title: Cloud Computing Pricing Models: A Survey

abstract: Cloud computing is emerging as a promising field offering a variety of computing services to end users. These services are offered at different prices using various pricing schemes and techniques. End users will favor the service provider offering the best QoS with the lowest price. Therefore, applying a fair pricing model will attract more customers and achieve higher revenues for service providers. This work focuses on comparing many employed and proposed pricing models techniques and highlights the pros and cons of each. The comparison is based on many aspects such as fairness, pricing approach, and utilization period. Such an approach provides a solid ground for designing better models in the future. We have found that most approaches are theoretical and not implemented in the real market, although their simulation results are very promising. Moreover, most of these approaches are biased toward the service provider.


D.: The uncanny valley: does it exist


title: Fluid dynamical view of pressure checkerboarding problem and smoothing pressure correction on meshes with colocated variables

abstract: This paper presents derivation of the pressure correction equation appropriate for colocated grids within the framework of the SIMPLE algorithm. It is shown that checkerboard prediction of pressure can be prevented by employing algebraic smoothing pressure correction [Numer. Heat Transfer, Part B 29 (1996) 441] that is very simple to implement on both the structured as well as unstructured grids. The ability of the smoothing correction (which is shown to be independent of transformations of the system of coordinates) in providing the necessary dissipation is explained and the connection of the former with requirements of the Stokess laws is established. � 2003 Elsevier Ltd. All rights reserved.


title: Complete analytical inverse kinematics for NAO

abstract: The design of complex dynamic motions for humanoid robots is achievable only through the use of robot kinematics. In this paper, we study the problems of forward and inverse kinematics for the Aldebaran NAO humanoid robot and present a complete, exact, analytical solution to both problems, including a software library implementation for realtime onboard execution. The forward kinematics allow NAO developers to map any configuration of the robot from its own joint space to the three-dimensional physical space, whereas the inverse kinematics provide closed-form solutions to finding joint configurations that drive the end effectors of the robot to desired target positions in the three-dimensional physical space. The proposed solution was made feasible through a decomposition into five independent problems (head, two arms, two legs), the use of the Denavit-Hartenberg method, and the analytical solution of a non-linear system of equations. The main advantage of the proposed inverse kinematics solution compared to existing approaches is its accuracy, its efficiency, and the elimination of singularities. In addition, we suggest a generic guideline for solving the inverse kinematics problem for other humanoid robots. The implemented, freely-available, NAO kinematics library, which additionally offers center-of-mass calculations, is demonstrated in two motion design tasks: basic center-of-mass balancing and pointing to the ball.


title: ArgumenText: Searching for Arguments in Heterogeneous Sources

abstract: Argument mining is a core technology for enabling argument search in large corpora. However, most current approaches fall short when applied to heterogeneous texts. In this paper, we present an argument retrieval system capable of retrieving sentential arguments for any given controversial topic. By analyzing the highest-ranked results extracted from Web sources, we found that our system covers 89% of arguments found in expert-curated lists of arguments from an online debate portal, and also identifies additional valid arguments.


title: Inductive reasoning in ontologies using conceptual spaces

abstract: Structured knowledge about concepts plays an increasingly ::: important role in areas such as information retrieval. The ::: available ontologies and knowledge graphs that encode such ::: conceptual knowledge, however, are inevitably incomplete. ::: This observation has led to a number of methods that aim ::: to automatically complete existing knowledge bases. Unfortunately, ::: most existing approaches rely on black box models, ::: e.g. formulated as global optimization problems, which ::: makes it difficult to support the underlying reasoning process ::: with intuitive explanations. In this paper, we propose a ::: new method for knowledge base completion, which uses interpretable ::: conceptual space representations and an explicit ::: model for inductive inference that is closer to human forms of ::: commonsense reasoning. Moreover, by separating the task of ::: representation learning from inductive reasoning, our method ::: is easier to apply in a wider variety of contexts. Finally, unlike ::: optimization based approaches, our method can naturally be ::: applied in settings where various logical constraints between ::: the extensions of concepts need to be taken into account.


title: A Study of Contact Network Generation for Cyber-bullying Detection

abstract: Cyber-bullying is widely recognized as a serious social problem, especially for adolescents and it is also becoming a threat to the viability of online social networks. Several researchers have proposed digital-communication based frameworks whose goal is to help detect bullying in social networks. In this paper, our study focuses on detecting relation-based cyber-bullying, which is an indirect attack on a human, e.g., isolating a victim by ignoring the victim's messages. Recently, relationship-based cyber-bullying has received attention as a new type of cyber-bullying, and detecting it is still a novel problem. As it attacks a human relationship, the detection should monitor the change of the human relationship. In this paper, for the first step of relation-based cyber-bullying detection, we propose a framework to generate a contact network. The framework consists of two phases for a reduction of false negative, i.e. Students are friends in the school but detected as non-friend in the Social Networking Service (SNS), which is a serious problem for the cyber-bullying detection. Finally, this paper analyzes the collected SNS data with the actual human relationships, and evaluates the proposed framework.


title: Service-Oriented Programming with Jolie

abstract: The wide adoption of service-oriented computing has led to a heterogeneous scenario formed by different technologies and specifications. Examples can be found both at the design level—the frameworks for defining services and those for defining their coordination feature fundamentally different primitives—and at the implementation level—different communication technologies are used depending on the context. In this chapter we present Jolie, a fully-fledged service-oriented programming language. Jolie addresses the aforementioned heterogeneity in two ways. On the one hand, it combines computation and composition primitives in an intuitive and concise syntax. On the other hand, the behaviour and deployment of a Jolie program are orthogonal: they can be independently defined and recombined as long as they have compatible typing.


title: True self-configuration for the IoT

abstract: For the Internet of Things to finally become a reality, obstacles on different levels need to be overcome. This is especially true for the upcoming challenge of leaving the domain of technical experts and scientists. Devices need to connect to the Internet and be able to offer services. They have to announce and describe these services in machine-understandable ways so that user-facing systems are able to find and utilize them. They have to learn about their physical surroundings, so that they can serve sensing or acting purposes without explicit configuration or programming. Finally, it must be possible to include IoT devices in complex systems that combine local and remote data, from different sources, in novel and surprising ways. We show how all of that is possible today. Our solution uses open standards and state-of-the art protocols to achieve this. It is based on 6LowPAN and CoAP for the communications part, semantic web technologies for meaningful data exchange, autonomous sensor correlation to learn about the environment, and software built around the Linked Data principles to be open for novel and unforeseen applications.


HFACS Analysis of Military and Civilian Aviation Accidents: A North American Comparison


title: A common parallel computing framework for modeling hydrological processes of river basins

abstract: Restricted computing power has become one of the primary factors obstructing advancement in basin simulations for majority of hydrological models. Parallel computing is one of the most available approaches to solve this problem. Using binary-tree theory, we present in this study a common parallel computing framework based on the message passing interface (MPI) protocol for modeling hydrological processes of river basins. A practical and dynamic spatial domain decomposition method, based on the binary-tree structure of the drainage network, is proposed. This framework is computationally efficient, and is independent of the type of physical models chosen. The framework is tested in the Chabagou river basin of China, where two years of runoff processes of the entire basin were simulated. Results demonstrate that the system may provide efficient computing performance. However, primarily because of the constraint of the binary-tree structure for drainage network, this study finds that unlimited enhancement of computing efficiency is impossible to realize.


title: A Generative Model for Non-Intrusive Load Monitoring in Commercial Buildings

abstract: Abstract In the recent years, there has been an increasing academic and industrial interest for analyzing the electrical consumption of commercial buildings. Whilst having similarities with the Non Intrusive Load Monitoring (NILM) tasks for residential buildings, the nature of the signals that are collected from large commercial buildings introduces additional difficulties to the NILM research causing existing NILM approaches to fail. On the other hand, the amount of publicly available datasets collected from commercial buildings is very limited, which makes the NILM research even more challenging for this type of large buildings. In this study, we aim at addressing these issues. We first present an extensive statistical analysis of both commercial and residential measurements from public and private datasets and show important differences. Secondly, we develop an algorithm for generating synthetic current data based on a modelization of the current flowing through an electrical device. We then demonstrate that our electrical device model fits well real measurements and that our simulations are realistic by using the quantitative metrics described in the previous section. Finally, to encourage research on commercial buildings we release a synthesized dataset called SHED that can be used to evaluate NILM algorithms.


title: Adversarial Risk and the Dangers of Evaluating Against Weak Attacks

abstract: This paper investigates recently proposed approaches for defending against adversarial examples and evaluating adversarial robustness. The existence of adversarial examples in trained neural networks reflects the fact that expected risk alone does not capture the model's performance against worst-case inputs. We motivate the use of adversarial risk as an objective, although it cannot easily be computed exactly. We then frame commonly used attacks and evaluation metrics as defining a tractable surrogate objective to the true adversarial risk. This suggests that models may be obscured to adversaries, by optimizing this surrogate rather than the true adversarial risk. We demonstrate that this is a significant problem in practice by repurposing gradient-free optimization techniques into adversarial attacks, which we use to decrease the accuracy of several recently proposed defenses to near zero. Our hope is that our formulations and results will help researchers to develop more powerful defenses.


title: Database System Implementation

abstract: From the Publisher: ::: Three well-known computer scientists at Stanford University-Hector Garcia-Molina, Jeffrey D. Ullman, and Jennifer Widom-have written one of the most comprehensive books on database system implementation. Hector Garcia- Molina pioneered this book at Stanford as a second database systems course for computer science majors and industry-based professionals. It focuses on the implementation of database systems, including storage structures, query processing, and transaction management. Database System Implementation is valuable as an academic textbook or a professional reference. ::: Noteworthy Features ::: Provides extensive coverage of query processing, including major algorithms for execution of queries and techniques for optimizing queries ::: Covers information integration, including warehousing and mediators, OLAP, and data-cube systems ::: Explains error-correction in RAID disks and covers bitmap indexes, data mining, data statistics, and pointer swizzling ::: Supports additional teaching materials found on the book's Web page at ...


title: Gait Analysis Using Floor Markers and Inertial Sensors

abstract: In this paper, a gait analysis system which estimates step length and foot angles is proposed. A measurement unit, which consists of a camera and inertial sensors, is installed on a shoe. When the foot touches the floor, markers are recognized by the camera to obtain the current position and attitude. A simple planar marker with 4,096 different codes is used. These markers printed on paper are placed on the floor. When the foot is moving off the floor, the position and attitude are estimated using an inertial navigation algorithm. For accurate estimation, a smoother is proposed, where vision information and inertial sensor data are combined. Through experiments, it is shown that the proposed system can both track foot motion and estimate step length.


title: The Impact of E-Learning in Medical Education

abstract: The authors provide an introduction to e-learning and its role in medical education by outlining key terms, the components of e-learning, the evidence for its effectiveness, faculty development needs for implementation, evaluation strategies for e-learning and its technology, and how e-learning might be considered evidence of academic scholarship. E-learning is the use of Internet technologies to enhance knowledge and performance. E-learning technologies offer learners control over content, learning sequence, pace of learning, time, and often media, allowing them to tailor their experiences to meet their personal learning objectives. In diverse medical education contexts, e-learning appears to be at least as effective as traditional instructor-led methods such as lectures. Students do not see e-learning as replacing traditional instructor-led training but as a complement to it, forming part of a blended-learning strategy. A developing infrastructure to support e-learning within medical education includes repositories, or digital libraries, to manage access to e-learning materials, consensus on technical standardization, and methods for peer review of these resources. E-learning presents numerous research opportunities for faculty, along with continuing challenges for documenting scholarship. Innovations in e-learning technologies point toward a revolution in education, allowing learning to be individualized (adaptive learning), enhancing learners' interactions with others (collaborative learning), and transforming the role of the teacher. The integration of e-learning into medical education can catalyze the shift toward applying adult learning theory, where educators will no longer serve mainly as the distributors of content, but will become more involved as facilitators of learning and assessors of competency.


title: A 27.6 MHz 297 μW 33 ppm/°C CMOS relaxation oscillator with an adjustable temperature compensation scheme

abstract: A 27.6 MHz 297 μW relaxation oscillator is presented in this paper by using an 180-nm CMOS technology. The proposed oscillator employs an adjustable temperature compensation feedforward scheme, in which the charging current can be set steady by a four-bit digital trimming signal. We have demonstrated a frequency variation lower than 33.5 ppm/°C which could be close to 0 ppm/°C in theory if the precision is high enough. In practical production, it is effective to calibrate the mismatching and deviation of fabrication because of the novel adjustable temperature compensation scheme.


title: 'On the ground' in Sidi Bouzid: investigating social media use during the tunisian revolution

abstract: We present a study conducted in Sidi Bouzid, the Tunisian town where the Arab Revolution, also known as 'Arab Spring', started, and where the role of Web 2.0 and social media applications in the people's uprising have been much discussed. We identify four relevant phenomena: (1) the publication of classified materials via WikiLeaks challenged the regime's legitimacy, (2) Web 2.0 connected local activists with Arab satellite TV, (3) social media linked the young activists with actors in other cities in Tunisia, (4) social media allowed organizing resistance inside Sidi Bouzid. Methodologically, we question a too deterministic view of the role of the new media and the representativeness of investigative techniques that uniquely use the new media in order to assess their impact. At the same time, rigorous investigations 'on the ground' are extremely difficult. We present a modest and initial attempt to provide such an 'on the ground' approach, cognizant of necessary limitations. We compare our findings with studies which analyze data downloaded out of social media applications and suggest that studies of the kind we describe offer additional insight and play an essential role in better understanding political uses of social media.


title: Peer influence on risk taking, risk preference, and risky decision making in adolescence and adulthood: an experimental study.

abstract: In this study, 306 individuals in 3 age groups—adolescents (13–16), youths (18–22), and adults (24 and older)—completed 2 questionnaire measures assessing risk preference and risky decision making, and 1 behavioral task measuring risk taking. Participants in each age group were randomly assigned to complete the measures either alone or with 2 same-aged peers. Analyses indicated that (a) risk taking and risky decision making decreased with age; (b) participants took more risks, focused more on the benefits than the costs of risky behavior, and made riskier decisions when in peer groups than alone; and (c) peer effects on risk taking and risky decision making were stronger among adolescents and youths than adults. These findings support the idea that adolescents are more inclined toward risky behavior and risky decision making than are adults and that peer influence plays an important role in explaining risky behavior during adolescence.


title: Health Informatics via Machine Learning for the Clinical Management of Patients

abstract: Objectives: To review how health informatics systems based on machine learning methods have impacted the clinical management of patients, by affecting clinical practice. Methods: We reviewed literature from 2010-2015 from databases such as Pubmed, IEEE xplore, and INSPEC, in which methods based on machine learning are likely to be reported. We bring together a broad body of literature, aiming to identify those leading examples of health informatics that have advanced the methodology of machine learning. While individual methods may have further examples that might be added, we have chosen some of the most representative, informative exemplars in each case. Results: Our survey highlights that, while much research is taking place in this high-profile field, examples of those that affect the clinical management of patients are seldom found. We show that substantial progress is being made in terms of methodology, often by data scientists working in close collaboration with clinical groups. Conclusions: Health informatics systems based on machine learning are in their infancy and the translation of such systems into clinical management has yet to be performed at scale.


title: Occupant behaviour prediction in ambient intelligence computing environment

abstract: In this paper, the application of ambient intelligence computing techniques in the prediction of occupant behaviours is addressed. It is aimed to deliver a wellbeing monitoring and assistive environment to support elderly lives independently, in control of their day to day activities. A wireless sensor network is constructed to collect the required occupancy data. Individual sensory data are combined to form an occupancy time series. In this paper different techniques in time series prediction are investigated. The prediction techniques include an Evolving Fuzzy Predictor (EFP) model along with Auto Regressive Moving Average (ARMA) model, Adaptive-Network-based Fuzzy Inference System (ANFIS), as well as Transductive Neuro-Fuzzy Inference model with Weighted data normalization (TWNFI). These prediction techniques are used to predict the occupancy time series representing anticipated occupancy of different areas of the environment, and the results are compared. Experimental results are presented based on a home environment with four separate areas and each area is equipped with a wireless passive infrared motion detector linked to a central processing unit. For wireless communication of the sensor network, ZigBee wireless modules are employed in the prototype ambient intelligence environment. © 2008 World Academic Press, UK. All rights reserved.


title: The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?

abstract: A successful grasp requires careful balancing of the contact forces. Deducing whether a particular grasp will be successful from indirect measurements, such as vision, is therefore quite challenging, and direct sensing of contacts through touch sensing provides an appealing avenue toward more successful and consistent robotic grasping. However, in order to fully evaluate the value of touch sensing for grasp outcome prediction, we must understand how touch sensing can influence outcome prediction accuracy when combined with other modalities. Doing so using conventional model-based techniques is exceptionally difficult. In this work, we investigate the question of whether touch sensing aids in predicting grasp outcomes within a multimodal sensing framework that combines vision and touch. To that end, we collected more than 9,000 grasping trials using a two-finger gripper equipped with GelSight high-resolution tactile sensors on each finger, and evaluated visuo-tactile deep neural network models to directly predict grasp outcomes from either modality individually, and from both modalities together. Our experimental results indicate that incorporating tactile readings substantially improve grasping performance.


title: Knowledge Architect: A Tool Suite for Managing Software Architecture Knowledge

abstract: Management of software architecture knowledge (AK) is vital for improving an organization’s architectural capabilities. To support the architecting process within our industrial partner: Astron, the Dutch radio astronomy institute, we implemented the Knowledge Architect (KA): a tool suite for creating, using, translating, sharing and managing AK. The KA tool suite entails specialized support for integrating the various process activities and supporting collaboration between the stakeholders. This report discusses the tools included and features of KA. We also discuss different usages of the KA for capturing and using AK to support the architecture process.


title: Adaptive control method for path-tracking control of an omni-directional walker compensating for center-of-gravity shifts and load changes

abstract: In previous studies, an omni-directional walker was developed for walking rehabilitation. Walking training programs are stored in the walker so that rehabilitation can be carried out without a physical therapist. However, the walker sometimes strays from the predefined path because of center-of-gravity shifts and load changes. It is necessary for the walker to precisely follow the paths defined in the walking training programs to guarantee the effectiveness of rehabilitation and user safety. Therefore, this paper describes a path-tracking control method for the omni-directional walker to compensate for center-of-gravity shifts and load changes. First, the kinematics and kinetics of the omni-directional walker motion are presented. Second, an adaptive control strategy is proposed. Finally, simulations show that the walker can be controlled accurately by using the proposed method.


title: Psychology And Economics

abstract: Because psychology systematically explores human judgment, behavior, and well-being, it can teach us important facts about how humans differ from traditional economic assumptions. In this essay I discuss a selection of psychological findings relevant to economics. Standard economics assumes that each person has stable, well-defined preferences, and that she rationally maximizes those preferences. Section 2 considers what psychological research teaches us about the true form of preferences, allowing us to make economics more realistic within the rational-choice framework. Section 3 reviews research on biases in judgment under uncertainty; because those biases lead people to make systematic errors in their attempts to maximize their preferences, thi s research poses a more radical challenge to the economics model. The array of psychological findings reviewed in Section 4 points to an even more radical critique of the economics model: Even if we are willing to modify our familiar assumptions about p references, or allow that people make systematic errors in their attempts to maximize those preferences, it is sometimes misleading to conceptualize people as attempting to maximize well-defined coherent, or stable preferences.


title: Using Deep Learning and Satellite Imagery to Quantify the Impact of the Built Environment on Neighborhood Crime Rates

abstract: The built environment has been postulated to have an impact on neighborhood crime rates, however, measures of the built environment can be subjective and differ across studies leading to varying observations on its association with crime rates. Here, we illustrate an accurate and straightforward approach to quantify the impact of the built environment on neighborhood crime rates from high-resolution satellite imagery. Using geo-referenced crime reports and satellite images for three United States cities, we demonstrate how image features consistently identified using a convolutional neural network can explain up to 82% of the variation in neighborhood crime rates. Our results suggest the built environment is a strong predictor of crime rates, and this can lead to structural interventions shown to reduce crime incidence in urban settings.


title: Ocean energy power take-off using oscillating paddle

abstract: This paper presents an ocean energy power take-off system using paddle like wave energy converter (WEC), magnetic gear and efficient power converter architecture. As the WEC oscillates at a low speed of about 5–25 rpm, the direct drive generator is not an efficient design. To increase the generator speed a cost effective flux focusing magnetic gear is proposed. Power converter architecture is discussed and integration of energy storage in the system to smooth the power output is elaborated. Super-capacitor is chosen as energy storage for its better oscillatory power absorbing capability than battery. WEC is emulated in hardware using motor generator set-up and energy storage integration in the system is demonstrated.


title: ObjectNet3D: A Large Scale Database for 3D Object Recognition

abstract: We contribute a large scale database for 3D object recognition, named ObjectNet3D, that consists of 100 categories, 90,127 images, 201,888 objects in these images and 44,147 3D shapes. Objects in the 2D images in our database are aligned with the 3D shapes, and the alignment provides both accurate 3D pose annotation and the closest 3D shape annotation for each 2D object. Consequently, our database is useful for recognizing the 3D pose and 3D shape of objects from 2D images. We also provide baseline experiments on four tasks: region proposal generation, 2D object detection, joint 2D detection and 3D object pose estimation, and image-based 3D shape retrieval, which can serve as baselines for future research using our database. Our database is available online at http://cvgl.stanford.edu/projects/objectnet3d.


title: PDDL+ Planning via Constraint Answer Set Programming

abstract: PDDL+ is an extension of PDDL that enables modelling planning domains with mixed discrete-continuous dynamics. In this paper we present a new approach to PDDL+ planning based on Constraint Answer Set Programming (CASP), i.e. ASP rules plus numerical constraints. To the best of our knowledge, ours is the first attempt to link PDDL+ planning and logic programming. We provide an encoding of PDDL+ models into CASP problems. The encoding can handle non-linear hybrid domains, and represents a solid basis for applying logic programming to PDDL+ planning. As a case study, we consider the EZCSP CASP solver and obtain promising results on a set of PDDL+ benchmark problems.


Extracting Thee-Structured Representations of Thained Networks


title: A Hybrid Knowledge-Guided Detection Technique for Screening of Infectious Pulmonary Tuberculosis From Chest Radiographs

abstract: Tuberculosis (TB) is a deadly infectious disease and the presence of cavities in the upper lung zones is a strong indicator that the disease has developed into a highly infectious state. Currently, the detection of TB cavities is mainly conducted by clinicians observing chest radiographs. Diagnoses performed by radiologists are labor intensive and very often there is insufficient healthcare personnel available, especially in remote communities. After assessing existing approaches, we propose an automated segmentation technique which takes a hybrid knowledge-based Bayesian classification approach to detect TB cavities automatically. We apply gradient inverse coefficient of variation (GICOV) and circularity measures to classify detected features and confirm true TB cavities. By comparing with non hybrid approaches and the classical active contour techniques for feature extraction in medical images, experimental results demonstrate that our approach achieves high accuracy with a low false positive rate in detecting TB cavities.


title: Biplanar Temple Lift for Lateral Brow Ptosis: Comparison with Uniplanar Dissection Technique

abstract: BACKGROUND ::: The amount of lift achievable in the temple region has been limited by traditional uniplanar dissection techniques. A biplanar temple-lifting technique (BTL), involving a biplanar dissection both deep and superficial to the superficial musculoaponeurotic system (SMAS) of the temporal region, is described. This study compares the amount of temporal lifting that can be achieved using a uniplanar dissection with that achieved using a biplanar dissection. ::: ::: ::: METHODS ::: Thirty-seven patients underwent bilateral temple lifting. Deep dissection was performed on the surface of the deep temporalis fascia. The skin flap was pulled in a superolateral direction and the skin overlap at the wound edge was measured. A SMAS flap was then dissected beneath the dermis from the anterior wound edge to the temporal hairline. The SMAS flap was suspended superolaterally and fixated to the deep temporalis fascia. The skin flap was again pulled in a superolateral direction and the amount of skin overlap was measured and compared. ::: ::: ::: RESULTS ::: The average potential temple skin that could be excised using the traditional dissection technique was 15.1 mm (range, 7-24 mm). The average temple skin that was excised using the biplanar dissection technique was 21.8 mm (range, 14-30 mm). The biplanar technique was shown to offer, on average, a 48% increase in lift relative to the skin-only approach. There were no cases of wound dehiscence, necrosis, or overcorrection. ::: ::: ::: CONCLUSION ::: Using the BTL technique to create a temporal SMAS flap, dissected free from overlying dermis as well as from deep temporal fascia, provides a more secure suspension of the temporal flap and significantly greater temple lift than a uniplanar dissection. The deep layering absorbs the tension of the lift, allowing for tensionless skin closure, thus decreasing the potential for scarring, hair loss, and necrosis. The increased mobility and higher suspension of the temporal flap allows for more skin excision and therefore a more pleasing lateral brow height.


title: Inductive Transfer Deep Hashing for Image Retrieval

abstract: With the explosive increase of online images, fast similarity search is increasingly critical for large scale image retrieval. Several hashing methods have been proposed to accelerate image retrieval, a promising way is semantic hashing which designs compact binary codes for a large number of images so that semantically similar images are mapped to similar codes. Supervised methods can handle such semantic similarity but they are prone to overfitting when the labeled data is few or noisy. In this paper, we concentrate on this issue and propose a novel Inductive Transfer Deep Hashing (ITDH) approach for semantic hashing based image retrieval. A transfer deep learning algorithm has been employed to learn the robust image representation, and the neighborhood-structure preserved method has been used to mapped the image into discriminative hash codes in hamming space. The combination of the two techniques ensures that we obtain a good feature representation and a fast query speed without depending on large amounts of labeled data. Experimental results demonstrate that the proposed approach is superior to some state-of-the-art methods.


title: Variable Penetrance of the 15q11.2 BP1-BP2 Microduplication in a Family with Cognitive and Language Impairment

abstract: The 15q11.2 BP1-BP2 region is found duplicated or deleted in people with cognitive, language, and behavioral impairment. We report on a family (a father and 3 male twin siblings) that presents with a


title: Illumination change robustness in direct visual SLAM

abstract: Direct visual odometry and Simultaneous Localization and Mapping (SLAM) methods determine camera poses by means of direct image alignment. This optimizes a photometric cost term based on the Lucas-Kanade method. Many recent works use the brightness constancy assumption in the alignment cost formulation and therefore cannot cope with significant illumination changes. Such changes are especially likely to occur for loop closures in SLAM. Alternatives exist which attempt to match images more robustly. In our paper, we perform a systematic evaluation of real-time capable methods. We determine their accuracy and robustness in the context of odometry and of loop closures, both on real images as well as synthetic datasets with simulated lighting changes. We find that for real images, a Census-based method outperforms the others. We make our new datasets available online3.


A simple animal model for characterizing gene regulatory control of an immune response


A PROGRAMMING INTERFACE FOR MEDICAL DIAGNOSIS PREDICTION


title: Omega-3 Polyunsaturated Fatty Acid (Fish Oil) Supplementation and the Prevention of Clinical Cardiovascular Disease: A Science Advisory From the American Heart Association

abstract: Multiple randomized controlled trials (RCTs) have assessed the effects of supplementation with eicosapentaenoic acid plus docosahexaenoic acid (omega-3 polyunsaturated fatty acids, commonly called fish oils) on the occurrence of clinical cardiovascular diseases. Although the effects of supplementation for the primary prevention of clinical cardiovascular events in the general population have not been examined, RCTs have assessed the role of supplementation in secondary prevention among patients with diabetes mellitus and prediabetes, patients at high risk of cardiovascular disease, and those with prevalent coronary heart disease. In this scientific advisory, we take a clinical approach and focus on common indications for omega-3 polyunsaturated fatty acid supplements related to the prevention of clinical cardiovascular events. We limited the scope of our review to large RCTs of supplementation with major clinical cardiovascular disease end points; meta-analyses were considered secondarily. We discuss the features of available RCTs and provide the rationale for our recommendations. We then use existing American Heart Association criteria to assess the strength of the recommendation and the level of evidence. On the basis of our review of the cumulative evidence from RCTs designed to assess the effect of omega-3 polyunsaturated fatty acid supplementation on clinical cardiovascular events, we update prior recommendations for patients with prevalent coronary heart disease, and we offer recommendations, when data are available, for patients with other clinical indications, including patients with diabetes mellitus and prediabetes and those with high risk of cardiovascular disease, stroke, heart failure, and atrial fibrillation.


title: Dielectric Elastomer Spring Roll Actuators for a Portable Force Feedback Device

abstract: In this paper, we present a new portable force feedback device for surgery simulations. Dielectric elastomer spring roll linear actua tors for this device were manufactured, and characterized via pas sive tensile tests and active isometric tests. The actuators exhibited a maximum force of 7.2 N, and a maximum elongation of 31%. Due to the high driving voltage, electrical safety issues were also considered. The results showed that sufficient electrical safety can be provided to the user. Two prototypes were built, which practi cally showed functionalities of the actuator and the proposed force feedback concept with actuators connected between the fingers.


Lessons from the Amazon Picking Challenge: Four Aspects of Building Robotic Systems.


title: Query simplification: graceful degradation for join-order optimization

abstract: Join ordering is one of the most important, but also most challenging problems of query optimization. In general finding the optimal join order is NP-hard. Existing dynamic programming algorithms exhibit exponential runtime even for the restricted, but highly relevant class of star joins. Therefore, it is infeasible to find the optimal join order when the query includes a large number of joins. Existing approaches for large queries switch to greedy heuristics or randomized algorithms at some point, which can degrade query execution performance by orders of magnitude. We propose a new paradigm for optimizing large queries: when a query is too complex to be optimized exactly, we simplify the query's join graph until the optimization problem becomes tractable within a given time budget. During simplification, we apply safe simplifications before more risky ones. This way join ordering problems are solved optimally if possible, and gracefully degrade with increasing query complexity. This paper presents a general framework for query simplification and a strategy for directing the simplification process. Extensive experiments with different kinds of queries, different join-graph structures, and different cost functions indicate that query simplification is very robust and outperforms previous methods for join-order optimization.


title: Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images

abstract: The three main tools in the single image restoration theory are the maximum likelihood (ML) estimator, the maximum a posteriori probability (MAP) estimator, and the set theoretic approach using projection onto convex sets (POCS). This paper utilizes the above known tools to propose a unified methodology toward the more complicated problem of superresolution restoration. In the superresolution restoration problem, an improved resolution image is restored from several geometrically warped, blurred, noisy and downsampled measured images. The superresolution restoration problem is modeled and analyzed from the ML, the MAP, and POCS points of view, yielding a generalization of the known superresolution restoration methods. The proposed restoration approach is general but assumes explicit knowledge of the linear space- and time-variant blur, the (additive Gaussian) noise, the different measured resolutions, and the (smooth) motion characteristics. A hybrid method combining the simplicity of the ML and the incorporation of nonellipsoid constraints is presented, giving improved restoration performance, compared with the ML and the POCS approaches. The hybrid method is shown to converge to the unique optimal solution of a new definition of the optimization problem. Superresolution restoration from motionless measurements is also discussed. Simulations demonstrate the power of the proposed methodology.


title: Real-Valued Negative Selection Algorithm with Variable-Sized Detectors

abstract: A new scheme of detector generation and matching mechanism for negative selection algorithm is introduced featuring detectors with variable properties. While detectors can be variable in different ways using this concept, the paper describes an algorithm when the variable parameter is the size of the detectors in real-valued space. The algorithm is tested using synthetic and real-world datasets, including time series data that are transformed into multiple-dimensional data during the preprocessing phase. Preliminary results demonstrate that the new approach enhances the negative selection algorithm in efficiency and reliability without significant increase in complexity.


title: Low-Overhead Asynchronous Checkpointing in Main-Memory Database Systems

abstract: As it becomes increasingly common for transaction processing systems to operate on datasets that fit within the main memory of a single machine or a cluster of commodity machines, traditional mechanisms for guaranteeing transaction durability---which typically involve synchronous log flushes---incur increasingly unappealing costs to otherwise lightweight transactions. Many applications have turned to periodically checkpointing full database state. However, existing checkpointing methods---even those which avoid freezing the storage layer---often come with significant costs to operation throughput, end-to-end latency, and total memory usage. This paper presents Checkpointing Asynchronously using Logical Consistency (CALC), a lightweight, asynchronous technique for capturing database snapshots that does not require a physical point of consistency to create a checkpoint, and avoids conspicuous latency spikes incurred by other database snapshotting schemes. Our experiments show that CALC can capture frequent checkpoints across a variety of transactional workloads with extremely small cost to transactional throughput and low additional memory usage compared to other state-of-the-art checkpointing systems.


title: Isotope effect in resonant Raman scattering and induced IR spectra of trans-polyacetylene

abstract: Abstract We present a full explanation of the isotope effect in resonant Raman scattering, doping induced, and photo-induced IR -active vibrations of previously reported data in trans-(CH) x , ( 13 CH) x and (CD) x . The interpretation is given in terms of Raman scattering from amplitude modes of the dimerized chains and absorption by charged induced IR -active modes. It is shown that the bare phonon frequencies are properly modified by the isotope mass whereas the observed frequencies are derived by the dressed phonon propagator, and therefore do not follow the usual mass rule.


title: Taking a deeper look at pedestrians

abstract: In this paper we study the use of convolutional neural networks (convnets) for the task of pedestrian detection. Despite their recent diverse successes, convnets historically underperform compared to other pedestrian detectors. We deliberately omit explicitly modelling the problem into the network (e.g. parts or occlusion modelling) and show that we can reach competitive performance without bells and whistles. In a wide range of experiments we analyse small and big convnets, their architectural choices, parameters, and the influence of different training data, including pretraining on surrogate tasks. We present the best convnet detectors on the Caltech and KITTI dataset. On Caltech our convnets reach top performance both for the Caltech1x and Caltech10x training setup. Using additional data at training time our strongest convnet model is competitive even to detectors that use additional data (optical flow) at test time.


title: Production of bioethanol from wheat straw: an overview on pretreatment, hydrolysis and fermentation.

abstract: Wheat straw is an abundant agricultural residue with low commercial value. An attractive alternative is utilization of wheat straw for bioethanol production. However, production costs based on the current technology are still too high, preventing commercialization of the process. In recent years, progress has been made in developing more effective pretreatment and hydrolysis processes leading to higher yield of sugars. The focus of this paper is to review the most recent advances in pretreatment, hydrolysis and fermentation of wheat straw. Based on the type of pretreatment method applied, a sugar yield of 74-99.6% of maximum theoretical was achieved after enzymatic hydrolysis of wheat straw. Various bacteria, yeasts and fungi have been investigated with the ethanol yield ranging from 65% to 99% of theoretical value. So far, the best results with respect to ethanol yield, final ethanol concentration and productivity were obtained with the native non-adapted Saccharomyses cerevisiae. Some recombinant bacteria and yeasts have shown promising results and are being considered for commercial scale-up. Wheat straw biorefinery could be the near-term solution for clean, efficient and economically-feasible production of bioethanol as well as high value-added products.


title: A Comparative Study of the Performance of IR Models on Duplicate Bug Detection

abstract: Open source projects incorporate bug triagers to help with the task of bug report assignment to developers. One of the tasks of a triager is to identify whether an incoming bug report is a duplicate of a pre-existing report. In order to detect duplicate bug reports, a triager either relies on his memory and experience or on the search capabilities of the bug repository. Both these approaches can be time consuming for the triager and may also lead to the misidentification of duplicates. In the literature, several approaches to automate duplicate bug report detection have been proposed. However, there has not been an exhaustive comparison of the performance of different IR models, especially with topic-based ones such as LSI and LDA. In this paper, we compare the performance of the traditional vector space model (using different weighting schemes) with that of topic based models, leveraging heuristics that incorporate exception stack frames, surface features, summary and long description from the free-form text in the bug report. We perform experiments on subsets of bug reports from Eclipse and Firefox and achieve a recall rate of 60% and 58% respectively. We find that word-based models, in particular a Log-Entropy based weighting scheme, outperform topic based ones such as LSI, LDA and Random Projections. Our findings also suggests that for the problem of duplicate bug detection, it is important to consider a project's domain and characteristics to devise a set of heuristics to achieve optimal results


title: Phylogenomic analysis reveals genome-wide purifying selection on TBE transposons in the ciliate Oxytricha

abstract: BackgroundTransposable elements are a major player contributing to genetic variation and shaping genome evolution. Multiple independent transposon domestication events have occurred in ciliates, recruiting transposases to key roles in cellular processes. In the ciliate Oxytricha trifallax, the telomere-bearing elements (TBE), a Tc1/mariner transposon, occupy a significant portion of the germline genome and are involved in programmed genome rearrangements that produce a transcriptionally active somatic nucleus from a copy of the germline nucleus during development.ResultsHere we provide a thorough characterization of the distribution and sequences of TBE transposons in the Oxytricha germline genome. We annotate more than 10,000 complete and 24,000 partial TBE sequences. TBEs cluster into four major families and display a preference for either insertion into DNA segments that are retained in the somatic genome or their maintenance at such sites. The three TBE-encoded genes in all four families display dN/dS ratios much lower than 1, suggesting genome-wide purifying selection. We also identify TBE homologs in other ciliate species for phylogenomic analysis.ConclusionsThis paper provides genome-wide characterization of a major class of ciliate transposons. Phylogenomic analysis reveals selective constraints on transposon-encoded genes, shedding light on the evolution and domesticated functions of these transposons.


title: Single-inductor multiple-output switching converters

abstract: An integrated single-inductor dual-output boost converter is presented. This converter adopts time-multiplexing control in providing two independent supply voltages (3.0 and 3.6 V) using only one 1-/spl mu/H off-chip inductor and a single control loop. This converter is analyzed and compared with existing counterparts in the aspects of integration, architecture, control scheme, and system stability. Implementation of the power stage, the controller, and the peripheral functional blocks is discussed. The design was fabricated with a standard 0.5-/spl mu/m CMOS n-well process. At an oscillator frequency of 1 MHz, the power conversion efficiency reaches 88.4% at a total output power of 350 mW. This topology can be extended to have multiple outputs and can be applied to buck, flyback, and other kinds of converters.


title: The Psychological Structure of Humility

abstract: Psychological inquiry into humility has advanced considerably over the past decade, yet this literature suffers from 2 notable limitations. First, there is no clear consensus among researchers about what humility is, and conceptualizations vary considerably across studies. Second, researchers have uniformly operationalized humility as a positive, socially desirable construct, while dismissing evidence from lay opinion and theological and philosophical traditions suggesting that humility may also have a darker side. To redress these issues, we conducted the first comprehensive, bottom-up analysis of the psychological structure of humility. Here we report 5 studies (total N = 1,479) that involve: (a) cluster analysis and categorization of humility-related words, generated by both lay persons and academic experts; (b) exploratory and confirmatory factor analyses of momentary and dispositional humility experiences; and (c) experimental induction of a momentary humility experience. Across these studies, we found converging evidence that humility can take 2 distinct forms, which we labeled “appreciative” and “self-abasing” humility. Appreciative humility tends to be elicited by personal success, involve action tendencies oriented toward celebrating others, and is positively associated with dispositions such as authentic pride, guilt, and prestige-based status. In contrast, self-abasing humility tends to be elicited by personal failure, involves negative self-evaluations and action tendencies oriented toward hiding from others’ evaluations, and is associated with dispositions such as shame, low self-esteem, and submissiveness. Together, these findings provide a systematic and empirically grounded understanding of humility. (PsycINFO Database Record (c) 2018 APA, all rights reserved)


title: The Bad Boys of Cyberspace: Deviant Behavior in a Multimedia Chat Community

abstract: A wide variety of deviant behavior may arise as the population of an online multimedia community increases. That behavior can span the range from simple mischievous antics to more serious expressions of psychopathology, including depression, sociopathy, narcissism, dissociation, and borderline dynamics. In some cases the deviant behavior may be a process of pathological acting out—in others, a healthy attempt to work through. Several factors must be taken into consideration when explaining online deviance, such as social/cultural issues, the technical infrastructure of the environment, transference reactions, and the effects of the ambiguous, anonymous, and fantasy-driven atmosphere of cyberspace life. In what we may consider an "online community psychology," intervention strategies for deviant behavior can be explored along three dimensions: preventative versus remedial, user versus superuser based, and automated versus interpersonal.


title: Validation of a New Type of Lagrangian Drifter Using a GPS Cellular Phone

abstract: A new type of Lagrangian drifter is developed that is suitable for observing water mass movement in coastal regions. A GPS (Global Positioning System) cellular phone is used for field data retrieval. The number of drifters used in observation is expected to increase, since these are relatively low-cost and can be used repeatedly. In order to examine the efficiency of the drifter for monitoring water mass movement in coastal regions, a field test was conducted in the Ariake Sea in October 2001. The drifter’s water-following capability and the reliability of the data acquisition/transmission unit are evaluated from the test results.


title: High-accuracy vehicle localization for autonomous warehousing

abstract: The research presented in this paper aims to bridge the gap between the latest scientific advances in autonomous vehicle localization and the industrial state of the art in autonomous warehousing. Notwithstanding great scientific progress in the past decades, industrial autonomous warehousing systems still rely on external infrastructure for obtaining their precise location. This approach increases warehouse installation costs and decreases system reliability, as it is sensitive to measurement outliers and the external localization infrastructure can get dirty or damaged. Several approaches, well studied in scientific literature, are capable of determining vehicle position based only on information provided by on board sensors, most commonly wheel encoders and laser scanners. However, scientific results published to date either do not provide sufficient accuracy for industrial applications, or have not been extensively tested in realistic, industrial-like operating conditions. In this paper, we combine several well established algorithms into a high-precision localization pipeline, capable of computing the pose of an autonomous forklift to sub-centimeter precision. The algorithms use only odometry information from wheel encoders and range readings from an on board laser scanner. The effectiveness of the proposed solution is evaluated by an extensive experiment that lasted for several days, and was performed in a realistic industrial-like environment. HighlightsWe describe an algorithm stack for high-precision autonomous vehicle localization.Localization accuracy satisfies industrial requirements of 1.5cm and 0.5°.The proposed methodology has been verified by extensive experiments.Experiments were performed in realistic industrial conditions.


title: Improved Particle Swarm Optimization Based K-Means Clustering

abstract: Clustering is a popular data analysis and data mining technique. K-Means is one of the most popular data mining algorithms for being simple, scalable and easily modifiable to a variety of contexts and application domains. The major issue of traditional K-Means algorithm is that its performance depends on the initialization of centroid and requires the number of clusters to be specified in advance. Many evolutionary based clustering algorithms have been developed in recent years for selecting optimum initial centroid to optimize clustering results. Particle Swarm Optimization algorithm is a population-based memetic-evolution-motivated meta-heuristic algorithm that mimics the capability of swarm. The K-Means algorithm typically uses Euclidean or squared Euclidean distance to measure the distortion between a data object and its cluster centroid. The Euclidean and squared Euclidean distances are usually computed from raw data and not from standardized data. Normalization is one of the important preprocessing steps, to transform values of all attributes. Effective data clustering can only occur if an equally effective technique for normalizing the data is applied. This paper proposes an effective partitional clustering algorithm which is developed by integrating the merits of Particle Swarm Optimization and normalization with traditional K-Means clustering algorithms. Experiments are conducted on real dataset to prove the efficiency of the proposed algorithm.


title: A new low-power, low-area, parallel prefix Sklansky adder with reduced inter-stage connections complexity

abstract: In this paper a relation between graph energy and electrical energy or power consumption with graph nodes and edges for PPA structures is introduced. The Sklansky PPA is selected as the target PPA and its recursion steps limited. This reduced the internal nodes of the PPA. The reduction of nodes and edges and limiting the recursive stages to maximum 8 steps, resulted in proposed adder I and using special dot and semi-dot operators for graph nodes in proposed adder I led to proposed adder II. The number of transistors, total power and critical delay path of our proposed adder II are reduced about 31%, 29% and 7% versus Sklansky adder respectively. Finally the power-delay-product (PDP) of proposed PPA II comes with about 35% improvement compared with Sklansky adder.


Visual inspection of metal surfaces


title: Smartphone-Based Food Diagnostic Technologies: A Review

abstract: A new generation of mobile sensing approaches offers significant advantages over traditional platforms in terms of test speed, control, low cost, ease-of-operation, and data management, and requires minimal equipment and user involvement. The marriage of novel sensing technologies with cellphones enables the development of powerful lab-on-smartphone platforms for many important applications including medical diagnosis, environmental monitoring, and food safety analysis. This paper reviews the recent advancements and developments in the field of smartphone-based food diagnostic technologies, with an emphasis on custom modules to enhance smartphone sensing capabilities. These devices typically comprise multiple components such as detectors, sample processors, disposable chips, batteries and software, which are integrated with a commercial smartphone. One of the most important aspects of developing these systems is the integration of these components onto a compact and lightweight platform that requires minimal power. To date, researchers have demonstrated several promising approaches employing various sensing techniques and device configurations. We aim to provide a systematic classification according to the detection strategy, providing a critical discussion of strengths and weaknesses. We have also extended the analysis to the food scanning devices that are increasingly populating the Internet of Things (IoT) market, demonstrating how this field is indeed promising, as the research outputs are quickly capitalized on new start-up companies.


title: A 0.27V 30MHz 17.7nJ/transform 1024-pt complex FFT core with super-pipelining

abstract: Recently, aggressive voltage scaling was shown as an important technique in achieving highly energy-efficient circuits. Specifically, scaling V dd to near or sub-threshold regions was proposed for energy-constrained sensor systems to enable long lifetime and small system volume [1][2][4]. However, energy efficiency degrades below a certain voltage, V min , due to rapidly increasing leakage energy consumption, setting a fundamental limit on the achievable energy efficiency. In addition, voltage scaling degrades performance and heightens delay variability due to large I d sensitivity to PVT variations in the ultra-low voltage (ULV) regime. This paper uses circuit and architectural methods to further reduce the minimum energy point, or E min , and establish a new lower limit on energy efficiency, while simultaneously improving performance and robustness. The approaches are demonstrated on an FFT core in 65nm CMOS.


title: Towards Lifelong Self-Supervision: A Deep Learning Direction for Robotics

abstract: Despite outstanding success in vision amongst other domains, many of the recent deep learning approaches have evident drawbacks for robots. This manuscript surveys recent work in the literature that pertain to applying deep learning systems to the robotics domain, either as means of estimation or as a tool to resolve motor commands directly from raw percepts. These recent advances are only a piece to the puzzle. We suggest that deep learning as a tool alone is insufficient in building a unified framework to acquire general intelligence. For this reason, we complement our survey with insights from cognitive development and refer to ideas from classical control theory, producing an integrated direction for a lifelong learning architecture.


title: Functional Anatomy of Human Auditory Attention Studied with PET

abstract: Positron emission tomography was used to investigate the functional anatomy of selective auditory attention in 17 right-handed male volunteers who submitted to different tasks: silent rest (REST), listening to frequent low- or rare high-pitched tones (LIS) delivered randomly to the right or the left ear, selective auditory attention where subjects had to attend to deviants in one ear, right (ATTR) or left (ATTL). Six subjects had the series REST, LIS, ATTR twice, eight subjects the series REST, LIS, ATTL, and the last three subjects the series REST, ATTR, ATTL. Event-related potentials were simultaneously recorded with PET and showed significant task and electrode site effects on the N100 amplitude. When compared to REST, LIS elicited bilateral temporal activations of the Heschl's gyri and theplanum temporale,with a significant rightward asymmetry, and of the posterior part of the superior temporal gyrus. Significant right precentral and anterior cingulate gyri normalized regional cerebral blood flow increases were observed in the frontal lobe. Both the ATTR and the ATTL conditions, compared to LIS, activated the supplementary motor area, bilateral precentral, and left postcentral cortices without any temporal cortex activation. In addition, the ATTL condition resulted in a right prefrontal cortex activation. Pooling the 14 subjects revealed an asymmetry in the superior temporal gyrus favoring the cortex contralateral to the attended ear. Two major networks seem thus to be involved during selective auditory attention: (1) a local temporal network, on which selective attention produces a modulation of the functional lateralization, and (2) a frontal network that could mediate the temporal cortex modulation by attention.


title: Practice Advisory for the Prevention and Management of Operating Room Fires A Report by the American Society of Anesthesiologists Task Force on Operating

abstract: Practice Advisory for the Prevention and Management of Operating Room Fires: An Updated Report by the American Society of Anesthesiologists Task Force on Operating Room Fires Jeffrey Apfelbaum;Robert Caplan;Steven Barker;Richard Connis;Charles Cowles;Jan Ehrenwerth;David Nickinovich;Donna Pritchard;David Roberson;Robert Caplan;Steven Barker;Richard Connis;Charles Cowles;Albert de Richemond;Jan Ehrenwerth;David Nickinovich;Donna Pritchard;David Roberson;Gerald Wolf; Anesthesiology


title: Recipes for the linear analysis of EEG

abstract: Abstract In this paper, we describe a simple set of “recipes” for the analysis of high spatial density EEG. We focus on a linear integration of multiple channels for extracting individual components without making any spatial or anatomical modeling assumptions, instead requiring particular statistical properties such as maximum difference, maximum power, or statistical independence. We demonstrate how corresponding algorithms, for example, linear discriminant analysis, principal component analysis and independent component analysis, can be used to remove eye-motion artifacts, extract strong evoked responses, and decompose temporally overlapping components. The general approach is shown to be consistent with the underlying physics of EEG, which specifies a linear mixing model of the underlying neural and non-neural current sources.


title: Floating charts: Data plotting using free-floating acoustically levitated representations

abstract: Charts are graphical representations of numbers that help us to extract trends, relations and in general to have a better understanding of data. For this reason, multiple systems have been developed to display charts in a digital or physical manner. Here, we introduce Floating Charts, a modular display that utilizes acoustic levitation for positioning free-floating objects. Multiple objects are individually levitated to compose a dynamic floating chart with the ability to move in real time to reflect changes in data. Floating objects can have different sizes and colours to represent extra information. Additionally, they can be levitated across other physical structures to improve depth perception. We present the system design, a technical evaluation and a catalogue of chart variations.


title: Comparing CSTA K-12 computer science standards with Austrian curricula

abstract: In 2011, the CSTA has published a review of its K-12 Computer Science Standards. Aiming to assess the situation of Computer Science Education in Austria, we used these standards as an external level rod. For this purpose, we investigated to which degree a well-chosen subset of these standards is implemented in Austrian curricula for computer science education. We analyzed curricula of several types of secondary schools (AHS, HTL for Chemistry, HTL for Informatics, HLW). Our findings reveal that the CSTA standards are quite poorly implemented at this point of time in Austria.


title: The aqueduct of Sylvius: a sonographic landmark for neural tube defects in the first trimester

abstract: Objectives ::: ::: To describe a new first-trimester sonographic landmark, posterior displacement of the midbrain and aqueduct of Sylvius, which may be useful in early screening for neural tube defects. ::: ::: ::: ::: Methods ::: ::: This was a prospective study of 457 normal fetuses at 11 + 0 to 13 + 6 weeks' gestation. We measured the distance from the posterior border of the aqueduct of Sylvius to the anterior border of the occiput (AOS-to-occiput distance) in the axial plane and created a reference range. In the nine fetuses with abnormal midbrain position identified in the first trimester and with neural tube defect subsequently confirmed, we analyzed ultrasound images to determine the position of the aqueduct of Sylvius. ::: ::: ::: ::: Results ::: ::: The lower limit of normal AOS-to-occiput distance (mean minus 2 SD) ranged from 1.7 mm at a crown–rump length (CRL) of 45 mm to 3.7 mm at a CRL of 84 mm. In the nine cases with abnormal position of the midbrain and confirmed neural tube defect, juxtaposition of the midbrain to the occiput was the clue to diagnosis of the spinal abnormality. In all nine cases, the AOS-to-occiput distance was below the established normal range. ::: ::: ::: ::: Conclusions ::: ::: Examination of the midbrain in an axial plane may prove a reliable marker for the first-trimester diagnosis of neural tube defects. In contrast to recently reported subtle changes in the mid-sagittal view of the posterior cranial fossa, axial imaging of the midbrain reveals striking displacement of this structure, with virtual juxtaposition to the occiput, in fetuses with confirmed open spina bifida. This anatomical distortion of the midbrain can be quantified by measurement of the AOS-to-occiput distance. Copyright © 2011 ISUOG. Published by John Wiley & Sons, Ltd.


title: Individual Tree Crown Detection and Delineation From Very-High-Resolution UAV Images Based on Bias Field and Marker-Controlled Watershed Segmentation Algorithms

abstract: Individual tree crown detection and delineation (ITCD) mainly depend on high-resolution aerial photos and satellite images or LiDAR, and these data can be costly to obtain. The advent of unmanned aerial vehicle (UAV) remote sensing technology provides an economic and effective method for data acquisition. Therefore, the research of ITCD based on UAV high-resolution images is of significance to improve the efficiency and accuracy of forest resource inventory and remote sensing validation. However, in very high-resolution (defined here with the pixel size smaller than 10 cm) images, the inhomogeneities in the canopy texture can be detrimental to the correct detection of individual trees by computer processing. We applied the bias field estimation, which is used in medical image segmentation, to reduce the within-canopy spectral heterogeneity in the very high-resolution UAV-derived orthophoto. By selecting young Osmanthus and Podocarpus trees that grow in a nursery as the study objects, we tested our method in an orthophoto (with a ground resolution of 2.5 cm) generated from overlapping UAV images. A local intensity clustering was first applied to produce a smoothed bias field image; then, by using morphological operation of opening and closing, the fine texture of the canopy was further smoothed. Finally, individual tree crowns were extracted by applying the marker-controlled watershed segmentation algorithm. The segmentation results were validated by comparing to the manually drawn individual tree crown polygons. The $F$ -scores of the detection rate for Osmanthus and Podocarpus were 98.2% and 93.1%, respectively. The result is considerably better than those achieved with similar processing steps but without the bias field treatment. Our study proves that it is feasible and effective to detect and delineate individual tree crowns based on the bias field and marker-controlled watershed segmentation in very-high-resolution images obtained from UAV.


title: Compact Circularly Polarized Symmetric-Slit Microstrip Antennas

abstract: Compact symmetric-slit square microstrip-patch antennas are proposed for circularly polarized (CP) radiation. A symmetric slit on a patch radiator can be used for circularly polarized radiation with a compact size. Various slit shapes embedded along the four diagonal directions on the patch radiators are realized for compact circularly polarized symmetric-slit microstrip antennas based on a single feed. Circularly polarized radiation is achieved by slightly varying the slit's circumference along one of the diagonal axes. The performance and sizes of the proposed antennas with various slit shapes are studied and compared. The proposed antennas were designed on a low-loss Rogers RO4003C substrate and on low-cost lossy FR4 substrate. The 3-dB axial ratio (AR) and 10-dB return-loss bandwidths of the antenna on the FR4 substrate were larger compared to those of the antenna on the RO4003C substrate. The gain of the antenna on the RO4003C substrate was higher compared to that of the antenna on the FR4 substrate with a fixed overall antenna size. The measured 10-dB return loss and 3-dB axial-ratio bandwidths were 3.85% and 1.5%, respectively, with a maximum gain of around 0.8 dBic, for the antenna on the FR4 substrate. For the antenna on the RO4003 substrate, the measured 10-dB return loss and 3-dB axial-ratio bandwidths were 1.5% and 0.5%, respectively, with a maximum gain of around 3.4 dBic.


LEAP for DEAF : A Sign Language To Speech Translator


title: Boost-Flyback Single-Stage PFC Converter with Large DC Bus Voltage Ripple

abstract: The life time of lighting emitting diode (LED) can reach 100,000 hours. Compared to other power supplies, the power supply for LED requires long life time in addition to high efficiency and high power factor. Electrolytic capacitor has the shortest life in switching power supplies. In order to prolong the life time of power supply, it is necessary to reduce the storage capacitance and use other kind of capacitor instead of electrolytic capacitor. In this paper the relationship between storage capacitor voltage ripple and capacitance is analyzed. The PF and DC bus voltage of the Boost-Flyback single-stage PFC converter is analyzed. When Flyback works in DCM, the DC bus voltage is independent of the load. Then the capacitance is reduced and the voltage ripple is increased, in this way, the high voltage ceramic capacitor can be used instead of electrolytic capacitor. The analysis is verified by the experiment.


title: Towards end-to-end speech recognition for Chinese Mandarin using long short-term memory recurrent neural networks

abstract: End-to-end speech recognition systems have been successfully designed for English. Taking into account the distinctive characteristics between Chinese Mandarin and English, it is worthy to do some additional work to transfer these approaches to Chinese. In this paper, we attempt to build a Chinese speech recognition system using end-to-end learning method. The system is based on a combination of deep Long Short-Term Memory Projected (LSTMP) network architecture and the Connectionist Temporal Classification objective function (CTC). The Chinese characters (the number is about 6,000) are used as the output labels directly. To integrate language model information during decoding, the CTC Beam Search method is adopted and optimized to make it more effective and more efficient. We present the first-pass decoding results which are obtained by decoding from scratch using CTC-trained network and language model. Although these results are not as good as the performance of DNN-HMMs hybrid system, they indicate that it is feasible to choose Chinese characters as the output alphabet in the end-toend speech recognition system.


title: CONSTRUCTIVISM VERSUS OBJECTIVISM: IMPLICATIONS FOR INTERACTION, COURSE DESIGN, AND EVALUATION IN DISTANCE EDUCATION.

abstract: This paper discusses the basic philosophical assumptions of objectivism and constructivism and their implications for course design, interaction, and evaluation in distance education. First, I provide a brief overview of the construct of interaction as it is used in the field of distance education. Second, I address the major philosophical ideas of objectivism and constructivism as they relate to education. Third, I discuss how curriculum designers from each paradigm design a distance education course. Finally, I compare and contrast the two approaches to distance education course design and provide suggestions for practitioners.


title: Embedding cryptographically secure matrix transformation in structured Compressive Sensing

abstract: In the context of secure communication using Compressive Sensing (CS) embedded Cryptography, the measurement matrix can be used as a secret key if it fulfills some requirements. This secret measurement matrix shall be generated at each communication entity using pre-shared information. For this reason, several measurement matrix generation mechanisms have been developed. In this paper we propose a mechanism for the design and the embedding of a cryptographically secure matrix transformation in structured CS. For this purpose, existing deterministic constructions of structured matrices, which are appropriate for specific applications, are used as input of the proposed mechanism. At the transmitter side, a secret matrix is generated using the designed secure matrix transformation. This matrix possesses the requirements of the corresponding CS-based application as well as the requirements of a cryptographic key. At the receiver side, the same secure matrix transformations are embedded into the CS reconstruction algorithm, so that the structure of the original non-secure measurement matrix can be reused during the recovery/decryption of the original signal. The proposed mechanism considers the fact that the Spark, the Null Space Property (NSP) order and the Restricted Isometry Property (RIP) order of CS measurement matrices, possess invariance under elementary column and row transformation of this matrices.


title: Factors Affecting Running Economy in Trained Distance Runners

abstract: velocity of submaximal running, and is determined by measuring the steady-state consumption of oxygen ( ˙ VO2) and the respiratory exchange ratio. Taking body mass (BM) into consideration, runners with good RE use less energy and therefore less oxygen than runners with poor RE at the same velocity. There is a strong association between RE and distance running performance, with RE being a better predictor of performance than maximal oxygen uptake ( ˙ VO2max) in elite runners


K-SRL: Instance-based Learning for Semantic Role Labeling


title: SHOP: Simple Hierarchical Ordered Planner

abstract: SHOP (Simple Hierarchical Ordered Planner) is a domain-independent HTN planning system with the following characteristics. • SHOP plans for tasks in the same order that they will later be executed. This avoids some goal-interaction issues that arise in other HTN planners, so that the planning algorithm is relatively simple. • Since SHOP knows the complete world-state at each step of the planning process, it can use highly expressive domain representations. For example, it can do planning problems that require complex numeric computations. • In our tests, SHOP was several orders of magnitude faster man Blackbox and several times faster than TLpian, even though SHOP is coded in Lisp and the other planners are coded in C.


Human-Computer Interaction design for emerging technologies: Virtual Reality, Augmented Reality and Mobile Computer Systems


title: Automated Playtesting With Procedural Personas Through MCTS With Evolved Heuristics

abstract: This paper describes a method for generative player modeling and its application to the automatic testing of game content using archetypal player models called procedural personas. Theoretically grounded in psychological decision theory, procedural personas are implemented using a variation of Monte Carlo tree search (MCTS) where the node selection criteria are developed using evolutionary computation, replacing the standard UCB1 criterion of MCTS. Using these personas, we demonstrate how generative player models can be applied to a varied corpus of game levels and demonstrate how different playstyles can be enacted in each level. In short, we use artificially intelligent personas to construct synthetic playtesters. The proposed approach could be used as a tool for automatic play testing when human feedback is not readily available or when quick visualization of potential interactions is necessary. Possible applications include interactive tools during game development or procedural content generation systems where many evaluations must be conducted within a short time span.


title: Visual and Infrared Sensor Data-Based Obstacle Detection for the Visually Impaired Using the Google Project Tango Tablet Development Kit and the Unity Engine

abstract: A novel visual and infrared sensor data-based system to assist visually impaired users in detecting obstacles in their path while independently navigating indoors is presented. The system has been developed for the recently introduced Google Project Tango Tablet Development Kit equipped with a powerful graphics processor and several sensors which allow it to track its motion and orientation in 3-D space in real-time. It exploits the inbuilt functionalities of the Unity engine in the Tango SDK to create a 3-D reconstruction of the surrounding environment, then associates a Unity collider component with the user and utilizes it to determine his interaction with the reconstructed mesh in order to detect obstacles. The user is warned about any detected obstacles via audio alerts. An extensive empirical evaluation of the obstacle detection component has yielded favorable results, thus, confirming the potential of this system for future development work.


title: Mean value coordinates for closed triangular meshes

abstract: Constructing a function that interpolates a set of values defined at vertices of a mesh is a fundamental operation in computer graphics. Such an interpolant has many uses in applications such as shading, parameterization and deformation. For closed polygons, mean value coordinates have been proven to be an excellent method for constructing such an interpolant. In this paper, we generalize mean value coordinates from closed 2D polygons to closed triangular meshes. Given such a mesh P, we show that these coordinates are continuous everywhere and smooth on the interior of P. The coordinates are linear on the triangles of P and can reproduce linear functions on the interior of P. To illustrate their usefulness, we conclude by considering several interesting applications including constructing volumetric textures and surface deformation.


title: Fear of being laughed at and social anxiety: A preliminary psychometric study

abstract: The present study examines the relationship between questionnaire measures of social phobia and gelotophobia. A sample of 211 Colombian adults filled in Spanish versions of the Social Anxiety and Distress scale (SAD; Watson & Friend, 1969), the Fear of Negative Evaluation scale (FNE; Watson & Friend, 1969) and the GELOPH (Ruch & Proyer, 2008). Results confirmed that both Social Anxiety and Distress and Fear of Negative Evaluation scale overlapped with the fear of being laughed at without being identical with it. The SAD and FNE correlated highly with the GELOPH but not all high scorers in these scales expressed a fear of being laughed at. Furthermore, an item factor analysis yielded three factors that were mostly loaded by items of the respective scales. This three-factor structure was verified using confirmatory factor analysis. A measure model where one general factor of social anxiety was specified, or another one where two different factors were defined (gelotophobia vs. social anxiety assessed by SAD and FNE) showed a very poor fit to the data. It is concluded that the fear of being laughed cannot fully be accounted for by these measures of social phobia.


title: Perception of Esthetic Impact of Smile Line in Complete Denture Wearers by Different Age Groups.

abstract: Purpose ::: ::: To evaluate esthetic perceptions based on tooth exposure when smiling of patients wearing complete dentures by evaluators in different age groups. ::: ::: ::: ::: Materials and Methods ::: ::: Alterations were made to a front view photograph of a smiling patient wearing complete maxillary and mandibular dentures. Alterations in the smile line were simulated to increase or decrease tooth exposure (increments of 0.5 mm). For this purpose, image manipulation software was used. After manipulation, images were printed on photo paper, attached to a questionnaire, and distributed to individuals in three age groups (n = 150). To evaluate the esthetic perception for each image, a visual analog scale was used, with 0 representing least attractive, 5 representing attractive, and 10 representing very attractive. Differences between examiners were analyzed using the Mann-Whitney test. All statistical analyses were performed with a degree of confidence of 95%. ::: ::: ::: ::: Results ::: ::: Two evaluators did not observe any differences between images. The images given the best and worst scores were E and O (alterations of 2 and 7 mm), respectively, in the 15- to 19-year-old group, B and O (alterations of 0.5 and 7 mm), respectively, in the 35- to 44-year-old group, and A and M (no alteration and 6 mm alteration), respectively, in the 65- to 74-year-old group. When the images were presented together (images 1 and 2), the unaltered image was selected by individuals of different age groups as the best, and the image with a change of 7 mm was selected as the worst. ::: ::: ::: ::: Conclusion ::: ::: In this study, complete dental prostheses with smile lines that coincided with the cervical margins of the anterior teeth were the most acceptable. Less exposure of the maxillary teeth when smiling corresponded with decreased attractiveness.


title: A Minimalistic Dataflow Programming Library for Python

abstract: Current work on parallel programming models are trending towards the dataflow paradigm. Previous works on that topic have shown that dataflow programming is indeed a natural way to exploit parallelism in programs. However, there is still a gap in terms of ease of programming between high level languages adopted by the scientific community and the languages and tools available for dataflow programming. In this paper we present Sucuri: a minimalistic Python library that provides dataflow programming with reasonably simple syntax. To parallelize applications using our library, the programmer needs only to identify functions of his code that are good candidates for parallelization and instantiate a dataflow graph where each node is associated with one of such functions, and the edges between nodes describe data dependencies between functions. We then proceed to implement two benchmarks that represent important parallel programming patterns using our library and execute them on a cluster of multicores. Experimental results are promising, proving that our library can be an interesting first option for parallelization.


title: αRoute: A name based routing scheme for Information Centric Networks

abstract: One of the crucial building blocks for Information Centric Networking (ICN) is a name based routing scheme that can route directly on content names instead of IP addresses. However, moving the address space from IP addresses to content names brings scalability issues to a whole new level, due to two reasons. First, name aggregation is not as trivial a task as the IP address aggregation in BGP routing. Second, the number of addressable contents in the Internet is several orders of magnitude higher than the number of IP addresses. With the current size of the Internet, name based, anycast routing is very challenging specially when routing efficiency is of prime importance. We propose a novel name-based routing scheme (αRoute) for ICN that offers efficient bandwidth usage, guaranteed content lookup and scalable routing table size.


title: Anatomy of the supratrochlear nerve: implications for the surgical treatment of migraine headaches.

abstract: BACKGROUND ::: Migraine headaches have been linked to compression, irritation, or entrapment of peripheral nerves in the head and neck at muscular, fascial, and vascular sites. The frontal region is a trigger for many patients' symptoms, and the possibility for compression of the supratrochlear nerve by the corrugator muscle has been indirectly implied. To further delineate their relationship, a fresh tissue anatomical study was designed. ::: ::: ::: METHODS ::: Dissection of the brow region was undertaken in 25 fresh cadaveric heads. The corrugator muscle was identified on both sides, and its relationship with the supratrochlear nerve was investigated. ::: ::: ::: RESULTS ::: The supratrochlear nerve was found in all 50 hemifaces. Three potential points of compression were uncovered in this investigation: the nerve entrance into the brow through the frontal notch or foramen, the entrance of the nerve into the corrugator muscle, and the exit of the nerve from the corrugator muscle. The nerve generally bifurcates within the retro-orbicularis oculi fat pad, and these branches enter into one of four relationships with the corrugator muscle: both branches enter the muscle, one branch enters the muscle and one remains deep, both branches remain deep, and the branches further branch into ever smaller filaments that cannot be identified cranially. ::: ::: ::: CONCLUSIONS ::: Some patients are nonresponders to migraine decompression techniques that address the supraorbital nerve. The supratrochlear nerve may be compressed in these patients. A standard corrugator resection that comes more medially within 1.8 cm of the midline may be beneficial. The morphology of the frontal notch/foramen must be examined and addressed if necessary.


title: Accuracy Analysis of Machine Learning Algorithms for Intrusion Detection System using NSL-KDD Dataset

abstract: Intrusion Detection System (IDS) that turns to be a vital component to secure the network. The lack of regular updation, less capability to detect unknown attacks, high non adaptable false alarm rate, more consumption of network resources etc., makes IDS to compromise. This paper aims to classify the NSL-KDD dataset with respect to their metric data by using the best six data mining classification algorithms like J48, ID3, CART, Bayes Net, Naive Bayes and SVM to find which algorithm will be able to offer more testing accuracy. NSL-KDD dataset has solved some of the inherent limitations of the available KDD’99 dataset.


title: Design and Control of Ball Wheel Omnidirectional Vehicles - Robotics and Automation, 1995. Proceedings., 1995 IEEE International Conference on

abstract: A new class of ball wheel mechanisms for omnidirectional vehicles is presented. This ball wheel mechanism can be designed to yield fully mobile vehicles that are not only free of any kinematic singularity but are configuration invariant in kinematic behavior. Invariant kinematics greatly simplifies the control of smooth and precise vehicle motion. Multiple displacement sensors are easily incorporated into each ball wheel mechanism. This unique feature enables the detection of slip between tires and the floor and also indicates at which tire the slip occurs. This allows traction control to be implemented to fully exploit the available floor friction while accurate dead reckoning navigation continues from two non slipping tires. A prototype vehicle with three ball wheel mechanisms is implemented. Smooth motion and precise dead reckoning are accomplished.


title: Designing Documentary Informatics

abstract: In this paper we describe a Research through Design inquiry about a speculative wedding documentation service, in the mode of the Quantified Self. We reflect on our design research, which included design ethnography, interviews, enactments of parts of the service, and the production of a concept brochure. In so doing, we explore the design of personal tracking as a documentary activity, one intended for longer-term self-expression and remembering -- rather than simply to monitor, regulate and motivate a data-driven life. Developing the Lived Informatics discourse, we use our design-led inquiry to propose "Documentary Informatics" as an alternative and longer-term design perspective on self-tracking tools.


title: Four paradigms of information systems development

abstract: Developing computer-based information systems necessarily involves making a number of implicit and explicit assumptions. The authors examine four different approaches to information systems development.


title: On the Time Series $K$-Nearest Neighbor Classification of Abnormal Brain Activity

abstract: Epilepsy is one of the most common brain disorders, but the dynamical transitions to neurological dysfunctions of epilepsy are not well understood in current neuroscience research. Uncontrolled epilepsy poses a significant burden to society due to associated healthcare cost to treat and control the unpredictable and spontaneous occurrence of seizures. The objective of this study is to develop and present a novel classification technique that is used to classify normal and abnormal (epileptic) brain activities through quantitative analyses of electroencephalogram (EEG) recordings. Such technique is based on the integration of sophisticated approaches from data mining and signal processing research (i.e., chaos theory, k-nearest neighbor, and statistical time series analysis). The proposed technique can correctly classify normal and abnormal EEGs with a sensitivity of 81.29% and a specificity of 72.86%, on average, across ten patients. Experimental results suggest that the proposed technique can be used to develop abnormal brain activity classification for detecting seizure precursors. Success of this study demonstrates that the proposed technique can excavate hidden patterns/relationships in EEGs and give greater understanding of brain functions from a system perspective, which will advance current diagnosis and treatment of epilepsy.


title: Deep neural network based malware detection using two dimensional binary program features

abstract: In this paper we introduce a deep neural network based malware detection system that Invincea has developed, which achieves a usable detection rate at an extremely low false positive rate and scales to real world training example volumes on commodity hardware. We show that our system achieves a 95% detection rate at 0.1% false positive rate (FPR), based on more than 400,000 software binaries sourced directly from our customers and internal malware databases. In addition, we describe a non-parametric method for adjusting the classifier’s scores to better represent expected precision in the deployment environment. Our results demonstrate that it is now feasible to quickly train and deploy a low resource, highly accurate machine learning classification model, with false positive rates that approach traditional labor intensive expert rule based malware detection, while also detecting previously unseen malware missed by these traditional approaches. Since machine learning models tend to improve with larger datasizes, we foresee deep neural network classification models gaining in importance as part of a layered network defense strategy in coming years.


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title: Four Element Dual-band Sub-6 GHz 5G MIMO Antenna Using SRR-loaded Slot-Loops

abstract: In this paper, a four element MIMO configuration is proposed for possible application in sub-6 GHz 5G wireless access points working in 3 – 4 GHz frequency bands. The proposed antenna uses microstrip-fed square slot-loops loaded with split-ring resonators (SRRs) to achieve miniaturized multiband performance, where extended bandwidth is achieved in the higher frequency range by merging of two narrow bands. Ansys HFSS simulations are used to explain the design methodology of the proposed antenna, by studying its impedance bandwidth, mutual coupling, simulated 3D radiation patterns as well as surface current distribution features. The proposed MIMO antenna exhibits excellent diversity performance (ECC < 0.1) and MEG-ratio within ±3 dB in uniform propagation scenario. The robustness of the proposed MIMO antenna is further checked for indoor Gaussian / Uniform propagation environment for a wide- range of scenario parameters.


title: Robot Rotation Decomposition Using Quaternions

abstract: To decompose the three-dimension robot rotation into two sub-rotations, the rotation of the robot's main axis and the rotation around this axis, we present a rotation decomposition method by quaternions. By the rotation matrix, we can use the quaternion computation to find the rotation angle of the robot main axis along the axis that is perpendicular to the robot main axis, and the rotation that turns along the robot main axis. In addition, we prove that the decomposition order of the two rotations will not affect the results of the rotation decomposition, and give the procedure for the decomposition. This method has been used to determine the orientation of the wireless capsule endoscope, whose rotation is determined by applying computer vision technique on the captured images.


title: Design of an Average-Current-Mode Noninverting Buck–Boost DC–DC Converter With Reduced Switching and Conduction Losses

abstract: With the rapid growth of battery-powered portable electronics, an efficient power management solution is necessary for extending battery life. Generally, basic switching regulators, such as buck and boost converters, may not be capable of using the entire battery output voltage range (e.g., 2.5-4.7 V for Li-ion batteries) to provide a fixed output voltage (e.g., 3.3 V). In this paper, an average-current-mode noninverting buck-boost dc-dc converter is proposed. It is not only able to use the full output voltage range of a Li-ion battery, but it also features high power efficiency and excellent noise immunity. The die area of this chip is 2.14 × 1.92 mm2, fabricated by using TSMC 0.35 μm 2P4M 3.3 V/5 V mixed-signal polycide process. The input voltage of the converter may range from 2.3 to 5 V with its output voltage set to 3.3 V, and its switching frequency is 500 kHz. Moreover, it can provide up to 400-mA load current, and the maximal measured efficiency is 92.01%.


title: Bioavailability of fluticasone propionate and mometasone furoate aqueous nasal sprays

abstract: To compare the systemic exposure for intranasal mometasone furoate (MF) and fluticasone propionate (FP) aqueous nasal sprays (ANS) in terms of serum and urinary cortisol parameters and plasma pharmacokinetics. Twelve healthy subjects completed this three-way, cross-over study. They received FPANS (50 μg/spray), MFANS (50 μg/spray) or placebo ANS, eight sprays per nostril every 8 h for 4 days. Cortisol measurements were made at baseline and day 4. FP and MF plasma concentrations were also measured on day 4. MFANS produced similar mean plasma AUC (123 pmol/l h) to FPANS (112 pmol/l h). Despite the use of high doses, necessary to generate adequate pharmacokinetic data, only minor reductions in cortisol parameters were found, with no difference between FPANS and MFANS. FP and MF have similar and very low systemic bioavailability when administered intranasally using a high-dose regimen. It is therefore unlikely that therapeutic doses of intranasal FP or MF will produce dissimilar or significant degrees of systemic exposure or systemic effects.