title: TransRev: Modeling Reviews as Translations from Users to Items

abstract: The text of a review expresses the sentiment a customer has towards a particular product. This is exploited in sentiment analysis where machine learning models are used to predict the review score from the text of the review. Furthermore, the products costumers have purchased in the past are indicative of the products they will purchase in the future. This is what recommender systems exploit by learning models from purchase information to predict the items a customer might be interested in. We propose TransRev, an approach to the product recommendation problem that integrates ideas from recommender systems, sentiment analysis, and multi-relational learning into a joint learning objective. TransRev learns vector representations for users, items, and reviews. The embedding of a review is learned such that (a) it performs well as input feature of a regression model for sentiment prediction; and (b) it always translates the reviewer embedding to the embedding of the reviewed items. This allows TransRev to approximate a review embedding at test time as the difference of the embedding of each item and the user embedding. The approximated review embedding is then used with the regression model to predict the review score for each item. TransRev outperforms state of the art recommender systems on a large number of benchmark data sets. Moreover, it is able to retrieve, for each user and item, the review text from the training set whose embedding is most similar to the approximated review embedding.


title: A Comparison of Parametric and Non-Parametric Methods Applied to a Likert Scale

abstract: A trenchant and passionate dispute over the use of parametric versus non-parametric methods for the analysis of Likert scale ordinal data has raged for the past eight decades. The answer is not a simple "yes" or "no" but is related to hypotheses, objectives, risks, and paradigms. In this paper, we took a pragmatic approach. We applied both types of methods to the analysis of actual Likert data on responses from different professional subgroups of European pharmacists regarding competencies for practice. Results obtained show that with "large" (>15) numbers of responses and similar (but clearly not normal) distributions from different subgroups, parametric and non-parametric analyses give in almost all cases the same significant or non-significant results for inter-subgroup comparisons. Parametric methods were more discriminant in the cases of non-similar conclusions. Considering that the largest differences in opinions occurred in the upper part of the 4-point Likert scale (ranks 3 "very important" and 4 "essential"), a "score analysis" based on this part of the data was undertaken. This transformation of the ordinal Likert data into binary scores produced a graphical representation that was visually easier to understand as differences were accentuated. In conclusion, in this case of Likert ordinal data with high response rates, restraining the analysis to non-parametric methods leads to a loss of information. The addition of parametric methods, graphical analysis, analysis of subsets, and transformation of data leads to more in-depth analyses.


title: Algorithms for Data-Race Detection in Multithreaded Programs

abstract: Two parallel accesses to the same location, at least one of which is a write, form a race. Debugging such races is complicated by atomic critical sections. In programs without critical sections, a race is usually a bug causing nondeterminism. In programs with critical sections, however, accesses in parallel critical sections are not considered bugs: the programmer, in specifying the critical sections, presumably intends them to run in parallel. Thus, a race detector should find "data races"---races between accesses not contained in atomic critical sections. We present algorithms for detecting data races in programs written in the Cilk multithreaded language. These algorithms do not verify programs; rather, they find data races in all schedulings of the computation generated when a program executes serially on a given input. The ALL-SETS algorithm checks programs in which atomicity is specified using locks. The UMBRELLA algorithm also checks programs using locks, gaining efficiency by detecting violations of a proposed locking discipline that precludes some race-free locking protocols as well as data races. The REVIEW-GUARDS algorithm checks programs using a proposed ``guard statement'''' language construct to specify atomicity at a higher level than locks. We also extend each algorithm to handle critical sections containing parallelism.


title: Flexible Induction Heating Using Magnetic Resonant Coupling

abstract: This paper proposes and implements the concept of flexible induction heating based on the magnetic resonant coupling (MRC) mechanism. In conventional induction heating systems, the variation of the relative position between the heater and workpiece significantly deteriorates the heating performance. In particular, the heating effect dramatically reduces with the increase of vertical displacement or horizontal misalignment. This paper utilizes the MRC mechanism to effectuate flexible induction heating; thus, handling the requirements of varying vertical displacement and horizontal misalignment for various cooking styles. Differing from a conventional induction heating, the proposed induction heating adopts one resonant coil in the heater and one resonant coil in the workpiece, which can significantly strengthen the coupling effect, and, hence, the heating effect. Both the simulation and experimental results are given to validate the feasibility and flexibility of the proposed induction heating.


title: Scaled Current Tracking Control for Doubly Fed Induction Generator to Ride-Through Serious Grid Faults

abstract: For doubly fed induction generator (DFIG)-based wind turbine, the main constraint to ride-through serious grid faults is the limited converter rating. In order to realize controllable low voltage ride through (LVRT) under the typical converter rating, transient control reference usually need to be modified to adapt to the constraint of converter's maximum output voltage. Generally, the generation of such reference relies on observation of stator flux and even sequence separation. This is susceptible to observation errors during the fault transient; moreover, it increases the complexity of control system. For this issue, this paper proposes a scaled current tracking control for rotor-side converter (RSC) to enhance its LVRT capacity without flux observation. In this method, rotor current is controlled to track stator current in a certain scale. Under proper tracking coefficient, both the required rotor current and rotor voltage can be constrained within the permissible ranges of RSC, thus it can maintain DFIG under control to suppress overcurrent and overvoltage. Moreover, during fault transient, electromagnetic torque oscillations can be greatly suppressed. Based on it, certain additional positive-sequence item is injected into rotor current reference to supply dynamic reactive support. Simulation and experimental results demonstrate the feasibility of the proposed method.


title: A Robust Optimization Perspective Of Stochastic Programming

abstract: In this paper, we introduce an approach for constructing uncertainty sets for robust optimization using new deviation measures for random variables termed the forward and backward deviations. These deviation measures capture distributional asymmetry and lead to better approximations of chance constraints. Using a linear decision rule, we also propose a tractable approximation approach for solving a class of multistage chance-constrained stochastic linear optimization problems. An attractive feature of the framework is that we convert the original model into a second-order cone program, which is computationally tractable both in theory and in practice. We demonstrate the framework through an application of a project management problem with uncertain activity completion time.


title: Optimal Brain Damage

abstract: We have used information-theoretic ideas to derive a class of practical and nearly optimal schemes for adapting the size of a neural network. By removing unimportant weights from a network, several improvements can be expected: better generalization, fewer training examples required, and improved speed of learning and/or classification. The basic idea is to use second-derivative information to make a tradeoff between network complexity and training set error. Experiments confirm the usefulness of the methods on a real-world application.


title: Service-Oriented Computing: Semantics, Processes, Agents

abstract: About the Authors.Preface.Note to the Reader.Acknowledgments.Figures.Tables.Listings.I Basics.1. Computing with Services.2. Basic Standards for Web Services.3. Programming Web Services.4. Enterprise Architectures.5. Principles of Service-Oriented Computing.II Description.6. Modeling and Representation.7. Resource Description Framework.8. Web Ontology Language.9. Ontology Management.III Engagement.10. Execution Models.11. Transaction Concepts.12. Coordination Frameworks for Web Services.13. Process Specifications.14. Formal Specification and Enactment.IV Collaboration.15. Agents.16. Multiagent Systems.17. Organizations.18. Communication.V Solutions.19. Semantic Service Solutions.20. Social Service Selection.21. Economic Service Selection.VI Engineering.22. Building SOC Applications.23. Service Management.24. Security.VII Directions.25. Challenge and Extensions.VIII Appendices.Appendix A: XML and XML Schema.Appendix B: URI, URN, URL and UUID.Appendix C: XML Namespace Abbreviations.Glossary.About the Authors.Bibliography.Index.


title: Modeling and simulation of rain for the test of automotive sensor systems

abstract: This paper presents a new approach for the test of automotive sensor systems in rain. The approach is based on an indoor test method, which helps to save test kilometers and test effort. For the activation of safety systems detailed information about the vehicles environment is necessary. Laser scanners provide precise information about the environment and a high angular resolution in contrast to radar sensors [1]. The performance of laser scanners depends on their local environment, because of the attenuation of the ambient atmosphere, precipitation and on the reflectivity of objects. False measurements in the field of vehicle safety can result in severe injury or death, so high reliability is essential. For this purpose a theoretical model is developed in order to determine the sensor behavior. Subsequently, a rain simulator is constructed to validate the theoretical model. Furthermore the developed rain simulator is validated by comparison with real rain. Based on determined rain disturbances benchmark tests of different sensor systems and algorithm approaches can be performed.


title: Linking employees' e-learning system use to their overall job outcomes: An empirical study based on the IS success model

abstract: e-Learning systems are increasingly being adopted by modern enterprises, because of their cost-down effect. However, the extant literature provides little insight into their beneficial consequences. It is expected that a transfer of acquired knowledge, skills and/or work attitudes to jobs will occur through e-learning systems use. This makes e-learning systems a useful tool for improving job outcomes, although this is not well documented. The IS success model, which captures both the human and technological elements of information systems, provides a theoretical basis for linking system use to system task. The current study then examines the relation between e-learning systems use and overall job outcomes based on the model. The data collected from one hundred and ninety-three e-learning system users were analyzed with partial least square (PLS). The results indicate that e-learning systems are perceived as useful and satisfying by employees, and employees' e-learning systems use is significantly associated with overall job outcomes. In practice, these results provide a basis for establishing a link between an organization's investment in e-learning and human capital management. They also lend empirical support to the IS success model.


title: 3D bioprinting directly onto moving human anatomy

abstract: This paper establishes the feasibility of robotically 3D printing biomaterials such as alginate hydrogels onto moving human anatomy and a stationary plane. The alginate hydrogels used are in-vivo compatible and a proven biomaterial for tissue scaffolds. We developed a control scheme for precision material deposition via piezo microjetting while tracking in real-time to continuously sense anatomy location and deposits material in a predefined trajectory derived from two pre-selected target geometries. We show that multilayer 3D structures can be created on a moving human hand with 1.6 mm average error and 87.8 % overall accuracy.


title: The RUBI project: a progress report

abstract: The goal of the RUBI project is to accelerate progress in the development of social robots by addressing the problem at multiple levels, including the development of a scientific agenda, research methods, formal approaches, software, and hardware. The project is based on the idea that progress will go hand-in-hand with the emergence of a new scientific discipline that focuses on understanding the organization of adaptive behavior in real-time within the environments in which organisms operate. As such, the RUBI project emphasizes the process of design by immersion, i.e., embedding scientists, engineers and robots in everyday life environments so as to have these environments shape the hardware, software, and scientific questions as early as possible in the development process. The focus of the project so far has been on social robots that interact with 18 to 24 month old toddlers as part of their daily activities at the Early Childhood Education Center at the University of California, San Diego. In this document we present an overall assessment of the lessons and progress through year two of the project.


title: Answering Complex Questions Using Open Information Extraction

abstract: While there has been substantial progress in factoid question-answering (QA), answering complex questions remains challenging, typically requiring both a large body of knowledge and inference techniques. Open Information Extraction (Open IE) provides a way to generate semi-structured knowledge for QA, but to date such knowledge has only been used to answer simple questions with retrieval-based methods. We overcome this limitation by presenting a method for reasoning with Open IE knowledge, allowing more complex questions to be handled. Using a recently proposed support graph optimization framework for QA, we develop a new inference model for Open IE, in particular one that can work effectively with multiple short facts, noise, and the relational structure of tuples. Our model significantly outperforms a state-of-the-art structured solver on complex questions of varying difficulty, while also removing the reliance on manually curated knowledge.


title: Development of an Augmented Reality Vehicle for Driver Performance Evaluation

abstract: Observing drivers' behaviors by reproducing traffic accidents and conflict situations is important for developing advanced driver assistance systems. For this purpose, driving simulators are frequently used to evaluate the effectiveness of driver assistance systems during product development. However, motion (simulator) sickness can be a serious practical problem with driving simulators. Therefore, an instrumented vehicle, the JARI-ARV (Japan Automobile Research Institute-Augmented Reality Vehicle), was developed to reproduce realistic traffic accident and conflict scenarios without endangering the driver. In this study, we examined level of control in the following cases: a right turn and encounter with a pedestrian by comparing the JARI-ARV with a standard (unaltered) same model vehicle. Results of the experiment indicated that drivers tend to react to virtual traffic participants in the same way as driving a standard vehicle. The study indicates that the JARI-ARV can play a useful role in human factors research.


title: A Qualitative Inquiry on the Multidimensional Racial Development among First-Year African American College Students Attending a Predominately White Institution.

abstract: While persistence and completion rates in postsecondary education are on the rise, gaps based on racial/ethnic demographics remain. This is particularly evident at predominately White institutions (PWIs), despite increasing enrollment of African Americans at these institutions. Previous studies have linked psychosocial health of African American students with positive educational outcomes. Using qualitative data from a study of first-year African American students at a PWI, this article examines the influence of racial identity development on the educational experiences through an interpretive theoretical framework-the Multidimensional Model of Racial Identity (MMRI). Five themes are presented, representing the influence of both internal reconceptualization of racial identity and external sources of support. Implications for policy development and future research are discussed. Keywords: African American students, postsecondary education, identity development, student persistence Postsecondary degree attainment has generated significant scholarly attention in recent years, particularly for traditionally underrepresented students (Bowen, Chingos, & McPherson, 2009; Rosenbaum, Deil-Amen, & Person, 2006; Sacks, 2008). While recent evidence suggests that persistence and completion rates at four-year colleges and universities are on the rise, a closer examination of the data reveals continued disparities based racial/ethnic demographics. In particular, persistence and degree completion rates for African American students continue to lag behind White students. Among a cohort of students who began at a four-year postsecondary institution in the 2003, 21 percent of African Americans dropped out of postsecondary education three years later, compared to just 1 1 percent of White students. A wider gap exists in graduation rates. Among a cohort of students who began at a four-year postsecondary institution in 2001, 42 percent of African American students completed a degree within six years, compared to 60 percent of White students (Aud et al., 2010). To address these observed educational inequities, considerable attention has been paid to the experiences of African American college students, particularly those who attend predominately White institutions (PWIs). Since McLaurin v. Oklahoma State Regents decision in 1950 and the Civil Rights Act of 1964 established de jure access to all institutions of higher education regardless of race, African American students have increasingly enrolled at PWIs. By 2001, approximately 87 percent of all African American students in postsecondary education were enrolled in a non-historically Black college or university (U.S. Department of Education, 2004). Despite increasing enrollment rates for African American students at PWIs, establishing relationships and maintaining engagement on campus continues to present unique difficulties (Alien, 1992; Cabrera et al. 1999; Fries-Britt & Turner, 2002; Guiffrida, 2005; Hurtado et al. 1 998). When compared to their White peers, African American students at PWIs are more likely to develop perceptions of a racially hostile climate on campus. Based on direct experiences and observations, these feelings often lead to a sense of alienation, inhibiting an establishment connection and commitment to the institution. The feeling of isolation among African American students attending PWIs is of particular concern during the first year of college. Most first-time college students face academic and social challenges as they transition to the institution (Elkins, Braxton, & James 2000; Pascarella & Terenzini, 2005; Upcraft, Gardner, & Barefoot, 2004). For African Americans attending PWIs, this difficult transition may become overwhelming, contributing to the persistence and degree completion disparities based on racial/ethnic categories. As first-year experiences have demonstrated a strong connection to persistence and degree attainment, it is critical to examine issues related to transitional experiences of African American students attending PWIs. …


title: Robust Feed-Forward Schemes for Anti-sway Control of Rotary Crane

abstract: This paper presents investigations into the development of robust feed-forward control schemes for anti-swaying control of a rotary crane system. A nonlinear rotary crane system is considered and the dynamic model of the system is derived using the Euler-Lagrange formulation. An unshaped square-pulse torque input is used to determine the characteristic parameters of the system for design and evaluation of the feed-forward control techniques. Feed-forward control schemes based on input shaping and filtering techniques are to be examined. Simulation results of the response of the rotary crane system to the shaped inputs are presented in time and frequency domains. Performances of the control schemes are examined in terms of sway angle reduction and time response specifications. Moreover, the robustness of the feed-forward control schemes is discussed. Finally, a comparative assessment of the proposed control techniques is presented and discussed.


title: Generative Multi-Adversarial Networks

abstract: Generative adversarial networks (GANs) are a framework for producing a generative model by way of a two-player minimax game. In this paper, we propose the \emph{Generative Multi-Adversarial Network} (GMAN), a framework that extends GANs to multiple discriminators. In previous work, the successful training of GANs requires modifying the minimax objective to accelerate training early on. In contrast, GMAN can be reliably trained with the original, untampered objective. We explore a number of design perspectives with the discriminator role ranging from formidable adversary to forgiving teacher. Image generation tasks comparing the proposed framework to standard GANs demonstrate GMAN produces higher quality samples in a fraction of the iterations when measured by a pairwise GAM-type metric.


title: K2Q: Generating Natural Language Questions from Keywords with User Refinements

abstract: Meeting: 5th International Joint Conference on Natural Language Processing, Chiang Mai, Thailand, November 8 - 13, 2011


title: Camera calibration by vanishing lines for 3-D computer vision

abstract: A novel approach to camera calibration by vanishing lines is proposed. Calibrated parameters include the orientation, position, and focal length of a camera. A hexagon is used as the calibration target to generate a vanishing line of the ground plane from its projected image. It is shown that the vanishing line includes useful geometric hints about the camera orientation parameters and the focal length, from which the orientation parameters can be solved easily and analytically. And the camera position parameters can be calibrated by the use of related geometric projective relationships. The simplicity of the target eliminates the complexity of the environment setup and simplifies the feature extraction in relevant image processing. The calibration formulas are also simple to compute. Experimental results show the feasibility of the proposed approach. >


title: Gain and directivity enhancement of microstrip patch array antenna with metallic ring for WLAN/Wi-Fi applications

abstract: A novel gain and directivity enhancement of microstrip patch array antenna with metallic ring is presented here for WLAN/Wi-Fi application. U-slot technique is used to achieve the necessary impedance transformation at preferred frequency. The designs have array and metallic ring coplanar to radiation patch to enhance the gain. The topology of the structure is dissimilar from conventional microstrip patch antenna. The structure present on the Rogers RO 4003; Using CST Microwave studio is designed & simulated. The simulated return loss is greater than 10 dB at center frequencies 5.9 GHz and 5.96 GHz. The results of microstrip patch antenna with array and metallic ring are simulated. In that microstrip patch antenna array design the improvement to gain and directivity of microstrip patch antenna with reference to 150% dB and 157.89% dBi frequencies as compared to conventional one. Using metallic ring around the patch on dielectric substrate realize gain enhancement and improved return loss. The substrate height is 1.3 mm and dielectric constant is 6.15.


title: Improving forecasts of GARCH family models with the artificial neural networks

abstract: In the study, we discussed the ARCH/GARCH family models and enhanced them with artificial neural networks to evaluate the volatility of daily returns for 23.10.1987-22.02.2008 period in Istanbul Stock Exchange. We proposed ANN-APGARCH model to increase the forecasting performance of APGARCH model. The ANN-extended versions of the obtained GARCH models improved forecast results. It is noteworthy that daily returns in the ISE show strong volatility clustering, asymmetry and nonlinearity characteristics.


title: Traffic Vehicle Counting in Jam Flow Conditions Using Low-Cost and Energy-Efficient Wireless Magnetic Sensors

abstract: The jam flow condition is one of the main traffic states in traffic flow theory and the most difficult state for sectional traffic information acquisition. Since traffic information acquisition is the basis for the application of an intelligent transportation system, research on traffic vehicle counting methods for the jam flow conditions has been worthwhile. A low-cost and energy-efficient type of multi-function wireless traffic magnetic sensor was designed and developed. Several advantages of the traffic magnetic sensor are that it is suitable for large-scale deployment and time-sustainable detection for traffic information acquisition. Based on the traffic magnetic sensor, a basic vehicle detection algorithm (DWVDA) with less computational complexity was introduced for vehicle counting in low traffic volume conditions. To improve the detection performance in jam flow conditions with a “tailgating effect” between front vehicles and rear vehicles, an improved vehicle detection algorithm (SA-DWVDA) was proposed and applied in field traffic environments. By deploying traffic magnetic sensor nodes in field traffic scenarios, two field experiments were conducted to test and verify the DWVDA and the SA-DWVDA algorithms. The experimental results have shown that both DWVDA and the SA-DWVDA algorithms yield a satisfactory performance in low traffic volume conditions (scenario I) and both of their mean absolute percent errors are less than 1% in this scenario. However, for jam flow conditions with heavy traffic volumes (scenario II), the SA-DWVDA was proven to achieve better results, and the mean absolute percent error of the SA-DWVDA is 2.54% with corresponding results of the DWVDA 7.07%. The results conclude that the proposed SA-DWVDA can implement efficient and accurate vehicle detection in jam flow conditions and can be employed in field traffic environments.


title: Scheduling Parallel Virtual Machine in Xen Based on Credit

abstract: With the development of multi-processor system, parallel program becomes a very important direction of software development. But in virtual machine systems, the performance of concurrent program scheduling is poor. In order to improve the performance of scheduling parallel applications in Virtual Machine (VM), this paper raises a parallel schedule algorism called PSA, which is based on Credit scheduler in Xen VMM. This new algorism support for scheduling the parallel Virtual CPUs (vCPUs) synchronously, it reduces the effect of asynchrony of parallel vCPUs by the interrupt of I/O bounded workloads, and it keeps the synchronization of parallel vCPUs when asynchrony emerges in the process of scheduling. The evaluation shows remarkable improvement of the PSA in scheduling the parallel vCPUs with different workloads, compared to the original Credit scheduler, co-scheduler and UVF: the demand-based coordinated scheduler.


abstract: Practical Digital Libraries Contents Practical Digital Libraries: Books, Bytes, and Bucks by Michael Lesk Preface 1 Evolution of Libraries 1.1 Why Digital Libraries? 1.2 History and Change 1.3 Advantages of Digital Libraries 1.4 Rise of Printing and Libraries 1.5 Vannevar Bush 1.6 Computer Technology 1.6.1 Processors 1.6.2 Memory Technology 1.6.3 Software 1.7 Information and Software 1.8 Summary 2 Text Access Methods 2.1 Computer Typesetting and Online Databases 2.2 Text Formats 2.3 Linear Text Searching 2.4 Inverted Files 2.5 Hash Coding 2.6 Other Text Search Issues 2.7 Thesauri 2.8 Document Conversion 2.9 Summary 3 Images of Pages 3.1 Scanning and Fax Machines 3.2 Image Formats for Scanned Printed Pages 3.3 Display Requirements 3.4 PostScript, Acrobat, and Reprinting 3.5 Indexing Images of Pages 3.6 Shared Text/Image Systems 3.7 Image Storage vs. Book Storage 3.8 Summary 4 Multimedia Storage and Access 4.1 Sound Formats: Vinyl, Tape, and CD 4.2 Pictures: GIF and JPEG 4.3 Automatic Speech Recognition 4.4 Moving Images: MPEG and Motion JPEG 4.5 Indexing Images: Motion Segmentation 4.6 Summary 5 Knowledge Representation Methods 5.1 Library Classification 5.2 Indexing Words and Thesauri 5.3 Artificial Intelligence Structures 5.4 Hypertext 5.5 Vector Models 5.6 History-Based Information Resources 5.7 New Information Techniques 5.8 Summary 6 Distribution 6.1 Physical Objects: Books and CD-ROMs 6.2 Computer Network Technologies 6.2.1 Packets vs. Circuits 6.2.2 Stars vs. Buses 6.2.3 Ethernet 6.2.4 Arpanet and Addressing 6.3 Security on the Net 6.4 Cryptography 6.5 Summary 7 Usability and Retrieval Evaluation 7.1 General Human Factors Considerations 7.2 Text Displays: Fonts and Highlighting 7.3 Image Displays and Compression Systems 7.3.1 Text 7.3.2 Speech 7.3.3 Images 7.4 Interface Methods: Menus and Keywords 7.5 Access Methods 7.6 Retrieval Evaluation 7.7 Summary 8 Collections and Preservation 8.1 Traditional Paper Collections 8.2 Traditional Preservation Problems: Acid Paper and Perfect Binding 8.3 Digitizing Special Collections and Archives 8.4 Sharing of Collections among Libraries 8.5 New Kinds of Material and Their Durability 8.6 Summary and Responsibilities to the Future 9 Economics 9.1 Traditional Economics and Economies of Scale 9.2 Scholarly Publications Today 9.3 Models for Library Funding 9.4 Paying for Electronic Information 9.5 Access versus Ownership 9.6 Importance of Administrative Costs 9.7 Electronic Commerce 9.8 The Future of Quality Information 9.9 Summary 10 Intellectual Property Rights 10.1 History of Copyright Law 10.2 History of Patent Law 10.3 Other Legal Risks 10.4 National Information Infrastructure Dangers 10.5 Intellectual Property Protection 10.5.1 Fractional Access 10.5.2 Control of Interface 10.5.3 Hardware Locks (Dongles) 10.5.4 Repositories 10.5.5 Steganography 10.5.6 Cryptolopes, or Secret Envelopes 10.5.7 Special Hardware 10.5.8 Economic Approaches 10.5.9 Flickering 10.6 Summary: Future Research and Law 11 International Activities 11.1 Information Policy, Not Industrial Policy 11.2 United States 11.2.1 University of California at Berkeley 11.2.2 University of Michigan 11.2.3 University of Illinois 11.2.4 Stanford University 11.2.5 University of California at Santa Barbara 11.2.6 Carnegie Mellon University 11.2.7 Other U.S. Projects 11.3 United Kingdom 11.4 France 11.5 Other EU Efforts 11.6 Japan 11.7 Australia 11.8 Elsewhere 11.9 International Cooperation 11.10 Summary 12 Future: Ubiquity, Diversity, Creativity, and Public Policy 12.1 Dream to Be Realized 12.2 Future Roles in Information Handling 12.3 Effect of Digital Technology on Universities 12.4 Society and Creativity 12.5 Public Policy Questions 12.6 Projections References Index


title: Ampicillin Plus Ceftriaxone Is as Effective as Ampicillin Plus Gentamicin for Treating Enterococcus faecalis Infective Endocarditis

abstract: (See the Editorial Commentary by Munita et al on pages 1269–72.) Background. The aim of this study was to compare the effectiveness of the ampicillin plus ceftriaxone (AC) and ampicillin plus gentamicin (AG) combinations for treating Enterococcus faecalis infective endocarditis (EFIE). Methods. An observational, nonrandomized, comparative multicenter cohort study was conducted at 17 Spanish and 1 Italian hospitals. Consecutive adult patients diagnosed of EFIE were included. Outcome measurements were death during treatment and at 3 months of follow-up, adverse events requiring treatment withdrawal, treatment failure requiring a change of antimicrobials, and relapse. Results. A larger percentage of AC-treated patients (n = 159) had previous chronic renal failure than AG-treated patients (n= 87) (33% vs 16%, P=.004), and AC patients had a higher incidence of cancer (18% vs 7%, P= .015), transplantation (6% vs 0%, P= .040), and healthcare-acquired infection (59% vs 40%, P= .006). Between AC and AGtreated EFIE patients, there were no differences in mortality while on antimicrobial treatment (22% vs 21%, P=.81) or at 3-month follow-up (8% vs 7%, P= .72), in treatment failure requiring a change in antimicrobials (1% vs 2%, P= .54), or in relapses (3% vs 4%, P=.67). However, interruption of antibiotic treatment due to adverse events was much more frequent in AG-treated patients than in those receiving AC (25% vs 1%, P< .001), mainly due to new renal failure (≥25% increase in baseline creatinine concentration; 23% vs 0%, P< .001). Conclusions. AC appears as effective as AG for treating EFIE patients and can be used with virtually no risk of renal failure and regardless of the high-level aminoglycoside resistance status of E. faecalis.


title: TriFlow: Triaging Android Applications using Speculative Information Flows

abstract: Information flows in Android can be effectively used to give an informative summary of an application's behavior, showing how and for what purpose apps use specific pieces of information. This has been shown to be extremely useful to characterize risky behaviors and, ultimately, to identify unwanted or malicious applications in Android. However, identifying information flows in an application is computationally highly expensive and, with more than one million apps in the Google Play market, it is critical to prioritize applications that are likely to pose a risk. In this work, we develop a triage mechanism to rank applications considering their potential risk. Our approach, called TriFlow, relies on static features that are quick to obtain. TriFlow combines a probabilistic model to predict the existence of information flows with a metric of how significant a flow is in benign and malicious apps. Based on this, TriFlow provides a score for each application that can be used to prioritize analysis. TriFlow also provides an explanatory report of the associated risk. We evaluate our tool with a representative dataset of benign and malicious Android apps. Our results show that it can predict the presence of information flows very accurately and that the overall triage mechanism enables significant resource saving.


title: Analysis of Buck Converters for On-Chip Integration With a Dual Supply Voltage Microprocessor

abstract: An analysis of an on-chip buck converter is presented in this paper. A high switching frequency is the key design parameter that simultaneously permits monolithic integration and high efficiency. A model of the parasitic impedances of a buck converter is developed. With this model, a design space is determined that allows integration of active and passive devices on the same die for a target technology. An efficiency of 88.4% at a switching frequency of 477 MHz is demonstrated for a voltage conversion from 1.2-0.9 volts while supplying 9.5 A average current. The area occupied by the buck converter is 12.6 mm/sup 2/ assuming an 80-nm CMOS technology. An estimate of the efficiency is shown to be within 2.4% of simulation at the target design point. Full integration of a high-efficiency buck converter on the same die with a dual-V/sub DD/ microprocessor is demonstrated to be feasible.


title: Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs

abstract: Activity-driven modifications in synaptic connections between neurons in the neocortex may occur during development and learning. In dual whole-cell voltage recordings from pyramidal neurons, the coincidence of postsynaptic action potentials (APs) and unitary excitatory postsynaptic potentials (EPSPs) was found to induce changes in EPSPs. Their average amplitudes were differentially up- or down-regulated, depending on the precise timing of postsynaptic APs relative to EPSPs. These observations suggest that APs propagating back into dendrites serve to modify single active synaptic connections, depending on the pattern of electrical activity in the pre- and postsynaptic neurons.


What Did You Mean? - Facing the Challenges of User-generated Software Requirements


title: Inverse halftoning algorithm using edge-based lookup table approach

abstract: The inverse halftoning algorithm is used to reconstruct a gray image from an input halftone image. Based on the recently published lookup table (LUT) technique, this paper presents a novel edge-based LUT method for inverse halftoning which improves the quality of the reconstructed gray image. The proposed method first uses the LUT-based inverse halftoning method as a preprocessing step to transform the given halftone image to a base gray image, and then the edges are extracted and classified from the base gray image. According to these classified edges, a novel edge-based LUT is built up to reconstruct the gray image. Based on a set of 30 real training images with both low-and high-frequency contents, experimental results demonstrated that the proposed method achieves a better image quality when compared to the currently published two methods, by Chang et al. and Meşe and Vaidyanathan.


Examining Drivers of Consumer Returns in E-Tailing with Real Shop Data


title: A new improved krill herd algorithm for global numerical optimization

abstract: This study presents an improved krill herd (IKH) approach to solve global optimization problems. The main improvement pertains to the exchange of information between top krill during motion calculation process to generate better candidate solutions. Furthermore, the proposed IKH method uses a new Levy flight distribution and elitism scheme to update the KH motion calculation. This novel meta-heuristic approach can accelerate the global convergence speed while preserving the robustness of the basic KH algorithm. Besides, the detailed implementation procedure for the IKH method is described. Several standard benchmark functions are used to verify the efficiency of IKH. Based on the results, the performance of IKH is superior to or highly competitive with the standard KH and other robust population-based optimization methods. (C) 2014 Elsevier B.V. All rights reserved.


title: TOWARDS A BUSINESS PROCESS MANGEMENT MATURITY Model

abstract: Business Process Management (BPM) has been identified as the number one business priority by a ::: recent Gartner study (Gartner, 2005). However, BPM has a plethora of facets as its origins are in ::: Business Process Reengineering, Process Innovation, Process Modelling, and Workflow Management ::: to name a few. Organisations increasingly recognize the requirement for an increased process ::: orientation and require appropriate comprehensive frameworks, which help to scope and evaluate ::: their BPM initiative. This research project aims toward the development of a holistic and widely ::: accepted BPM maturity model, which facilitates the assessment of BPM capabilities. This paper ::: provides an overview about the current model with a focus on the actual model development utilizing ::: a series of Delphi studies. The development process includes separate studies that focus on further ::: defining and expanding the six core factors within the model, i.e. strategic alignment, governance, ::: method, Information Technology, people and culture.


title: An Introduction to MCMC for Machine Learning

abstract: This purpose of this introductory paper is threefold. First, it introduces the Monte Carlo method with emphasis on probabilistic machine learning. Second, it reviews the main building blocks of modern Markov chain Monte Carlo simulation, thereby providing and introduction to the remaining papers of this special issue. Lastly, it discusses new interesting research horizons.


title: Video Captioning with Multi-Faceted Attention

abstract: Video captioning has attracted an increasing amount of interest, due in part to its potential for improved accessibility and information retrieval. While existing methods rely on different kinds of...


Phonological Features of in-, un-and non -


title: Correlation filters with limited boundaries

abstract: Correlation filters take advantage of specific properties in the Fourier domain allowing them to be estimated efficiently: O(N D log D) in the frequency domain, versus O(D3 + N D2) spatially where D is signal length, and N is the number of signals. Recent extensions to correlation filters, such as MOSSE, have reignited interest of their use in the vision community due to their robustness and attractive computational properties. In this paper we demonstrate, however, that this computational efficiency comes at a cost. Specifically, we demonstrate that only 1/D proportion of shifted examples are unaffected by boundary effects which has a dramatic effect on detection/tracking performance. In this paper, we propose a novel approach to correlation filter estimation that: (i) takes advantage of inherent computational redundancies in the frequency domain, (ii) dramatically reduces boundary effects, and (iii) is able to implicitly exploit all possible patches densely extracted from training examples during learning process. Impressive object tracking and detection results are presented in terms of both accuracy and computational efficiency.


Combination of 595-nm pulsed dye laser, long-pulsed 755-nm alexandrite laser and microdermabrasion treatment for keratosis pilaris.


title: Machine Learning Approach to Identify Users Across Their Digital Devices

abstract: This paper discusses methods to identify individualusers across their digital devices as part of the ICDM 2015 competition hosted on Kaggle. The competition's data set and prize poolwere provided by http://www.drawbrid.ge/ in sponsorship withthe ICDM 2015 conference. The methods described in this paperfocuses on feature engineering and generic machine learningalgorithms like Extreme Gradient Boosting (xgboost), Follow theReguralized Leader Proximal etc. Machine learning algorithmsdiscussed in this paper can help improve the marketer's abilityto identify individual users as they switch between devices andshow relevant content/recommendation to users wherever they go.


Probabilistic latent semantic indexing


title: A hybrid wavelet convolution network with sparse-coding for image super-resolution

abstract: This paper proposes a hybrid wavelet convolution network (HWCN) which is composed of a scattering convolution component and a convolution neural component. The hierarchical end-to-end network implements sparse-coding and high-dimensional reconstruction for inverse problem through cascade convolutions. With the pre-defined scattering convolutions from nonlinear operators, the network can be tailored in accordance with the frequency property to provide sparse code candidates, and the convolution neural component could automatically select and weight these candidates for sparse coding. Given a tiny dataset, HWCN could train complex deep network with better generalization by regularization from scattering convolutions, and thereby is a competitive alternative to convolutional neural networks (CNN). Moreover, we further demonstrate that HWCN is a superior selection of sparse-coding based image super-resolution and achieves state-of-the-art performance.


title: Biometric Authentication: System Security and User Privacy

abstract: While biometric systems aren't foolproof, the research community has made significant strides to identify vulnerabilities and develop measures to counter them.


title: A Distributed, Cooperative Frequency and Voltage Control for Microgrids

abstract: This paper presents a novel secondary frequency and voltage control method for islanded microgrids based on distributed cooperative control. The proposed method utilizes a sparse communication network where each DG unit only requires local and its neighbors’ information to perform control actions. The frequency controller restores the system frequency to the nominal value while maintaining the equal generation cost increment value among DG units. The voltage controller simultaneously achieves the critical bus voltage restoration and accurate reactive power sharing. Subsequently, the case when the DG unit ac-side voltage reaches its limit value is discussed and a controller output limitation method is correspondingly provided to selectively realize the desired control objective. This paper also provides a small-signal dynamic model of the microgrid with the proposed controller to evaluate the system dynamic performance. Finally, simulation results on a microgrid test system are presented to validate the effectiveness of the proposed method.


Application of Data Mining and Process Mining approaches for improving e-Learning Processes


title: MRI tumor segmentation with densely connected 3D CNN

abstract: Glioma is one of the most common and aggressive types of primary brain tumors. The accurate segmentation of subcortical brain structures is crucial to the study of gliomas in that it helps the monitoring of the progression of gliomas and aids the evaluation of treatment outcomes. However, the large amount of required human labor makes it difficult to obtain the manually segmented Magnetic Resonance Imaging (MRI) data, limiting the use of precise quantitative measurements in the clinical practice. In this work, we try to address this problem by developing a 3D Convolutional Neural Network~(3D CNN) based model to automatically segment gliomas. The major difficulty of our segmentation model comes with the fact that the location, structure, and shape of gliomas vary significantly among different patients. In order to accurately classify each voxel, our model captures multi-scale contextual information by extracting features from two scales of receptive fields. To fully exploit the tumor structure, we propose a novel architecture that hierarchically segments different lesion regions of the necrotic and non-enhancing tumor~(NCR/NET), peritumoral edema~(ED) and GD-enhancing tumor~(ET). Additionally, we utilize densely connected convolutional blocks to further boost the performance. We train our model with a patch-wise training schema to mitigate the class imbalance problem. The proposed method is validated on the BraTS 2017 dataset and it achieves Dice scores of 0.72, 0.83 and 0.81 for the complete tumor, tumor core and enhancing tumor, respectively. These results are comparable to the reported state-of-the-art results, and our method is better than existing 3D-based methods in terms of compactness, time and space efficiency.


title: XNMT: The eXtensible Neural Machine Translation Toolkit

abstract: This paper describes XNMT, the eXtensible Neural Machine Translation toolkit. XNMT distin- guishes itself from other open-source NMT toolkits by its focus on modular code design, with the purpose of enabling fast iteration in research and replicable, reliable results. In this paper we describe the design of XNMT and its experiment configuration system, and demonstrate its utility on the tasks of machine translation, speech recognition, and multi-tasked machine translation/parsing. XNMT is available open-source at this https URL


title: A New Design Method of LCL Filter for Single Phase Grid Connected Power Converter

abstract: This paper proposes a new design method of LCL filter for a grid connected single phase inverter to improve reduction of switching ripple current. LCL filter is designed from the viewpoint of Total Harmonic Distortion(THD), reactive power and minimization of magnetic materials. The converter side filter inductor L1 depends on a ripple ratio of the switching frequency component of the converter side current to the rated current, which varies by the modulation index and PWM method of the inverter. Filter capacitor limits the amount of reactive power absorbed from the grid. The grid side filter inductor L2 depends on an attenuation ratio of the switching frequency component of the converter side current and the grid side current. The validity of filter design is verified by simulation of current control stability and current ripple characteristics by applying designed LCL filter to the single phase inverter, which can be applied to a railway application.


title: How to Train Triplet Networks with 100K Identities?

abstract: Training triplet networks with large-scale data is challenging in face recognition. Due to the number of possible triplets explodes with the number of samples, previous studies adopt the online hard negative mining(OHNM) to handle it. However, as the number of identities becomes extremely large, the training will suffer from bad local minima because effective hard triplets are difficult to be found. To solve the problem, in this paper, we propose training triplet networks with subspace learning, which splits the space of all identities into subspaces consisting of only similar identities. Combined with the batch OHNM, hard triplets can be found much easier. Experiments on the large-scale MS-Celeb-1M challenge with 100K identities demonstrate that the proposed method can largely improve the performance. In addition, to deal with heavy noise and large-scale retrieval, we also make some efforts on robust noise removing and efficient image retrieval, which are used jointly with the subspace learning to obtain the state-of-the-art performance on the MS-Celeb-1M competition (without external data in Challenge1).


title: Self-regulated and Informal Learning: understanding How New Digital Contexts Support Autonomous Learning Processes

abstract: The aim of this paper is to reflect on the new challenges associated to online digital content creation and use and its impact on learning based on some of the findings of the ongoing eContentplus European project KeyToNature. This three-year project aims at developing interactive e-tools for learning and teaching biodiversity providing common access to data and interactive educational tools for the identification of organisms tailored to the needs of different educational users. A theoretical approach based on self-regulated and informal learning will be the basis upon which our findings will be framed.


title: miRBase: tools for microRNA genomics

abstract: miRBase is the central online repository for microRNA (miRNA) nomenclature, sequence data, annotation and target prediction. The current release (10.0) contains 5071 miRNA loci from 58 species, expressing 5922 distinct mature miRNA sequences: a growth of over 2000 sequences in the past 2 years. miRBase provides a range of data to facilitate studies of miRNA genomics: all miRNAs are mapped to their genomic coordinates. Clusters of miRNA sequences in the genome are highlighted, and can be defined and retrieved with any inter-miRNA distance. The overlap of miRNA sequences with annotated transcripts, both protein- and non-coding, are described. Finally, graphical views of the locations of a wide range of genomic features in model organisms allow for the first time the prediction of the likely boundaries of many miRNA primary transcripts. miRBase is available at http://microrna.sanger.ac.uk/.


title: Impact of Different Cell Isolation Techniques on Lymphocyte Viability and Function

abstract: Abstract The outcome of immunological assays is markedly influenced by the method of isolation of lymphocytes. It is, therefore, important to comparatively assess various techniques of isolation of lymphocytes, an aspect thus far not thoroughly addressed. In particular, the potential of isolation techniques to influence cell recovery, viability, and function has not yet been evaluated. These studies were designed to determine the effect of different mechanical tissue dissociation methods on the viability and function of lymphocytes. Following spleen and thymus removal, the lymphoid organs were dissociated by one of four different tissue dissociation techniques: metallic screen, sheer force slide, commercial stomacher, or plunger‐screen. Cells were then enumerated and a trypan blue exclusion technique and 7‐amino‐actinomycin D (7‐AAD) were both employed to assess viability. Mitogen‐induced lymphocyte proliferation was measured using the Alamar Blue™ assay. Cell viability and lymphocyte surface antigen expr...


title: Dataset Augmentation in Feature Space

abstract: Dataset augmentation, the practice of applying a wide array of domain-specific transformations to synthetically expand a training set, is a standard tool in supervised learning. While effective in tasks such as visual recognition, the set of transformations must be carefully designed, implemented, and tested for every new domain, limiting its re-use and generality. In this paper, we adopt a simpler, domain-agnostic approach to dataset augmentation. We start with existing data points and apply simple transformations such as adding noise, interpolating, or extrapolating between them. Our main insight is to perform the transformation not in input space, but in a learned feature space. A re-kindling of interest in unsupervised representation learning makes this technique timely and more effective. It is a simple proposal, but to-date one that has not been tested empirically. Working in the space of context vectors generated by sequence-to-sequence models, we demonstrate a technique that is effective for both static and sequential data.


title: Energy-efficient cluster-based security mechanism for intra-WBAN and inter-WBAN communications for healthcare applications

abstract: Wireless body area networks (WBANs) are formed by using tiny health monitoring sensors on the human body in order to collect and communicate the human personal data. WBANs serve as a solution to facilitate the tasks performed in the medical sector, and minimize the chances of errors during the process of medical diagnosis. Due to the unreliable wireless media, the communication in a WBAN is exposed to a variety of attacks. These attacks pose major threats to WBAN security. In order to overcome these threats, several cryptographic techniques have been proposed in the recent past. Effectiveness of these cryptographic techniques largely depends on a good key management scheme. However, using an expensive key management scheme is not feasible in highly resource-constrained WBANs. Therefore, we propose and evaluate an energy-efficient key management scheme for WBANs that takes into account available resources of a node during the whole life cycle of key management. Our proposed scheme is a cluster-based hybrid security framework that supports both intra-WBAN and inter-WBAN communications. By using multiple clusters, energy-efficiency can be ensured. The cluster formation process itself is secured by using electrocardiogram (EKG)-based key agreement scheme. The proposed technique is hybrid because we use both preloading of keys and physiological value-based generated keys. We use highly dynamic and random EKG values of the human body for pairwise key generation and refreshment. The performance comparison of our proposed cluster-based key management scheme and low-energy adaptive clustering hierarchy (LEACH)-based key agreement scheme shows that the proposed scheme is secure, more energy-efficient, and provides better network lifetime.


title: Inertial Particle Behavior in a an Unsteady Separated Flow

abstract: A new inertial particle behavior is identified in a particle-laden, unsteady separated flow behind a backward-facing step. In a numerical investigation, it is shown that inertial particles eject away from the wall along a distinct material line that originates from a fixed wall location. The fixed separation location coincides with the zero averaged wall skin friction location of the carrier flow. At Stokes numbers larger than one, inertial particles separate at moderate angles. At Stokes numbers smaller than one, the inertial particle dispersion compares to the fluid particle traces showing a moving separation origin of the material line. The Saffman lift forces particles away from the wall and pollutes the distinct character of the separation material line.


title: Machine learning of shooting technique for controlling a robot camera

abstract: We propose a machine learning method of a TV cameraman's shooting technique with a neural network so that a robot camera can automatically shoot like an experienced cameraman. An experiment using a simulator that we developed shows that, by using our method, the cameraman's shooting technique can be quickly and easily embodied in the control system of the robot camera. Moreover, we derive guidelines for performing machine learning for shooting actual TV programs by analyzing the relationship of the learning data and the learning parameter with the performance of the learned neural network.


title: Power electronics' circuit topology - the basic switching cells

abstract: Abstract—In this paper, two basic switching cells, P-cell and Ncell, are presented to investigate the topological nature of power electronics circuits. Both cells consist of a switching device and a diode and are the basic building blocks for almost all power electronics circuits. The mirror relationship of the P-cell and Ncell will be revealed. This paper describes the two basic switching cells and shows how all dc-dc converters, voltage source inverters, current source inverters, and multilevel converters are constituted from the two basic cells. Through these two basic cells, great insights about the topology of all power electronics circuits can be obtained for the construction and decomposition of existing power electronic circuits. New power conversion circuits can now be easily derived and invented.


title: 8.2 Batteryless Sub-nW Cortex-M0+ processor with dynamic leakage-suppression logic

abstract: Recent low-voltage design techniques have enabled dramatic improvements in miniaturization and lifetime of wireless sensor nodes [1-3]. These systems typically use a secondary battery to provide energy when the sensor is awake and operating; the battery is then recharged from a harvesting source when the sensor is asleep. In these systems, the key requirement is to minimize energy per operation of the sensor. This extends the number of operations on one battery charge and/or reduces the time to recharge the battery between awake cycles. This requirement has driven significant advances in energy efficiency [1-2] and standby power consumption [3].


A Mobile Knowledge-based System for On-Board Diagnostics and Car Driving Assistance


title: Learning Deep $\ell_0$ Encoders

abstract: Despite its nonconvex nature, l0 sparse approximation is desirable in many theoretical and application cases. We study the l0 sparse approximation problem with the tool of deep learning, by proposing Deep l0 Encoders. Two typical forms, the l0 regularized problem and the M-sparse problem, are investigated. Based on solid iterative algorithms, we model them as feed-forward neural networks, through introducing novel neurons and pooling functions. Enforcing such structural priors acts as an effective network regularization. The deep encoders also enjoy faster inference, larger learning capacity, and better scalability compared to conventional sparse coding solutions. Furthermore, under task-driven losses, the models can be conveniently optimized from end to end. Numerical results demonstrate the impressive performances of the proposed encoders.


title: Deconstructing the Blockchain to Approach Physical Limits

abstract: Transaction throughput, confirmation latency and confirmation reliability are fundamental performance measures of any blockchain system in addition to its security. In a decentralized setting, these measures are limited by two underlying physical network attributes: communication capacity and speed-of-light propagation delay. Existing systems operate far away from these physical limits. In this work we introduce Prism, a new proof-of-work blockchain protocol, which can achieve 1) security against up to 50% adversarial hashing power; 2) optimal throughput up to the capacity C of the network; 3) confirmation latency for honest transactions proportional to the propagation delay D, with confirmation error probability exponentially small in CD ; 4) eventual total ordering of all transactions. Our approach to the design of this protocol is based on deconstructing the blockchain into its basic functionalities and systematically scaling up these functionalities to approach their physical limits.


title: Road Centerline Extraction via Semisupervised Segmentation and Multidirection Nonmaximum Suppression

abstract: Accurate road centerline extraction from remotely sensed images plays a significant role in road map generation and updating. In the road extraction problem, the acquisition of labeled data is time consuming and costly; thus, there are only a small amount of labeled samples in reality. In the existing centerline extraction algorithms, the thinning-based algorithms always produce small spurs that reduce the smoothness and accuracy of the road centerline; the regression-based algorithms can extract a smooth road network, but they are time consuming. To solve the aforementioned problems, we propose a novel road centerline extraction method, which is constructed based on semisupervised segmentation and multiscale filtering (MF) and multidirection nonmaximum suppression (M-NMS) (MF&M-NMS). Specifically, a semisupervised method, which explores the intrinsic structures between the labeled samples and the unlabeled ones, is introduced to obtain the segmentation result. Then, a novel MF&M-NMS-based algorithm is proposed to gain a smooth and complete road centerline network. Experimental results on a public data set demonstrate that the proposed method achieves comparable or better performances by comparing with the state-of-the-art methods. In addition, our method is nearly ten times faster than the state-of-the-art methods.


title: Fast and Accurate Multiplicative Decomposition for Fringe Removal in Interferometric Images

abstract: Airborne hyperspectral images can be efficiently obtained with imaging static Fourier transform spectrometers. However, to be effective on any location, this technology requires to know the relief of the scene. This is not a straightforward process, as the horizontal interference fringes on the images, which are necessary for spectrum construction, prevent efficient stereoscopic processing. We present a novel variational model for multiplicative image decomposition to separate the fringes from the panchromatic image of the scene. This multiplicative model is much more physically accurate than previous additive decomposition models inspired by cartoon-texture decomposition. It combines fully smoothed total variation operators and one-dimensional (1-D) Fourier transform. Smoothed total variation is adopted to avoid staircasing artifacts caused by traditional total variation regularization. The use of a 1-D Fourier transform is suggested by the geometry of the fringes, in order to circumvent the lack of horizontal periodicity in the interferometric pattern. We also present an optimization algorithm. Finally, a second algorithm is introduced, whose convergence is not mathematically guaranteed. However, it systematically approaches the solution of the first one in much less computation time. Our experimental evaluation on real and simulated images shows that the proposed model separates fringes from the panchromatic image very accurately and that this accuracy significantly improves subpixel stereo matching results.


title: The textcat Package for n-Gram Based Text Categorization in R

abstract: Identifying the language used will typically be the first step in most natural language processing tasks. Among the wide variety of language identification methods discussed in the literature, the ones employing the Cavnar and Trenkle (1994) approach to text categorization based on character n-gram frequencies have been particularly successful. This paper presents the R extension package textcat for n-gram based text categorization which implements both the Cavnar and Trenkle approach as well as a reduced n-gram approach designed to remove redundancies of the original approach. A multi-lingual corpus obtained from the Wikipedia pages available on a selection of topics is used to illustrate the functionality of the package and the performance of the provided language identification methods.


title: Classification of grass pollen through the quantitative analysis of surface ornamentation and texture

abstract: Taxonomic identification of pollen and spores uses inherently qualitative descriptions of morphology. Consequently, identifications are restricted to categories that can be reliably classified by multiple analysts, resulting in the coarse taxonomic resolution of the pollen and spore record. Grass pollen represents an archetypal example; it is not routinely identified below family level. To address this issue, we developed quantitative morphometric methods to characterize surface ornamentation and classify grass pollen grains. This produces a means of quantifying morphological features that are traditionally described qualitatively. We used scanning electron microscopy to image 240 specimens of pollen from 12 species within the grass family (Poaceae). We classified these species by developing algorithmic features that quantify the size and density of sculptural elements on the pollen surface, and measure the complexity of the ornamentation they form. These features yielded a classification accuracy of 77.5%. In comparison, a texture descriptor based on modelling the statistical distribution of brightness values in image patches yielded a classification accuracy of 85.8%, and seven human subjects achieved accuracies between 68.33 and 81.67%. The algorithmic features we developed directly relate to biologically meaningful features of grass pollen morphology, and could facilitate direct interpretation of unsupervised classification results from fossil material.


title: A 28 GHz compact vertically-polarized dipole for 5G smartphone edge

abstract: This paper presents a compact 28 GHz vertically-polarized SIW-fed dipole for future 5G smartphones. The dipole is implemented using vias in standard PCB process to fit at the edge of the smartphone providing fan-beam in the elevation plane for wide coverage. Additional parasitic vias are employed in front of the dipole to serve as directors for enhanced gain achieving 7 dBi peak gain. Also a via fence is added surrounding the dipole to suppress back radiation. The fabricated prototype achieves 2.7 GHz matching bandwidth, 96% radiation efficiency, 16 dB front-to-back radiation ratio and 96° fan-beam coverage in the elevation plane.


THE SOCIOTECHNICAL NATURE OF MOBILE COMPUTING AND WORK : EVIDENCE FROM A STUDY OF POLICING IN THE UNITED STATES INTRODUCTION


title: Reliability, availability and maintenance aspects of large-scale offshore wind farms, a concepts study

abstract: The DOWEC projects aims at implementation of large wind turbines in large scale wind farms. part of the DOWEC project a concepts study was performed regarding the achievable reliability and availability levels. A reduction with a factor of 2 with regard to the present state of the art seems fairly easy achievable. This is however not sufficient for application at more exposed sites. Availability levels are lower than targeted, but moreover the O&M cost turn out to be substantially higher than initially anticipated. The main cause for the high O&M costs is the rather frequent need for an expensive external crane vessel. A second design round is necessary to reconsider the reliability levels adopted for almost all concepts. Furthermore a more "farm like design approach" is needed to reduce major maintenance cost and increase availability.


title: Access to Financial Services in the UK

abstract: In a market-based system, consumers do not have an automatic right to ::: receive products and services, nor firms an obligation to provide them unless specific universal obligations are legislated for (for example in telecoms). This means it is often not obvious who should take responsibility for access issues. One of this paper’s key purposes is to explore which stakeholders may be able to drive improvements and, in particular, how much access issues may come within the scope of the financial regulator. It aims to open up discussion about the nature, scale and impact of access issues, and how to resolve them, using four key areas as examples: digital innovation, compliance and crime prevention, automated processes and segmented markets. The paper develops a new taxonomy of access issues, tracing each type through to appropriate solutions.


title: An unusual case of transient neonatal pustular melanosis: a diagnostic puzzle

abstract: A newborn's skin may exhibit a variety of changes during the first weeks of life, and rashes are extremely common in the neonatal period, representing a significant source of parental concern. In particular, a variety of skin eruptions can present as pustules. Most of them are innocuous and self-limiting, while others can be the manifestation of an infectious disease or even indicative of serious underlying disorders. Transient neonatal pustular melanosis is an uncommon vesiculopustular rash characterized by small pustules on a non-erythematous base, noted at birth or during the first day of life, without systemic symptoms. The lesions rupture spontaneously, leaving hyperpigmented macules that usually fade within few weeks. Clinical recognition of this disease can help physicians avoid unnecessary diagnostic testing and treatment for infectious etiologies because no specific therapy is recommended. The clinical aspect and time of onset are generally sufficient to make the correct diagnosis. Nevertheless, peculiar clinical presentations may require additional work-up to rule out life-threatening conditions, and dermatological consultation and histological examination are required for the final diagnosis. Conclusion: We report an exceedingly unusual presentation of transient neonatal pustular melanosis, suggesting the importance of a systematic diagnostic approach to allow a confident recognition of this benign condition.


title: SkyRAN: a self-organizing LTE RAN in the sky

abstract: We envision a flexible, dynamic airborne LTE infrastructure built upon Unmanned Autonomous Vehicles (UAVs) that will provide on-demand, on-time, network access, anywhere. In this paper, we design, implement and evaluate SkyRAN, a self-organizing UAV-based LTE RAN (Radio Access Network) that is a key component of this UAV LTE infrastructure network. SkyRAN determines the UAV's operating position in 3D airspace so as to optimize connectivity to all the UEs on the ground. It realizes this by overcoming various challenges in constructing and maintaining radio environment maps to UEs that guide the UAV's position in real-time. SkyRAN is designed to be scalable in that it can be quickly deployed to provide efficient connectivity even over a larger area. It is adaptive in that it reacts to changes in the terrain and UE mobility, to maximize LTE coverage performance while minimizing operating overhead. We implement SkyRAN on a DJI Matrice 600 Pro drone and evaluate it over a 90 000 m2 operating area. Our testbed results indicate that SkyRAN can place the UAV in the optimal location with about 30 secs of a measurement flight. On an average, SkyRAN achieves a throughput of 0.9 - 0.95X of optimal, which is about 1.5 - 2X over other popular baseline schemes.


A Fundamental Algorithm for Dependency Parsing (with corrections)


title: A temporal modal defeasible logic for formalizing social commitments in dialogue and argumentation models

abstract: In this paper, we extend a temporal defeasible logic with a modal operator Committed to formalize commitments that agents undertake as a consequence of communicative actions (speech acts) during dialogues. We represent commitments as modal sentences. The defeasible dual of the modal operator Committed is a modal operator called Exempted. The logical setting makes the social-commitment based semantics of speech acts verifiable and practical; it is possible to detect if, and when, a commitment is violated and/or complied with. One of the main advantages of the proposed system is that it allows for capturing the nonmonotonic behavior of the commitments induced by the relevant speech acts.


title: Re-id: Hunting Attributes in the Wild

abstract: © 2014. The copyright of this document resides with its authors. ::: It may be distributed unchanged freely in print or electronic forms.


title: Local Word Vectors Guide Keyphrase Extraction

abstract: Automated keyphrase extraction is a fundamental textual information processing task concerned with the selection of representative phrases from a document that summarize its content. This work presents a novel unsupervised method for keyphrase extraction, whose main innovation is the use of local word embeddings (in particular GloVe vectors), i.e. embeddings trained from the single document under consideration. We argue that such local representation of words and keyphrases are able to accurately capture their semantics in the context of the document they are part of, and can therefore help in improving keyphrase extraction quality. Empirical results offer evidence that indeed local representations lead to better keyphrase extraction results compared to both embeddings trained on very large third corpora or larger corpora consisting of several documents of the same scientific field and to other state-of-the-art unsupervised keyphrase extraction methods.


title: Impact of Urban Nature on Executive Functioning in Early and Middle Childhood

abstract: According to attention restoration theory, directed attention can become fatigued and then be restored by spending time in a restorative environment. This study examined the restorative effects of nature on children’s executive functioning. Seven- to 8-year-olds (school aged, n = 34) and 4- to 5-year-olds (preschool, n = 33) participated in two sessions in which they completed an activity to fatigue attention, then walked along urban streets (urban walk) in one session and in a park-like area (nature walk) in another session, and finally completed assessments of working memory, inhibitory control, and attention. Children responded faster on the attention task after a nature walk than an urban walk. School-aged children performed significantly better on the attention task than preschoolers following the nature walk, but not urban walk. Walk type did not affect inhibitory control or verbal working memory. However, preschoolers’ spatial working memory remained more stable following the nature walk than the u...


title: Design and Characterization of 1.8–3.2 THz Schottky-Based Harmonic Mixers

abstract: A room-temperature Schottky diode-based WM-86 (WR-0.34) harmonic mixer was developed to build high-resolution spectrometers, and multipixel receivers in the terahertz (THz) region for applications such as radio astronomy, plasma diagnostics, and remote sensing. The mixer consists of a quartz-based local oscillator (LO), intermediate-frequency (IF) circuits, and a GaAs-based beam-lead THz circuit with an integrated diode. Measurements of the harmonic mixer were performed using a 2 THz solid-state source and 2.6906 THz QCL. A conversion loss of 27 dB for the third harmonic mixing and a conversion loss of 30 dB for the fourth harmonic mixing were achieved. This is the first development of a wideband WM-86 (WR-0.34) harmonic mixer with a planar Schottky diode integrated on a beam-lead THz circuit that uses a lower LO harmonic factor for 1.8–3.2 THz RF frequency. Furthermore, this result represents the best Schottky-based mixer in this frequency range.


title: Executive and attentional contributions to Theory of Mind deficit in attention deficit/hyperactivity disorder (ADHD)

abstract: Attention deficit/hyperactivity disorder (ADHD) in children has been associated with attentional and executive problems, but also with socioemotional difficulties possibly associated with deficits in Theory of Mind (ToM). Socioemotional problems in ADHD are associated with more negative prognoses, notably interpersonal, educational problems, and an increased risk of developing other psychiatric disorders that emphasize the need to clarify the nature of their ToM deficits. In this study, we hypothesized that ToM dysfunction in children with ADHD is largely attributable to their attentional and/or executive deficits. Thirty-one children with ADHD (8–12 years, IQ > 85) and 31 typically developing (TD) children were assessed using executive functions (inhibition, planning, and flexibility) and attentional tasks, as well as two advanced ToM tasks (Reading the Mind in the Eyes and Faux Pas) involving different levels of executive control. Children with ADHD performed more poorly than TD children in attentional,...


title: MARLA: MapReduce for Heterogeneous Clusters

abstract: MapReduce has gradually become the framework of choice for "big data". The MapReduce model allows for efficient and swift processing of large scale data with a cluster of compute nodes. However, the efficiency here comes at a price. The performance of widely used MapReduce implementations such as Hadoop suffers in heterogeneous and load-imbalanced clusters. We show the disparity in performance between homogeneous and heterogeneous clusters in this paper to be high. Subsequently, we present MARLA, a MapReduce framework capable of performing well not only in homogeneous settings, but also when the cluster exhibits heterogeneous properties. We address the problems associated with existing MapReduce implementations affecting cluster heterogeneity, and subsequently present through MARLA the components and trade-offs necessary for better MapReduce performance in heterogeneous cluster and cloud environments. We quantify the performance gains exhibited by our approach against Apache Hadoop and MARIANE in data intensive and compute intensive applications.


title: Sorting of finely-grinded granular mixtures using a belt-type corona-electrostatic separator

abstract: The commercial roll-type corona-electrostatic separators, which are currently employed for the recovery of metals and plastics from mm-size granular mixtures, are inappropriate for the processing of finely-grinded wastes. The aim of the present work is to demonstrate that a belt-type corona-electrostatic separator could be an appropriate solution for the selective sorting of conductive and non-conductive products contained in micronized wastes. The experiments are carried out on a laboratory-scale multi-functional electrostatic separator designed by the authors. The corona discharge is generated between a wire-type dual electrode and the surface of the metal belt conveyor. The distance between the wire and the belt and the applied voltage are adjusted to values that permit particles charging without having an electric wind that puts them into motion on the surface of the belt. The separation is performed in the electric field generated between a high-voltage roll-type electrode (diameter 30 mm) and the grounded belt electrode. The study is conducted according to experimental design methodology, to enable the evaluation of the effects of the various factors that affect the efficiency of the separation: position of the roll-type electrode and applied high-voltage. The conclusions of this study will serve at the optimum design of an industrial belt-type corona-electrostatic separator for the recycling of metals and plastics from waste electric and electronic equipment.


title: 3DCapsule: Extending the Capsule Architecture to Classify 3D Point Clouds

abstract: This paper introduces the 3DCapsule, which is a 3D extension of the recently introduced Capsule concept that makes it applicable to unordered point sets. The original Capsule relies on the existence of a spatial relationship between the elements in the feature map it is presented with, whereas in point permutation invariant formulations of 3D point set classification methods, such relationships are typically lost. Here, a new layer called ComposeCaps is introduced that, in lieu of a spatially relevant feature mapping, learns a new mapping that can be exploited by the 3DCapsule. Previous works in the 3D point set classification domain have focused on other parts of the architecture, whereas instead, the 3DCapsule is a drop-in replacement of the commonly used fully connected classifier. It is demonstrated via an ablation study, that when the 3DCapsule is applied to recent 3D point set classification architectures, it consistently shows an improvement, in particular when subjected to noisy data. Similarly, the ComposeCaps layer is evaluated and demonstrates an improvement over the baseline. In an apples-to-apples comparison against state-of-the-art methods, again, better performance is demonstrated by the 3DCapsule.


title: Linguistic Signals under Misinformation and Fact-Checking: Evidence from User Comments on Social Media

abstract: Misinformation and fact-checking are opposite forces in the news environment: the former creates inaccuracies to mislead people, while the latter provides evidence to rebut the former. These news articles are often posted on social media and attract user engagement in the form of comments. In this paper, we investigate linguistic (especially emotional and topical) signals expressed in user comments in the presence of misinformation and fact-checking. We collect and analyze a dataset of 5,303 social media posts with 2,614,374 user comments from Facebook, Twitter, and YouTube, and associate these posts to fact-check articles from Snopes and PolitiFact for veracity rulings (i.e., from true to false). We find that linguistic signals in user comments vary significantly with the veracity of posts, e.g., we observe more misinformation-awareness signals and extensive emoji and swear word usage with falser posts. We further show that these signals can help to detect misinformation. In addition, we find that while there are signals indicating positive effects after fact-checking, there are also signals indicating potential "backfire" effects.


title: Clustering of streaming time series is meaningless

abstract: Time series data is perhaps the most frequently encountered type of data examined by the data mining community. Clustering is perhaps the most frequently used data mining algorithm, being useful in it's own right as an exploratory technique, and also as a subroutine in more complex data mining algorithms such as rule discovery, indexing, summarization, anomaly detection, and classification. Given these two facts, it is hardly surprising that time series clustering has attracted much attention. The data to be clustered can be in one of two formats: many individual time series, or a single time series, from which individual time series are extracted with a sliding window. Given the recent explosion of interest in streaming data and online algorithms, the latter case has received much attention.In this work we make a surprising claim. Clustering of streaming time series is completely meaningless. More concretely, clusters extracted from streaming time series are forced to obey a certain constraint that is pathologically unlikely to be satisfied by any dataset, and because of this, the clusters extracted by any clustering algorithm are essentially random. While this constraint can be intuitively demonstrated with a simple illustration and is simple to prove, it has never appeared in the literature.We can justify calling our claim surprising, since it invalidates the contribution of dozens of previously published papers. We will justify our claim with a theorem, illustrative examples, and a comprehensive set of experiments on reimplementations of previous work. Although the primary contribution of our work is to draw attention to the fact that an apparent solution to an important problem is incorrect and should no longer be used, we also introduce a novel method which, based on the concept of time series motifs, is able to meaningfully cluster some streaming time series datasets.


title: High-dimensional covariance estimation by minimizing $\ell_1$-penalized log-determinant divergence

abstract: Given i.i.d. observations of a random vector $X \in \mathbb{R}^p$, we study the problem of estimating both its covariance matrix $\Sigma^*$, and its inverse covariance or concentration matrix {$\Theta^* = (\Sigma^*)^{-1}$.} We estimate $\Theta^*$ by minimizing an $\ell_1$-penalized log-determinant Bregman divergence; in the multivariate Gaussian case, this approach corresponds to $\ell_1$-penalized maximum likelihood, and the structure of $\Theta^*$ is specified by the graph of an associated Gaussian Markov random field. We analyze the performance of this estimator under high-dimensional scaling, in which the number of nodes in the graph $p$, the number of edges $s$ and the maximum node degree $d$, are allowed to grow as a function of the sample size $n$. In addition to the parameters $(p,s,d)$, our analysis identifies other key quantities covariance matrix $\Sigma^*$; and (b) the $\ell_\infty$ operator norm of the sub-matrix $\Gamma^*_{S S}$, where $S$ indexes the graph edges, and $\Gamma^* = (\Theta^*)^{-1} \otimes (\Theta^*)^{-1}$; and (c) a mutual incoherence or irrepresentability measure on the matrix $\Gamma^*$ and (d) the rate of decay $1/f(n,\delta)$ on the probabilities $ \{|\hat{\Sigma}^n_{ij}- \Sigma^*_{ij}|>\delta \}$, where $\hat{\Sigma}^n$ is the sample covariance based on $n$ samples. Our first result establishes consistency of our estimate $\hat{\Theta}$ in the elementwise maximum-norm. This in turn allows us to derive convergence rates in Frobenius and spectral norms, with improvements upon existing results for graphs with maximum node degrees $d = o(\sqrt{s})$. In our second result, we show that with probability converging to one, the estimate $\hat{\Theta}$ correctly specifies the zero pattern of the concentration matrix $\Theta^*$.


BEHAVIORAL DESIGN AND ADAPTIVE ROBOTIC FABRICATION OF A FIBER COMPOSITE COMPRESSION SHELL WITH PNEUMATIC FORMWORK


title: Recognizing speculative language in biomedical research articles: a linguistically motivated perspective

abstract: BackgroundDue to the nature of scientific methodology, research articles are rich in speculative and tentative statements, also known as hedges. We explore a linguistically motivated approach to the problem of recognizing such language in biomedical research articles. Our approach draws on prior linguistic work as well as existing lexical resources to create a dictionary of hedging cues and extends it by introducing syntactic patterns.Furthermore, recognizing that hedging cues differ in speculative strength, we assign them weights in two ways: automatically using the information gain (IG) measure and semi-automatically based on their types and centrality to hedging. Weights of hedging cues are used to determine the speculative strength of sentences.ResultsWe test our system on two publicly available hedging datasets. On the fruit-fly dataset, we achieve a precision-recall breakeven point (BEP) of 0.85 using the semi-automatic weighting scheme and a lower BEP of 0.80 with the information gain weighting scheme. These results are competitive with the previously reported best results (BEP of 0.85). On the BMC dataset, using semi-automatic weighting yields a BEP of 0.82, a statistically significant improvement (p <0.01) over the previously reported best result (BEP of 0.76), while information gain weighting yields a BEP of 0.70.ConclusionOur results demonstrate that speculative language can be recognized successfully with a linguistically motivated approach and confirms that selection of hedging devices affects the speculative strength of the sentence, which can be captured reasonably by weighting the hedging cues. The improvement obtained on the BMC dataset with a semi-automatic weighting scheme indicates that our linguistically oriented approach is more portable than the machine-learning based approaches. Lower performance obtained with the information gain weighting scheme suggests that this method may benefit from a larger, manually annotated corpus for automatically inducing the weights.


Chapter 2 CAMERA CALIBRATION Zhengyou Zhang


title: A Scalable Approach to Activity Recognition based on Object Use

abstract: We propose an approach to activity recognition based on detecting and analyzing the sequence of objects that are being manipulated by the user. In domains such as cooking, where many activities involve similar actions, object-use information can be a valuable cue. In order for this approach to scale to many activities and objects, however, it is necessary to minimize the amount of human-labeled data that is required for modeling. We describe a method for automatically acquiring object models from video without any explicit human supervision. Our approach leverages sparse and noisy readings from RFID tagged objects, along with common-sense knowledge about which objects are likely to be used during a given activity, to bootstrap the learning process. We present a dynamic Bayesian network model which combines RFID and video data to jointly infer the most likely activity and object labels. We demonstrate that our approach can achieve activity recognition rates of more than 80% on a real-world dataset consisting of 16 household activities involving 33 objects with significant background clutter. We show that the combination of visual object recognition with RFID data is significantly more effective than the RFID sensor alone. Our work demonstrates that it is possible to automatically learn object models from video of household activities and employ these models for activity recognition, without requiring any explicit human labeling.


title: Extraction of Beta Carotene from Palm Mesocarp via Green Sub-critical Carbon Dioxide

abstract: Experiments involving subcritical carbon dioxide extraction of palm oil from treated palm mesocarp were done to determine palm oil yield and concentration of beta-carotene. As comparison, the conventional method of Soxhlet Extraction with six different solvents was used. The overall oil yield was measured as weight of oil per weight of sample, while anti-oxidant activity was determined using 2,2-diphenyl-1-picryihydrazyl (DPPH) radical scavenging method. The sub-critical extraction was conducted at a constant temperature of 30˚C and four different pressures at 10 MPa, 15 MPa, 20 MPa and 25 MPa. The sub-critical extraction produced high concentration of beta-carotene than soxhlet extraction, even though the oil yield was lower. Furthermore, the anti-oxidant analysis showed a similar trend as the concentration of beta carotene. The best condition of sub-critical extraction was obtained at 25 MPa and 30˚C.


title: Automatic registration of 2-D with 3-D imagery in urban environments

abstract: We are building a system that can automatically acquire 3D range scans and 2D images to build geometrically correct, texture mapped 3D models of urban environments. This paper deals with the problem of automatically registering the 3D range scans with images acquired at other times and with unknown camera calibration and location. The method involves the utilization of parallelism and orthogonality constraints that naturally exist in urban environments. We present results for building a texture mapped 3-D model of an urban building.


title: Queries and Concept Learning

abstract: We consider the problem of using queries to learn an unknown concept. Several types of queries are described and studied: membership, equivalence, subset, superset, disjointness, and exhaustiveness queries. Examples are given of efficient learning methods using various subsets of these queries for formal domains, including the regular languages, restricted classes of context-free languages, the pattern languages, and restricted types of propositional formulas. Some general lower bound techniques are given. Equivalence queries are compared with Valiant's criterion of probably approximately correct identification under random sampling.


title: The Epithelial-Mesenchymal Transition Mediator S100A4 Maintains Cancer-Initiating Cells in Head and Neck Cancers

abstract: A calcium-binding molecule that is prognostic in several cancers is found to be critical to maintain stemness properties, with implications for new cancer targeting strategies.


title: High temperature fatigue of three nickel-base eutectic composites

abstract: Abstract The high cycle fatigue behavior of three advanced nickel-base eutectic composites, Nitac 14B, Cotac 744 and AG-170, all in the as-directionally solidified condition, has been investigated at 25 °C and 825 °C in vacuum. At 25 °C, little difference in fatigue resistance was noted between the three alloys, but Nitac 14B displayed superior properties at 825 °C. The ductility of the three reinforcing fibers (TaC, NbC and α-Mo respectively) does not play a significant role in fatigue crack nucleation or total life. Instead, the γ/γ′ matrix properties dominate the resistance to fatigue. Room temperature failures were surface initiated and propagation proceeded along crystallographic planes (stage I), whereas stage II crack propagation was observed at 825 °C. Dislocation substructures indicated by transmission electron microscopy determined differences with alloy type and test temperature. The high cycle fatigue behavior of eutectics is compared with that of early generation eutectic compounds.


title: Natural Language Processing with Small Feed-Forward Networks

abstract: We show that small and shallow feed-forward neural networks can achieve near state-of-the-art results on a range of unstructured and structured language processing tasks while being considerably cheaper in memory and computational requirements than deep recurrent models. Motivated by resource-constrained environments like mobile phones, we showcase simple techniques for obtaining such small neural network models, and investigate different tradeoffs when deciding how to allocate a small memory budget.


Microsoft Word - 13-16 hoofdstuk 1.doc


title: Aromatherapy: The Effect of Lavender on Anxiety and Sleep Quality in Patients Treated With Chemotherapy

abstract: BACKGROUND ::: A cancer diagnosis is a serious stressor that is associated with anxiety, depression, sleep disorders, and inability to fulfill daily routines. Many pharmacologic and nonpharmacologic options are available to help patients with cancer manage anxiety. ::: ::: ::: OBJECTIVES ::: This randomized, controlled trial examined the effects of lavender oil aromatherapy on anxiety and sleep quality in patients undergoing chemotherapy. ::: ::: ::: METHODS ::: 70 patients were randomly assigned to a lavender oil group, a tea tree oil group, and a control group with no oil. A patient identification form, the State-Trait Anxiety Inventory, and the Pittsburgh Quality Sleep Index (PSQI) were used to measure anxiety and sleep quality before and after chemotherapy. ::: ::: ::: FINDINGS ::: State anxiety before and after chemotherapy did not vary among groups. The authors compared trait anxiety values before and after chemotherapy and found a significant difference in the lavender group. In addition, a significant change in PSQI measurements before and after chemotherapy was observed.


title: The Marulan Data Sets: Multi-sensor Perception in a Natural Environment with Challenging Conditions

abstract: In this paper we present large, accurately calibrated and time-synchronized data sets, gathered outdoors in controlled and variable environmental conditions, using an unmanned ground vehicle (UGV), equipped with a wide variety of sensors. These include four 2D laser scanners, a radar scanner, a color camera and an infrared camera. It provides a full description of the system used for data collection and the types of environments and conditions in which these data sets have been gathered, which include the presence of airborne dust, smoke and rain.


title: Joint Alignment of Multiple Point Sets with Batch and Incremental Expectation-Maximization

abstract: This paper addresses the problem of registering multiple point sets. Solutions to this problem are often approximated by repeatedly solving for pairwise registration, which results in an uneven treatment of the sets forming a pair: a model set and a data set. The main drawback of this strategy is that the model set may contain noise and outliers, which negatively affects the estimation of the registration parameters. In contrast, the proposed formulation treats all the point sets on an equal footing. Indeed, all the points are drawn from a central Gaussian mixture, hence the registration is cast into a clustering problem. We formally derive batch and incremental EM algorithms that robustly estimate both the GMM parameters and the rotations and translations that optimally align the sets. Moreover, the mixture's means play the role of the registered set of points while the variances provide rich information about the contribution of each component to the alignment. We thoroughly test the proposed algorithms on simulated data and on challenging real data collected with range sensors. We compare them with several state-of-the-art algorithms, and we show their potential for surface reconstruction from depth data.