometrics combinations is given for a wide number of MFA-MB real applications.


title: ZVS phase shift full bridge converter with separated primary winding

abstract: Generally additional leakage inductance and two clamp diodes are adopted into the conventional phase shift full bridge (PSFB) converter for reducing the voltage stress of secondary rectifier diodes and extending the range of zero voltage switching (ZVS) operation. However, the core and copper loss caused by additional leakage inductor can be high enough to decrease the whole efficiency of DC/DC converter. Therefore, a new ZVS PSFB converter with separated primary winding (SPW) is proposed. The proposed converter makes both the transformer and additional leakage inductor with same ferrite core by separating the primary winding method. Using this method, leakage inductance is controlled by the winding ratio of SPW. Moreover, by using this integrated magnetic component with single core, size and core loss can be greatly reduced and it results in the improvement of efficiency and power density of DC/DC converter. The operational principle and analysis of proposed converter are presented and verified by the 1.2kW prototype.


title: The Relationship between Total Quality Management and Quality Performance in the Service Industry: A Theoretical Model

abstract: Total quality management (TQM) has been widely considered as the strategic, tactical and operational tool in the quality management research field. It is one of the most applied and well accepted approaches for business excellence besides Continuous Quality Improvement (CQI), Six Sigma, Just-in-Time (JIT), and Supply Chain Management (SCM) approaches. There is a great enthusiasm among manufacturing and service industries in adopting and implementing this strategy in order to maintain their sustainable competitive advantage. The aim of this study is to develop and propose the conceptual framework and research model of TQM implementation in relation to company performance particularly in context with the Indian service companies. It examines the relationships between TQM and company's performance by measuring the quality performance as performance indicator. A comprehensive review of literature on TQM and quality performance was carried out to accomplish the objectives of this study and a research model and hypotheses were generated. Two research questions and 34 hypotheses were proposed to re-validate the TQM practices. The adoption of such a theoretical model on TQM and company's quality performance would help managers, decision makers, and practitioners of TQM in better understanding of the TQM practices and to focus on the identified practices while implementing TQM in their companies. Further, the scope for future study is to test and validate the theoretical model by collecting the primary data from the Indian service companies and using Structural Equation Modeling (SEM) approach for hypotheses testing.


title: Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections

abstract: In this paper, we propose a very deep fully convolutional encoding-decoding framework for image restoration such as denoising and super-resolution. The network is composed of multiple layers of convolution and de-convolution operators, learning end-to-end mappings from corrupted images to the original ones. The convolutional layers act as the feature extractor, which capture the abstraction of image contents while eliminating noises/corruptions. De-convolutional layers are then used to recover the image details. We propose to symmetrically link convolutional and de-convolutional layers with skip-layer connections, with which the training converges much faster and attains a higher-quality local optimum. First, The skip connections allow the signal to be back-propagated to bottom layers directly, and thus tackles the problem of gradient vanishing, making training deep networks easier and achieving restoration performance gains consequently. Second, these skip connections pass image details from convolutional layers to de-convolutional layers, which is beneficial in recovering the original image. Significantly, with the large capacity, we can handle different levels of noises using a single model. Experimental results show that our network achieves better performance than all previously reported state-of-the-art methods.


title: 3D resistive RAM cell design for high-density storage class memory—a review

abstract: In this article, we comprehensively review recent progress in the ReRAM cell technology for 3D integration focusing on a material/device level. First we briefly mention pioneering work on high-density crossbar ReRAM arrays which paved the way to 3D integration. We discuss the two main proposed 3D integration schemes—3D horizontally stacked ReRAM vs 3D Vertical ReRAM and their respective advantages and disadvantages. We follow with the detailed memory cell design on important work in both areas, utilizing either filamentary or interface-limited switching mechanisms. We also discuss our own contributions on HfO2-based filamentary 3D Vertical ReRAM as well as TaO x /TiO2 bilayer-based self-rectifying 3D Vertical ReRAM. Finally, we summarize the present status and provide an outlook for the nearterm future.


title: Detailed Study on Dynamic Characteristics of a High-Performance SGT-MOSFET With Under-the-Trench Floating P-Pillar

abstract: We proposed a 150 V shielded-gate trench (SGT) power MOSFET with floating P-pillars under the trench and studied its static and dynamic characteristics, especially the transient capacitance and dynamic ${\mathrm {R}}_{{\mathrm {ds}}({\mathrm {on}})}$ at high switching frequency. TCAD simulation demonstrated over 33% ${\mathrm {R}}_{{\mathrm {ds}}({\mathrm {on}})}$ reduction compared with regular SGT-MOSFET. Up to MHz switching frequency, simulation indicates almost no ${\mathrm {R}}_{{\mathrm {ds}}({\mathrm {on}})}$ increase in this device, meaning negligible dynamic ${\mathrm {R}}_{{\mathrm {ds}}({\mathrm {on}})}$ penalty. On the other hand, elevated ${\mathrm {C}}_{{\mathrm {oss}}}$ is observed within a particular ${\mathrm {V}}_{{\mathrm {ds}}}$ range (40–90 V) as switching frequency increases, which translates to $\text{Q}_{{\mathrm {oss}}}$ penalty. We investigated the underlying physical process for this ${\mathrm {Q}}_{{\mathrm {oss}}}$ change, and attributed it to the hole pilings in the undepleted region of the P-pillar. The overall parameters of this device, including ${\mathrm {R}}_{{\mathrm {ds}}({\mathrm {on}})}$ and ${\mathrm {R}}_{{\mathrm {ds}}({\mathrm {on}})} \ast {\mathrm {Q}}_{{\mathrm {oss}}}$ , are superior to regular SGT-MOSFET and are close to superjunction MOSFET, which is more expensive and challenging in manufacturing. Although a 150 V MOSFET is used as our study vehicle, the analyses and conclusions apply to any power devices that employ the proposed device architecture.


title: food cold-chain and climate change

abstract: Any noticeable increase in ambient temperature resulting from climatic change will have a substantial effect on the current and developing food cold-chain. A rise in temperature will increase the risk of food poisoning and food spoilage unless the cold-chain is extended and improved. The little data that is available suggests that currently the cold-chain accounts for approximately 1% of CO2 production in the world, however this is likely to increase if global temperatures increase significantly. Using the most energy efficient refrigeration technologies it would be possible to substantially extend and improve the cold-chain without any increase in CO2, and possibly even a decrease.


title: ForceHand Glove: A Wearable Force-Feedback Glove With Pneumatic Artificial Muscles (PAMs)

abstract: The upper limb of the human body has a higher likeliness of getting affected by muscle fatigue and injury, due to repetitive motion. This letter presents an interactive glove for the wrist of the human body. The assistive model is enabled with pneumatic actuators and stretch sensors, which support the user in performing wrist flexion, extension, pronation, and supination. The purpose of this glove is to provide the subject with a force-feedback feeling, and while a repetitive practice of such training is done, rehabilitation can be achieved. For evaluation, surface electromyographical signals were first used to study the effect of force feedback on associated muscle activation. Average % maximum voluntary isometric contraction of all subjects showed statistically reduced muscle activity in at least one associated muscle while performing all motions, enabled with actuators. Second, the force feedback generated by the glove at the user's wrist has been modeled using experiments with a force transducer. An application was then developed where the user was given the ability to interact with virtual objects and receive feedback through the glove. The evaluation of the feasibility of this application was done by measuring and comparing force profiles while generating an equal force in an ideal and a predicted environment.


title: Analysis of modulation schemes for Bluetooth-LE module for Internet-of-Things (IoT) applications

abstract: Bluetooth transceivers have been in the active area of recent research being the key component of physical layer in the Bluetooth technology. The low power consumption of Bluetooth low energy (LE) devices compared to the conventional Bluetooth devices has enhanced its importance in Internet-of-Things (IoT) applications. Therefore, Bluetooth low energy device based solution needs expansion in the IoT network infrastructure. The transceivers in the literature and modulation schemes are compared and summarized. Energy consumption of modulation schemes in Bluetooth communication are analyzed and compared using the model presented in this work. In this approach considering both circuit and signal power consumption, optimum modulation order for minimum energy consumption has been found using numerical calculation and relation between signal to noise ratio (SNR) and channel capacity. Battery life for IoT sensors using Bluetooth LE technology as a wireless link has been analyzed considering multiple transaction times for transmitters having different power consumption. MFSK and more bandwidth-efficient GFSK are identified as low energy solution for all smart devices.


title: Deployment of the PON with an optical fibre G-652.B

abstract: The aim of this article was to create two passive optical networks PON (Passive Optical Network). In the LOS (Laboratory of Optoelectronic Systems) at KEMT (Department of Electronics and Multimedia Communications), FEI (Faculty of Electrical Engineering and Informatics), TUKE (Technical University of Kosice) were created the optical networks of different lengths. For implementation was used the optical fibre G-652.B with a length of 706.4m, PLC (Planar Lightwave Circuit) splitter with a splitting ratio of 1:4, connectors SC/PC, and RISER indoor switchgear. The optical networks were first designed, and afterwards simulated and implemented. Design and simulation were created in OptSim software from Rsoft company. The main contribution of this paper was comparison of the difference between simulated and measured parameters of optical networks and demonstration when it is better to use an optical splitter, and when it is better to connect the fibre directly. The distances were set by the buildings based at FEI which were UVT (Institute of Computer Science) and the LOS laboratory at V4 (Vysokoskolska 4).


title: Antiproliferative effects of aspirin and diclofenac against the growth of cancer and fibroblast cells: In vitro comparative study

abstract: Non-steroidal anti-inflammatory drugs (NSAIDs) inhibit the growth of several cancer cell lines. The aim of this study is to compare the cytotoxic effect of aspirin with diclofenac on the growth of HeLa cell, mammary cell carcinoma, rhabdomyosarcoma and fibroblast cell lines in the culture media. The cells are cultured in RPMI-1640 culture media supplemented with 5% fetal calf serum and antibiotics. Aspirin (5 mg/well) and diclofenac (0.625 mg/well) significantly inhibit the growth of HeLa, rhabdomyosarcoma and fibroblast cells. The cytotoxic effect of aspirin against rhabdomyosarcoma is significantly (p < 0.001) higher than that of diclofenac with a potency approximated 2.6. It concludes that aspirin and diclofenac inhibit the growth of fibroblast and cancer cell by inhibiting the up-regulation of cyclooxygenases enzymes in cancer cells. Aspirin is more effective than diclofenac against the growth of rhabdomyosarcoma cell line.


title: Patterns of motor commands during various types of voluntary movement in man

abstract: Electrophysiological techniques applied to the alert human subject can provide a useful method of assessing the behavioural correlates ofselected brain mechanisms. For example, the co-operating normal human volunteer can be asked to execute a wide range of voluntary movements whilst the experimenter explores motor control parameters under conditions that preserve the entire range of normal physiological interactions. Here, John Desmedt describes some of his recent work on the control of individual muscle units during voluntary movement.


title: Insights into the late Holocene vegetation history of the East European forest-steppe: case study Sudzha (Kursk region, Russia)

abstract: Today, the East European forest-steppe is an agricultural landscape with very few remains of its former natural vegetation. The history of the transformation from natural vegetation to a human-made landscape in the area of Sudzha (Kursk region, Russia) is studied here. We compare the off-site pollen record Sudzha with three on-site pollen records obtained from the archaeological site Kurilovka-2. The sediment core Sudzha covering the last 2,500 years was taken from an oxbow lake in an area with archaeological sites of the early Slavonic period (3rd–8th centuries ce). The Sudzha pollen record indicates dominance of broadleaf forests and meadow steppes in the area from 2,500 to 200 cal year bp with two major settlement phases one between ~ 2,000 and 1,600 cal year bp (~ 50 bce to 350 ce) and the other between 1,100 and 600 cal year bp (850 and 1350 ce) followed by a total deforestation and transformation to an agricultural landscape over the last 200–300 years. Similar changes in the last 300–400 years are indicated by the three on-site pollen records. It is noteworthy, however, that the record Sudzha does not provide an intensive signal of human impact during the 5th–8th centuries ce. This points to a quite restricted spatial influence of the Early Slavonic settlements on the vegetation, leading to a relatively low contribution of palynological anthropogenic indicators to the regional pollen rain signal.


Overview of Physical Characterization Methodology


title: A Task-oriented Search Engine for Evidence-based Medicine

abstract: Evidence-based medicine (EBM) is the practice of making clinical decisions based on rigorous scientific evidence. EBM relies on effective access to peer-reviewed literature - a task hampered by both the exponential growth of medical literature and a lack of efficient and effective means of searching and presenting this literature. This paper describes a search engine specifically designed for searching medical literature for the purpose of EBM and in a clinical decision support setting.


Machine Learning Methods for Data-Driven Turbulence Modeling


title: A Distributed Sensor Data Search Platform for Internet of Things Environments

abstract: Recently, the number of devices has grown increasingly and it is hoped that, between 2015 and 2016, 20 billion devices will be connected to the Internet and this market will move around 91.5 billion dollars. The Internet of Things (IoT) is composed of small sensors and actuators embedded in objects with Internet access and will play a key role in solving many challenges faced in today's society. However, the real capacity of IoT concepts is constrained as the current sensor networks usually do not exchange information with other sources. In this paper, we propose the Visual Search for Internet of Things (ViSIoT) platform to help technical and non-technical users to discover and use sensors as a service for different application purposes. As a proof of concept, a real case study is used to generate weather condition reports to support rheumatism patients. This case study was executed in a working prototype and a performance evaluation is presented.


title: Aggregate dynamics for dense crowd simulation

abstract: Large dense crowds show aggregate behavior with reduced individual freedom of movement. We present a novel, scalable approach for simulating such crowds, using a dual representation both as discrete agents and as a single continuous system. In the continuous setting, we introduce a novel variational constraint called unilateral incompressibility, to model the large-scale behavior of the crowd, and accelerate inter-agent collision avoidance in dense scenarios. This approach makes it possible to simulate very large, dense crowds composed of up to a hundred thousand agents at near-interactive rates on desktop computers.


title: Mobile bin picking with an anthropomorphic service robot

abstract: Grasping individual objects from an unordered pile in a box has been investigated in static scenarios so far. In this paper, we demonstrate bin picking with an anthropomorphic mobile robot. To this end, we extend global navigation techniques by precise local alignment with a transport box. Objects are detected in range images using a shape primitive-based approach. Our approach learns object models from single scans and employs active perception to cope with severe occlusions. Grasps and arm motions are planned in an efficient local multiresolution height map. All components are integrated and evaluated in a bin picking and part delivery task.


title: Deep learning for ocean remote sensing: an application of convolutional neural networks for super-resolution on satellite-derived SST data

abstract: In this paper, we propose to address the downscaling of ocean remote sensing data using image super-resolution models based on deep learning, and more particularly Convolutional Neural Networks (CNNs). The goal of this study, for which we focus on satellite-derived Sea Surface Temperature (SST) data, is to evaluate the efficiency and the relevance of deep learning architectures applied to oceanographic remote sensing data. By using a CNN architecture, namely SRCNN (Super Resolution CNN), on a large-scale dataset of SST fields, we show that it allows a considerable gain in terms of PSNR compared to classical downscaling techniques. These results point out the relevance of deep learning models specifically trained for ocean remote sensing data and advocate for other applications to the reconstruction of high-resolution sea surface geophysical fields from multi-sensor satellite observations.


title: 3-Dimensional Blow Torch-Molding of Fused Silica Microstructures

abstract: This paper presents a new and simple microfabrication process for creating various 3-D microstructures with high-aspect ratios . The key feature of this process is the use of a blow torch, which provides very intense localized heat for a short amount of time . The flame temperature can be up to 2500 °C for a propane-oxygen torch, above the melting temperatures of many high- Q materials. We demonstrate the fabrication of hemispherical and half-toroid (birdbath) shells from 100- μm-thick fused silica substrates. The structures have an rms surface roughness of 5.3 Å, which is crucial for achieving both high mechanical and optical Q. We create microbirdbath resonators by batch-level releasing the birdbath shells. We verify the resonance mode shapes of the degenerate n=2 wineglass modes using laser vibrometry and measure the frequency and Q of these modes using laser vibrometry and a capacitive measurement method. We demonstrate one of the best mechanical Q and smallest frequency split between the n=2 wineglass modes among existing micromechanical resonators. The birdbath resonator is promising for emerging applications such as the microrate-integrating gyroscope.


title: Towards the Design of Affective Survival Horror Games: An Investigation on Player Affect

abstract: An upcoming trend of affective gaming is where a player's emotional state is used to manipulate game play. This is an interesting field to explore especially for the survival horror genre that is excellent at producing player's intense emotions. In this research, we analyzed different player affective states prior to (i.e., Neutral, Anxiety, Suspense) and after (i.e., Low-Fear, Mid-Fear, High-Fear) a scary event using an affect annotation tool to collect player self-reports of their affective states during the game. Brainwave signals, heart rate and keyboard-mouse activity were also collected for analyzing the potential of automatically detecting horror-related affect. Results indicated that players were more likely to experience fear from a scary event when they were in a suspense state compared to when they were in a neutral state. In this state, players only experienced fear after experiencing surprise. Heart rate data gave the best result in classifying player affect, which achieved up to 90% overall accuracy. This highlights the potential of using player affect in survival horror games to adapt a scary event to evoke more fear from players.


title: Automatic Pixelwise Object Labeling for Aerial Imagery Using Stacked U-Nets

abstract: Automation of objects labeling in aerial imagery is a computer vision task with numerous practical applications. Fields like energy exploration require an automated method to process a continuous stream of imagery on a daily basis. In this paper we propose a pipeline to tackle this problem using a stack of convolutional neural networks (U-Net architecture) arranged end-to-end. Each network works as post-processor to the previous one. Our model outperforms current state-of-the-art on two different datasets: Inria Aerial Image Labeling dataset and Massachusetts Buildings dataset each with different characteristics such as spatial resolution, object shapes and scales. Moreover, we experimentally validate computation time savings by processing sub-sampled images and later upsampling pixelwise labeling. These savings come at a negligible degradation in segmentation quality. Though the conducted experiments in this paper cover only aerial imagery, the technique presented is general and can handle other types of images.


title: Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands

abstract: The term 'synergy' - from the Greek synergia - means 'working together'. The concept of multiple elements working together towards a common goal has been extensively used in neuroscience to develop theoretical frameworks, experimental approaches, and analytical techniques to understand neural control of movement, and for applications for neuro-rehabilitation. In the past decade, roboticists have successfully applied the framework of synergies to create novel design and control concepts for artificial hands, i.e., robotic hands and prostheses. At the same time, robotic research on the sensorimotor integration underlying the control and sensing of artificial hands has inspired new research approaches in neuroscience, and has provided useful instruments for novel experiments. The ambitious goal of integrating expertise and research approaches in robotics and neuroscience to study the properties and applications of the concept of synergies is generating a number of multidisciplinary cooperative projects, among which the recently finished 4-year European project "The Hand Embodied" (THE). This paper reviews the main insights provided by this framework. Specifically, we provide an overview of neuroscientific bases of hand synergies and introduce how robotics has leveraged the insights from neuroscience for innovative design in hardware and controllers for biomedical engineering applications, including myoelectric hand prostheses, devices for haptics research, and wearable sensing of human hand kinematics. The review also emphasizes how this multidisciplinary collaboration has generated new ways to conceptualize a synergy-based approach for robotics, and provides guidelines and principles for analyzing human behavior and synthesizing artificial robotic systems based on a theory of synergies.


title: Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear

abstract: To use deep reinforcement learning in the wild, we might hope for an agent that can avoid catastrophic mistakes. Unfortunately, even in simple environments, the popular deep Q-network (DQN) algorithm is doomed by a Sisyphean curse. Owing to the use of function approximation, these agents may eventually forget experiences as they become exceedingly unlikely under a new policy. Consequently, for as long as they continue to train, DQNs may periodically repeat avoidable catastrophic mistakes. In this paper, we learn a \emph{reward shaping} that accelerates learning and guards oscillating policies against repeated catastrophes. First, we demonstrate unacceptable performance of DQNs on two toy problems. We then introduce \emph{intrinsic fear}, a new method that mitigates these problems by avoiding dangerous states. Our approach incorporates a second model trained via supervised learning to predict the probability of catastrophe within a short number of steps. This score then acts to penalize the Q-learning objective. Equipped with intrinsic fear, our DQNs solve the toy environments and improve on the Atari games Seaquest, Asteroids, and Freeway.


title: The OWL API: A Java API for OWL ontologies

abstract: We present the OWL API, a high level Application Programming Interface (API) for working with OWL ontologies. The OWL API is closely aligned with the OWL 2 structural specification. It supports parsing and rendering in the syntaxes defined in the W3C specification (Functional Syntax, RDF/XML, OWL/XML and the Manchester OWL Syntax); manipulation of ontological structures; and the use of reasoning engines. The reference implementation of the OWL API, written in Java, includes validators for the various OWL 2 profiles - OWL 2 QL, OWL 2 EL and OWL 2 RL. The OWL API has widespread usage in a variety of tools and applications.


title: GNAS: A Greedy Neural Architecture Search Method for Multi-Attribute Learning

abstract: A key problem in deep multi-attribute learning is to effectively discover the inter-attribute correlation structures. Typically, the conventional deep multi-attribute learning approaches follow the pipeline of manually designing the network architectures based on task-specific expertise prior knowledge and careful network tunings, leading to the inflexibility for various complicated scenarios in practice. Motivated by addressing this problem, we propose an efficient greedy neural architecture search approach (GNAS) to automatically discover the optimal tree-like deep architecture for multi-attribute learning. In a greedy manner, GNAS divides the optimization of global architecture into the optimizations of individual connections step by step. By iteratively updating the local architectures, the global tree-like architecture gets converged where the bottom layers are shared across relevant attributes and the branches in top layers more encode attribute-specific features. Experiments on three benchmark multi-attribute datasets show the effectiveness and compactness of neural architectures derived by GNAS, and also demonstrate the efficiency of GNAS in searching neural architectures.


The Neonatal Connectome During Preterm Brain Development


title: Cloud Verifier: Verifiable Auditing Service for IaaS Clouds

abstract: Cloud computing has commoditized compute, storage, and networking resources creating an on-demand utility. Despite the attractiveness of this new paradigm, its adoption has been stymied by cloud platform's lack of transparency, which leaves customers unsure if their sensitive data and computation can be entrusted to the cloud. While techniques like encryption can protect customers' data at rest, clouds still lack mechanisms for customers to verify that their computations are being executed as expected, a guarantee one could obtain if they were running the computation in their own data center. In this paper, we present the cloud verifier (CV), a flexible framework that cloud vendors can configure to provide cloud monitoring services for customers to validate that their computations are configured and being run as expected in Infrastructure as a Service (IaaS) clouds. The CV builds a chain of trust from the customer to their hosted virtual machine (VM) instances through the cloud platform, enabling it to check customer-specified requirements against a comprehensive view of both the VM's load-time and run-time properties. In addition, the CV enables cloud vendors to provide more responsive remediation techniques than traditional attestation mechanisms. We built a proof of concept CV for the OpenStack cloud platform whose evaluation demonstrates that a single CV enables over 20,000 simultaneous customers to verify numerous properties with little impact on cloud application performance. As a result, the CV gives cloud customers a low-overhead method for assuring that their instances are running according to their requirements.


An End-to-End Multi-task Learning Model for Fact Checking


title: Multi-vehicle Flocking Control with Deep Deterministic Policy Gradient Method

abstract: Flocking control has been studied extensively along with the wide application of multi-vehicle systems. In this paper the Multi-vehicles System (MVS) flocking control with collision avoidance and communication preserving is considered based on the deep reinforcement learning framework. Specifically the deep deterministic policy gradient (DDPG) with centralized training and distributed execution process is implemented to obtain the flocking control policy. First, to avoid the dynamically changed observation of state, a three layers tensor based representation of the observation is used so that the state remains constant although the observation dimension is changing. A reward function is designed to guide the way-points tracking, collision avoidance and communication preserving. The reward function is augmented by introducing the local reward function of neighbors. Finally, a centralized training process which trains the shared policy based on common training set among all agents. The proposed method is tested under simulated scenarios with different setup.


title: Smart grid communication over DC powerline: Evaluation of powerline communication OFDM PAPR for new types of destabilizing electrical loads

abstract: In this paper, the channel peak-to-average-power-ratio (PAPR) of powerline communication (PLC) for smart grid and industrial Internet of Things (IoT) communication is evaluated in view of new types of destabilizing, non-stable, nonlinear electrical loads recently reported in literature. These types of loads may occur due to faults or varying bandwidth of controllers on the powerline and the presence of such loads always leads to spurious group delay, increasing channel distortion, noise and very high PAPR on the PLC channel. This work is an attempt towards better understanding of the impact of such load types on powerline communication over a direct current (DC) powerline channel. The effect of having such loads on powerline channel implementing Orthogonal Frequency Division Multiplexing (OFDM) scheme is also examined with the aid of Matlab. It is discovered that while PLC amplifiers normally introduce high PAPR, having first order lag loads will exacerbate this problem. The result of this study will be useful for more effective modulation, coding and signal processing for PLC. It will also be useful to other researchers working on designing more effective transmission channel's group delay equalizers, channel digital to analogue converters (DACs) and to standard bodies such as the US National Institute of Standards and Technology (NIST).


title: Personality traits and psychological motivations predicting selfie posting behaviors on social networking sites

abstract: This study examined the psychological predictors of selfie posting behaviors on SNSs.Narcissism predicted greater levels of posting solo selfies and editing selfies.Extraversion and agreeableness were predictors of posting group selfies.The need for popularity predicted posting both solo and group selfies.The need to belong was not associated with any of the selfie behaviors. This study examined the relationships between narcissism, the Big 5 personality traits (extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience), the need for popularity, the need to belong, and various types of selfie posting behaviorsposting solo selfies, selfies with a group, and editing selfies. Results of the survey (N=260) indicated that after controlling for overall social media use and demographic factors (i.e., age, gender), narcissism significantly predicted the frequency of posting solo selfies and editing selfies. Age moderated the relationship between narcissism and the frequency of posting group selfies. Posting group selfies was predicted by extraversion and agreeableness and the need for popularity. The need for popularity also predicted the frequency of posting solo selfies, but not of selfie editing. Furthermore, findings revealed that gender moderated the relationship between the need for popularity and posting solo selfies, such that the need for popularity predicted posting solo selfies among men, but not among women. The need to belong was not associated with any of the selfie behaviors. Interpretations and implications of these findings are discussed.


title: High-Dimensional Data Classification

abstract: Recently, high-dimensional classification problems have been ubiquitous due to significant advances in technology. High dimensionality poses significant statistical challenges and renders many traditional classification algorithms impractical to use. In this chapter, we present a comprehensive overview of different classifiers that have been highly successful in handling high-dimensional data classification problems. We start with popular methods such as Support Vector Machines and variants of discriminant functions and discuss in detail their applications and modifications to several problems in high-dimensional settings. We also examine regularization techniques and their integration to several existing algorithms. We then discuss more recent methods, namely the hybrid classifiers and the ensemble classifiers. Feature selection techniques, as a part of hybrid classifiers, are introduced and their relative merits and drawbacks are examined. Lastly, we describe AdaBoost and Random Forests in the ensemble classifiers and discuss their recent surge as useful algorithms for solving high-dimensional data problems.


title: Induction cooking systems with single switch inverter using new driving techniques

abstract: In this paper, some novel and modified driving techniques for a single switch zero voltage switching (ZVS) topology are introduced. These medium/high frequency and digitally synthesized driving techniques can be applied to decrease the dangers of peak currents that may damage the switching circuit when switching in out of nominal conditions. The technique is fully described and evaluated experimentally in a 2500W prototype intended for a domestic induction cooking application.


title: Linking vulnerability, adaptation, and resilience science to practice: Pathways, players, and partnerships $

abstract: Vulnerability, adaptation and resilience are concepts that are finding increasing currency in several fields of research as well as in various policy and practitioner communities engaged in global environmental change science, climate change, sustainability science, disaster risk-reduction and famine interventions. As scientists and practitioners increasingly work together in this arena a number of questions are emerging: What is credible, salient and legitimate knowledge, how is this knowledge generated and how is it used in decision making? Drawing on important science in this field, and including a case study from southern Africa, we suggest an alternative mode of interaction to the usual one-way interaction between science and practice often used. In this alternative approach, different experts, risk-bearers, and local communities are involved and knowledge and practice is contested, co-produced and reflected upon. Despite some successes in the use and negotiation of such knowledge for ‘real’ world issues, a number of problems persist that require further investigation including the difficulties of developing consensus on the methodologies used by a range of stakeholders usually across a wide region (as the case study of southern Africa shows, particularly in determining and identifying vulnerable groups, sectors, and systems); slow delivery of products that could enhance resilience to change that reflects not only a lack of data, and need for scientific credibility, but also the time-consuming process of coming to a negotiated understanding in science–practice interactions and, finally, the need to clarify the role of ‘external’ agencies, stakeholders, and scientists at the outset of the dialogue process and subsequent interactions. Such factors, we argue, all hinder the use of vulnerability and resilience ‘knowledge’ that is being generated and will require much more detailed investigation by both producers and users of such knowledge. r 2007 Published by Elsevier Ltd.


title: Efficient hardware implementation strategy for local normalization of fingerprint images

abstract: Global techniques do not produce satisfying and definitive results for fingerprint image normalization due to the non-stationary nature of the image contents. Local normalization techniques are employed, which are a better alternative to deal with local image statistics. Conventional local normalization techniques involve pixelwise division by the local variance and thus have the potential to amplify unwanted noise structures, especially in low-activity background regions. To counter the background noise amplification, the research work presented here introduces a correction factor that, once multiplied with the output of the conventional normalization algorithm, will enhance only the feature region of the image while avoiding the background area entirely. In essence, its task is to provide the job of foreground segmentation. A modified local normalization has been proposed along with its efficient hardware structure. On the way to achieve real-time hardware implementation, certain important computationally efficient approximations are deployed. Test results show an improved speed for the hardware architecture while sustaining reasonable enhancement benchmarks.


Catchment Yield Prediction in an Ungauged Basin Using PyTOPKAPI


title: A passive UHF tag for RFID-based train axle temperature measurement system

abstract: A fully integrated passive UHF RFID tag with embedded temperature sensor, compatible with the ISO/IEC 18000 type 6C protocol, is developed in a standard 0.18µm CMOS process, which is designed to measure the axle temperature of a running train. The consumption of RF/analog front-end circuits is 1.556µA@1.0V, and power dissipation of digital part is 5µA@1.0V. The CMOS temperature sensor exhibits a conversion time under 2 ms, less than 7 µW power dissipation, resolution of 0.31°C/LSB and error of +2.3/−1.1°C with a 1.8 V power supply for range from −35°C to 105°C. Measured sensitivity of tag is −5dBm at room temperature.


title: Emergence of hysteresis in gait transition of a hexapod robot driven by nonlinear oscillators with phase resetting

abstract: Our previous work has revealed that a quadruped robot, whose legs are controlled by nonlinear oscillators with phase resetting, shows a walk-trot transition induced by changing the locomotion speed through dynamic interactions among the robot's mechanical system, the oscillator control system, and the environment. Furthermore, the gait-pattern transition exhibited a hysteresis similar to that observed in the locomotion of humans and animals. In the present study, we apply this dynamic analysis to a hexapod robot model to investigate general properties of the hysteresis embedded in the locomotion dynamics of legged robotic systems. Our simulation results show that the hexapod robot produces the metachronal and tripod gaits depending on the locomotion speed and the gait transition is induced by changing the locomotion speed. In addition, the gait transition shows a hysteresis similar to the quadruped robot. We examined the mechanisms of the hysteresis from the perspective of dynamics.


title: G4LTL-ST: Automatic Generation of PLC Programs

abstract: G4LTL-ST automatically synthesizes control code for industrial Programmable Logic Controls (PLC) from timed behavioral specifications of input-output signals. These specifications are expressed in a linear temporal logic (LTL) extended with non-linear arithmetic constraints and timing constraints on signals. G4LTL-ST generates code in IEC 61131-3-compatible Structured Text, which is compiled into executable code for a large number of industrial field-level devices. The synthesis algorithm of G4LTL-ST implements pseudo-Boolean abstraction of data constraints and the compilation of timing constraints into LTL, together with a counterstrategy-guided abstraction-refinement synthesis loop. Since temporal logic specifications are notoriously difficult to use in practice, G4LTL-ST supports engineers in specifying realizable control problems by suggesting suitable restrictions on the behavior of the control environment from failed synthesis attempts.


title: Concept-based knowledge discovery in texts extracted from the Web

abstract: ABSTRACT This paper presents an approach for knowledge discovery in texts extracted from the Web. Instead of analyzing words or attribute values, the approach is based on concepts, which are extracted from texts to be used as characteristics in the mining process. Statistical techniques are applied on concepts in order to find interesting patterns in concept distributions or associations. In this way, users can pe rform discovery in a high level, since concepts describe real world events, objects, thoughts, etc. For identifying concepts in texts, a categorization algorithm is used associated to a previous classification task for concept definitions. Two experiments are presented: one for political analysis and other for competitive intelligence. At the end, the approach is discussed, examining its problems and advantages in the Web context. Keywords Knowledge discovery, data mining, information extraction, categorization, text mining. 1. INTRODUCTION The Web is a large and growing collection of texts. This amount of text is becoming a valuable resource of information and knowledge. As Garofalakis and partners comment, "


title: Fundamental aspects of HfO2-based high-k metal gate stack reliability and implications on tinv-scaling

abstract: Experimental reliability trends indicate that t inv -scaling with HKMG stacks remains challenging because NBTI, PBTI and TDDB reliability margins rapidly decrease with decreasing t inv values and increasing gate leakage current. A case is made that these observed trends arise from the layer structure and the materials properties of the SiO(N)/HfO 2 dual dielectric. Therefore, fundamental reliability limitations appear to increasingly impact HKMG stack scaling.


title: Roll rate thresholds and perceived realism in driving simulation

abstract: Due to limited operational space, in dynamic driving simulators it is common practice to implement motion cueing algorithms that tilt the simulator cabin to reproduce sustained accelerations. In order to avoid conflicting inertial cues, the tilt rate is kept below drivers' perceptual thresholds, which are typically derived from the results of classical vestibular research where additional sensory cues to self-motion are removed. Here we conduct two experiments in order to assess whether higher tilt limits can be employed to expand the user's perceptual workspace of dynamic driving simulators. In the first experiment we measure detection thresholds for roll in conditions that closely resemble typical driving. In the second experiment we measure drivers' perceived realism in slalom driving for sub-, near- and supra-threshold roll rates. Results show that detection threshold for roll in an active driving task is remarkably higher than the limits currently used in motion cueing algorithms to drive simulators. Supra-threshold roll rates in the slalom task are also rated as more realistic. Overall, our findings suggest that higher tilt limits can be successfully implemented in motion cueing algorithms to better optimize simulator operational space.


title: An Access Control Language for a General Provenance Model

abstract: Provenance access control has been recognized as one of the most important components in an enterprise-level provenance system. However, it has only received little attention in the context of data security research. One important challenge in provenance access control is the lack of an access control language that supports its specific requirements, e.g., the support of both fine-grained policies and personal preferences, and decision aggregation from different applicable policies. In this paper, we propose an access control language tailored to these requirements.


title: Fusion of Video and Inertial Sensing for Deep Learning–Based Human Action Recognition

abstract: This paper presents the simultaneous utilization of video images and inertial signals that are captured at the same time via a video camera and a wearable inertial sensor within a fusion framework in order to achieve a more robust human action recognition compared to the situations when each sensing modality is used individually. The data captured by these sensors are turned into 3D video images and 2D inertial images that are then fed as inputs into a 3D convolutional neural network and a 2D convolutional neural network, respectively, for recognizing actions. Two types of fusion are considered—Decision-level fusion and feature-level fusion. Experiments are conducted using the publicly available dataset UTD-MHAD in which simultaneous video images and inertial signals are captured for a total of 27 actions. The results obtained indicate that both the decision-level and feature-level fusion approaches generate higher recognition accuracies compared to the approaches when each sensing modality is used individually. The highest accuracy of 95.6% is obtained for the decision-level fusion approach.


title: Intra-nasal midazolam in conscious sedation of young paediatric dental patients

abstract: Summary.Objectives. To compare the effects of 3 different doses of intra-nasal midazolam in the conscious sedation of young paediatric dental patients and to compare the effectiveness of the sedation in the fasting and non-fasting child. ::: ::: Design. Double blind random controlled trial. ::: ::: Sample and Methods. Thirty-eight uncooperative young children aged 2–5 years (mean age 4·02 years) were randomly assigned to one of 3 groups. The groups and the doses of midazolam administered intra-nasally were A: 0·3 mg/kg, B: 0·4 mg/kg, and C: 0·5 mg/kg body weight. Each child in each group had two visits for restorative treatment: one without food (fasting) and the other with soft drink and light food (non-fasting) before treatment. Child behaviour and sedative effects were evaluated using the scoring system of Houpt. The vital signs were monitored continuously using a pulse oximeter and Dinamap machine. ::: ::: Results. There was rapid onset of sedation with the maximal effect between 8 and 15 minutes. This sedation lasted for 25–40 minutes in Groups A and B and for 60 minutes in Group C. Conscious sedation and dental treatment were achieved in 79%, 96% and 100% of the children in Groups A, B and C, respectively. Consistently higher Houpt scores were seen in Groups B and C, with statistically significant differences between Groups A and C, and B and C (Tukey’s range test, P 0·05). All the vital signs were within normal physiological limits and there were no significant adverse effects either with or without fasting. ::: ::: Conclusions. All 3 doses of intranasal midazolam were effective in modifying the behaviour of the uncooperative child patient to accept dental treatment. This was irrespective of fasting.


title: Large-scale training to increase speech intelligibility for hearing-impaired listeners in novel noises.

abstract: Supervised speech segregation has been recently shown to improve human speech intelligibility in noise, when trained and tested on similar noises. However, a major challenge involves the ability to generalize to entirely novel noises. Such generalization would enable hearing aid and cochlear implant users to improve speech intelligibility in unknown noisy environments. This challenge is addressed in the current study through large-scale training. Specifically, a deep neural network (DNN) was trained on 10 000 noises to estimate the ideal ratio mask, and then employed to separate sentences from completely new noises (cafeteria and babble) at several signal-to-noise ratios (SNRs). Although the DNN was trained at the fixed SNR of - 2 dB, testing using hearing-impaired listeners demonstrated that speech intelligibility increased substantially following speech segregation using the novel noises and unmatched SNR conditions of 0 dB and 5 dB. Sentence intelligibility benefit was also observed for normal-hearing listeners in most noisy conditions. The results indicate that DNN-based supervised speech segregation with large-scale training is a very promising approach for generalization to new acoustic environments.


title: Learning dexterous in-hand manipulation

abstract: We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies that can perform vision-based object reorientation on a physical Shadow Dexterous Hand. The training is performed...


title: Evaluation of suicide by hanging : From the video recording.

abstract: Most evaluations of the pathophysiological processes of suicidal hangings are based on animal experiments and only a few have examined the hanging process. The little information available is mostly the result of animal experiments that have focused on tracheal obstructions. In the study presented here, a camcorder was set up by the victim to record the entire hanging process. According to the analysis from the recording, the complete asphyxial process lasted only 2 minutes and 43 seconds. Stage 1 (the prodromal stage) was especially short. Typical convulsions were not observed in this case. Body movements, such as clonic and tonic spasms, were not noticed in stage 2 (the dyspnea stage). Because the body did not touch the ground, the pressure on the neck by the ligature caused cerebral ischemia as a result of the obstruction of the neck vessels. Consequently, it can be hypothesized that sui cide by hanging may not be based on the effect of changed concentration in the blood of carbon dioxide and oxygen, but on the effect of cerebral ischemia.


title: Car-to-Car Safety Broadcast with Interference Using Raptor Codes

abstract: Car-to-car safety applications that demand real-time and reliable communications in vehicular ad hoc networks (VANETs) requires a new paradigm of coding techniques. In this paper, we propose a novel coding approach using a systematic Raptor code for car-to- car post-crash warning broadcast applications. A cross-layer simulator model is developed to evaluate the performance of Raptor codes against repetition codes using also multiple antennas spatial diversity techniques. The end-to-end delay and packet delivery ratio are used as performance metrics to demonstrate the latency and reliability problems of repetition codes that are addressed using Raptor codes.


title: Mars Sample Return campaign status

abstract: The proposed Mars Sample Return (MSR) Campaign would be the next big step in the Mars Exploration Program (MEP). Identified by the Planetary Decadal Survey of 2011 as the highest science priority for large missions, the Mars Program has been working in partnership with the European Space Agency (ESA) to define a campaign to return a surface sample of martian surface material to Earth for analysis. As currently envisioned, the MSR campaign concept consists of a series of flight missions to collect and return the sample, and a ground based project to develop a sample receiving facility. The first mission in this proposed campaign would be baselined for launch in 2018, and would consist of a rover capable of scientifically selecting and caching the sample for retrieval by a later mission. Subsequent missions would retrieve the cache, launch it into orbit around Mars, where it would be captured by an orbiter and returned to the Earth. This paper discusses the current status of the MSR Campaign architecture definition and requirements development and near term plans for technical definition and review. Also discussed are selected design drivers and proposed key requirements which would have to be addressed in the definition of the campaign.1, 2


title: Minimally invasive dentistry

abstract: ABSTRACT Background During the past few decades, scientific developments in cariology, dental materials and diagnostic systems have changed dentistry's approach to diagnosis and management of dental caries. The authors summarize these developments. Overview Dental adhesives and restorative materials, new understanding of the caries process and remineralization, and changes in caries prevlance have catalyzed the evolution in caries management from G.V. Black's “extension for prevention” to “minimally invasive.” The authors describe the scientific basis for early diagnosis; a modified classification of caries based on site and size of lesion remineralization; reduction of cariogenic bacteria; and minimally invasive cavity preparation design, techniques and material selection. Conclusions and Practice Implications Minimally invasive dentistry is based on advances in science. Emerging technologies will facilitate evolution to primary prevention of caries, though technical, cultural and economic obstacles to full implementation in clinical practice now exist.


title: Extracting sub-glottal and Supra-glottal features from MFCC using convolutional neural networks for speaker identification in degraded audio signals

abstract: We present a deep learning based algorithm for speaker recognition from degraded audio signals. We use the commonly employed Mel-Frequency Cepstral Coefficients (MFCC) for representing the audio signals. A convolutional neural network (CNN) based on 1D filters, rather than 2D filters, is then designed. The filters in the CNN are designed to learn inter-dependency between cepstral coefficients extracted from audio frames of fixed temporal expanse. Our approach aims at extracting speaker dependent features, like Sub-glottal and Supra-glottal features, of the human speech production apparatus for identifying speakers from degraded audio signals. The performance of the proposed method is compared against existing baseline schemes on both synthetically and naturally corrupted speech data. Experiments convey the efficacy of the proposed architecture for speaker recognition.


title: A Comparison of Modeling Units in Sequence-to-Sequence Speech Recognition with the Transformer on Mandarin Chinese

abstract: The choice of modeling units is critical to automatic speech recognition (ASR) tasks. Conventional ASR systems typically choose context-dependent states (CD-states) or context-dependent phonemes (CD-phonemes) as their modeling units. However, it has been challenged by sequence-to-sequence attention-based models, which integrate an acoustic, pronunciation and language model into a single neural network. On English ASR tasks, previous attempts have already shown that the modeling unit of graphemes can outperform that of phonemes by sequence-to-sequence attention-based model. In this paper, we are concerned with modeling units on Mandarin Chinese ASR tasks using sequence-to-sequence attention-based models with the Transformer. Five modeling units are explored including context-independent phonemes (CI-phonemes), syllables, words, sub-words and characters. Experiments on HKUST datasets demonstrate that the lexicon free modeling units can outperform lexicon related modeling units in terms of character error rate (CER). Among five modeling units, character based model performs best and establishes a new state-of-the-art CER of $26.64\%$ on HKUST datasets without a hand-designed lexicon and an extra language model integration, which corresponds to a $4.8\%$ relative improvement over the existing best CER of $28.0\%$ by the joint CTC-attention based encoder-decoder network.


title: An Analysis of Angle-Based With Ratio-Based Vegetation Indices

abstract: Remotely sensed, angle-based vegetation indices that measure vegetation amounts by the angle between an approximated soil line and a simulated vegetation isoline in the red-near-infrared reflectance space were developed and evaluated in this paper. Unsalan and Boyer previously proposed an angle-based vegetation index, thetas (denoted as thetas NDVI in this paper), based on the normalized difference vegetation index (NDVI) with the objective of overcoming the saturation problem in the NDVI. However, thetasNDVI did not consider strong soil background influences present in the NDVI. To reduce soil background noise, an angle-based vegetation index, thetasSAVI , based on the soil-adjusted vegetation index (SAVI), was derived using trigonometric analysis. The performance of thetasNDVI and thetasSAVI was evaluated and compared with their corresponding vegetation indices, NDVI and SAVI. The soil background influence on thetasNDVI was found to be as significant as that on the NDVI. thetasNDVI was found to be more sensitive to vegetation amount than the NDVI at low vegetation density levels, but less sensitive to vegetation fraction at high vegetation density levels. Thus, the saturation effect at high vegetation density levels encountered in the NDVI was not mitigated by thetasNDVI. By contrast, thetasSAVI exhibited insignificant soil background effects and weaker saturation, as in SAVI, but also improved upon the dynamic range of SAVI. Analyses and evaluation suggest that thetasSAVI is an optimal vegetation index to assess and monitor vegetation cover across the entire range of vegetation fraction density levels and over a wide variety of soil backgrounds


title: Research on personalized referral service and big data mining for e-commerce with machine learning

abstract: Machine learning (ML) is one of the main methods to address the problem of big data mining. ML can enable the e-commerce system upon self-innovate and improvement by accumulating prior knowledge. The big data produced by transaction, interaction and observation from e-commerce enterprises can greatly provide decision-making service for marketing strategy. In this paper we take the e-commerce data of tea-device enterprise as an example, use the FP-grow algorithm to get the frequent item sets, so as to mining and analyze association rule of user behavior to get feature vector as the basis of user classification, then use Naive Bayesian algorithm on the feature vector to implement clustering learning for precision marketing and personalized online referral services. Finally, we evaluate the feasibility of big data mining with ML through the profit produced by the sale of goods. Experimental results show that ML can not only greatly improve the performance in big data mining, can also achieve precise marketing, and can further increasing about 20% marginal profit for each type of goods.


title: Trust between humans and machines, and the design of decision aids

abstract: Abstract A problem in the design of decision aids is how to design them so that decision makers will trust them and therefore use them appropriately. This problem is approached in this paper by taking models of trust between humans as a starting point, and extending these to the human-machine relationship. A definition and model of human-machine trust are proposed, and the dynamics of trust between humans and machines are examined. Based upon this analysis, recommendations are made for calibrating users' trust in decision aids.


title: Hyperbolic centroidal Voronoi tessellation

abstract: The centroidal Voronoi tessellation (CVT) has found versatile applications in geometric modeling, computer graphics, and visualization. In this paper, we extend the concept of the CVT from Euclidean space to hyperbolic space. A novel hyperbolic CVT energy is defined, and the relationship between minimizing this energy and the hyperbolic CVT is proved. We also show by our experimental results that the hyperbolic CVT has the similar property as its Euclidean counterpart where the sites are uniformly distributed according to given density values. Two algorithms -- Lloyd's algorithm and the L-BFGS algorithm -- are adopted to compute the hyperbolic CVT, and the convergence of Lloyd's algorithm is proved. As an example of the application, we utilize the hyperbolic CVT to compute uniform partitions and high-quality remeshing results for high-genus (genus>1) surfaces.


title: Adaptive teams of autonomous aerial and ground robots for situational awareness

abstract: This is a preprint of an article accepted for publication in the Journal of Field Robotics, copyright 2007. ::: Journal of Field Robotics 24(11), 991–1014 (2007) © 2007 Wiley Periodicals, Inc. Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/rob.202222


title: Disaggregated water sensing from a single , pressure-based sensor : An extended analysis of HydroSense using staged experiments

abstract: We present an extended analysis of our previous work on the HydroSense technology, which is a low-cost and easily installed single-point sensor of pressure for automatically disaggregating water usage activities in the home (Froehlich et al., 2009 [53]). We expand upon this work by providing a survey of existing and emerging water disaggregation techniques, a more comprehensive description of the theory of operation behind our approach, and an expanded analysis section that includes hot versus cold water valve usage classification and a comparison between two classification approaches: the template-based matching scheme used in Froehlich et al. (2009) [53] and a new stochastic approach using a Hidden Markov Model. We show that both are successful in identifying valve- and fixture-level water events with greater than 90% accuracies. We conclude with a discussion of the limitations in our experimental methodology and open problems going forward.


title: Automated plant Watering system

abstract: In daily operations related to farming or gardening Watering is the most important cultural practice and the most labor-intensive task. No matter whichever weather it is, either too hot and dry or too cloudy and wet, you want to be able to control the amount of water that reaches your plants. Modern watering systems could be effectively used to water plants when they need it. But this manual process of watering requires two important aspects to be considered: when and how much to water. In order to replace manual activities and making gardener's work easier, we have create automatic plant watering system. By adding automated plant watering system to your garden or agricultural field, you will help all of your plants reach their fullest potential as well as conserving water. Using sprinklers drip emitters, or a combination of both, we can design a system that is ideal for every plant in our yard. For implementation of automatic plant watering system, we have used combination of sprinkler systems, pipes, and nozzles. This project uses the ATmega328 microcontroller. It is programmed to sense moisture level of plants at particular instance of time, if the moisture content is less than specified threshold which is predefined according to particular plant's water need then desired amount of water is supplied till it reaches threshold. Generally, plants need to be watered twice a day, morning and evening. Thus, the microcontroller is programmed to water plants two times per day. System is designed in such a way that it reports its current state as well as remind the user to add water to the tank. All this notifications are made through mobile application. We hope that through this prototype we all can enjoy having plants, without being worried about absent or forgetfulness.


Approximate convex decomposition


title: A Deep-Learning Approach for Operation of an Automated Realtime Flare Forecast

abstract: Automated forecasts serve important role in space weather science, by providing statistical insights to flare-trigger mechanisms, and by enabling tailor-made forecasts and high-frequency forecasts. Only by realtime forecast we can experimentally measure the performance of flare-forecasting methods while confidently avoiding overlearning. ::: We have been operating unmanned flare forecast service since August, 2015 that provides 24-hour-ahead forecast of solar flares, every 12 minutes. We report the method and prediction results of the system.


Approximating s-t minimum cuts in Õ(n2) time


Low-resource Post Processing of Noisy OCR Output for Historical Corpus Digitisation.


title: Nonlinear Dynamics of Information Diffusion in Social Networks

abstract: The recent explosion in the adoption of search engines and new media such as blogs and Twitter have facilitated the faster propagation of news and rumors. How quickly does a piece of news spread over these media? How does its popularity diminish over time? Does the rising and falling pattern follow a simple universal law? In this article, we propose S pike M, a concise yet flexible analytical model of the rise and fall patterns of information diffusion. Our model has the following advantages. First, unification power: it explains earlier empirical observations and generalizes theoretical models including the SI and SIR models. We provide the threshold of the take-off versus die-out conditions for S pike M and discuss the generality of our model by applying it to an arbitrary graph topology. Second, practicality: it matches the observed behavior of diverse sets of real data. Third, parsimony: it requires only a handful of parameters. Fourth, usefulness: it makes it possible to perform analytic tasks such as forecasting, spotting anomalies, and interpretation by reverse engineering the system parameters of interest (quality of news, number of interested bloggers, etc.). We also introduce an efficient and effective algorithm for the real-time monitoring of information diffusion, namely S pike S tream , which identifies multiple diffusion patterns in a large collection of online event streams. Extensive experiments on real datasets demonstrate that S pike M accurately and succinctly describes all patterns of the rise and fall spikes in social networks.


title: Comparison of Neutral-Point-Clamped, Symmetrical, and Hybrid Asymmetrical Multilevel Inverters

abstract: This paper presents a comparison of three topologies of multilevel inverters applied to drive an induction motor of 500 kVA/4.16 kV rating. The multilevel inverters analyzed are the following: a neutral-point-clamped inverter, a symmetrical cascaded multilevel inverter, and a hybrid asymmetrical cascaded multilevel inverter. The performance indexes used in the comparison are total harmonic distortion, first-order distortion factor, second-order distortion factor, common-mode voltage, semiconductor power loss distribution, and heat-sink volume. The multilevel inverters are designed to present 99% efficiency at the nominal operating point, and the aforementioned performance indexes are compared for distinct values of amplitude modulation depth.


title: Efficient Attention using a Fixed-Size Memory Representation

abstract: The standard content-based attention mechanism typically used in sequence-to-sequence models is computationally expensive as it requires the comparison of large encoder and decoder states at each time step. In this work, we propose an alternative attention mechanism based on a fixed size memory representation that is more efficient. Our technique predicts a compact set of K attention contexts during encoding and lets the decoder compute an efficient lookup that does not need to consult the memory. We show that our approach performs on-par with the standard attention mechanism while yielding inference speedups of 20% for real-world translation tasks and more for tasks with longer sequences. By visualizing attention scores we demonstrate that our models learn distinct, meaningful alignments.


title: New Predictive Models for Blood–Brain Barrier Permeability of Drug-like Molecules

abstract: PURPOSE ::: The goals of the present study were to apply a generalized regression model and support vector machine (SVM) models with Shape Signatures descriptors, to the domain of blood-brain barrier (BBB) modeling. ::: ::: ::: MATERIALS AND METHODS ::: The Shape Signatures method is a novel computational tool that was used to generate molecular descriptors utilized with the SVM classification technique with various BBB datasets. For comparison purposes we have created a generalized linear regression model with eight MOE descriptors and these same descriptors were also used to create SVM models. ::: ::: ::: RESULTS ::: The generalized regression model was tested on 100 molecules not in the model and resulted in a correlation r2 = 0.65. SVM models with MOE descriptors were superior to regression models, while Shape Signatures SVM models were comparable or better than those with MOE descriptors. The best 2D shape signature models had 10-fold cross validation prediction accuracy between 80-83% and leave-20%-out testing prediction accuracy between 80-82% as well as correctly predicting 84% of BBB+ compounds (n = 95) in an external database of drugs. ::: ::: ::: CONCLUSIONS ::: Our data indicate that Shape Signatures descriptors can be used with SVM and these models may have utility for predicting blood-brain barrier permeation in drug discovery.


title: Text Localization Based on Fast Feature Pyramids and Multi-Resolution Maximally Stable Extremal Regions

abstract: Text localization from scene images is a challenging task that finds application in many areas. In this work, we propose a novel hybrid text localization approach that exploits Multi-resolution Maximally Stable Extremal Regions to discard false-positive detections from the text confidence maps generated by a Fast Feature Pyramid based sliding window classifier. The use of a multi-scale approach during both feature computation and connected component extraction allows our method to identify uncommon text elements that are usually not detected by competing algorithms, while the adoption of approximated features and appropriately filtered connected components assures a low overall computational complexity of the proposed system.


title: Gradient descent GAN optimization is locally stable

abstract: Despite the growing prominence of generative adversarial networks (GANs), optimization in GANs is still a poorly understood topic. In this paper, we analyze the "gradient descent" form of GAN optimization i.e., the natural setting where we simultaneously take small gradient steps in both generator and discriminator parameters. We show that even though GAN optimization does not correspond to a convex-concave game (even for simple parameterizations), under proper conditions, equilibrium points of this optimization procedure are still \emph{locally asymptotically stable} for the traditional GAN formulation. On the other hand, we show that the recently proposed Wasserstein GAN can have non-convergent limit cycles near equilibrium. Motivated by this stability analysis, we propose an additional regularization term for gradient descent GAN updates, which \emph{is} able to guarantee local stability for both the WGAN and the traditional GAN, and also shows practical promise in speeding up convergence and addressing mode collapse.


title: Is Big Data the Next Big Thing in Performance Measurement Systems

abstract: This paper explores the relationship between big data and performance measurement systems. Generally, big data is associated to data that are produced in big velocity, variety, and volume. Companies nowadays are searching for ways to extract useful information from large amount of data. Nevertheless, big data requires advanced computational resources, statistical techniques, and data visualization techniques in order to store, process, and, in special, analyze all different kinds of data. The capability to create value from such high quantity of data is critical to get competitive advantages from the big data. In fact, big data is probably a new revolution in information management that can influence the way in which companies do their businesses. Consequently, the performance measurement system can also be affected. These systems are composed of a multidimensional set of performance measures used for planning, controlling, and improving business performance. Therefore, big data could contribute to different ways of using the performance measurement systems. Finally, this paper provides some insights into how big data influences the changes in performance measurement systems such as new kind of analytics, strategy challenge, and support to continuous improvement.


title: Wearable Device-Based System to Monitor a Driver’s Stress, Fatigue, and Drowsiness

abstract: This paper proposes a wearable device-based system to monitor the abnormal conditions of a driver, including stress, fatigue, and drowsiness. The system measures the motional and physiological information of the driver using the developed wearable device on the wrist. Preprocessing is used to distinguish the valid signal parts of the measured signals, because various noises can occur in wearable sensors. Features are extracted from the signal parts, and an optimal feature set is determined by an analysis of variance and a sequential floating forward selection algorithm. To classify the driver’s state, a support vector machine-based classification method is used to obtain high generalization performance considering interdriver variance. Experiments were conducted on an indoor driving simulator, with 28 subjects, to gather data for each state. The classification accuracy was 98.43% for fivefold cross validation on the data. In a subject-independent test, the accuracy was 68.31% for the four states and 84.46% for the three states consisting of normal, stressed, and fatigued or drowsy states. Using the proposed system, the abnormal conditions of the driver can be detected and distinguished. This advantage contributes to safer and more comfortable driving. Furthermore, the utilization of the wearable device makes the system easy to use.


title: Adaptive SNN Torque Control for Tendon-Driven Fingers

abstract: Tendon-driven robot manipulators are often used to actuate distal joints. The tendons allow the actuators to be located outside the fingers. Conventionally, the use of the tendons of the fingers allows for the significant reduction to the size and weight, in this case, which approximately similar to that of the human. To achieve the interaction with unstructured environments, a torque control system is presented based on the single neuron networks (SNN) in this paper. The torque control allows the system maintain proper torques on the joints. Meanwhile, this controller calculates actuator positions based on the error measured by the actual joint torques and desired joint torques. Simulations have been conducted on a tendon-driven finger model to demonstrate that the proposed controller can achieve the faster response, and then decrease overshoot comparing to a PI controller.


title: Clustering is Efficient for Approximate Maximum Inner Product Search

abstract: Locality Sensitive Hashing (LSH) techniques have recently become a popular solution for solving the approximate Maximum Inner Product Search (MIPS) problem, which arises in many situations and have in particular been used as a speed-up for the training of large neural probabilistic language models. In this paper we propose a new approach for solving approximate MIPS based on a variant of the $k$-means algorithm. We suggest using spherical $k$-means which is an algorithm that can efficiently be used to solve the approximate Maximum Cosine Similarity Search (MCSS), and basing ourselves on previous work by Shrivastava and Li we show how it can be adapted for approximate MIPS. Our new method compares favorably with LSH-based methods on a simple recall rate test, by providing a more accurate set of candidates for the maximum inner product. The proposed method is thus likely to benefit the wide range of problems with very large search spaces where a robust approximate MIPS heuristic could be of interest, such as for providing a high quality short list of candidate words to speed up the training of neural probabilistic language models.


title: On Stochastic Geometry Analysis and Optimization of Wireless-Powered Cellular Networks

abstract: In this paper, a new mathematical framework to the analysis and optimization of wireless-powered cellular networks is introduced. The proposed approach leverages stochastic geometry for system-level analysis of cellular networks, by modeling base station locations as points of a Poisson point process. The trade-offs emerging from simultaneous wireless information and power transfer are characterized through the joint cumulative distribution function of average harvested energy and average rate, which is conveniently formulated in terms of an easy-to-compute two-fold integral. The analysis shows that an optimal operating point for system-level optimization exists, as well as that network densification and directional beamforming constitute essential enablers for enhancing the performance. Finally, the fundamental trade-offs emerging in wireless-powered cellular networks are quantified through the concept of feasibility regions.


title: A simple method for mass propagation of Spathiphyllum cannifolium using an airlift bioreactor

abstract: A method for the micropropagation of Spathiphyllum cannifolium is presented using shoot tip proliferation onto Murashige and Skoog (MS) medium supplemented with different plant growth regulator concentrations and combinations. The proliferation responses were significantly influenced by the cytokinin type and concentrations. Supplementation of the medium with benzyladenine (BA; 4.44–13.32 μM) increased the shoot proliferation rate significantly as compared to other treatments. When cytokinins were used with auxin (indole-3-butyric acid, IBA and naphthalene acetic acid. NAA), the number of shoots per explant increased in comparison with treatments with BA alone. The largest number of shoots, 9.3 per explant, was obtained with 13.32 μM BA and 4.9 μM IBA. Different MS medium strengths and sucrose concentrations were used with the aim to stimulate in vitro shoot proliferation. Full MS medium with 30 gl−1 sucrose was found to be suitable for shoot tip culture of Spathiphyllum. Comparative studies between gelled medium and bioreactor culture [continuous immersion (with or without net) and temporary immersion in liquid media using ebb and flood] revealed that shoot multiplication and growth were more efficient in continuous immersion (with net) bioreactor with low cytokinin-supplemented media. Plantlets from the bioreactor were cultured hydroponically for 30 d and 100% of plants were rooted and acelimatized successfully. Rapid and efficient multiplication rate in bioreactor and successful transfer to greenhouse makes this protocol suitable for large-scale multiplication of this important foliage plant.


title: Self-taught learning: transfer learning from unlabeled data

abstract: We present a new machine learning framework called "self-taught learning" for using unlabeled data in supervised classification tasks. We do not assume that the unlabeled data follows the same class labels or generative distribution as the labeled data. Thus, we would like to use a large number of unlabeled images (or audio samples, or text documents) randomly downloaded from the Internet to improve performance on a given image (or audio, or text) classification task. Such unlabeled data is significantly easier to obtain than in typical semi-supervised or transfer learning settings, making self-taught learning widely applicable to many practical learning problems. We describe an approach to self-taught learning that uses sparse coding to construct higher-level features using the unlabeled data. These features form a succinct input representation and significantly improve classification performance. When using an SVM for classification, we further show how a Fisher kernel can be learned for this representation.


title: Anticipating many futures: Online human motion prediction and synthesis for human-robot collaboration

abstract: Fluent and safe interactions of humans and robots require both partners to anticipate the others' actions. A common approach to human intention inference is to model specific trajectories towards known goals with supervised classifiers. However, these approaches do not take possible future movements into account nor do they make use of kinematic cues, such as legible and predictable motion. The bottleneck of these methods is the lack of an accurate model of general human motion. In this work, we present a conditional variational autoencoder that is trained to predict a window of future human motion given a window of past frames. Using skeletal data obtained from RGB depth images, we show how this unsupervised approach can be used for online motion prediction for up to 1660 ms. Additionally, we demonstrate online target prediction within the first 300-500 ms after motion onset without the use of target specific training data. The advantage of our probabilistic approach is the possibility to draw samples of possible future motions. Finally, we investigate how movements and kinematic cues are represented on the learned low dimensional manifold.


title: Many-Goals Reinforcement Learning

abstract: All-goals updating exploits the off-policy nature of Q-learning to update all possible goals an agent could have from each transition in the world, and was introduced into Reinforcement Learning (RL) by Kaelbling (1993). In prior work this was mostly explored in small-state RL problems that allowed tabular representations and where all possible goals could be explicitly enumerated and learned separately. In this paper we empirically explore 3 different extensions of the idea of updating many (instead of all) goals in the context of RL with deep neural networks (or DeepRL for short). First, in a direct adaptation of Kaelbling's approach we explore if many-goals updating can be used to achieve mastery in non-tabular visual-observation domains. Second, we explore whether many-goals updating can be used to pre-train a network to subsequently learn faster and better on a single main task of interest. Third, we explore whether many-goals updating can be used to provide auxiliary task updates in training a network to learn faster and better on a single main task of interest. We provide comparisons to baselines for each of the 3 extensions.