title: SVF-Net: Learning Deformable Image Registration Using Shape Matching

abstract: In this paper, we propose an innovative approach for registration based on the deterministic prediction of the parameters from both images instead of the optimization of a energy criteria. The method relies on a fully convolutional network whose architecture consists of contracting layers to detect relevant features and a symmetric expanding path that matches them together and outputs the transformation parametrization. Whereas convolutional networks have seen a widespread expansion and have been already applied to many medical imaging problems such as segmentation and classification, its application to registration has so far faced the challenge of defining ground truth data on which to train the algorithm. Here, we present a novel training strategy to build reference deformations which relies on the registration of segmented regions of interest. We apply this methodology to the problem of inter-patient heart registration and show an important improvement over a state of the art optimization based algorithm. Not only our method is more accurate but it is also faster - registration of two 3D-images taking less than 30 ms second on a GPU - and more robust to outliers.


title: Encoding emotional information for sequence-to-sequence response generation

abstract: This paper introduces an alternative approach on embedding emotional information at the encoder stage of a sequence-to-sequence based emotional response generation. It explores different positioning and styles of the embedding, which represent associations of emotion with specific words or the whole sentence. The experiment was set up with standard dataset as well as dataset annotated with emotional classifiers. Preliminary results showed that this new approach should better represent sentence level emotional and work well with standard Recurrent Neural network (RNN) with Long Short Term Memory (LSTM) architecture.


title: TapDance: end-to-middle anticensorship without flow blocking

abstract: In response to increasingly sophisticated state-sponsored Internet censorship, recent work has proposed a new approach to censorship resistance: end-to-middle proxying. This concept, developed in systems such as Telex, Decoy Routing, and Cirripede, moves anticensorship technology into the core of the network, at large ISPs outside the censoring country. In this paper, we focus on two technical obstacles to the deployment of certain end-to-middle schemes: the need to selectively block flows and the need to observe both directions of a connection. We propose a new construction, TapDance, that removes these requirements. TapDance employs a novel TCP-level technique that allows the anticensorship station at an ISP to function as a passive network tap, without an inline blocking component. We also apply a novel steganographic encoding to embed control messages in TLS ciphertext, allowing us to operate on HTTPS connections even under asymmetric routing. We implement and evaluate a TapDance prototype that demonstrates how the system could function with minimal impact on an ISP's network operations.


title: Logic Programming for Robot Control

abstract: This paper proposes logic programs as a specification for robot control. These provide a formal specification of what an agent should do depending on what it senses, and its previous sensory inputs and actions. We show how to axiomatise reactive agents, events as an interface between continuous and discrete time, and persistence, as well as axiomatising integration and differentiation over time (in terms of the limit of sums and differences). This specification need not be evaluated as a Prolog program; we use can the fact that it will be evaluated in time to get a more efficient agent. We give a detailed example of a nonholonomic maze travelling robot, where we use the same language to model both the agent and the environment. One of the main motivations for this work is that there is a clean interface between the logic programs here and the model of uncertainty embedded in probabilistic Horn abduction. This is one step towards building a decision-theoretic planning system where the output of the planner is a plan suitable for actually controlling a robot.


title: Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection

abstract: To cope with the increasing variability and sophistication of modern attacks, machine learning has been widely adopted as a statistically-sound tool for malware detection. However, its security against well-crafted attacks has not only been recently questioned, but it has been shown that machine learning exhibits inherent vulnerabilities that can be exploited to evade detection at test time. In other words, machine learning itself can be the weakest link in a security system. In this paper, we rely upon a previously-proposed attack framework to categorize potential attack scenarios against learning-based malware detection tools, by modeling attackers with different skills and capabilities. We then define and implement a set of corresponding evasion attacks to thoroughly assess the security of Drebin, an Android malware detector. The main contribution of this work is the proposal of a simple and scalable secure-learning paradigm that mitigates the impact of evasion attacks, while only slightly worsening the detection rate in the absence of attack. We finally argue that our secure-learning approach can also be readily applied to other malware detection tasks.


title: Predicting the impact of scientific concepts using full-text features

abstract: New scientific concepts, interpreted broadly, are continuously introduced in the literature, but relatively few concepts have a long-term impact on society. The identification of such concepts is a challenging prediction task that would help multiple parties-including researchers and the general public-focus their attention within the vast scientific literature. In this paper we present a system that predicts the future impact of a scientific concept, represented as a technical term, based on the information available from recently published research articles. We analyze the usefulness of rich features derived from the full text of the articles through a variety of approaches, including rhetorical sentence analysis, information extraction, and time-series analysis. The results from two large-scale experiments with 3.8 million full-text articles and 48 million metadata records support the conclusion that full-text features are significantly more useful for prediction than metadata-only features and that the most accurate predictions result from combining the metadata and full-text features. Surprisingly, these results hold even when the metadata features are available for a much larger number of documents than are available for the full-text features.


title: Software defined enterprise passive optical network

abstract: In the last few years, changing infrastructure and business requirements are forcing enterprises to rethink their networks. Enterprises look to passive optical networks (PON) for increased network efficiency, flexibility, and cost reduction. At the same time, the emergence of Cloud and mobile in enterprise networks calls for dynamic network control and management following a centralized and software-defined paradigm. In this context, we propose a software-defined edge network (SDEN) design that operates on top of PON. SDEN leverages PON benefits while overcoming its lack of dynamic control. This paper is a work-in-progress focusing on enabling key flow control functions over PON: dynamic traffic steering, service dimensioning and realtime re-dimensioning. We also discuss how SDEN edge network can integrate with core SDN solutions to achieve end-to-end manageability. Through case experiment studies conducted on a live PON testbed deployment, we show the practical benefits and potentials that SDEN can offer to enterprise networks redesign.


title: Experience with ketanserin and ritanserin in hypertensive patients.

abstract: In patients with essential hypertension, ketanserin (40 mg once or twice daily) reduces the blood pressure in mono- and combination therapy. Despite evidence of peripheral vasodilation, the heart rate is reduced. This would suggest that ketanserin has an additional sympathoinhibitory effect. However, the influence of ketanserin on plasma catecholamine levels at rest and during exercise or stress was small, and to date there is little evidence for a central sympathoinhibitory effect of ketanserin in humans. The antihypertensive effects of ritanserin, a selective 5-HT2-receptor antagonist, were investigated in a double-blind, placebo-controlled crossover study (4 weeks). Treatment with ritanserin, 10 mg twice daily, was ineffective in hypertensive patients, which indirectly suggests that the 5-HT2-blocking properties of ketanserin cannot alone be responsible for its antihypertensive effects.


title: EXTRACTING RULES FROM MULTILAYER PERCEPTRONS IN CLASSIFICATION PROBLEMS: A CLUSTERING-BASED APPROACH

abstract: Abstract Multilayer perceptrons adjust their internal parameters performing vector mappings from the input to the output space. Although they may achieve high classification accuracy, the knowledge acquired by such neural networks is usually incomprehensible for humans. This fact is a major obstacle in data mining applications, in which ultimately understandable patterns (like classification rules) are very important. Therefore, many algorithms for rule extraction from neural networks have been developed. This work presents a method to extract rules from multilayer perceptrons trained in classification problems. The rule extraction algorithm basically consists of two steps. First, a clustering genetic algorithm is applied to find clusters of hidden unit activation values. Then, classification rules describing these clusters, in relation to the inputs, are generated. The proposed approach is experimentally evaluated in four datasets that are benchmarks for data mining applications and in a real-world meteorological dataset, leading to interesting results.


title: Exploiting Contextual Independence In Probabilistic Inference

abstract: Bayesian belief networks have grown to prominence because they provide compact representations for many problems for which probabilistic inference is appropriate, and there are algorithms to exploit this compactness. The next step is to allow compact representations of the conditional probabilities of a variable given its parents. In this paper we present such a representation that exploits contextual independence in terms of parent contexts; which variables act as parents may depend on the value of other variables. The internal representation is in terms of contextual factors (confactors) that is simply a pair of a context and a table. The algorithm, contextual variable elimination, is based on the standard variable elimination algorithm that eliminates the non-query variables in turn, but when eliminating a variable, the tables that need to be multiplied can depend on the context. This algorithm reduces to standard variable elimination when there is no contextual independence structure to exploit. We show how this can be much more efficient than variable elimination when there is structure to exploit. We explain why this new method can exploit more structure than previous methods for structured belief network inference and an analogous algorithm that uses trees.


title: Physical Activity, Cognitive Activity, and Cognitive Decline in a Biracial Community Population

abstract: Background Findings from studies investigating whether physical activity reduces the risk of cognitive decline in old age have been inconsistent. Objective To examine whether participation in physical activity by older adults reduces the rate of cognitive decline after accounting for participation in cognitively stimulating activities. Design A prospective population study conducted from August 1993 to January 2003, with an average follow-up of 6.4 years. Setting A biracial community population on the south side of Chicago. Participants Participants were 4055 community-dwelling adults 65 years and older who were able to walk across a small room and had participated in at least 2 of the 3 follow-up assessments. Main Outcome Measure Annual rate of cognitive change as measured by a global cognitive score, which consisted of averaged standardized scores from 4 cognitive tests. Results In a mixed model adjusted for age, sex, race, and education, each additional physical activity hour per week was associated with a slower rate of cognitive decline by 0.0007 U/y ( P = .04). However, with further adjustments (1) for participation in cognitive activities (β = .0006, P = .10),(2) for depression and vascular diseases (β = .0005, P = .19), and (3) by excluding participants whose global cognitive score at baseline was at or below the 10th percentile (β = .0002, P = .45), the coefficients were smaller and no longer statistically significant. Conclusion These data do not support the hypothesis that physical activity alone protects against cognitive decline among older adults.


title: Human tracking using floor sensors based on the Markov chain Monte Carlo method

abstract: The aim of this paper is to develop a human tracking system that is resistant to environmental changes and covers wide area. Simply structured floor sensors are low-cost and can track people in a wide area. However, the sensor reading is discrete and missing; therefore, footsteps do not represent the precise location of a person. A Markov chain Monte Carlo method (MCMC) is a promising tracking algorithm for these kinds of signals. We applied two prediction models to the MCMC: a linear Gaussian model and a highly nonlinear bipedal model. The Gaussian model was efficient in terms of computational cost while the bipedal model discriminated people more accurate than the Gaussian model. The Gaussian model can be used to track a number of people, and the bipedal model can be used in situations where more accurate tracking is required.


title: Multi-criteria decision making with skyline computation

abstract: Multi-criteria decision making is one of the most critical and yet most challenging components in modern enterprise business intelligence. It is well known that complex business decisions are often made based on multi-dimensional criteria. The competitiveness of optimal business decision making typically resorts to finding a good trade-off among many different and possibly contradicting criteria, e.g., maximum profit, minimum price, minimum resource consumption. A skyline query operator is by design to find the set of interesting data points (objects) over a large dimensional data collection, satisfying a set of possibly contradicting conditions. In this paper, we provide an in-depth coverage of skyline computation models, algorithms and optimization techniques for improving both efficiency and quality of multi-criteria decision making. By reviewing and revising the state of art research in multi-criteria decision making using skyline operations, we describe the essential concepts, the alternative models, and the suite of techniques for providing scalable and elastic skyline computation in massively distributed computing environments. The paper consists of four parts. First, we provide an overview of skyline query operators in terms of concepts, basic processing algorithms and representative application scenarios. Second, we review the state of art literature in skyline query processing research and development, outline the most representative classes of skyline query processing and optimization techniques and discuss the pros and cons of existing approaches. Third, we provide a comprehensive analysis on the inherent limitations of some existing skyline models and algorithms and discuss why scaling skyline query processing over large high dimensional datasets continues to pose daunting challenges. Finally, we present optimization techniques for designing parallel skyline query processing algorithms and how to utilize GPUs to support and scale parallel skyline computations over high dimensional large datasets. We also introduce a novel dominance test technique, called GNL (GPU-based Nested Loop), which can drastically reduce the cost of dominance tests by leveraging GPUs, and outperform CPU-based dominance tests.


title: Recent Advances on Neuromorphic Systems Using Phase-Change Materials

abstract: Realization of brain-like computer has always been human’s ultimate dream. Today, the possibility of having this dream come true has been significantly boosted due to the advent of several emerging non-volatile memory devices. Within these innovative technologies, phase-change memory device has been commonly regarded as the most promising candidate to imitate the biological brain, owing to its excellent scalability, fast switching speed, and low energy consumption. In this context, a detailed review concerning the physical principles of the neuromorphic circuit using phase-change materials as well as a comprehensive introduction of the currently available phase-change neuromorphic prototypes becomes imperative for scientists to continuously progress the technology of artificial neural networks. In this paper, we first present the biological mechanism of human brain, followed by a brief discussion about physical properties of phase-change materials that recently receive a widespread application on non-volatile memory field. We then survey recent research on different types of neuromorphic circuits using phase-change materials in terms of their respective geometrical architecture and physical schemes to reproduce the biological events of human brain, in particular for spike-time-dependent plasticity. The relevant virtues and limitations of these devices are also evaluated. Finally, the future prospect of the neuromorphic circuit based on phase-change technologies is envisioned.


title: Taylor-fourier analysis of blood pressure oscillometric waveforms

abstract: Blood pressure oscillometric waveforms behave as amplitude modulated nonlinear signals with frequency fluctuations. Their oscillating nature can be better analyzed by the digital Taylor-Fourier transform (DTFT), recently proposed for phasor estimation in oscillating power systems. Based on a relaxed signal model that includes Taylor components greater than zero, the DTFT is able to estimate not only the oscillation itself, as does the digital Fourier transform (DFT), but also its derivatives included in the signal model. In this paper, an oscillometric waveform is analyzed with the DTFT, and its zeroth and first oscillating harmonics are illustrated. The results show that the breathing activity can be separated from the cardiac one through the critical points of the first component, determined by the zero crossings of the amplitude derivatives estimated from the third Taylor order model. On the other hand, phase derivative estimates provide the fluctuations of the cardiac frequency and its derivative, new parameters that could improve the precision of the systolic and diastolic blood pressure assignment. The DTFT envelope estimates uniformly converge from K=3, substantially improving the harmonic separation of the DFT.


title: IK-PSO, PSO Inverse Kinematics Solver with Application to Biped Gait Generation

abstract: This paper describes a new approach allowing the generation of a simplified Biped gait. This approach combines a classical dynamic modeling with an inverse kinematics' solver based on particle swarm optimization, PSO. First, an inverted pendulum, IP, is used to obtain a simplified dynamic model of the robot and to compute the target position of a key point in biped locomotion, the Centre Of Mass, COM. The proposed algorithm, called IK-PSO, Inverse Kinematics PSO, returns and inverse kinematics solution corresponding to that COM respecting the joints constraints. In This paper the inertia weight PSO variant is used to generate a possible solution according to the stability based fitness function and a set of joints motions constraints. The method is applied with success to a leg motion generation. Since based on a pre-calculated COM, that satisfied the biped stability, the proposal allowed also to plan a walk with application on a small size biped robot.


title: A single GluN2 subunit residue controls NMDA receptor channel properties via intersubunit interaction

abstract: The authors investigate the basis of the variations in the channel properties of NMDAR subtypes and report that the specificity of the Mg2+ block, the selective permeability to Ca2+ and the single-channel conductance are all primarily controlled by the residue at a single GluN2 site in the M3 transmembrane region.


title: Geometric particle swarm optimization for the sudoku puzzle

abstract: Geometric particle swarm optimization (GPSO) is a recentlyintroduced generalization of traditional particle swarm optimization(PSO) that applies to all combinatorial spaces. The aim of thispaper is to demonstrate the applicability of GPSO to non-trivialcombinatorial spaces. The Sudoku puzzle is a perfect candidate totest new algorithmic ideas because it is entertaining andinstructive as well as a non-trivial constrained combinatorialproblem. We apply GPSO to solve the sudoku puzzle.


title: Megatrend and Intervention Impact Analyser for Jobs: A European Big Data Hackathon Entry

abstract: This paper presents results of a European Big Data Hackathon entry to facilitate data analysis and visualization of patterns in provided and external datasets about European jobs and skills mismatch and labour market in general. The main contributions of this work are: the development of a method to represent the complex labour market internal structure from the perspective of occupations sharing skills; developing and presenting the prototype, together with an extended description of constructing a graph and related necessary data processing. Since labour market is not an isolated phenomenon and is constantly impacted with external trends and interventions, the presented tool is designed to enable adding extra layers of external information: what is the impact of a megatrend or an intervention to the labour market? Which parts of labour market of what country is most vulnerable to approaching megatrend or planned intervention? A case of analysing the labour market together with the megatrend of automation and computerization of jobs is presented. The source code of the prototype is released as open source for better repeat-ability.


title: File Transfer over Dual-Stack IPv6 Tunnelling in Real Network Environment: Router to Router Performance Analysis Using Best Effort Approach

abstract: In this research, we propose an architectural solution to implement file transfer service (FTP) in IPv6 environment network. IPv6 is considered to be the next-generation Internet protocol. Thus, this study is to analyze the size of files transfer performance and to measure Quality of Service (QoS) delivered by IPv6 using best effort approach in comparison to IPv4. This study primarily focuses on file transfer speed quality of FTP. In the experiment, both host clients and routers utilize the same technical specification. In the same study also, network management system (NMS) is used to monitor and to capture the performance of file transfer in IPv6 and IPv4 environment. Based on the finding result, it shows that there is a slight but significant difference in file transfer performance between dual stack tunnelling IPv4 and IPv6 protocol. Small size file transfer will result in lower and same delay performance outcome for both IPv4 and IPv6, while large size file transfer over IPv6 will result in higher delay performance as compared to IPv4. In short, the significant result of IPv6 delay is slightly higher than IPv4. Hence, the quality of FTP might be decreased if dual stack tunnelling is implemented in IPv6 environment. Nevertheless, Link Efficiency and compression technique are able to lower the delay performance on file transfer over IPv6 environment.


title: Investigation on the electrical properties of palm oil and coconut oil based TiO2 nanofluids

abstract: This paper presents an examination on the electrical properties of Palm Oil (PO) and Coconut Oil (CO) under the presence of semi-conductive nanoparticle, Titanium (IV) Oxide (TiO 2 ). The type of PO used in this study was Refined, Bleached and Deodorized Palm Oil (RBDPO) Olein. TiO2 was dispersed into RBDPO and CO at volume concentrations ranging from 0.001% to 0.05%. The effect of surfactant, Cetrimonium Bromide (CTAB) was also examined and the morphology of RBDPO and CO based TiO 2 nanofluid was observed through Transmission Electron Microscope (TEM). A number of measurements were carried out including the AC/lightning breakdown voltages, dielectric dissipation factor, relative permittivity and resistivity. Statistical analysis was carried out through Weibull distribution to determine the AC and lightning withstand voltages at 1% probability for RBDPO and CO. It is found that without CTAB, there are only slight improvements on the AC breakdown voltages of the RBDPO and CO with introduction of TiO2 but could decrease with presence of CTAB. The same pattern is found for lightning breakdown voltages of RBDPO and CO under non-uniform field where the highest percentages of increments decrease with introduction of CTAB while the effect is not apparent under quasi-uniform field. Significant effect of CTAB is found on the dielectric dissipation factors and resistivity of RBDPO and CO based TiO 2 nanofluid while the relative permittivity are not affected. The statistical analyses reveal that RBDPO and CO experience apparent increments of the AC breakdown voltages at 1% probability after the introduction of TiO 2 regardless with or without CTAB. The improvements done by TiO2 on the lightning breakdown voltages at 1% probability for all samples with CTAB under quasi-uniform field are more apparent than non-uniform field.


title: A Dual-Band U-Slot PIFA Antenna with Ground Slit for RFID Applications

abstract: In this paper, a Planar Inverted-F Antenna (PIFA) with U-Slot (U-PIFA) and slit in ground is introduced. The proposed PIFA is compact with single coaxial feed, single short strip, and single short pin. The main achievement of this research was the design of ISM Microwave bands 2.45/5.8 GHz dual band PIFA antenna by using slit in ground to control the selection of lower resonance frequency without any mutual effect on the upper resonance frequency which determined from U-slot in the patch. The dielectric constant of the substrate is 4.3 and loss tangent of 0.013, FR-4 substrate. The characteristics (input impedance, and the far field radiation pattern) of the U-PIFA were analyzed by a CST (Computer Simulation technology) Microwave Studio package. The maximum impedance bandwidths achieved are ~8.44% centered at 2.45 GHz and ~ 6.78% centered at 5.8 GHz for a return loss less than 10 dB with nearly omnidirectional pattern suitable for RFID applications. The antenna gains for frequency 2.45 GHz and 5.8 GHz are 2.2 dB and 6.55 dB, respectively.


title: Comparative Performance Analysis of Reference Voltage-Controlled Pulse Width Modulation for High-Speed Single-Phase Brushless DC Motor Drive

abstract: The recent advancements in power switching device material and manufacturing technology have enabled the design of highly efficient and compact high-speed motor direct drives. The use of high-switching frequency pulse width modulation in these drives provides speed and torque control capabilities and it can also serve as an effective phase current shaping method for torque ripple minimization. This paper builds upon the previous paper introducing a torque ripple minimization technique for gallium nitride high-electron mobility transistor-based high-speed single-phase brushless dc motor drive. It presents an in-depth performance analysis in comparison with the conventional high-speed control techniques. Four different high-speed control techniques are implemented and tested to quantify the maximum speed, efficiency, torque ripple, and vibration. The vibration of the motor drive is measured using a microelectromechanical systems accelerometer with a ferroelectric random access memory for high-speed data acquisition with the maximum bandwidth of 1 kHz.


title: Design Trade-offs in Ultra-Low-Power Digital Nanoscale CMOS

abstract: While the general trend in CMOS technology scaling is mostly focused on high-performance and high-speed circuits, the potential use of advanced nanoscale technologies for ultra-low power (ULP) applications with lower operating frequencies is still debated. In these types of applications, the supply voltage is generally reduced well below threshold voltage of MOS devices in order to limit dissipation and to control the device leakage current due to the subthreshold channel residual current. However, recent studies show that reducing the supply voltage increases the device susceptibility to process variations, resulting in delay spread and decreased noise margin. This article presents an analytical approach for studying the effect of technology scaling and variability on performance of ULP integrated systems. Unlike the conventional design methodologies, we include the effect of process variation on circuit performance (such as on noise margin and delay) in each step of design and optimization. Here, the power dissipation and noise margin are both calculated as a function of turn-on and turn-off current of devices. This approach helps to explore the effect of these two quantities on performance of CMOS digital circuits. The trade-offs between the choice of supply voltage, threshold voltage, device dimensions, delay performance, activity rate, and power consumption are analytically examined using predictive device models, for different technology nodes. Taking into account the circuit reliability requirements, this analysis can be used to optimize the system performance with proper device sizing and selecting supply voltage.


title: Comparative analysis of the fecal microbiota from different species of domesticated and wild suids

abstract: Most of the microorganisms living in a symbiotic relationship in different animal body sites (microbiota) reside in the gastrointestinal tract (GIT). Several studies have shown that the microbiota is involved in host susceptibilities to pathogens. The fecal microbiota of domestic and wild suids was analyzed. Bacterial communities were determined from feces obtained from domestic pigs (Sus scrofa) raised under different conditions: specific-pathogen-free (SPF) pigs and domestic pigs from the same bred, and indigenous domestic pigs from a backyard farm in Kenya. Secondly, the fecal microbiota composition of the African swine fever (ASF) resistant warthogs (Phacochoerus africanus) from Africa and a European zoo was determined. African swine fever (ASF) is a devastating disease for domestic pigs. African animals showed the highest microbial diversity while the SPF pigs the lowest. Analysis of the core microbiota from warthogs (resistant to ASF) and pigs (susceptible to ASF) showed 45 shared OTUs, while 6 OTUs were exclusively present in resistant animals. These six OTUs were members of the Moraxellaceae family, Pseudomonadales order and Paludibacter, Anaeroplasma, Petrimonas, and Moraxella genera. Further characterization of these microbial communities should be performed to determine the potential involvement in ASF resistance.


title: Inkjet printed 24 GHz rectenna on paper for millimeter wave identification and wireless power transfer applications

abstract: The use of millimeter wave bands presents an exciting possibility for Radio Frequency Identification (RFID) and wireless power transfer technologies since it allows for low power, low profile yet high performance circuits and provides a larger bandwidth for communication. Furthermore, inkjet printing fabrication permits the fast prototyping of millimeter wave circuits using a variety of low cost and flexible substrate materials. In this work we present the design of a millimeter wave rectenna inkjet-printed on glossy photo-paper substrate and integrating an off-the-shelf Schottky diode. The design has been optimized using harmonic balance optimization combined with electromagnetic simulation in order to maximize the RF-dc conversion efficiency. The simulated rectenna RF-dc efficiency was 35.2% for 15 dBm input power across an optimal load of 280 Ohm.


title: Hierarchical VampPrior Variational Fair Auto-Encoder

abstract: Decision making is a process that is extremely prone to different biases. In this paper we consider learning fair representations that aim at removing nuisance (sensitive) information from the decision process. For this purpose, we propose to use deep generative modeling and adapt a hierarchical Variational Auto-Encoder to learn these fair representations. Moreover, we utilize the mutual information as a useful regularizer for enforcing fairness of a representation. In experiments on two benchmark datasets and two scenarios where the sensitive variables are fully and partially observable, we show that the proposed approach either outperforms or performs on par with the current best model.


title: NimbRo-OP2: Grown-up 3D Printed Open Humanoid Platform for Research

abstract: The versatility of humanoid robots in locomotion, full-body motion, interaction with unmodified human environments, and intuitive human-robot interaction led to increased research interest. Multiple smaller platforms are available for research, but these require a miniaturized environment to interact with---and often the small scale of the robot diminishes the influence of factors which would have affected larger robots. Unfortunately, many research platforms in the larger size range are less affordable, more difficult to operate, maintain and modify, and very often closed-source. In this work, we introduce NimbRo-OP2X, an affordable, fully open-source platform in terms of both hardware and software. Being almost 135cm tall and only 18kg in weight, the robot is not only capable of interacting in an environment meant for humans, but also easy and safe to operate and does not require a gantry when doing so. The exoskeleton of the robot is 3D printed, which produces a lightweight and visually appealing design. We present all mechanical and electrical aspects of the robot, as well as some of the software features of our well-established open-source ROS software. The NimbRo-OP2X performed at RoboCup 2017 in Nagoya, Japan, where it won the Humanoid League AdultSize Soccer competition and Technical Challenge.


title: Generalized classical axially-symmetric dual-reflector antennas

abstract: Classical axially-symmetric Cassegrain and Gregorian reflectors are widely used in high-gain antenna applications. The main disadvantage of these configurations is the subreflector blockage, which causes a number of deleterious effects. However, this problem can be reduced by decreasing the main-reflector radiation toward the subreflector. This may be accomplished either by shaping both reflectors or by using alternative classical configurations. This work considers the second option by presenting, in an unified way, generalized classical axially-symmetric configurations that prevent, from a geometrical optics (GO) stand point, the main-reflector scattered energy from striking the subreflector surface. Starting from initial design variables, closed-form expressions are derived for the relevant surface parameters, as well as for the corresponding aperture field distributions. These expressions can be used as effective design tools to determine the final antenna geometry or even to establish an initial configuration for a shaping procedure.


title: Modeling temporal aspects of sensor data for MongoDB NoSQL database

abstract: Proliferation of structural, semi-structural and no-structural data, has challenged the scalability, flexibility and processability of the traditional relational database management systems (RDBMS). The next generation systems demand horizontal scaling by distributing data over autonomously addable nodes to a running system. For schema flexibility, they also want to process and store different data formats along the sequence factor in the data. NoSQL approaches are solutions to these, hence big data solutions are vital nowadays. But in monitoring scenarios sensors transmit the data continuously over certain intervals of time and temporal factor is the main property of the data. Therefore the key research aspect is to investigate schema flexibility and temporal data integration aspects together. We need to know that: what data modelling should we adopt for a data driven real-time scenario; that we could store the data effectively and evolve the schema accordingly during data integration in NoSQL environments without losing big data advantages. In this paper we explain a middleware based schema model to support the temporal oriented storage of real-time data of ANT+ sensors as hierarchical documents. We explain how to adopt a schema for the data integration by using an algorithm based approach for flexible evolution of the model for a document oriented database, i.e, MongoDB. The proposed model is logical, compact for storage and evolves seamlessly upon new data integration.


title: Beyond the French paradox: the impact of moderate beverage alcohol and wine consumption in the prevention of cardiovascular disease

abstract: Cardiovascular disease (CVD) is the leading cause of death and disability in the United States and is responsible for 53% of deaths in women and 46% of deaths in men. [1] CVD is a primary or contributing cause in 60% of all deaths, and claims as many lives as the next leading causes of death including cancers, accidents, infections, and pulmonary disease. Coronary heart disease (CHD) affects 12 million people in the United States of which 1.1 million have a myocardial infarction annually and about one third die. Worldwide, CVD is the foremost cause of death, accounting for 57% of deaths among developing nations, and the second most cause of disability [2]. Over the past two decades, studies have consistently demonstrated an inverse relationship between alcohol consumption and the occurrence of myocardial infarction and cardiac death, with a J-shaped curve relating alcohol intake to mortality, favoring moderate alcohol drinkers compared with non-drinkers or heavy drinkers [3–5]. Millions of persons living in the United States drink alcohol; most consume fewer than three alcoholic drinks per day [4]. The American Heart Association’s Science Advisory on alcohol and health cites the widely acknowledged harmful effects of heavy drinking [6]. This report underemphasizes, however, the risk reduction for cardiac and all cause mortality that has been associated with moderate alcohol consumption. Plausible biologic mechanisms for vascular protection from beverage alcohol is discussed in this article. The medical community has been cautioned not to expound the health benefits of alcohol, and particularly wine. In a letter, Ellison countered that, ‘‘telling people to avoid any alcohol consumption, because of the potential dangers of heavy use may not be in the best health interest of the public’’ [7]. Most agree that in the absence of a well-executed, randomized, doubleblinded, intervention trial, controlled for confounding variables, the debate as to the health benefits of wine and beverage alcohol, as a part of a healthy lifestyle, will undoubtedly continue.


title: Understanding user privacy in Internet of Things environments

abstract: During the past decade, user privacy has become an important issue in networked computing environments. For instance, mobile applications and devices are increasingly asking users to provide personal information, as well as monitoring users through behavioral tracking. This privacy-invasive practice is likely to increase with the proliferation of sensor devices in the upcoming era of Internet of Things (IoT). However, there has been comparatively little research so far aimed at understanding people's notion of privacy in connection with IoT. In earlier work, we unveiled five contextual parameters that characterize IoT service scenarios, and five reaction parameters that describe people's attitudes toward the scenarios. In this paper, we aim to understand how these contextual parameters impact people's privacy perceptions of IoT scenarios. To this end, we conducted a survey with 200 respondents on 2800 hypothetical IoT scenarios (mostly about information monitoring activities), and analyzed them using a K-modes clustering algorithm. We identified four clusters of scenarios, with clearly distinctive associated user reactions. By comparing the different clusters, we can identify contextual parameters that are associated with higher or lower acceptance of sensor tracking in IoT environments.


title: Theoretical, Statistical, and Practical Perspectives on Pattern-based Classification Approaches to the Analysis of Functional Neuroimaging Data

abstract: Abstract The goal of pattern-based classification of functional neuroimaging data is to link individual brain activation patterns to the experimental conditions experienced during the scans. These “brain-reading” analyses advance functional neuroimaging on three fronts. From a technical standpoint, pattern-based classifiers overcome fatal f laws in the status quo inferential and exploratory multivariate approaches by combining pattern-based analyses with a direct link to experimental variables. In theoretical terms, the results that emerge from pattern-based classifiers can offer insight into the nature of neural representations. This shifts the emphasis in functional neuroimaging studies away from localizing brain activity toward understanding how patterns of brain activity encode information. From a practical point of view, pattern-based classifiers are already well established and understood in many areas of cognitive science. These tools are familiar to many researchers and provide a quantitatively so...


title: Community Detection in Networks: The Leader-Follower Algorithm

abstract: Traditional spectral clustering methods cannot naturally learn the number of communities in a network and often fail to detect smaller community structure in dense networks because they are based upon external community connectivity properties such as graph cuts. We propose an algorithm for detecting community structure in networks called the leader-follower algorithm which is based upon the natural internal structure expected of communities in social networks. The algorithm uses the notion of network centrality in a novel manner to differentiate leaders (nodes which connect different communities) from loyal followers (nodes which only have neighbors within a single community). Using this approach, it is able to naturally learn the communities from the network structure and does not require the number of communities as an input, in contrast to other common methods such as spectral clustering. We prove that it will detect all of the communities exactly for any network possessing communities with the natural internal structure expected in social networks. More importantly, we demonstrate the effectiveness of the leader-follower algorithm in the context of various real networks ranging from social networks such as Facebook to biological networks such as an fMRI based human brain network. We find that the leader-follower algorithm finds the relevant community structure in these networks without knowing the number of communities beforehand. Also, because the leader-follower algorithm detects communities using their internal structure, we find that it can resolve a finer community structure in dense networks than common spectral clustering methods based on external community structure.


title: Multimodal Content Analysis for Effective Advertisements on YouTube

abstract: The rapid advances in e-commerce and Web 2.0 technologies have greatly increased the impact of commercial advertisements on the general public. As a key enabling technology, a multitude of recommender systems exists which analyzes user features and browsing patterns to recommend appealing advertisements to users. In this work, we seek to study the characteristics or attributes that characterize an effective advertisement and recommend a useful set of features to aid the designing and production processes of commercial advertisements. We analyze the temporal patterns from multimedia content of advertisement videos including auditory, visual and textual components, and study their individual roles and synergies in the success of an advertisement. The objective of this work is then to measure the effectiveness of an advertisement, and to recommend a useful set of features to advertisement designers to make it more successful and approachable to users. Our proposed framework employs the signal processing technique of cross modality feature learning where data streams from different components are employed to train separate neural network models and are then fused together to learn a shared representation. Subsequently, a neural network model trained on this joint feature embedding representation is utilized as a classifier to predict advertisement effectiveness. We validate our approach using subjective ratings from a dedicated user study, the sentiment strength of online viewer comments, and a viewer opinion metric of the ratio of the Likes and Views received by each advertisement from an online platform.


Solitary Cutaneous Metastasis as the First Sign of Relapse of Thyroid Carcinoma: A Clinical, Dermoscopic-Pathologic Case Study


title: Layers of Silence, Arenas of Voice: The Ecology of Visible and Invisible Work

abstract: No work is inherently either visible or invisible. We always ’’see‘‘ work through a selection of indicators: straining muscles, finished artifacts, a changed state of affairs. The indicators change with context, and that context becomes a negotiation about the relationship between visible and invisible work. With shifts in industrial practice these negotiations require longer chains of inference and representation, and may become solely abstract. ::: ::: This article provides a framework for analyzing invisible work in CSCW systems. We sample across a variety of kinds of work to enrich the understanding of how invisibility and visibility operate. Processes examined include creating a ’’non-person‘‘ in domestic work; disembedding background work; and going backstage. Understanding these processes may inform the design of CSCW systems and the development of related social theory.


Part of Speech Tagging and Shallow Parsing of Indian Languages


title: Enhancing group recommendation by incorporating social relationship interactions

abstract: Group recommendation, which makes recommendations to a group of users instead of individuals, has become increasingly important in both the workspace and people's social activities, such as brainstorming sessions for coworkers and social TV for family members or friends. Group recommendation is a challenging problem due to the dynamics of group memberships and diversity of group members. Previous work focused mainly on the content interests of group members and ignored the social characteristics within a group, resulting in suboptimal group recommendation performance. In this work, we propose a group recommendation method that utilizes both social and content interests of group members. We study the key characteristics of groups and propose (1) a group consensus function that captures the social, expertise, and interest dissimilarity among multiple group members; and (2) a generic framework that automatically analyzes group characteristics and constructs the corresponding group consensus function. Detailed user studies of diverse groups demonstrate the effectiveness of the proposed techniques, and the importance of incorporating both social and content interests in group recommender systems.


title: Automatically mining software-based, semantically-similar words from comment-code mappings

abstract: Many software development and maintenance tools involve matching between natural language words in different software artifacts (e.g., traceability) or between queries submitted by a user and software artifacts (e.g., code search). Because different people likely created the queries and various artifacts, the effectiveness of these tools is often improved by expanding queries and adding related words to textual artifact representations. Synonyms are particularly useful to overcome the mismatch in vocabularies, as well as other word relations that indicate semantic similarity. However, experience shows that many words are semantically similar in computer science situations, but not in typical natural language documents. In this paper, we present an automatic technique to mine semantically similar words, particularly in the software context. We leverage the role of leading comments for methods and programmer conventions in writing them. Our evaluation of our mined related comment-code word mappings that do not already occur in WordNet are indeed viewed as computer science, semantically-similar word pairs in high proportions.


title: Pattern Mining with Evolutionary Algorithms

abstract: This book provides a comprehensive overview of the field of pattern mining with evolutionary algorithms. To do so, it covers formal definitions about patterns, patterns mining, type of patterns and the usefulness of patterns in the knowledge discovery process. As it is described within the book, the discovery process suffers from both high runtime and memory requirements, especially when high dimensional datasets are analyzed. To solve this issue, many pruning strategies have been developed. Nevertheless, with the growing interest in the storage of information, more and more datasets comprise such a dimensionality that the discovery of interesting patterns becomes a challenging process. In this regard, the use of evolutionary algorithms for mining pattern enables the computation capacity to be reduced, providing sufficiently good solutions. This book offers a survey on evolutionary computation with particular emphasis on genetic algorithms and genetic programming. Also included is an analysis of the set of quality measures most widely used in the field of pattern mining with evolutionary algorithms. This book serves as a review of the most important evolutionary algorithms for pattern mining. It considers the analysis of different algorithms for mining different type of patterns and relationships between patterns, such as frequent patterns, infrequent patterns, patterns defined in a continuous domain, or even positive and negative patterns. A completely new problem in the pattern mining field, mining of exceptional relationships between patterns, is discussed. In this problem the goal is to identify patterns which distribution is exceptionally different from the distribution in the complete set of data records. Finally, the book deals with the subgroup discovery task, a method to identify a subgroup of interesting patterns that is related to a dependent variable or target attribute. This subgroup of patterns satisfies two essential conditions: interpretability and interestingness.


Modeling the Influence of Format and Depth during Effortful Retrieval Practice


title: CSR Reporting Practices of Polish Energy and Mining Companies

abstract: Corporate Social Responsibility (CSR) reporting receives much attention nowadays. Communication with stakeholders is a part of assumed social responsibility, thus the quality of information disclosed in CSR reports has a significant impact on fulfilment of the responsibility. The authors use content analysis of selected CSR reports to describe and assess patterns and structure of information disclosed in them. CSR reports of Polish companies have similar structures at a very high level of analysis, but a more detailed study reveals much diversity in approaches to the report’s content. Even fairly similar companies may devote significantly different amounts of space to the same issue. The number of similar stakeholders varies irrespectively of the company’s size. Considerable diversity of reporting patterns results from the nature of CSR reporting, because it concerns highly entity-specific issues. Thus, such considerable diversity is not surprising. However, many initiatives and efforts are devoted to greater comparability of reporting, so a greater degree of uniformity can be expected. Similar conclusions may be drawn from integrated reports’ analysis, though a small sample reflects the relative novelty of this trend.


Educating K-12 Students about Glacier Dynamics in a Changing Climate


title: Organizational Culture: The Focus Questionnaire

abstract: This article describes two studies. The first study concerns the development of an internationally useful questionnaire for measuring organizational culture on the basis of Quinn's (l988) competing values model. The competing values model describes four cultural orientations. These are the support, innovation, rules, and goal orientation. The questionnaire is called FOCUS, and was developed by an international research group from 12 countries. The questionnaire consists of two parts: descriptive part (measuring organizational practices) and an evaluative part (measuring characteristics of the organization). The first study shows that seven of the eight scales meet psychometric criteria. The second study shows preliminary results regarding the influence of country and sector on organizational culture. Organizational culture is again divided into practices and values (characteristics). Sector and organization are expected to explain differences in organization are expected to explain differences in organiza...


title: The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools

abstract: The Arabidopsis Information Resource (TAIR, http://arabidopsis.org) is a genome database for Arabidopsis thaliana, an important reference organism for many fundamental aspects of biology as well as basic and applied plant biology research. TAIR serves as a central access point for Arabidopsis data, annotates gene function and expression patterns using controlled vocabulary terms, and maintains and updates the A. thaliana genome assembly and annotation. TAIR also provides researchers with an extensive set of visualization and analysis tools. Recent de- velopments include several new genome releases (TAIR8, TAIR9 and TAIR10) in which the A. thaliana assembly was updated, pseudogenes and transposon genes were re-annotated, and new data from proteomics and next generation transcriptome sequencing were incorporated into gene models and splice variants. Other highlights include progress on functional anno- tation of the genome and the release of sev- eral new tools including Textpresso for Arabidopsis which provides the capability to carry out full text searches on a large body of research literature.


title: Supervised methods for multi-relational link prediction

abstract: Many important real-world systems, modeled naturally as complex networks, have heterogeneous interactions and complicated dependency structures. Link prediction in such networks must model the influences between heterogenous relationships and distinguish the formation mechanisms of each link type, a task which is beyond the simple topological features commonly used to score potential links. In this paper, we introduce a novel probabilistically weighted extension of the Adamic/Adar measure for heterogenous information networks, which we use to demonstrate the potential benefits of diverse evidence, particularly in cases where homogeneous relationships are very sparse. However, we also expose some fundamental flaws of traditional unsupervised link prediction. We develop supervised learning approaches for relationship (link) prediction in multi-relational networks, and demonstrate that a supervised approach to link prediction can enhance performance. We present results on three diverse, real-world heterogeneous information networks and discuss the trends and tradeoffs of supervised and unsupervised link prediction in a multi-relational setting.


title: Cyber security for smart grid systems: Status, challenges and perspectives

abstract: The transformation of traditional energy networks to smart grids revolutionizes the energy industry in terms of reliability, performance, and manageability by providing bi-directional communications to operate, monitor, and control power flow and measurements. However, communication networks in smart grid bring increased connectivity with increased severe security vulnerabilities and challenges. Smart grid can be a prime target for cyber terrorism because of its critical nature. As a result, smart grid security is already getting a lot of attention from governments, energy industries, and consumers. There have been several research efforts for securing smart grid systems in academia, government and industries. This article provides a comprehensive study of challenges in smart grid security, which we concentrate on the problems and proposed solutions. Then, we outline current state of the research and future perspectives.With this article, readers can have a more thorough understanding of smart grid security and the research trends in this topic.


title: Obstacle avoidance of autonomous vehicles with CQP-based model predictive control

abstract: In this paper, an approach for real time obstacle avoidance of autonomous vehicles is presented. A model predictive control (MPC) scheme based on convex quadratic programming (CQP) is developed to generate safety trajectories. To reduce the computational burden in optimizing the performance index of MPC, linear time-varying MPC is adopted and a unique single dimension artificial potential fields (SDAPF) method to utilize the obstacle information is proposed. Autonomous vehicles with proposed method can track the desired path if there is no obstacle on it and avoid both static and dynamic obstacles if the path is occupied. Simulation results show the validity of the approach and its superior real time performance, which is critical to autonomous vehicles.


title: Acupuncture analgesia: I. The scientific basis.

abstract: Acupuncture has been used in China and other Asian countries for the past 3000 yr. Recently, this technique has been gaining increased popularity among physicians and patients in the United States. Even though acupuncture-induced analgesia is being used in many pain management programs in the United States, the mechanism of action remains unclear. Studies suggest that acupuncture and related techniques trigger a sequence of events that include the release of neurotransmitters, endogenous opioid-like substances, and activation of c-fos within the central nervous system. Recent developments in central nervous system imaging techniques allow scientists to better evaluate the chain of events that occur after acupuncture-induced stimulation. In this review article we examine current biophysiological and imaging studies that explore the mechanisms of acupuncture analgesia.


title: Modeling user interests from web browsing activities

abstract: Browsing sessions are rich in elements useful to build profiles of user interests, but at the same time HTML pages include noisy data such as advertisements, navigation menus and privacy notes. Moreover, some pages cover several different topics making it difficult to identify the most relevant to the user. For these reasons, they are often ignored by personalized search and recommender systems. We propose a novel approach for recognizing valuable text descriptions of current user information needs--namely cues--based on the data mined from browsing interactions over the web. The approach combines page clustering techniques based on Document Object Model-based representations for acquiring evidence about relevant correlations between text contents. This evidence is exploited for better filtering out irrelevant information and facilitating the construction of interest profiles. A comparative framework proves the accuracy of the extracted cues in the personalize search task, where results are re-ranked according to the last browsed resources.


title: The 2nd to 4th digit ratio and autism

abstract: It has been suggested that autism may arise as the result of exposure to high concentrations of prenatal testosterone. There is evidence that the ratio of the lengths of the 2nd and 4th digit (2D:4D) may be negatively correlated with prenatal testosterone. We measured 2D:4D in 95 families recruited via the National Autistic Society, UK. The sample comprised a total 72 children with autism (62 males, 10 females; age range 2 to 14 years), including 23 children (20 males, three females) with Asperger syndrome(AS), 34 siblings, 88 fathers, 88 mothers and sex- and age-matched control participants. We found that the 2D:4D ratios of children with autism, their siblings, fathers and mothers were lower than population normative values. Children with AS, who share the social and communicative symptoms of autism but have normal or even high IQ, had higher 2D:4D ratios than children with autism but lower ratios than population normative values. There were positive associations between 2D:4D ratios of children with autism and the ratios of their relatives. Children with autism had lower than expected 2D:4D ratios and children with AS higher ratios than expected in relation to their fathers’2D:4D ratio. It was concluded that 2D:4D ratio may be a possible marker for autism which could implicate prenatal testosterone in its aetiology.


title: Ultrathin low cost EBG structure for insulating UHF RFID tag from metal objects

abstract: In this paper, an ultrathin low cost EBG structure is used to insulate the RFID tag antenna from backside objects. The reflection phase of TM waves and the transmission coefficient of a suspended stripline over an EBG surface are both used to simulate the stopband of such structures. A used dipole antenna placed on top of our proposed EBG structure with an ultrathin substrate is simulated by HFSS.


title: GNSS-Based Attitude Determination Techniques—A Comprehensive Literature Survey

abstract: GNSS-based Attitude Determination (AD) of a mobile object using the readings of the Global Navigation Satellite Systems (GNSS) is an active area of research. Numerous attitude determination methods have been developed lately by making use of various sensors. However, the last two decades have witnessed an accelerated growth in research related to GNSS-based navigational equipment as a reliable and competitive device for determining the attitude of any outdoor moving object using data demodulated from GNSS signals. Because of constantly increasing number of GNSS-based AD methods, algorithms, and techniques, introduced in scientific papers worldwide, the problem of choosing an appropriate approach, that is optimal for the given application, operational environment, and limited financial funding becomes quite a challenging task. The work presents an extensive literature survey of the methods mentioned above which are classified in many different categories. The main aim of this survey is to help researchers and developers in the field of GNSS applications to understand pros and cons of the current state of the art methods and their computational efficiency, the scope of use and accuracy of the angular determination.


title: Design for RTDB Memory Structure of DCS

abstract: Real-time database (RTDB) is an important part of the distributed control system (DCS), which is equivalent to a data processing center and functions as a data exchange area. The RTDB kernel carries the main features of the RTDB, supports task timing constraints, maintains a large amount of shared data, and ensures the integrity, consistency and real-time performance of the data. It also provides data access services for historical databases and human-computer interaction client programs, and is a bridge for data interaction between controllers and clients. The data in the real-time library is stored in memory. Therefore, the design of the RTDB memory structure directly affects the quality of the entire DCS system.


title: National and Surgical Health Care Expenditures, 2005–2025

abstract: OBJECTIVE AND BACKGROUND ::: Health care expenditures for 2005 in the United States were $1.9733 trillion and 15.9% of the gross domestic product (GDP). Twenty-nine percent of those expenditures were secondary to surgical revenues. Health care expenditures are increasing 2(1/2) times the rate of the general US economy and are being fed by new technologies, new medications, the aging population, more services provided per patient, defensive medicine and little tort reform, the insurance system, and the free rider problem, ie, patients are cared for as emergencies regardless of insurance coverage and legality, which all have contributed to rising health care and surgical expenditures over the last 50 years. ::: ::: ::: METHODS ::: The purpose of this study was to project aggregate national health care expenditures, aggregate surgical health care expenditures, and the United States GDP for the years 2005-2025. Model building and existing state and national data were used. Aggregate surgical health care expenditures were computed as 29% of aggregate health care expenditures using a unique model developed by the late Dr. Francis D. Moore. The model of Dr. Moore which used 1981 federal data was verified/tested using data from UMDNJ-University Hospital, and New Jersey and national data from 2005. From 1965 to 2005 mean health care expenditures increased at 4.9% per year, and US GDP increased at a mean of 2.1% per year. ::: ::: ::: RESULTS ::: Aggregate surgical expenditures are expected to grow from $572 billion in 2005 (4.6% of US GDP) to $912 billion (2005 dollars) in the year 2025 (7.3% of US GDP). Aggregate health care expenditures are projected to increase from $5572 per capita (15.9% of GDP) in 2005 to $8832 per capita (2005 dollars) in 2025 (25.2% of US GDP). Both surgery and national health care expenditures are expected to expand by almost 60% during the period 2005-2025. Thus, surgical health care expenditures by 2025 are likely to be 1/14 of the US economy, and health care expenditures will be (1/4) of the US economy. ::: ::: ::: CONCLUSIONS ::: Real per capita GDP growth is relatively flat in the United States. Rising surgical health care expenditures and national health care expenditures are a significant issue for the US population. Unfortunately, programs at the state and federal level as well as private programs, for the last 50 years have not been able to slow the growth in health care expenditures. These trends are likely to continue and the effects will be: * A change in the US standard of living as surgical and health care expenditures become a larger part of the earned dollar per American especially with the current volatility of the US economy, * A rise in the cost of products made in the United States to pay the rising health care bill with a concomitant change in our national and international standard of living, and * An increasing debt and increases in federal and state taxes which will be required to maintain the current health care system, ie, Medicare, Medicaid, and the private health care insurance payment scheme, which has not changed substantially over the past 40 to 50 years. Surgeons must look at the incremental benefit of new technologies and procedures and determine which to choose if we are to slow the growth of surgical health care expenditures.


title: Grounded Cognition

abstract: Grounded cognition rejects traditional views that cognition is computation on amodal symbols in a modular system, independent of the brain's modal systems for perception, action, and introspection. Instead, grounded cognition proposes that modal simulations, bodily states, and situated action underlie cognition. Accumulating behavioral and neural evidence supporting this view is reviewed from research on perception, memory, knowledge, language, thought, social cognition, and development. Theories of grounded cognition are also reviewed, as are origins of the area and common misperceptions of it. Theoretical, empirical, and methodological issues are raised whose future treatment is likely to affect the growth and impact of grounded cognition.


title: A review of the recent research on vibration energy harvesting via bistable systems

abstract: The investigation of the conversion of vibrational energy into electrical power has become a major field of research. In recent years, bistable energy harvesting devices have attracted significant attention due to some of their unique features. Through a snap-through action, bistable systems transition from one stable state to the other, which could cause large amplitude motion and dramatically increase power generation. Due to their nonlinear characteristics, such devices may be effective across a broad-frequency bandwidth. Consequently, a rapid engagement of research has been undertaken to understand bistable electromechanical dynamics and to utilize the insight for the development of improved designs. This paper reviews, consolidates, and reports on the major efforts and findings documented in the literature. A common analytical framework for bistable electromechanical dynamics is presented, the principal results are provided, the wide variety of bistable energy harvesters are described, and some remaining challenges and proposed solutions are summarized.


title: Design of fully printable chipless RFID tag on flexible substrate for secure banknote applications

abstract: A fully printable, passive and planar chipless RFID tag on thin flexible substrate is presented. The chipless tag encodes data into the spectral signature using a multiresonating circuit. Both amplitude and phase components of the spectral signature are used for data encoding. The tag comprises of a microstrip multiresonating circuit with 6 multi-spiral resonators and two cross-polarized UWB disc loaded monopole transmitting and receiving antennas. The chipless tag is designed to be printed on the Australian banknote and provide an extra layer of security against counterfeiting.


title: Trustless Machine Learning Contracts; Evaluating and Exchanging Machine Learning Models on the Ethereum Blockchain

abstract: Using blockchain technology, it is possible to create contracts that offer a reward in exchange for a trained machine learning model for a particular data set. This would allow users to train machine learning models for a reward in a trustless manner. The smart contract will use the blockchain to automatically validate the solution, so there would be no debate about whether the solution was correct or not. Users who submit the solutions won't have counterparty risk that they won't get paid for their work. Contracts can be created easily by anyone with a dataset, even programmatically by software agents. This creates a market where parties who are good at solving machine learning problems can directly monetize their skillset, and where any organization or software agent that has a problem to solve with AI can solicit solutions from all over the world. This will incentivize the creation of better machine learning models, and make AI more accessible to companies and software agents.


title: Superoxide dismutase activity in Pseudomonas putida affects utilization of sugars and growth on root surfaces.

abstract: To investigate the role of superoxide dismutases (SOD) in root colonization and oxidative stress, mutants of Pseudomonas putida lacking manganese-superoxide dismutase (MnSOD) (sodA), iron-superoxide dismutase (FeSOD) (sodB), or both were generated. The sodA sodB mutant did not grow on components washed from bean root surfaces or glucose in minimal medium. The sodB and sodA sodB mutants were more sensitive than wild type to oxidative stress generated within the cell by paraquat treatment. In single inoculation of SOD mutants on bean, only the sodA sodB double mutant was impaired in growth on root surfaces. In mixed inoculations with wild type, populations of the sodA mutant were equal to those of the wild type, but levels of the sodB mutant and, to a great extent, the sodA sodB mutant, were reduced. Confocal microscopy of young bean roots inoculated with green fluorescent protein-tagged cells showed that wild type and SOD single mutants colonized well predominantly at the root tip but that the sodA sodB double mutant grew poorly at the tip. Our results indicate that FeSOD in P. putida is more important than MnSOD in aerobic metabolism and oxidative stress. Inhibition of key metabolic enzymes by increased levels of superoxide anion may cause the impaired growth of SOD mutants in vitro and in planta.


title: The low radio frequency limit of solar type III bursts: Ulysses observations in and out of the ecliptic

abstract: We measure the low frequency limit flo of 1028 type III bursts and compare it with the observed plasma frequency at the spacecraft fp. We consider three periods: when Ulysses was in the ecliptic plane near the Sun, far from the Sun, and out of the ecliptic; the average plasma frequency was 20, 10, and 5 kHz respectively. The major results are: 1) During the entire period of Ulysses flight November 1990 to March 1995, 9 kHz is the lowest frequency at which type III electromagnetic radiation was ever seen, even though fp was often 3–4 kHz. 2) The distribution of cutoffs is independent of ecliptic latitude, and of fp at the spacecraft (excepting of course that flo ≥ fp) and is broadly spread between 17 and 300 kHz. 3) When fp is about 20, 10 and 5 kHz, only 15, 5 and 0% respectively of type III burst radiation approaches within a factor of two of fp. ::: ::: We develop three hypotheses to explain these results: 1) that the low-frequency cutoff of type III bursts is intrinsic to the radiation mechanism, 2) that it is due to directivity of the radiation, and 3) that it is due to propagation conditions between the burst source and the spacecraft. The evidence for intrinsic cutoffs is strong, but propagation effects play a role in some cases.


title: OctNet: Learning Deep 3D Representations at High Resolutions

abstract: We present OctNet, a representation for deep learning with sparse 3D data. In contrast to existing models, our representation enables 3D convolutional networks which are both deep and high resolution. Towards this goal, we exploit the sparsity in the input data to hierarchically partition the space using a set of unbalanced octrees where each leaf node stores a pooled feature representation. This allows to focus memory allocation and computation to the relevant dense regions and enables deeper networks without compromising resolution. We demonstrate the utility of our OctNet representation by analyzing the impact of resolution on several 3D tasks including 3D object classification, orientation estimation and point cloud labeling.


title: Identifying Prominent Arguments in Online Debates Using Semantic Textual Similarity

abstract: Online debates sparkle argumentative discussions from which generally accepted arguments often emerge. We consider the task of unsupervised identification of prominent argument in online debates. As a first step, in this paper we perform a cluster analysis using semantic textual similarity to detect similar arguments. We perform a preliminary cluster evaluation and error analysis based on cluster-class matching against a manually labeled dataset.


title: Word Embedding Composition for Data Imbalances in Sentiment and Emotion Classification

abstract: Text classification often faces the problem of imbalanced training data. This is true in sentiment analysis and particularly prominent in emotion classification where multiple emotion categories are very likely to produce naturally skewed training data. Different sampling methods have been proposed to improve classification performance by reducing the imbalance ratio between training classes. However, data sparseness and the small disjunct problem remain obstacles in generating new samples for minority classes when the data are skewed and limited. Methods to produce meaningful samples for smaller classes rather than simple duplication are essential in overcoming this problem. In this paper, we present an oversampling method based on word embedding compositionality which produces meaningful balanced training data. We first use a large corpus to train a continuous skip-gram model to form a word embedding model maintaining the syntactic and semantic integrity of the word features. Then, a compositional algorithm based on recursive neural tensor networks is used to construct sentence vectors based on the word embedding model. Finally, we use the SMOTE algorithm as an oversampling method to generate samples for the minority classes and produce a fully balanced training set. Evaluation results on two quite different tasks show that the feature composition method and the oversampling method are both important in obtaining improved classification results. Our method effectively addresses the data imbalance issue and consequently achieves improved results for both sentiment and emotion classification.


title: Lateralization of auditory language functions: A dynamic dual pathway model

abstract: Spoken language comprehension requires the coordination of different subprocesses in time. After the initial acoustic analysis the system has to extract segmental information such as phonemes, syntactic elements and lexical-semantic elements as well as suprasegmental information such as accentuation and intonational phrases, i.e., prosody. According to the dynamic dual pathway model of auditory language comprehension syntactic and semantic information are primarily processed in a left hemispheric temporo-frontal pathway including separate circuits for syntactic and semantic information whereas sentence level prosody is processed in a right hemispheric temporo-frontal pathway. The relative lateralization of these functions occurs as a result of stimulus properties and processing demands. The observed interaction between syntactic and prosodic information during auditory sentence comprehension is attributed to dynamic interactions between the two hemispheres.


title: Maintenance scheduling of geographically distributed assets with prognostics information

abstract: Abstract Maintenance scheduling for high value assets has been studied for decades and is still a crucial area of research with new technological advancements. The main dilemma of maintenance scheduling is to avoid failures while preventing unnecessary maintenance. The technological advancements in real time monitoring and computational science make tracking asset health and forecasting asset failures possible. The usage and maintenance of assets can be planned more efficiently with the forecasted failure probability and remaining useful life (i.e., prognostic information). The prognostic information is time sensitive. Geographically distributed assets such as off-shore wind farms and railway switches add another complexity to the maintenance scheduling problem with the required time of travel to reach these assets. Thus, the travel time between geographically distributed assets should be incorporated in the maintenance scheduling when one technician (or team) is responsible for the maintenance of multiple assets. This paper presents a methodology to schedule the maintenance of geographically distributed assets using their prognostic information. Genetic Algorithm based solution incorporating the daily work duration of the maintenance team is also presented in the paper.


title: ACaaS: Access Control as a Service for IaaS Cloud

abstract: Organizations and enterprises have been outsourcing their computation, storage, and workflows to Infrastructure-as-a-Service (IaaS) based cloud platforms. The heterogeneity and high diversity of IaaS cloud environment demand a comprehensive and fine-grained access control mechanism, in order to meet dynamic, extensible, and highly configurable security requirements of these cloud consumers. However, existing security mechanisms provided by IaaS cloud providers do not satisfy these requirements. To address such an emergent demand, we propose a new cloud service called access control as a service (ACaaS), a service-oriented architecture in cloud to support multiple access control models, with the spirit of plug gable access control modules in modern operating systems. As a proof-of-concept reference prototype, we design and implement ACaaS_RBAC to provide role-based access control (RBAC) for Amazon Web Services (AWS), where cloud customers can easily integrate the service into enterprise applications in order to extend RBAC policy enforcement in AWS.


title: Futures risk premia in the era of shale oil

abstract: The advent of shale oil in the United States triggered a structural transformation in the oil market. We show, both theoretically and empirically, that this process has relevant consequences on oil risk premia. We construct a consumption-based model with shale producers interacting with financial speculators in the futures market. Compared to conventionals, shale producers have a more flexible technology, but higher risk aversion and additional costs due to their reliance on external finance. Our model helps to explain the observed pattern of aggregate hedging by US firms in the last decade. The empirical analysis shows that the hedging pressure of shale producers has become more relevant than that of conventional producers in explaining the oil futures risk premium.


title: The SWAN biomedical discourse ontology

abstract: Developing cures for highly complex diseases, such as neurodegenerative disorders, requires extensive interdisciplinary collaboration and exchange of biomedical information in context. Our ability to exchange such information across sub-specialties today is limited by the current scientific knowledge ecosystem's inability to properly contextualize and integrate data and discourse in machine-interpretable form. This inherently limits the productivity of research and the progress toward cures for devastating diseases such as Alzheimer's and Parkinson's. SWAN (Semantic Web Applications in Neuromedicine) is an interdisciplinary project to develop a practical, common, semantically structured, framework for biomedical discourse initially applied, but not limited, to significant problems in Alzheimer Disease (AD) research. The SWAN ontology has been developed in the context of building a series of applications for biomedical researchers, as well as in extensive discussions and collaborations with the larger bio-ontologies community. In this paper, we present and discuss the SWAN ontology of biomedical discourse. We ground its development theoretically, present its design approach, explain its main classes and their application, and show its relationship to other ongoing activities in biomedicine and bio-ontologies.


title: Forecasting urban dynamics with mobility logs by bilinear Poisson regression

abstract: Understanding people flow in a city (urban dynamics) is of great importance in urban planning, emergency management, and commercial activity. With the spread of smart devices, many studies on urban dynamics modeling with mobility logs have been conducted. It is predictive analysis, not analysis of the past, that enables various applications contributing to a more prosperous society. To deal with the non-linear effects on urban dynamics from external factors, such as day of the week, national holiday, or weather, we propose a low-rank bilinear Poisson regression model, for a novel and flexible representation of urban dynamics predictive analysis. The results obtained from an experiment with one year's worth of mobility records suggest the high prediction accuracy of the proposed model. We also introduce the following applications: regional event detection via irregularities, visualization of urban dynamics corresponding to urban demographics, and extraction of urban demographics of unknown point of interests.


title: A Unified Theory of Exogenous and Endogenous Attentional Control

abstract: Although diverse, theories of visual attention generally share the notion that attention is controlled by some combination of three distinct strategies: (1) exogenous cueing from locallycontrasting primitive visual features, such as abrupt onsets or color singletons (e.g., Itti & Koch, 2001); (2) endogenous gain modulation of exogenous activations, used to guide attention to task relevant features (e.g., Navalpakkam & Itti, 2005; Wolfe, 1994); and (3) endogenous prediction of likely locations of interest, based on task and scene gist (e.g., Torralba, Oliva, Catelhano, & Henderson, 2006). Because these strategies can make conflicting suggestions, theories posit arbitration and combination rules. We propose an alternative conceptualization consisting of a single unified mechanism that is controlled along two dimensions: the degree of task focus, and the spatial scale of operation. Previously proposed strategies—and their combinations—can be viewed as instances of this mechanism. Our theory offers a means of integrating data from a wide range of attentional phenomena. More importantly, the theory yields an unusual perspective on attention that places a fundamental emphasis on the role of experience and task-related knowledge.


The Problem with Solutions to the Frame Problem


title: An assist-as-needed control scheme for robot-assisted rehabilitation

abstract: This paper addresses the assist-as-needed (AAN) control problem for robotic orthoses. The objective is to design a stable AAN controller with an adjustable assistance level. The controller aims to follow a desired trajectory while allowing an adjustable tracking error with low control effort to provide a freedom zone for the user. By ensuring the stability of the system and providing the freedom zone, the controller combines the advantages of both model-based and non-model-based AAN controllers existing in the literature. Furthermore, the controller provides a priori bounded control command, and includes an adaptive neural network term to compensate for the uncertainties of dynamic model of the system, mainly when a precise tracking is of interest. The stability of the closed-loop system is well analysed based on the Lyapunov method. The effectiveness of the proposed control scheme is validated through experiments using a lower extremity robotic exoskeleton.


title: Low-level Software Security by Example

abstract: Computers are often subject to external attacks that aim to control software behavior. Typically, such attacks arrive as data over a regular communication channel and, once resident in program memory, trigger pre-existing, low-level software vulnerabilities. By exploiting such flaws, these low-level attacks can subvert the execution of the software and gain control over its behavior. The combined effects of these attacks make them one of the most pressing challenges in computer security. As a result, in recent years, many mechanisms have been proposed for defending against these attacks.


title: Nonhuman primates prefer slow tempos but dislike music overall

abstract: Abstract Human adults generally find fast tempos more arousing than slow tempos, with tempo frequently manipulated in music to alter tension and emotion. We used a previously published method [McDermott, J., & Hauser, M. (2004). Are consonant intervals music to their ears? Spontaneous acoustic preferences in a nonhuman primate. Cognition, 94 (2), B11–B21] to test cotton-top tamarins and common marmosets, two new-World primates, for their spontaneous responses to stimuli that varied systematically with respect to tempo. Across several experiments, we found that both tamarins and marmosets preferred slow tempos to fast. It is possible that the observed preferences were due to arousal, and that this effect is homologous to the human response to tempo. In other respects, however, these two monkey species showed striking differences compared to humans. Specifically, when presented with a choice between slow tempo musical stimuli, including lullabies, and silence, tamarins and marmosets preferred silence whereas humans, when similarly tested, preferred music. Thus despite the possibility of homologous mechanisms for tempo perception in human and nonhuman primates, there appear to be motivational ties to music that are uniquely human.


title: Classification-driven Active Contour for Dress Segmentation

abstract: In this work we propose a a dedicated object extractor for dress segmentation in fashion images by combining local information with a prior learning. First, a person detector is applied to localize sites in the image that are likely to contain the object. Then, an intra-image two-stage learning process is developed to roughly separate foreground pixels from the background. Finally, the object is finely segmented by employing an active contour algorithm that takes into account the previous segmentation and injects specific knowledge about local curvature in the energy function. The method is validated on a database of manually segmented images. We show examples of both successful segmentation and difficult cases. We quantitatively analyze each component and compare with the well-known GrabCut foreground extraction method.


title: The Flash Crash: A New Deconstruction

abstract: On May 6, 2010, in the span of a mere four and half minutes, the Dow Jones Industrial Average lost approximately 1,000 points. In the following fifteen minutes it recovered essentially all of its losses. This “Flash Crash” occurred in the absence of fundamental news that could explain the observed price pattern and is generally viewed as the result of endogenous factors related to the complexity of modern equity market trading. We present the first analysis of the entire order book at millisecond granularity, and not just of executed transactions, in an effort to explore the causes of the Flash Crash. We also examine information flows as reflected in a variety of data feeds provided to market participants during the Flash Crash. While assertions relating to causation of the Flash Crash must be accompanied by significant disclaimers, we suggest that it is highly unlikely that, as alleged by the United States Government, Navinder Sarao’s spoofing orders, even if illegal, could have caused the Flash Crash, or that the crash was a foreseeable consequence of his spoofing activity. Instead, we find that the explanation offered by the joint CFTC-SEC Staff Report, which relies on prevailing market conditions combined with the introduction of a large equity sell order implemented in a particularly dislocating manner, is consistent with the data. We offer a simulation model that formalizes the process by which large sell orders of the sort observed in the CFTC-SEC Staff Report, combined with prevailing market conditions, could generate a Flash Crash in the absence of fundamental information. Our research also documents the emergence of heretofore unobserved anomalies in market data feeds that correlate very closely with the initiation of and recovery from the Flash Crash. Our analysis of these data feed anomalies is ongoing as we attempt to discern whether they were a symptom of the rapid trading that accompanied the Flash Crash or whether they were causal in the sense that they rationally contributed to traders’ decisions to withdraw liquidity and then restore it after the anomalies were resolved.


title: A review of ferroresonance

abstract: This article covers the basics of a ferroresonant condition: the network circuits involved; two simple analysis processes - linear circuit analysis concept and dual X/sub M/ analysis concept. A review of how ferroresonance is prevented and a variety of other issues were discussed.


title: Crank-wheel: A brand new mobile base for field robots

abstract: In the accident of nuclear disasters or biochemical terrors, there is the strong need for robots which can move around and collect information at the disaster site. The robot should have toughness and high mobility in a location of stairs and obstacles. In this study, we propose a brand new type of mobile base named “crank-wheel” suitable for such use. Crank-wheel consists of wheels and connecting coupler link named crank-leg. Crank-wheel makes simple and smooth wheeled motion on flat ground and automatically transforms to the walking motion on rugged terrain as the crank-legs starts to contact the surface of the rugged terrain and acts as legs. This mechanism features its simple, easiness to maintain water and dust proof structure, and limited danger of biting rubbles in the driving mechanism just as the case of tracked vehicles. Effectiveness of the Crank-wheel is confirmed by several driving experiments on debris, sand and bog.


title: Norton Zone: Symantec's Secure Cloud Storage System

abstract: Cloud storage services are the way of the future, if not the present, but broad adoption is limited by a stark trade-off between privacy and functionality. Many popular cloud services provide search capabilities, but make only nominal efforts to keep user data fully private. Alternatives that search private user data on an untrusted server sacrifice functionality and/or scalability. We describe Norton Zone, Symantec's secure and scalable public storage system based on our valet security model. Whereas most commercial cloud storage systems secure user data with access control and legal mechanisms, Zone's cryptographic techniques provide proven privacy guarantees. This gives users an extra layer of security without compromising functionality. Zone's performance is comparable to unencrypted cloud storage systems that support search and sharing. We report on the design of Zone and the lessons learned in developing and deploying it in commercial, distributed datacenters scalable to millions of users.


Designing a research protocol in clinical dermatology: Common errors and how to avoid them


title: Credit card fraud detection at merchant side using neural networks

abstract: With the growth and widespread of the internet the credit card payment industry has expanded rapidly during the course of time. Most of the companies and industries have transformed their business into online services to provide e-commerce, easy access and communication allowing their customers better efficiency and accessibility. This evolution is of great importance since it has credited towards increased efficiency, and profitability but it also has its own shortcomings. Along with this evolution comes a greater possibility of threats. One of the most crucial challenges in making business on the internet is that neither the card nor the cardholder needs to be present when the purchase is being made. This makes it impossible for the merchant to verify whether the customer making a purchase is the authentic cardholder or not. This makes it easy for a fraudster to carry out a fraudulent transaction anonymously.


PAIRING EMERGENCY SEVERITY INDEX 5-LEVEL TRIAGE DATA WITH COMPUTER AIDED SYSTEM DESIGN TO IMPROVE EMERGENCY DEPARTMENT ACCESS AND THROUGHPUT


title: DEBAR: A scalable high-performance de-duplication storage system for backup and archiving

abstract: Driven by the increasing demand for large-scale and high-performance data protection, disk-based de-duplication storage has become a new research focus of the storage industry and research community where several new schemes have emerged recently. So far these systems are mainly inline de-duplication approaches, which are centralized and do not lend themselves easily to be extended to handle global de-duplication in a distributed environment. We present DEBAR, a de-duplication storage system designed to improve capacity, performance and scalability for de-duplication backup/archiving. DEBAR performs post-processing de-duplication, where backup streams are de-duplicated and cached on server-disks through an in-memory preliminary filter in phase I, and then completely de-duplicated in-batch in phase II. By decentralizing fingerprint lookup and update, DEBAR supports a cluster of servers to perform de-duplication backup in parallel, and is shown to scale linearly in both write throughput and physical capacity, achieving an aggregate throughput of 1.7GB/s and supporting a physical capacity of 2PB with 16 backup servers.


title: Charge transfer between Li ions and Li atoms in the 14-1000-eV energy region

abstract: Supported by Advanced Research Project Agency, Project Defender. OH wishes to acknowledge support received from the Office of Naval Research though the U.S. Naval Postgraduate School Research Foundation.


title: Bounded distortion mapping spaces for triangular meshes

abstract: The problem of mapping triangular meshes into the plane is fundamental in geometric modeling, where planar deformations and surface parameterizations are two prominent examples. Current methods for triangular mesh mappings cannot, in general, control the worst case distortion of all triangles nor guarantee injectivity. This paper introduces a constructive definition of generic convex spaces of piecewise linear mappings with guarantees on the maximal conformal distortion, as-well as local and global injectivity of their maps. It is shown how common geometric processing objective functionals can be restricted to these new spaces, rather than to the entire space of piecewise linear mappings, to provide a bounded distortion version of popular algorithms.


title: Unrolled Generative Adversarial Networks

abstract: We introduce a method to stabilize Generative Adversarial Networks (GANs) by defining the generator objective with respect to an unrolled optimization of the discriminator. This allows training to be adjusted between using the optimal discriminator in the generator's objective, which is ideal but infeasible in practice, and using the current value of the discriminator, which is often unstable and leads to poor solutions. We show how this technique solves the common problem of mode collapse, stabilizes training of GANs with complex recurrent generators, and increases diversity and coverage of the data distribution by the generator.