title: Prey stage preference and functional response of Euseius hibisci to Tetranychus urticae (Acari: Phytoseiidae, Tetranychidae)

abstract: The aims of this study were: (a) determine the prey stage preference of female Euseius hibisci (Chant) (Phytoseiidae) at constant densities of different stages of Tetranychus urticae Koch (Tetranychidae), (b) assess the functional response of the predator females to the varying densities of eggs, larvae, or protonymphs of T. urticae, and (c) estimate the functional response of E. hibisci when pollen of Ligustrum ovalifolium was present as well. We conducted experiments on excised pieces of strawberry leaf arenas (Fragaria ananassa) under laboratory conditions of 25+/-2 degrees C, 60+/-5% RH and 12 h photophase. Our results indicated that the predator consumed significantly more prey eggs than other prey stages. Consumption of prey deutonymphs and adults was so low that they were excluded from the non-choice functional response experiments. The functional response on all food items was of type II. The two parameters of the functional response were estimated for each prey type by means of the adjusted non-linear regression model. The highest estimated value a' (instantaneous rate of discovery) and the lowest value of Th (handling time, including digestion) were found for the predator feeding on prey eggs, and a' was lowest and Th highest when fed protonymphs. Using the jack-knife method, the values for the functional response parameters were estimated. The values of a' and Th produced by the model were similar among all prey types except for the eggs, which were different. Using pollen simultaneously with prey larvae decreased the consumption of the latter over the full range of prey densities The suitability of this predator for biological control of T. urticae on strawberry is discussed.


title: An improved wavelet based shock wave detector

abstract: In this paper, the detection of shock wave that generated by supersonic bullet is considered. A wavelet based multi-scale products method has been widely used for detection. However, the performance of method decreased at low signal-to-noise ratio (SNR). It is noted that the method does not consider the distribution of the signal and noise. Thus we analyze the method under the standard likelihood ratio test in this paper. It is found that the multi-scale product method is made in an assumption that is extremely restricted, just hold for a special noise condition. Based on the analysis, a general condition is considered for the detection. An improved detector under the standard likelihood ratio test is proposed. Monte Carlo simulations is conducted with simulated shock waves under additive white Gaussian noise. The result shows that this new detection algorithm outperforms the conventional detection algorithm.


title: Car to car communication in VANET using Co-operative Mobility Services of the Future (CoMoSeF)

abstract: Every year, the number of traffic accidents in the world may increase as the number of vehicles. These was caused by improper use of vehicles such as the location awareness of road users to obey traffic signs or a lack of tolerance or mutual respect fellow road users. Wireless communication between vehicles is expected to help reduce the number of accidents. In the near future the vehicle can be used as a means of communication and transportation. CoMoSeF is a method that use a sensor controller area network (CAN) BUS instilled in every vehicle where the sensor is functioning as communication between vehicles. This is considered as a new approach for car to car communication in VANET. The sensors connected to a mobile device that serves riders by giving information when there is a vehicle which may experience a collision with him. This study presents a simulation designed for inter-vehicle communication system using the system of CoMoSeF 802.11p. The results of this study are expected to provide benefits to the smart-city of transportation in reducing the accident rate.


title: Bioequivalence study of 500 mg cefuroxime axetil film-coated tablets in healthy volunteers.

abstract: The aim of the study was to investigate the bioavailability of a generic product of 500 mg cefuroxime axetil film-coated tablets (test) as compared to that of a branded product (reference) at the same strength to determine bioequivalence and to apply for regulatory approval. The secondary objective of the study was to evaluate tolerability of both products. A double blinded, randomized, crossover, two-period, single-dose, comparative study was conducted in Caucasian healthy volunteers in fasting conditions. A single oral dose administration of the test or reference product was followed by 7-day wash-out period. The cefuroxime concentration was determined using a validated HPLC-UV method. The results of the single-dose study in healthy volunteers indicated that the film-coated tablets of Tarsime 500 mg manufactured by Tarchomińskie Zakłady Farmaceutyczne Polfa S.A. (test product) are bioequivalent to those of Zinnat manufactured by GlaxoSmithKline Export Ltd. (reference product). Both products were well tolerated.


title: BAGAN: Data Augmentation with Balancing GAN

abstract: Image classification datasets are often imbalanced, characteristic that negatively affects the accuracy of deep-learning classifiers. In this work we propose balancing GAN (BAGAN) as an augmentation tool to restore balance in imbalanced datasets. This is challenging because the few minority-class images may not be enough to train a GAN. We overcome this issue by including during the adversarial training all available images of majority and minority classes. The generative model learns useful features from majority classes and uses these to generate images for minority classes. We apply class conditioning in the latent space to drive the generation process towards a target class. The generator in the GAN is initialized with the encoder module of an autoencoder that enables us to learn an accurate class-conditioning in the latent space. We compare the proposed methodology with state-of-the-art GANs and demonstrate that BAGAN generates images of superior quality when trained with an imbalanced dataset.


title: Generative Partition Networks for Multi-Person Pose Estimation

abstract: This paper proposes a new Generative Partition Network (GPN) to address the challenging multi-person pose estimation problem. Different from existing models that are either completely top-down or bottom-up, the proposed GPN introduces a novel strategy--it generates partitions for multiple persons from their global joint candidates and infers instance-specific joint configurations simultaneously. The GPN is favorably featured by low complexity and high accuracy of joint detection and re-organization. In particular, GPN designs a generative model that performs one feed-forward pass to efficiently generate robust person detections with joint partitions, relying on dense regressions from global joint candidates in an embedding space parameterized by centroids of persons. In addition, GPN formulates the inference procedure for joint configurations of human poses as a graph partition problem, and conducts local optimization for each person detection with reliable global affinity cues, leading to complexity reduction and performance improvement. GPN is implemented with the Hourglass architecture as the backbone network to simultaneously learn joint detector and dense regressor. Extensive experiments on benchmarks MPII Human Pose Multi-Person, extended PASCAL-Person-Part, and WAF, show the efficiency of GPN with new state-of-the-art performance.


Application of two inversion schemes to determine the absorption coefficient distribution in flames


title: Fundamentals of Digital Forensic Evidence

abstract: Digital forensic evidence (DFE) is composed of exhibits, each consisting of a sequence of bits, presented by witnesses in a legal matter, to help jurors understand the facts of the case and support or refute legal theories of the case. The exhibits should be introduced and presented and/or challenged by properly qualified people using a properly applied methodology that addresses the legal theories involved. Building the connection between technical issues associated with the DFE and the legal theories is the job of expert witnesses.


title: c ○ Springer-Verlag Evaluating the CMA Evolution Strategy on Multimodal Test Functions

abstract: In this paper the performance of the CMA evolution strategy with rank-μ-update and weighted recombination is empirically investigated on eight multimodal test functions. In particular the effect of the population size A on the performance is investigated. Increasing the population size remarkably improves the performance on six of the eight test functions. The optimal population size takes a wide range of values, but, with one exception, scales sub-linearly with the problem dimension. The global optimum can be located in all but one function. The performance for locating the global optimum scales between linear and cubic with the problem dimension. In a comparison to state-of-the-art global search strategies the CMA evolution strategy achieves superior performance on multimodal, non-separable test functions without intricate parameter tuning.


title: Ferrimagnetic Resonance Modes in Spheres

abstract: The magnetostatic solutions of ferrimagnetic resonance in ferrite spheres are briefly derived. General and specific solutions are given for values of n up to five for the magnetostatic potentials, rf distributions of magnetization, and fields for resonance. Some experimental results are given and compared with the theory.


title: Development of antimicrobial polypropylene sutures by graft polymerization. I. Influence of grafting conditions and characterization

abstract: Modification of polypropylene monofilament was carried out by the graft polymerization of 1-vinylimidazole (VIm) using simultaneous radiation grafting method. The effect of radiation dose, monomer concentration, and the grafting medium on the degree of grafting was evaluated. It was observed that the presence of organics as additives in the reaction medium had significant influence on the graft levels. These grafted sutures were characterized using several techniques, such as infrared spectroscopy (IR), X-ray diffraction, and differential scanning calorimetry (DSC). It was found that the grafts are confined to the amorphous region of the monofilament and the crystalline regions remain intact. The surface morphology of sutures was evaluated by scanning electron microscopy. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 3895–3901, 2006


title: Blockchain based approach to enhance big data authentication in distributed environment

abstract: Existing authentication protocols for Big Data system such as Apache Hadoop is based on Kerberos. In the Kerberos protocol, there are numerous security issues that have remained unsolved; replay attacks, DDoS and single point of failure are some examples. These indicate potential security vulnerabilities and Big Data risks in using Hadoop. This paper presents drawbacks of Kerberos implementations and identifies authentication requirements that can enhance the security of Big Data in distributed environments. The enhancement proposed is based on the rising technology of blockchain that overcomes shortcomings of Kerberos.


title: Architectural support for SWAR text processing with parallel bit streams: the inductive doubling principle

abstract: Parallel bit stream algorithms exploit the SWAR (SIMD within a register) capabilities of commodity processors in high-performance text processing applications such as UTF-8 to UTF-16 transcoding, XML parsing, string search and regular expression matching. Direct architectural support for these algorithms in future SWAR instruction sets could further increase performance as well as simplifying the programming task. A set of simple SWAR instruction set extensions are proposed for this purpose based on the principle of systematic support for inductive doubling as an algorithmic technique. These extensions are shown to significantly reduce instruction count in core parallel bit stream algorithms, often providing a 3X or better improvement. The extensions are also shown to be useful for SWAR programming in other application areas, including providing a systematic treatment for horizontal operations. An implementation model for these extensions involves relatively simple circuitry added to the operand fetch components in a pipelined processor.


Applying specialization to process models


title: The Sorcerer's Apprentice Guide to Fault Attacks

abstract: The effect of faults on electronic systems has been studied since the 1970s when it was noticed that radioactive particles caused errors in chips. This led to further research on the effect of charged particles on silicon, motivated by the aerospace industry, which was becoming concerned about the effect of faults in airborne electronic systems. Since then various mechanisms for fault creation and propagation have been discovered and researched. This paper covers the various methods that can be used to induce faults in semiconductors and exploit such errors maliciously. Several examples of attacks stemming from the exploiting of faults are explained. Finally a series of countermeasures to thwart these attacks are described.


Ultra-wideband, Wide Angle and Polarization-insensitive Specular Reflection Reduction by Metasurface based on Parameter-adjustable Meta-Atoms


title: A 77GHz 4-channel automotive radar transceiver in SiGe

abstract: A fully integrated 4-channel automotive radar transceiver chip, integrated in a 200-GHz SiGe:C production technology, is presented. With a typical transmit power of 2 x +7 dBm at the antenna ports and all functions active, the chip draws a current of about 600 mA from a single 5.5 V supply. The design permits FMCW operation in the 76 to 77 GHz band at chip-backside temperatures from -40degC to +125degC.


title: GAUSSIAN QUADRATURE RULES AND A-STABILITY OF GALERKIN SCHEMES FOR ODE

abstract: The A-stability properties of continuous and discontinuous Galerkin methods for solving ordinary differential equations (ODEs) are established using properties of Legendre polynomials and Gaussian quadrature rules. The influence on the Astability of the numerical integration using Gaussian quadrature rules involving a parameter is analyzed.


title: A Case for Malleable Thread-Level Linear Algebra Libraries: The LU Factorization With Partial Pivoting

abstract: We propose two novel techniques for overcoming load-imbalance encountered when implementing so-called look-ahead mechanisms in relevant dense matrix factorizations for the solution of linear systems. Both techniques target the scenario where two thread teams are created/activated during the factorization, with each team in charge of performing an independent task/branch of execution. The first technique promotes worker sharing (WS) between the two tasks, allowing the threads of the task that completes first to be reallocated for use by the costlier task. The second technique allows a fast task to alert the slower task of completion, enforcing the early termination (ET) of the second task, and a smooth transition of the factorization procedure into the next iteration. The two mechanisms are instantiated via a new malleable thread-level implementation of the basic linear algebra subprograms, and their benefits are illustrated via an implementation of the LU factorization with partial pivoting enhanced with look-ahead. Concretely, our experimental results on an Intel-Xeon system with 12 cores show the benefits of combining WS+ET, reporting competitive performance in comparison with a task-parallel runtime-based solution.


A Survey of E-book Digital Right Management


title: Virtual Reality for Training and Lifelong Learning

abstract: This article covers the application of virtual reality (VR) to training and lifelong learning. A number of considerations concerning the design of VR applications are included. The introduction is dedicated to the more general aspects of applying VR to training. From multiple perspectives, we will provide an overview of existing applications with their main purposes and go into more depth on certain learning areas. Recent developments of virtual environments for training and lifelong learning are analyzed, followed by an analytical viewpoint on design, advocating more explicit paradigmatic considerations and development of generic design methods. These approaches and proposals are aimed at better exploiting the uniqueness of VR and designing more effective virtual environments. Finally, a number of conclusions will be drawn for future technology-enhanced training and for lifelong learning using VR.


Efficient Visual-Inertial Navigation using a Rolling-Shutter Camera with Inaccurate Timestamps


What is the best method to treat CSF leaks following resection of an acoustic neuroma?


title: How to define and study farmers' management capacity: theory and use in agricultural economics

abstract: Textbooks and articles on farm management stress the importance of the management capacity of the fanner with respect to his farm results. However, explicit definitions together with an elaboration of this concept are hard to find. In this article, aspects of management capacity are grouped into: (1) personal aspects, consisting of fanner's drives and motivations, fanner's abilities and capabilities and his biographical facts such as age and education; and (2) aspects of the decision-making process, consisting of practices and procedures with respect to planning, implementation and control of decisions at the farm. Empirical studies on the role of management capacity in relation to farm results are reviewed. Frontier production functions are widely used in recent literature to estimate technical and economic efficiency of farms. However, in explaining differences in efficiency most studies do not go further than adding a biographical variable (e.g., level of education). This study concludes that a next step would be to include aspects of the decision-making process. Longitudinal on-farm observations, which give possibilities for studying the dynamic aspects of the decision-making, are suggested to further analyze the concept of management capacity. © 1998 Elsevier Science B.V. All rights reserved.


title: Determination of Optimal Process Parameters of Friction Stir Welding to Join Dissimilar Aluminum Alloys Using Artificial Bee Colony Algorithm

abstract: This paper presents the efforts of joining dissimilar aluminum alloys (AA6351-T6 and AA6061-T6) by friction stir welding (FSW) process. FSW experiments are conducted according to the three factors five level central composite rotatable design method, and the response surface methodology was used to establish the empirical relationship between FSW process parameters such as tool rotational speed (N), tool traverse speed (S) and axial force (F), and the response variables such as ultimate tensile strength, yield strength, and percentage of elongation. The developed empirical models’ adequacies are estimated using the analysis of variance technique. This paper also presents the application of the artificial bee colony algorithm to estimate the optimal process parameters to achieve good mechanical properties of FS weld joints. Results suggest that the estimations of the algorithm are in good agreement with the experimental findings.


title: Link-layer protection in 802.11i WLANS with dummy authentication

abstract: The current 802.11i standard can provide data confidentiality, integrity and mutual authentication in enterprise Wireless Local Area Networks (WLANs). However, secure communication can only be provided after successful authentication and a robust security network association is established. In general, the wireless link layer is not protected by the current standard in WLANs, which leads to many possible attacks, especially in public open-access wireless networks. We argue that regardless of the type of network under consideration, link-layer protection and data confidentiality are of great importance in wireless applications. In this paper, we first identify and analyze the security issues ignored by the current 802.11 security standard. Then we propose our solution to patch the current 802.11i standard and address all those issues with a new dummy authentication key-establishment algorithm. Dummy means no real authentication for a user. In dummy authentication, we apply public-key cryptography's key-establishment technique to the 802.11 MAC protocol. Our solution can provide link-layer data encryption in open-access wireless networks, separate session encryption keys for different users, and protection for important frames such as management and null data frames as well as Extensible Authentication Protocol (EAP) messages.


title: End to End Automation on Cloud with Build Pipeline: The Case for DevOps in Insurance Industry, Continuous Integration, Continuous Testing, and Continuous Delivery

abstract: In modern environment, delivering innovative idea in a fast and reliable manner is extremely significant for any organizations. In the existing scenario, Insurance industry need to better respond to dynamic market requirements, faster time to market for new initiatives and services, and support innovative ways of customer interaction. In past few years, the transition to cloud platforms has given benefits such as agility, scalability, and lower capital costs but the application lifecycle management practices are slow with this disruptive change. DevOps culture extends the agile methodology to rapidly create applications and deliver them across environment in automated manner to improve performance and quality assurance. Continuous Integration (CI) and Continuous delivery (CD) has emerged as a boon for traditional application development and release management practices to provide the capability to release quality artifacts continuously to customers with continuously integrated feedback. The objective of the paper is to create a proof of concept for designing an effective framework for continuous integration, continuous testing, and continuous delivery to automate the source code compilation, code analysis, test execution, packaging, infrastructure provisioning, deployment, and notifications using build pipeline concept.


title: Mechanism of Collapse of Tall Steel Moment-Frame Buildings under Earthquake Excitation

abstract: The mechanism of collapse of tall steel moment-frame buildings is explored through three-dimensional nonlinear analyses of two 18-story steel moment-frame buildings under earthquake excitation. Both fracture-susceptible and perfect-connection conditions are investigated. Classical energy-balance analysis shows that only long-period excitation imparts energy to tall buildings large enough to cause collapse. Under such long-period motion, the shear-beam analogy alludes to the existence of a characteristic mechanism of collapse or a few preferred ::: mechanisms of collapse for these buildings. Numerical evidence from parametric analyses of the buildings under a suite of idealized sawtooth-like ground-motion time histories, with varying period (T), amplitude (peak ground velocity, PGV), and duration (number of cycles, N), is ::: presented to support this hypothesis. Damage localizes to form a quasi-shear band over a few stories. When the band is destabilized, sidesway collapse is initiated, and gravity takes over. Only one to five collapse mechanisms occur out of a possible 153 mechanisms in either principal ::: direction of the buildings considered. Where two or more preferred mechanisms do exist, they have significant story-overlap, typically separated by just 1 story. It is shown that a simple work-energy relation applied to all possible quasi-shear bands combined with plastic analysis principles ::: can systematically identify all the preferred collapse mechanisms.


title: “Knock! Knock! Who is it?” probabilistic person identification in TV-series

abstract: We describe a probabilistic method for identifying characters in TV series or movies. We aim at labeling every character appearance, and not only those where a face can be detected. Consequently, our basic unit of appearance is a person track (as opposed to a face track). We model each TV series episode as a Markov Random Field, integrating face recognition, clothing appearance, speaker recognition and contextual constraints in a probabilistic manner. The identification task is then formulated as an energy minimization problem. In order to identify tracks without faces, we learn clothing models by adapting available face recognition results. Within a scene, as indicated by prior analysis of the temporal structure of the TV series, clothing features are combined by agglomerative clustering. We evaluate our approach on the first 6 episodes of The Big Bang Theory and achieve an absolute improvement of 20% for person identification and 12% for face recognition.


title: Adoption of mobile social networking sites for learning

abstract: Purpose – The purpose of this paper is to explore the factors that influence users’ behavioral intention (BI) to adopt mobile social networking sites (mSNS) in facilitating formal/informal learning. Specifically, the study also investigates the association of mobility, reachability and convenience with performance expectancy (PE) and effort expectancy (EE). Design/methodology/approach – Partial least squares structural equation modeling (PLS-SEM) approach was applied to test on 266 valid responses. Findings – The findings indicated that learning compatibility (LC), PE, EE and copyright clearance (CC) has a significant effect on BI. The results also revealed that EE is influenced by mobility, reachability and convenience. PE however was found to be influenced by convenience. Practical implications – The results of this study provides valuable insights and references for practitioners and mobile network providers in developing mSNS in facilitating learning. Originality/value – While mSNS have the potential ...


title: Asphyxial suicide by inhalation of chloroform inside a plastic bag.

abstract: Asphyxia suicide by placing a plastic bag over the head in addition with inhalation of gases or use of sedative substances is an unusual method of committing suicide, but frequently referenced by right to die groups in the Internet. This article reports 2 suicides in which chloroform was used to induce unconsciousness and subsequent asphyxia by placing the head in a plastic bag. Case histories of 2 males, ages 23 and 28, are described with special emphasis on characteristics death related to suffocation using plastic bags and chloroform. The final remarkable point in both cases is that the victims previously searched the WEB for instructions of suicide methods. The importance of the phenomenon of misuse of Internet by young people who commit suicide is stressed.


PID Controllers: Theory, Design, and Tuning


title: Visual analytics for the big data era — A comparative review of state-of-the-art commercial systems

abstract: Visual analytics (VA) system development started in academic research institutions where novel visualization techniques and open source toolkits were developed. Simultaneously, small software companies, sometimes spin-offs from academic research institutions, built solutions for specific application domains. In recent years we observed the following trend: some small VA companies grew exponentially; at the same time some big software vendors such as IBM and SAP started to acquire successful VA companies and integrated the acquired VA components into their existing frameworks. Generally the application domains of VA systems have broadened substantially. This phenomenon is driven by the generation of more and more data of high volume and complexity, which leads to an increasing demand for VA solutions from many application domains. In this paper we survey a selection of state-of-the-art commercial VA frameworks, complementary to an existing survey on open source VA tools. From the survey results we identify several improvement opportunities as future research directions.


title: Determinates of inner city butterfly and bee species richness

abstract: Although urbanization is increasing worldwide, relatively few studies have investigated patterns of urban biodiversity outside of city parks and reserves, in urban neighborhoods where people live and work. We evaluated models including local and landscape factors that might influence the bee and butterfly richness of community gardens located within densely populated neighborhoods of the Bronx and East Harlem in New York City (>10,000 people/km2). The gardens were surrounded by buildings and limited amounts of green space (3,600–17,400 building units and 10–32% green space within a 500 m radius). Contrary to our initial prediction that landscape green space might be especially influential in this heavily urbanized setting, the most highly supported models for both bee and butterfly richness (based on Akaike Information Criterion) included just the local, within-garden variables of garden floral area and sunlight availability. There was marginal support for models of bee richness including the number of building units surrounding gardens within a 500 m radius (which exhibited a negative association with bee richness). In addition, perhaps because bees are central place foragers that may nest within or near gardens, supported models of bee species richness also included total garden area, canopy cover, and the presence of wild/unmanaged area in the garden. Generally, our findings indicate that sunlight and floral abundance are the major factors limiting local pollinator diversity in this setting. This suggests that rooftop and other “open” urban habitats might be managed to increase local pollinator diversity, even if seemingly “isolated” within heavily developed neighborhoods.


title: (1)H, (13)C and (15)N random coil NMR chemical shifts of the common amino acids. I. Investigations of nearest-neighbor effects.

abstract: In this study we report on the 1H, 13C and 15N NMR chemical shifts for the random coil state and nearest-neighbor sequence effects measured from the protected linear hexapeptide Gly-Gly-X-Y-Gly-Gly (where X and Y are any of the 20 common amino acids). We present data for a set of 40 peptides (of the possible 400) including Gly-Gly-X-Ala-Gly-Gly and Gly-Gly-X-Pro-Gly-Gly, measured under identical aqueous conditions. Because all spectra were collected under identical experimental conditions, the data from the Gly-Gly-X-Ala-Gly-Gly series provide a complete and internally consistent set of 1H, 13C and 15N random coil chemical shifts for all 20 common amino acids. In addition, studies were also conducted into nearest-neighbor effects on the random coil shift arising from a variety of X and Y positional substitutions. Comparisons between the chemical shift measurements obtained from Gly-Gly-X-Ala-Gly-Gly and Gly-Gly-X-Pro-Gly-Gly reveal significant systematic shift differences arising from the presence of proline in the peptide sequence. Similarly, measurements of the chemical shift changes occurring for both alanine and proline (i.e., the residues in the Y position) are found to depend strougly on the type of amino acid substituted into the X position. These data lend support to the hypothesis that sequence effects play a significant role in determining peptide and protein chemical shifts.


title: Deep Scene Text Detection with Connected Component Proposals

abstract: A growing demand for natural-scene text detection has been witnessed by the computer vision community since text information plays a significant role in scene understanding and image indexing. Deep neural networks are being used due to their strong capabilities of pixel-wise classification or word localization, similar to being used in common vision problems. In this paper, we present a novel two-task network with integrating bottom and top cues. The first task aims to predict a pixel-by-pixel labeling and based on which, word proposals are generated with a canonical connected component analysis. The second task aims to output a bundle of character candidates used later to verify the word proposals. The two sub-networks share base convolutional features and moreover, we present a new loss to strengthen the interaction between them. We evaluate the proposed network on public benchmark datasets and show it can detect arbitrary-orientation scene text with a finer output boundary. In ICDAR 2013 text localization task, we achieve the state-of-the-art performance with an F-score of 0.919 and a much better recall of 0.915.


title: A dual-phase-shift control strategy for dual-active-bridge DC-DC converter in wide voltage range

abstract: This paper analyzes the dual-phase-shift (DPS) control strategy for a dual-active-bridge (DAB) converter in whole operation range. The DPS has two degree of freedom to control the transferring power, which can improve the performance of the DAB converter than single-phase-shift (SPS) control strategy. This paper derives the reactive power, the rms and the peak current flowing on the transformer and the zero-voltage-switching (ZVS) condition according to the operating conditions. Using this analysis and simulation results, a suitable control strategy for DAB converter is obtained. Experiments are performed to verify the proposed control strategy and to compare with the conventional SPS control strategy. The experimental results show that the proposed method can enhance the overall efficiency and expand the ZVS operation range.


title: On the Exploitation of User Personality in Recommender Systems

abstract: In this paper we revise the state of the art on personality-aware recommender systems, identifying main research trends and achievements up to date, and discussing open issues that may be addressed in the future.


title: Reducing latency in an SRAM/DRAM cache hierarchy via a novel Tag-Cache architecture

abstract: Memory speed has become a major performance bottleneck as more and more cores are integrated on a multi-core chip. The widening latency gap between high speed cores and memory has led to the evolution of multi-level SRAM/DRAM cache hierarchies that exploit the latency benefits of smaller caches (e.g. private L1 and L2 SRAM caches) and the capacity benefits of larger caches (e.g. shared L3 SRAM and shared L4 DRAM cache). The main problem of employing large L3/L4 caches is their high tag lookup latency. To solve this problem, we introduce the novel concept of small and low latency SRAM/DRAM Tag-Cache structures that can quickly determine whether an access to the large L3/L4 caches will be a hit or a miss. The performance of the proposed Tag-Cache architecture depends upon the Tag-Cache hit rate and to improve it we propose a novel Tag-Cache insertion policy and a DRAM row buffer mapping policy that reduce the latency of memory requests. For a 16-core system, this improves the average harmonic mean instruction per cycle throughput of latency sensitive applications by 13.3% compared to state-of-the-art.


title: CommCube 1 and 2: A CubeSat series of missions to enhance communication capabilities for CubeSat

abstract: CubeSats and small satellites are becoming a way to explore space and to perform science more affordably. As the goals for these spacecraft become more ambitious in terms of physical distance (moving from Low Earth Orbit (LEO) to Geostationary Earth Orbit (GEO) or further), and of the amount of data to relay back to Earth (from Kbits to Mbits), the communication systems currently implemented will not be able to fully support those missions. Two of the possible strategies to solve this issue are the following: 1. Increasing the time available to communicate to the ground by using existing satellite networks as communication relays. 2. Equipping the satellite with a more powerful antenna compatible with the volume and mass constraints imposed by CubeSats and small satellites.


title: Automatic question generation for intelligent tutoring systems

abstract: In this paper, we present the system of automatic MCQs (Multiple Choice Questions) generation for any given input text along with a set of distractors. The system is trained on a Wikipedia-based dataset consisting of URLs of Wikipedia articles. The important words (keywords) which consist of both bigrams and unigrams are extracted and stored in a dictionary along with many other components of the knowledge base. We have used Inverse Document Frequency (IDF) measure for ranking the extracted keywords and Context-Based Similarity approach using Paradigmatic Relation discovery techniques for generation of distractors. In addition, the question generation phase includes eliminating sentences starting with Discourse Connectives to avoid a question with incomplete information. We have obtained significant accuracy compared to many similar approaches. The results are quite promising considering that there is no human intervention. The task of automatic question generation can be of quite an importance in MOOCs (Massive Open Online Courses), Intelligent Tutoring Systems and for self-assessment while learning a new concept. Though we have developed our system for the field of physics, it can be extended to any field.


The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology.


title: Feedback Control Applied to the Bluff Body Wake

abstract: In the present study the flow around a 2D bluff body with blunt stern is investigated experimentally and theoretically. The goal is to decrease and stabilize drag by active control. Low-dimensional vortex models are used to describe actuation effects on the coherent structures and the pressure field. Open-loop actuation as well as feedback control is applied using robust H ∞-controllers and slope-seeking feedback for a range of Reynolds numbers based on the height from 20 000 to 60 000. As expected, a decreased drag is observed to be related to delayed vortex shedding, i.e. an extended recirculation zone. Intriguingly, a control which mitigates the natural coupling between separating upper and lower shear-layer and the vortex street serves that purpose.


title: The development of a service supply chain model for a manufacturing SME

abstract: Purpose – Services are increasingly growing in importance in the global economy. However, in the context of manufacturing industry supply chains, services are still little discussed. Therefore, the purpose of this paper is to increase the understanding of service business development for manufacturing small- and medium-sized enterprises by further developing previous conceptual frameworks for service supply chain management, based on the current scientific literature and empirical cases. Design/methodology/approach – The study is based mainly on qualitative interviews and expert group sessions set up to analyze the acquired data and test the developed model. The developed model is based on a multidisciplinary literature review, to provide a holistic perspective on the affecting factors in new business development in the service supply chain context. Findings – The implications of the study are beneficial from both the scientific and practical perspectives, which help to understand better the process and r...


title: Three-phase single-stage four-switch PFC buck-boost-type rectifier

abstract: This paper proposes a new three-phase single-stage power-factor corrector buck-boost-type rectifier topology. The typical topology uses a bridge configuration with six switches. This new topology only requires four switches, improving the rectifier efficiency as only one reverse-blocking power semiconductor conducts at any time. A vector-based sliding-mode control method for the three-phase input currents is also proposed. This fast and robust technique uses sliding mode to generate /spl alpha//spl beta/ space-vector modulation, which forces the input line currents to track a suitable sinusoidal reference. A near-unity power-factor operation of the rectifier is obtained using a sinusoidal reference in phase with the input source voltages. A proportional-integral controller is adopted to regulate the output voltage of the converter. This external voltage controller modulates the amplitude of the current references. The characteristics of the new rectifier are verified with experimental results.


title: Algorithmic clothing: hybrid recommendation, from street-style-to-shop

abstract: In this paper we detail Cortexica's (this https URL) recommendation framework -- particularly, we describe how a hybrid visual recommender system can be created by combining conditional random fields for segmentation and deep neural networks for object localisation and feature representation. The recommendation system that is built after localisation, segmentation and classification has two properties -- first, it is knowledge based in the sense that it learns pairwise preference/occurrence matrix by utilising knowledge from experts (images from fashion blogs) and second, it is content-based as it utilises a deep learning based framework for learning feature representation. Such a construct is especially useful when there is a scarcity of user preference data, that forms the foundation of many collaborative recommendation algorithms.


title: Massive MIMO: An Introduction

abstract: Demand for wireless throughput, both mobile and fixed, will always increase. One can anticipate that, in five or ten years, millions of augmented reality users in a large city will want to transmit and receive 3D personal high-definition video more or less continuously, say 100 megabits per second per user in each direction. Massive MIMO-also called Large-Scale Antenna Systems-is a promising candidate technology for meeting this demand. Fifty-fold or greater spectral efficiency improvements over fourth generation (4G) technology are frequently mentioned. A multiplicity of physically small, individually controlled antennas performs aggressive multiplexing/demultiplexing for all active users, utilizing directly measured channel characteristics. Unlike today's Point-to-Point MIMO, by leveraging time-division duplexing (TDD), Massive MIMO is scalable to any desired degree with respect to the number of service antennas. Adding more antennas is always beneficial for increased throughput, reduced radiated power, uniformly great service everywhere in the cell, and greater simplicity in signal processing. Massive MIMO is a brand new technology that has yet to be reduced to practice. Notwithstanding, its principles of operation are well understood, and surprisingly simple to elucidate.


title: Barista: An implementation framework for enabling new tools, interaction techniques and views in code editors

abstract: Recent advances in programming environments have focused on improving programmer productivity by utilizing the inherent structure in computer programs. However, because these environments represent code as plain text, it is difficult and sometimes impossible to embed interactive tools, annotations, and alternative views in the code itself. Barista is an implementation framework that enables the creation of such user interfaces by simplifying the implementation of editors that represent code internally as an abstract syntax tree and maintain a corresponding, fully structured visual representation on-screen. Barista also provides designers of editors with a standard text-editing interaction technique that closely mimics that of conventional text editors, overcoming a central usability issue of previous structured code editors.


title: What Is Technological Pedagogical Content Knowledge

abstract: This paper describes a framework for teacher knowledge for technology integration called technological pedagogical content knowledge (originally TPCK, now known as TPACK, or technology, pedagogy, and content knowledge). This framework builds on Lee Shulman’s construct of pedagogical content knowledge (PCK) to include technology knowledge. The development of TPACK by teachers is critical to effective teaching with technology. The paper begins with a brief introduction to the complex, illstructured nature of teaching. The nature of technologies (both analog and digital) is considered, as well as how the inclusion of technology in pedagogy further complicates teaching. The TPACK framework for teacher knowledge is described in detail, as a complex interaction among three bodies of knowledge: Content, pedagogy, and technology. The interaction of these bodies of knowledge, both theoretically and in practice, produces the types of flexible knowledge needed to successfully integrate technology use into teaching.


title: Model-Driven Prototyping Based Requirements Elicitation

abstract: This paper presents a requirements elicitation approach that is based on model-driven prototyping. Model-driven development fits naturally in evolutionary prototyping because modeling and design are not treated merely as documents but as key parts of the development process. A novel rapid program synthesis approach is applied to speed up the prototype development. MDA, AI planning, and component-based software development techniques are seamlessly integrated together in the approach to achieve rapid prototyping. More importantly, the rapid program synthesis approach can ensure the correctness of the generated code, which is another favorable factor in enabling the development of a production quality prototype in a timely manner.


title: Grey-level hit-or-miss transforms—part II: Application to angiographic image processing

abstract: The hit-or-miss transform (HMT) is a fundamental operation on binary images, widely used since 40 years. As it is not increasing, its extension to grey-level images is not straightforward, and very few authors have considered it. Moreover, despite its potential usefulness, very few applications of the grey-level HMT have been proposed until now. Part I of this paper [B. Naegel, N. Passat, C. Ronse, Grey-level hit-or-miss transforms-part I: unified theory. Pattern Recogn., in press doi:10.1016/j.patcog.2006.06.004] was devoted to the description of a theory enabling to unify the main definitions of the grey-level HMT, mainly proposed by Ronse and Soille, respectively. Part II of this paper, developed hereafter, deals with the applicative potential of the grey-level HMT, illustrated by its use for vessel segmentation from 3D angiographic data. Different HMT-based segmentation methods are then described and analysed, leading to concrete analysis techniques for brain and liver vessels, but also providing algorithmic strategies which could further be used for many other kinds of image processing applications.


title: An unusual cause of thigh pain in colonic cancer. Inflammatory oncotaxis?

abstract: Colonic carcinoma metastasis in order of frequency to the liver, lung, bone and brain. Metastases elsewhere are unusual. We report a very rare case of metastatic spread to skeletal muscle and skin. There follows a review of the theories of anomalous metastatic localization. Inflammatory oncotaxis is offered as the most likely explanation of this phenomenon. The importance of good history taking in colorectal cancer follow up clinics is emphasised.


title: Predicting the 2011 Dutch Senate Election Results with Twitter

abstract: To what extend can one use Twitter in opinion polls for political elections? Merely counting Twitter messages mentioning political party names is no guarantee for obtaining good election predictions. By improving the quality of the document collection and by performing sentiment analysis, predictions based on entity counts in tweets can be considerably improved, and become nearly as good as traditionally obtained opinion polls.


title: Baselines for Reinforcement Learning in Text Games

abstract: The ability to learn optimal control policies in systems where action space is defined by sentences in natural language would allow many interesting real-world applications such as automatic optimisation of dialogue systems. Text-based games with multiple endings and rewards are a promising platform for this task, since their feedback allows us to employ reinforcement learning techniques to jointly learn text representations and control policies. We argue that the key property of AI agents, especially in the text-games context, is their ability to generalise to previously unseen games. We present a minimalistic text-game playing agent, testing its generalisation and transfer learning performance and showing its ability to play multiple games at once. We also present pyfiction, an open-source library for universal access to different text games that could, together with our agent that implements its interface, serve as a baseline for future research.


title: A Recurrent Convolutional Neural Network for Land Cover Change Detection in Multispectral Images

abstract: In this paper, we propose a novel network architecture, a recurrent convolutional neural network, which is trained to learn a joint spectral-spatial-temporal feature representation in a unified framework for change detection of multispectral images. To this end, we bring together a convolutional neural network (CNN) and a recurrent neural network (RNN) into one end-to-end network. The former is able to generate rich spectral-spatial feature representations while the latter effectively analyzes temporal dependency in bi-temporal images. Although both CNN and RNN are well-established techniques for remote sensing applications, to the best of our knowledge, we are the first to combine them for multitemporal data analysis in the remote sensing community. Both visual and quantitative analysis of experimental results demonstrates competitive performance in the proposed mode.


title: TWITOBI: A Recommendation System for Twitter Using Probabilistic Modeling

abstract: Twitter provides search services to help people find new users to follow by recommending popular users or their friends' friends. However, these services do not offer the most relevant users to follow for a user. Furthermore, Twitter does not provide yet the search services to find the most interesting tweet messages for a user either. In this paper, we propose TWITOBI, a recommendation system for Twitter using probabilistic modeling for collaborative filtering which can recommend top-K users to follow and top-K tweets to read for a user. Our novel probabilistic model utilizes not only tweet messages but also the relationships between users. We develop an estimation algorithm for learning our model parameters and present its parallelized algorithm using MapReduce to handle large data. Our performance study with real-life data sets confirms the effectiveness and scalability of our algorithms.


title: An online adaptive approach to alert correlation

abstract: The current intrusion detection systems (IDSs) generate a tremendous number of intrusion alerts. In practice, managing and analyzing this large number of low-level alerts is one of the most challenging tasks for a system administrator. In this context alert correlation techniques aiming to provide a succinct and high-level view of attacks gained a lot of interest. Although, a variety of methods were proposed, the majority of them address the alert correlation in the off-line setting. In this work, we focus on the online approach to alert correlation. Specifically, we propose a fully automated adaptive approach for online correlation of intrusion alerts in two stages. In the first online stage, we employ a Bayesian network to automatically extract information about the constraints and causal relationships among alerts. Based on the extracted information, we reconstruct attack scenarios on-the-fly providing network administrator with the current network view and predicting the next potential steps of the attacker. Our approach is illustrated using both the well known DARPA 2000 data set and the live traffic data collected from a Honeynet network.


title: The Robustness of Google CAPTCHAs

abstract: We report a novel attack on two CAPTCHAs that have been widely deployed on the Internet, one being Google's home design and the other acquired by Google (i.e. reCAPTCHA). With a minor change, our attack program also works well on the latest ReCAPTCHA version, which uses a new defence mechanism that was unknown to us when we designed our attack. This suggests that our attack works in a fundamental level. Our attack appears to be applicable to a whole family of text CAPTCHAs that build on top of the popular segmentation-resistant mechanism of "crowding character together" for security. Next, we propose a novel framework that guides the application of our well-tested security engineering methodology for evaluating CAPTCHA robustness, and we propose a new general principle for CAPTCHA design.


title: An automatic parameter identification method for a PMSM drive with LC-filter

abstract: This paper presents a method for stand-still identification of parameters in a permanent magnet synchronous motor (PMSM) fed from an inverter equipped with an three-phase LC-type output filter.


Using E-Mail to Enhance Class Participation


title: Flipped-Adversarial AutoEncoders

abstract: We propose a flipped-Adversarial AutoEncoder (FAAE) that simultaneously trains a generative model G that maps an arbitrary latent code distribution to a data distribution and an encoder E that embodies an"inverse mapping"that encodes a data sample into a latent code vector. Unlike previous hybrid approaches that leverage adversarial training criterion in constructing autoencoders, FAAE minimizes re-encoding errors in the latent space and exploits adversarial criterion in the data space. Experimental evaluations demonstrate that the proposed framework produces sharper reconstructed images while at the same time enabling inference that captures rich semantic representation of data.


title: A 450 fs 65-nm CMOS Millimeter-Wave Time-to-Digital Converter Using Statistical Element Selection for All-Digital PLLs

abstract: This paper presents a time-to-digital converter (TDC) that operates with a 20–64 GHz input and underpins the phase digitization function in a millimeter-wave all-digital fractional-N frequency synthesizer. A self-calibrated inductor-less frequency divider using dynamic CML latches provides an eight-phase input to a 3-bit “coarse” TDC, which is interfaced to a 5-bit “fine” TDC through a sub-sampling coarse-fine interface circuit. A wide bandwidth low dropout (LDO) on-chip regulator is used to decrease the effect of supply noise on TDC performance. A synthesized digital engine implements calibration using statistical element selection with mean adaptation to alleviate TDC nonlinearity that results from random mismatches and PVT variations. The TDC is fabricated in 65-nm CMOS along with the divider and calibration circuits, and achieves 450-fs resolution. The measured DNL and INL of the TDC are 0.65 and 1.2 LSB, respectively. The TDC consumes 11 mA from 1-V supply voltage. The TDC features a figure-of-merit of 0.167 (0.47) pJ per conversion step without (with) the frequency divider. A single-shot experiment shows that the on-chip LDO reduces the effect of TDC noise by reducing the standard deviation from 0.856 to 0.167 LSB for constant input. The prototype occupies an active area of $502\times 110~\mu \text{m}^{\mathbf {2}}$ excluding pads.


title: An operational definition of learning

abstract: Abstract An operational definition offered in this paper posits learning as a multi-dimensional and multi-phase phenomenon occurring when individuals attempt to solve what they view as a problem. To model someone's learning accordingly to the definition, it suffices to characterize a particular sequence of that person's disequilibrium–equilibrium phases in terms of products of a particular mental act, the characteristics of the mental act inferred from the products, and intellectual and psychological needs that instigate or result from these phases. The definition is illustrated by analysis of change occurring in three thinking-aloud interviews with one middle-school teacher. The interviews were about the same task: “Make up a word problem whose solution may be found by computing 4/5 divided by 2/3.”


title: Empirical evaluation of dissimilarity measures for color and texture

abstract: This paper empirically compares nine image dissimilarity measures that are based on distributions of color and texture features summarizing over 1,000 CPU hours of computational experiments. Ground truth is collected via a novel random sampling scheme for color and via an image partitioning method for texture. Quantitative performance evaluations are given for classification, image retrieval, and segmentation tasks, and for a wide variety of dissimilarity measures. It is demonstrated how the selection of a measure, based on large scale evaluation, substantially improves the quality of classification, retrieval, and unsupervised segmentation of color and texture images.


title: A deep architecture for audio-visual voice activity detection in the presence of transients

abstract: Abstract We address the problem of voice activity detection in difficult acoustic environments including high levels of noise and transients, which are common in real life scenarios. We consider a multimodal setting, in which the speech signal is captured by a microphone, and a video camera is pointed at the face of the desired speaker. Accordingly, speech detection translates to the question of how to properly fuse the audio and video signals, which we address within the framework of deep learning. Specifically, we present a neural network architecture based on a variant of auto-encoders, which combines the two modalities, and provides a new representation of the signal, in which the effect of interferences is reduced. To further encode differences between the dynamics of speech and interfering transients, the signal, in this new representation, is fed into a recurrent neural network, which is trained in a supervised manner for speech detection. Experimental results demonstrate improved performance of the proposed deep architecture compared to competing multimodal detectors.


title: Online reliability assessment and reliability-aware fusion for Ego-Lane detection using influence diagram and Bayes filter

abstract: Within the context of road estimation, the present paper addresses the problem of the fusion of several sources with different reliabilities. Thereby, reliability represents a higher-level uncertainty. This problem arises in automated driving and ADAS due to changing environmental conditions, e.g., road type or visibility of lane markings. Thus, we present an online sensor reliability assessment and reliability-aware fusion to cope with this challenge. First, we apply a boosting algorithm to select the highly discriminant features among the extracted information. Using them we apply different classifiers to learn the reliabilities, such as Bayesian Network and Random Forest classifiers. To stabilize the estimated reliabilities over time, we deploy approaches such as Dempster-Shafer evidence theory and Influence Diagram combined with a Bayes Filter. Using a big collection of real data recordings, the experimental results support our proposed approach.


title: Evaluation of neonatal mortality in Buenos Aires City by place of residence and use of a health system subsector

abstract: INTRODUCTION ::: Neonatal mortality is the most important component of infant mortality. Analyzing neonatal mortality is complex and does not depend exclusively on the health system. In Buenos Aires City (CABA), between2000 and 2012, neonatal mortality rate was lower than the national mean rate but no changes were recorded. Besides, the difference is narrowing: in 2000, it was 46% lower but in 2012, it was 21% lower. ::: ::: ::: OBJECTIVE ::: To assess the relationship among the place of maternal residence, the use of a health system subsector, and mortality rate among newborn infants younger than 28 days old in CABA. ::: ::: ::: METHODS ::: Cross-sectional, population-based study conducted in 2011 and 2012 using data from the Office of Vital Records and the Department of Statistics and Surveys of CABA. ::: ::: ::: RESULTS ::: A total of 164 837 births were recorded. The ratio of births in public and private facilities has remained stable; the private subsector accounts for 57% ofbirths. The ratiobetweenbothsubsectors was also similar in terms of gender, birthweight, and average gestational age. Neonatal mortalitywas higher among mothers who lived outside CABA (6.55%o versus 5.42%, odds ratio: 1.21, 95% confidence interval: 1.07-1.37, p 0.0039). Among mothers living in CABA, neonatal mortality was higher in the public health subsector (7.8% versus 4.4%, odds ratio: 1.77, 95% confidence interval: 1.48-2.11, p < 0.001). ::: ::: ::: CONCLUSION ::: Unlike any other district in Argentina, CABA has a very high rate of births and deaths from other jurisdictions, especially from Greater Buenos Aires, which is not reflected in official statistics that only consider the place of parental residence.


title: A 500µW neural tag with 2µVrms AFE and frequency-multiplying MICS/ISM FSK transmitter

abstract: Advances in electronic-neural interfaces have shown great potential for both neuroscience research and medical devices. Much of the work to date has focused on short-range inductive links for power and communication transfer [1]. There is an emerging need for active miniaturized systems that stream neural data in the far field, which would enable the observation of brain activity in unconstrained animals such as mice or moths. Such systems cannot rely on near-field power transfer, and must be powered by small batteries or energy harvesters. We present a 500µW fully integrated neural interface that wirelessly streams a digitized neural waveform over 15m.


title: Single Multi-feature detector for Amodal 3D Object Detection in RGB-D Images

abstract: This paper aims at fast and high-accuracy amodal 3D object detections in RGB-D images, which requires a compact 3D bounding box around the whole object even under partial observations. To avoid the time-consuming proposals preextraction, we propose a single end-to-end framework based on the deep neural networks which hierarchically incorporates appearance and geometric features from 2.5D representation to 3D objects. The depth information has helped on reducing the output space of 3D bounding boxes into a manageable set of 3D anchor boxes with different sizes on multiple feature layers. At prediction time, in a convolutional fashion, the network predicts scores for categories and adjustments for locations, sizes and orientations of each 3D anchor box, which has considered multi-scale 2D features. Experiments on the challenging SUN RGB-D datasets show that our algorithm outperforms the state-of-the-art by 10.2 in mAP and is 88x faster than the Deep Sliding Shape. In addition, experiments suggest our algorithm even with a smaller input image size performs comparably but is 454x faster than the state-of-art on NYUV2 datasets.


title: Three naive Bayes approaches for discrimination-free classification

abstract: In this paper, we investigate how to modify the naive Bayes classifier in order to perform classification that is restricted to be independent with respect to a given sensitive attribute. Such independency restrictions occur naturally when the decision process leading to the labels in the data-set was biased; e.g., due to gender or racial discrimination. This setting is motivated by many cases in which there exist laws that disallow a decision that is partly based on discrimination. Naive application of machine learning techniques would result in huge fines for companies. We present three approaches for making the naive Bayes classifier discrimination-free: (i) modifying the probability of the decision being positive, (ii) training one model for every sensitive attribute value and balancing them, and (iii) adding a latent variable to the Bayesian model that represents the unbiased label and optimizing the model parameters for likelihood using expectation maximization. We present experiments for the three approaches on both artificial and real-life data.


title: Unsupervised prediction of citation influences

abstract: Publication repositories contain an abundance of information about the evolution of scientific research areas. We address the problem of creating a visualization of a research area that describes the flow of topics between papers, quantifies the impact that papers have on each other, and helps to identify key contributions. To this end, we devise a probabilistic topic model that explains the generation of documents; the model incorporates the aspects of topical innovation and topical inheritance via citations. We evaluate the model's ability to predict the strength of influence of citations against manually rated citations.


title: Fuzzy control of an autonomous car using a smart phone

abstract: This paper presents preliminary results on the development of a vision-based controller with a smart phone in the loop for autonomous vehicles. Our approach involves the use of the low cost vision sensors available on a smart phone to implement speed control for a lead-follower application, under stop-and-go traffic conditions. The controller uses fuzzy logic to determine commands based on estimated car position and speed with respect to the target vehicle being followed. We define constraints for system parameters associated with the minimum resolution and distance required for the controller to operate efficiently. The proposed design was successfully demonstrated on the University of Arizona CAT Vehicle by having it identify and follow a chosen human-driven lead vehicle.


title: An improved scheme for high-efficiency push-pull converter using single winding self-driven synchronous rectification

abstract: This paper presents a method of synchronous rectifier (SR) for improving the efficiency in DC/DC converter. The proposed method is used push-pull topology on each half primary as a single winding self-driven synchronous rectification (SWSDSK). Specially, this method can improve efficiency to let SR switch turn on during dead time. Based on the novel design scheme for auxiliary winding in the power transformer, The SR's MOSFET is turn on fully even when the voltage in the transformer is zero. For verification, there are analyzed and compared improved single winding self-driven synchronous rectification (ISDSR) with various conventional SR topology.


title: Emotion-Focused Family Therapy for Eating Disorders in Children and Adolescents

abstract: Family-based therapy (FBT) is regarded as best practice for the treatment of eating disorders in children and adolescents. In FBT, parents play a vital role in bringing their child or adolescent to health; however, a significant minority of families do not respond to this treatment. This paper introduces a new model whereby FBT is enhanced by integrating emotion-focused therapy (EFT) principles and techniques with the aims of helping parents to support their child's refeeding and interruption of symptoms. Parents are also supported to become their child's ‘emotion coach’; and to process any emotional ‘blocks’ that may interfere with their ability to take charge of recovery. A parent testimonial is presented to illustrate the integration of the theory and techniques of EFT in the FBT model. EFFT (Emotion-Focused Family Therapy) is a promising model of therapy for those families who require a more intense treatment to bring about recovery of an eating disorder. ::: ::: Key Practitioner Message ::: More intense therapeutic models exist for treatment-resistant eating disorders in children and adolescents. ::: Emotion is a powerful healing tool in families struggling with an eating disorder. ::: Working with parent's emotions and emotional reactions to their child's struggles has the potential to improve child outcomes. Copyright © 2013 John Wiley & Sons, Ltd.


title: Secure image retrieval using pyramid histogram of oriented gradient descriptor

abstract: Secure Content Based Image Retrieval (CBIR) deals with retrieving similar images from an image database without revealing the contents of the query image. In the traditional CBIR, it is not practically possible to store large volumes of image database locally. Hence, image database has to be stored in a remote server. In this paper, the images are encrypted using double key RSA algorithm and are stored in the remote servers. The pyramid histogram of oriented gradients (PHoG) descriptor is used as feature for representing the images. The PHoG descriptor is computed on the original image and are encrypted and then stored in the remote server. The double key RSA ensures security and the attacker cannot effortlessly decrypt the images stored in the public server.


title: Turning and side motion of snake-like robot

abstract: With high adaptability to environments snakelike robots offer a variety of advantages over other mobile robots. Such a robot with passive wheels has quite different mechanism in locomotion from that of other locomotion systems. We have developed a snakelike robot for rescue applications. The unit composing the snakelike robot of Shenyang Institute of Automation (SIA) is a module including actuating system and control system. To let the snakelike robot perform turning motion and compensate offset and orientation errors of the robot, we propose an amplitude modulation method and a phase modulation method based on analysis of the serpenoid curve. The side motion of the snakelike robot can also be generated by the amplitude modulation. The tracking control method is also proposed based on sensor information. Computer simulations and experimental tests are performed to show the validity of the proposed methods.


title: Support Vector Machines for Classification of Geometric Primitives in Point Clouds

abstract: Classification of point clouds by different types of geometric primitives is an essential part in the reconstruction process of CAD geometry. We use support vector machines (SVM) to label patches in point clouds with the class labels tori, ellipsoids, spheres, cones, cylinders or planes. For the classification features based on different geometric properties like point normals, angles, and principal curvatures are used. These geometric features are estimated in the local neighborhood of a point of the point cloud. Computing these geometric features for a random subset of the point cloud yields a feature distribution. Different features are combined for achieving best classification results. To minimize the time consuming training phase of SVMs, the geometric features are first evaluated using linear discriminant analysis (LDA).


title: Activities on Facebook Reveal the Depressive State of Users

abstract: ▶ Background: ::: Depression, which currently is the most commonly diagnosed mental disorder in many developed countries, is expected to be the second leading cause of disability worldwide by 2020 according to the World Health Organization. Given the growing social interest and the scale of the problem, much effort has been paid toward the early diagnosis, treatment, and prevention of depression. One such effort is to utilize online social network data to reach and detect a large number of individuals with depression at low cost. In doing so, identifying the markers of depression in online social networks is crucial. ::: ::: ▶ Objectives: ::: The goal of this study was to determine depression-related features or activities on the Facebook social network platform, by utilizing a newly developed web application, named EmotionDiary. ::: ::: ▶ Methods: ::: We recruited 55 participants on Facebook, of whom 40 were males aged between 19 and 36 (mean age=24.9±4.4) and 15 were females aged between 19 and 28 (mean age=23.3±2.2). The participants were recruited through both online and offline advertisements made available at the authors’ university. Through the EmotionDiary app, participants could evaluate their depressive symptoms by CES-D (Center for Epidemiological Studies Depression) scale. The app also provided tips about depression and all the profile and activity logs of participants (e.g., Likes, Interests, Viewed tips) were stored for analysis. Correlation analyses were performed between the CES-D scales and the level of various social network activities. Lastly, a psychiatrist interviewed two participants who showed definite depression (CES-D scales were 32 and 25, respectively) to qualitatively assess their symptoms. ::: ::: ▶ Results: ::: Facebook activities had predictive power in distinguishing depressed and non-depressed individuals. Participants’ activities on the app (e.g., the number of viewed tips and points gathered) had a positive correlation with the CES-D scales (P=.04 for both), while the number of Friends and Location tagging had a negative correlation (P=.08 and P=.045, respectively). In terms of group-level markers, the number of viewed tips and points gathered showed a significant difference (P=.01 and P=.03, respectively) in determining the depressed (i.e., CES-D≥21) and non-depressed individuals (i.e., CES-D<21). ::: ::: ▶ Conclusions: ::: Our study demonstrates that while depressed individuals had significantly fewer interpersonal interactions with others (e.g., number of Friends and Location tagging), they were more active in terms of seeking information about depression (e.g., reading tips). These results open the door for utilizing various online social activities in determining depressive symptoms. In the future, we plan to repeat the experiment across multiple cultures. []


title: WINd ENErgy aNd jusTIcE fOr dIsadVaNTagEd cOmmuNITIEs

abstract: Providing benefits to communities affected by wind-farm development is a matter of justice: a means of redressing the impacts on communities adversely affected by wind farms, not simply a means of cultivating acceptance and expediting planning consent. The scale of community benefit funds from developers is rising but more needs to be done to ensure that these funds are used to support lasting improvements in the areas around wind farms.


title: Prototyping a Software Defined Utility

abstract: The smart grid can be seen as a hybrid system composed by many systems. From a large scale point of view, it combines the electric power system itself and a heterogeneous information and communication technology (ICT) infrastructure. Additionally, these systems are composed by many building blocks that are designed and managed as separated systems which are hard to fully integrate between each other. Relying on the experiences arisen and the knowledge gathered from the partners during the development of the FP7 European projects INTEGRIS (intelligent electrical grid sensor communications) and FINESCE (future internet smart utility services), this paper presents the software defined utility (SDU) concept for the management of the smart grid and its security, which advocates for the migration of the utility infrastructure to software systems instead of relying on complex and rigid hardware based systems. Following this approach, SDU proposes the evolution of power systems’ ICT and the usage of programmable commodity hardware, low-cost sensors, and reliable high-speed IP-based communications underneath. More concretely, this paper proposes some building blocks for the deployment of the SDU (flexible data management infrastructure, context-aware security and web of things interface) and evaluates their functionalities and benefits for the smart grids of the future.


title: New Trends in Neutrosophic Theory and Applications

abstract: Neutrosophic theory and applications have been expanding in all directions at an astonishing rate especially after the introduction the journal entitled “Neutrosophic Sets and Systems”. New theories, techniques, algorithms have been rapidly developed. One of the most striking trends in the neutrosophic theory is the hybridization of neutrosophic set with other potential sets such as rough set, bipolar set, soft set, hesitant fuzzy set, etc. The different hybrid structure such as rough neutrosophic set, single valued neutrosophic rough set, bipolar neutrosophic set, single valued neutrosophic hesitant fuzzy set, etc. are proposed in the literature in a short period of time. Neutrosophic set has been a very important tool in all various areas of data mining, decision making, e-learning, engineering, medicine, social science, and some more. ::: The book “New Trends in Neutrosophic Theories and Applications” focuses on theories, methods, algorithms for decision making and also applications involving neutrosophic information. Some topics deal with data mining, decision making, e-learning, graph theory, medical diagnosis, probability theory, topology, and some more. ::: 30 papers by 39 authors and coauthors.


title: Enhancing Student Engagement Using the Flipped Classroom

abstract: The flipped classroom is an innovative pedagogical approach that focuses on learner-centered instruction. The purposes of this report were to illustrate how to implement the flipped classroom and to describe students' perceptions of this approach within 2 undergraduate nutrition courses. The template provided enables faculty to design before, during, and after class activities and assessments based on objectives using all levels of Bloom's taxonomy. The majority of the 142 students completing the evaluation preferred the flipped method compared with traditional pedagogical strategies. The process described in the report was successful for both faculty and students.


title: Semantic Extraction from Texts

abstract: Text documents are one of the means to store information. These documents can be found on personal desktop computers, intranets and in the Web. Thus the valuable knowledge is embedded in an unstructured form. Having an automated system that can extract information from the texts is very desirable. However, the major challenging issue in developing such an automated system is a natural language is not free from ambiguity and uncertainty problems. Thus semantic extraction remains a challenging task to researchers in this area. In this paper, a new framework to extract semantics for information extraction is proposed, where possibility theory, fuzzy sets, and knowledge about the subject and preceding sentence have been used as the key in resolving the ambiguity and uncertainty problems.


title: The effects of neural gain on attention and learning

abstract: Here the authors show that measures of pupil diameter, which are thought to track levels of LC-NE activity and neural gain, are correlated with the degree to which learning is focused on stimulus dimensions that individual human participants are more predisposed to process. They further show that the pupillary and behavioral variables are correlated with global changes in the strength and clustering of functional connectivity, as brain-wide fluctuations of gain would predict.


title: Keyframe-Based Visual-Inertial Online SLAM with Relocalization

abstract: Complementing images with inertial measurements has become one of the most popular approaches to achieve highly accurate and robust real-time camera pose tracking. In this paper, we present a keyframe-based approach to visual-inertial simultaneous localization and mapping (SLAM) for monocular and stereo cameras. Our visual-inertial SLAM system is based on a real-time capable visual-inertial odometry method that provides locally consistent trajectory and map estimates. We achieve global consistency in the estimate through online loop-closing and non-linear optimization. Furthermore, our system supports relocalization in a map that has been previously obtained and allows for continued SLAM operation. We evaluate our approach in terms of accuracy, relocalization capability and run-time efficiency on public indoor benchmark datasets and on newly recorded outdoor sequences. We demonstrate state-of-the-art performance of our system compared to a visual-inertial odometry method and baseline visual SLAM approaches in recovering the trajectory of the camera.


title: Neuroimaging and Neuromonitoring Effects of Electro and Manual Acupuncture on the Central Nervous System: A Literature Review and Analysis

abstract: The aim of this review is to provide an overview of the different effects of manual and electroacupuncture on the central nervous system in studies with different neuroimaging interventions. The Database PubMed was searched from 1/1/2000 to 1/6/2014 with restriction to human studies in English language. Data collection for functional magnetic resonance (fMRI) studies was restricted to the period from 1/1/2010 to 1/6/2014 due to a recently published review which included all published randomized and nonrandomized controlled clinical studies as well as observational studies with control groups, no blinding required. Only studies comparing manual or electroacupuncture with sham acupuncture were eligible. All participants were healthy adult men and women. A majority of 25 studies compared manual versus sham, a minority of 7 trials compared electro versus sham and only 1 study compared electro versus manual acupuncture. In 29 out of 33 studies verum acupuncture results were found to present either more or different modulation effects on neurological components measured by neuroimaging and neuromonitoring methods than sham acupuncture. Only four studies reported no effects of verum in comparison to sham acupuncture. Evaluation of the very heterogeneous results shows evidence that verum acupuncture elicits more modulation effects on neurological components than sham acupuncture.


title: Upper-Limb Powered Exoskeleton Design

abstract: An exoskeleton is an external structural mechanism with joints and links corresponding to those of the human body. With applications in rehabilitation medicine and virtual reality simulation, exoskeletons offer benefits for both disabled and healthy populations. A pilot database defining the kinematics and dynamics of the upper limb during daily living activities was one among several factors guiding the development of an anthropomorphic, 7-DOF, powered arm exoskeleton. Additional design inputs include anatomical and physiological considerations, workspace analyses, and upper limb joint ranges of motion. The database was compiled from 19 arm activities of daily living. The cable-actuated dexterous exoskeleton for neurorehabilitation (CADEN)-7 offers remarkable opportunities as a versatile human-machine interface and as a new generation of assistive technology. Proximal placement of motors and distal placement of cable-pulley reductions were incorporated into the design, leading to low inertia, high-stiffness links, and backdrivable transmissions with zero backlash. The design enables full glenohumeral, elbow, and wrist joint functionality. Potential applications of the exoskeleton as a wearable robot include: 1) a therapeutic and diagnostics device for physiotherapy, 2) an assistive (orthotic) device for human power amplifications, 3) a haptic device in virtual reality simulation, and 4) a master device for teleoperation.


title: Model-Driven Architecture in Practice: A Software Production Environment Based on Conceptual Modeling

abstract: This book introduces all the relevant information required to understand and put Model Driven Architecture (MDA) into industrial practice. It clearly explains which conceptual primitives should be present in a system specification, how to use UML to properly represent this subset of basic conceptual constructs, how to identify just those diagrams and modeling constructs that are actually required to create a meaningful conceptual schema, and how to accomplish the transformation process between the problem space and the solution space. The approach is fully supported by commercially available tools.


title: Composite Hierarchical Antidisturbance Control for Magnetic Bearing System Subject to Multiple External Disturbances

abstract: This paper explores a composite hierarchical antidisturbance control (CHADC) scheme for the resonance vibration suppression of a high-speed rotor system supported by active magnetic bearings (AMBs) in the magnetically suspended double-gimbal control moment gyroscope (MSDGCMG). The rotor dynamics with a fictitious integral term is introduced to meet the steady-state performance requirements. Resonance disturbances caused by harmonic drive transmission and gyroscopic torque disturbances caused by gimbal motions are also analyzed. The augmented composite AMB system with two types of disturbances and nonlinear electromagnetic dynamics is developed. Then, a disturbance observer is constructed and a stability analysis of the closed-loop system is performed using a Lyapunov approach. Simulation results show that the exogenous disturbance can be estimated and compensated with the appropriate control parameters. Rotor run-up experiments demonstrate that the proposed method has a good performance for disturbance rejection of harmonic drive resonance vibrations under the excitation of coupling torques. The gyroscopic torque disturbances with the bounded norm can also be attenuated effectively.


title: Pose Flow: Efficient Online Pose Tracking

abstract: Multi-person articulated pose tracking in complex unconstrained videos is an important and challenging problem. In this paper, going along the road of top-down approaches, we propose a decent and efficient pose tracker based on pose flows. First, we design an online optimization framework to build association of cross-frame poses and form pose flows. Second, a novel pose flow non maximum suppression (NMS) is designed to robustly reduce redundant pose flows and re-link temporal disjoint pose flows. Extensive experiments show our method significantly outperforms best reported results on two standard Pose Tracking datasets (PoseTrack dataset and PoseTrack Challenge dataset) by 13 mAP 25 MOTA and 6 mAP 3 MOTA respectively. Moreover, in the case of working on detected poses in individual frames, the extra computation of proposed pose tracker is very minor, requiring 0.01 second per frame only.


title: Graspan: A Single-machine Disk-based Graph System for Interprocedural Static Analyses of Large-scale Systems Code

abstract: There is more than a decade-long history of using static analysis to find bugs in systems such as Linux. Most of the existing static analyses developed for these systems are simple checkers that find bugs based on pattern matching. Despite the presence of many sophisticated interprocedural analyses, few of them have been employed to improve checkers for systems code due to their complex implementations and poor scalability. In this paper, we revisit the scalability problem of interprocedural static analysis from a "Big Data" perspective. That is, we turn sophisticated code analysis into Big Data analytics and leverage novel data processing techniques to solve this traditional programming language problem. We develop Graspan, a disk-based parallel graph system that uses an edge-pair centric computation model to compute dynamic transitive closures on very large program graphs. We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations. Moreover, we show that these analyses can be used to augment the existing checkers; these augmented checkers uncovered 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.


title: Choosing the path to bargaining power: an empirical comparison of BATNAs and contributions in negotiation.

abstract: Although the negotiations literature identifies a variety of approaches for improving one's power position, the relative benefits of these approaches remains largely unexplored. The empirical study presented in this paper begins to address this issue by examining how the size of the bargaining zone affects the relative benefit of an advantage in one's BATNA (i.e. having a better alternative than one's opponent) versus Contribution (i.e. contributing more to the relationship than one's opponent) for negotiator performance. Results indicate that whereas BATNAs exerted a stronger effect on resource allocations than Contributions when the bargaining zone was small, an advantage in Contributions exerted a stronger effect on resource allocations than BATNAs when the bargaining zone was large. These findings provide needed insight and supporting evidence for how to alter one's power relationship in negotiation.


title: Topoisomerases in the treatment of metastatic or recurrent squamous carcinoma of the head and neck.

abstract: Camptothecins are a class of antineoplastic agents that function via inhibition of topoisomerase I, a critical enzyme involved in DNA replication, transcription and chromosomal structure. Two topoisomerase I inhibitors, topotecan and irinotecan, have been developed and approved for the treatment of solid tumour malignancies. Because of their known activity, these and other camptothecins, such as 9-aminocamptothecin (9-AC), have been evaluated for the treatment of patients with recurrent or metastatic squamous carcinomas of the head and neck. Both 9-AC and topotecan failed to demonstrate consistent activity in this cohort of patients. Irinotecan demonstrated single-agent activity of 20% as first-line therapy for patients with metastatic or recurrent disease; however, efficacy was lacking as a second-line agent. Irinotecan is now being evaluated in combination regimens as first-line therapy for metastatic and recurrent disease. In addition, irinotecan is a potent radiation sensitising agent. Two Phase I trials using irinotecan-containing chemotherapy regimens with concurrent radiation in the primary treatment of head and neck cancer (HNC) patients have been reported. Clarification of the role of irinotecan in the treatment of HNC awaits further studies.


abstract: WEAVER, DIANE N., M.S., March 2008, Food and Nutrition Leadership Characteristics of Dietetic Professionals in Ohio (76 pp.) Director of Thesis: Annette Graham The field of dietetics, which has existed in the United States for under a century, faces the challenge of developing and using strong leadership in facing nutrition objectives (Erickson-Weerts, 1999; Calabro, Bright, & Bahl 2001). While the literature generally calls for further exploration of dietetic leadership issues, a notable lack of research compares leadership attributes of registered dietitians (RDs) of various specialties. To assess characteristics and factors of dietetic professionals and their leadership behaviors, a survey was mailed to 1000 Ohio dietitians. The survey’s first three sections investigated demographics, dietetic specialty, and dietetic leadership, and the final component was the Leadership Practices Inventory, used with permission of the authors, Kouzes and Posner. With a response rate of 44.4%, the findings showed that many of the investigated factors—including dietetic specialty—significantly correlated with leadership practices of RDs. Approved: _____________________________________________________________ Annette Graham Associate Professor of Human and Consumer Sciences


title: Deep Haar Scattering Networks

abstract: An orthogonal Haar scattering transform is a deep network, computed with a hierarchy of additions, subtractions and absolute values, over pairs of coefficients. It provides a simple mathematical model for unsupervised deep network learning. It implements non-linear contractions, which are optimized for classification, with an unsupervised pair matching algorithm, of polynomial complexity. A structured Haar scattering over graph data computes permutation invariant representations of groups of connected points in the graph. If the graph connectivity is unknown, unsupervised Haar pair learning can provide a consistent estimation of connected dyadic groups of points. Classification results are given on image data bases, defined on regular grids or graphs, with a connectivity which may be known or unknown.


title: From Collision To Exploitation: Unleashing Use-After-Free Vulnerabilities in Linux Kernel

abstract: Since vulnerabilities in Linux kernel are on the increase, attackers have turned their interests into related exploitation techniques. However, compared with numerous researches on exploiting use-after-free vulnerabilities in the user applications, few efforts studied how to exploit use-after-free vulnerabilities in Linux kernel due to the difficulties that mainly come from the uncertainty of the kernel memory layout. Without specific information leakage, attackers could only conduct a blind memory overwriting strategy trying to corrupt the critical part of the kernel, for which the success rate is negligible. In this work, we present a novel memory collision strategy to exploit the use-after-free vulnerabilities in Linux kernel reliably. The insight of our exploit strategy is that a probabilistic memory collision can be constructed according to the widely deployed kernel memory reuse mechanisms, which significantly increases the success rate of the attack. Based on this insight, we present two practical memory collision attacks: An object-based attack that leverages the memory recycling mechanism of the kernel allocator to achieve freed vulnerable object covering, and a physmap-based attack that takes advantage of the overlap between the physmap and the SLAB caches to achieve a more flexible memory manipulation. Our proposed attacks are universal for various Linux kernels of different architectures and could successfully exploit systems with use-after-free vulnerabilities in kernel. Particularly, we achieve privilege escalation on various popular Android devices (kernel version>=4.3) including those with 64-bit processors by exploiting the CVE-2015-3636 use-after-free vulnerability in Linux kernel. To our knowledge, this is the first generic kernel exploit for the latest version of Android. Finally, to defend this kind of memory collision, we propose two corresponding mitigation schemes.


title: Real-life voice activity detection with LSTM Recurrent Neural Networks and an application to Hollywood movies

abstract: A novel, data-driven approach to voice activity detection is presented. The approach is based on Long Short-Term Memory Recurrent Neural Networks trained on standard RASTA-PLP frontend features. To approximate real-life scenarios, large amounts of noisy speech instances are mixed by using both read and spontaneous speech from the TIMIT and Buckeye corpora, and adding real long term recordings of diverse noise types. The approach is evaluated on unseen synthetically mixed test data as well as a real-life test set consisting of four full-length Hollywood movies. A frame-wise Equal Error Rate (EER) of 33.2% is obtained for the four movies and an EER of 9.6% is obtained for the synthetic test data at a peak SNR of 0 dB, clearly outperforming three state-of-the-art reference algorithms under the same conditions.


title: Investigating different types of research collaboration and citation impact: a case study of Harvard University’s publications

abstract: This study aims to investigate the influence of different patterns of collaboration on the citation impact of Harvard University's publications. Those documents published by researchers affiliated with Harvard University in WoS from 2000---2009, constituted the population of the research which was counted for 124,937 records. Based on the results, only 12% of Harvard publications were single author publications. Different patterns of collaboration were investigated in different subject fields. In all 22 examined fields, the number of co-authored publications is much higher than single author publications. In fact, more than 60% of all publications in each field are multi-author publications. Also, the normalized citation per paper for co-authored publications is higher than that of single author publications in all fields. In addition, the largest number of publications in all 22 fields were also published through inter-institutional collaboration and were as a result of collaboration among domestic researchers and not international ones. In general, the results of the study showed that there was a significant positive correlation between the number of authors and the number of citations in Harvard publications. In addition, publications with more number of institutions have received more number of citations, whereas publications with more number of foreign collaborators were not much highly cited.


title: A Day in the Life of PubMed: Analysis of a Typical Day's Query Log

abstract: Objective: To characterize PubMed usage over a typical day and compare it to previous studies of user behavior on Web search engines. Design: We performed a lexical and semantic analysis of 2,689,166 queries issued on PubMed over 24 consecutive hours on a typical day. Measurements: We measured the number of queries, number of distinct users, queries per user, terms per query, common terms, Boolean operator use, common phrases, result set size, MeSH categories, used semantic measurements to group queries into sessions, and studied the addition and removal of terms from consecutive queries to gauge search strategies. Results: The size of the result sets from a sample of queries showed a bimodal distribution, with peaks at approximately 3 and 100 results, suggesting that a large group of queries was tightly focused and another was broad. Like Web search engine sessions, most PubMed sessions consisted of a single query. However, PubMed queries contained more terms. Conclusion: PubMed's usage profile should be considered when educating users, building user interfaces, and developing future biomedical information retrieval systems. J Am Med Inform Assoc. 2007;14:212-220. DOI 10.1197/jamia.M2191.