title: The 2018 DAVIS Challenge on Video Object Segmentation

abstract: We present the 2018 DAVIS Challenge on Video Object Segmentation, a public competition specifically designed for the task of video object segmentation. It builds upon the DAVIS 2017 dataset, which was presented in the previous edition of the DAVIS Challenge, and added 100 videos with multiple objects per sequence to the original DAVIS 2016 dataset. Motivated by the analysis of the results of the 2017 edition, the main track of the competition will be the same than in the previous edition (segmentation given the full mask of the objects in the first frame -- semi-supervised scenario). This edition, however, also adds an interactive segmentation teaser track, where the participants will interact with a web service simulating the input of a human that provides scribbles to iteratively improve the result.


title: 2.5D building modeling with topology control

abstract: 2.5D building reconstruction aims at creating building models composed of complex roofs and vertical walls. In this paper, we define 2.5D building topology as a set of roof features, wall features, and point features; together with the associations between them. Based on this definition, we extend 2.5D dual contouring into a 2.5D modeling method with topology control. Comparing with the previous method, we put less restrictions on the adaptive simplification process. We show results under intense geometry simplifications. Our results preserve significant topology structures while the number of triangles is comparable to that of manually created models or primitive-based models.


title: Wideband Excitation Technology of ${\rm TE}_{20}$ Mode Substrate Integrated Waveguide (SIW) and Its Applications

abstract: The higher order mode substrate integrated waveguide (SIW) has advantages in the applications, which are: simplifying the structure with reduced fabrication cost and enhancing the stability of performance by relaxed fabrication tolerance. In this paper, two wideband TE ::: 20 ::: mode excitation structures are presented and investigated for the SIW. A slot along the mid line in the longitudinal direction of the SIW is employed to convert the electromagnetic field pattern between slotline and the TE ::: 20 ::: mode SIW. The second structure is based on the slot aperture coupling and can realize wideband direct transition between the TE ::: 20 ::: mode SIW and microstrip line. Both transitions have a simple and compact structure, as well as broadband characteristics. The wideband transition performance is demonstrated in this paper. As application examples of the transitions, two broadband substrate integrated baluns are presented and fabricated based on the TE ::: 20 ::: mode excitation structures and TE ::: 20 ::: mode characteristics. The 180 ::: ° ::: out-of-phase and equal magnitude of the balun outputs in a wide frequency range can be achieved inherently. The balun based on the slotline excitation has a fractional bandwidth of 50.2%, from 7.3 to 12.2 GHz, with measured return loss, amplitude imbalance, and phase imbalance better than 10 and 0.45 dB and 3.8 ::: ° ::: . The balun based on the aperture coupling excitation shows a fractional bandwidth of 50.3%, from 7 to 11.7 GHz, with measured return loss, insertion loss, amplitude imbalance, and phase imbalance better than 10, 1, and 0.27 dB and 2.4 ::: ° ::: , respectively. Both baluns not only show good performance, but also demonstrate the existence of the TE ::: 20 ::: mode in the SIW.


title: A Flexible AC Distribution System Device for a Microgrid

abstract: This paper presents a flexible ac distribution system device for microgrid applications. The device aims to improve the power quality and reliability of the overall power distribution system that the microgrid is connected to. The control design employs a new model predictive control algorithm which allows faster computational time for large power systems by optimizing the steady-state and the transient control problems separately. Extended Kalman filters are also employed for frequency tracking and to extract the harmonic spectra of the grid voltage and the load currents in the microgrid. The design concept is verified through different test case scenarios to demonstrate the capability of the proposed device and the results obtained are discussed.


title: The Graph Neural Network Model

abstract: Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains. This GNN model, which can directly process most of the practically useful types of graphs, e.g., acyclic, cyclic, directed, and undirected, implements a function tau(G,n) isin IRm that maps a graph G and one of its nodes n into an m-dimensional Euclidean space. A supervised learning algorithm is derived to estimate the parameters of the proposed GNN model. The computational cost of the proposed algorithm is also considered. Some experimental results are shown to validate the proposed learning algorithm, and to demonstrate its generalization capabilities.


title: BOHB: Robust and Efficient Hyperparameter Optimization at Scale

abstract: Modern deep learning methods are very sensitive to many hyperparameters, and, due to the long training times of state-of-the-art models, vanilla Bayesian hyperparameter optimization is typically computationally infeasible. On the other hand, bandit-based configuration evaluation approaches based on random search lack guidance and do not converge to the best configurations as quickly. Here, we propose to combine the benefits of both Bayesian optimization and bandit-based methods, in order to achieve the best of both worlds: strong anytime performance and fast convergence to optimal configurations. We propose a new practical state-of-the-art hyperparameter optimization method, which consistently outperforms both Bayesian optimization and Hyperband on a wide range of problem types, including high-dimensional toy functions, support vector machines, feed-forward neural networks, Bayesian neural networks, deep reinforcement learning, and convolutional neural networks. Our method is robust and versatile, while at the same time being conceptually simple and easy to implement.


title: Simulink based LTE system simulator

abstract: A MATLAB Simulink-based simulator for an antenna system has been developed at Chalmers, followed by the implementation of a WCDMA system in it. Naturally, 3GPP LongTerm Evolution (LTE) as a new standard for communication system should be considered next. Therefore, a Simulink-based LTE system simulator connected to the existing antenna blocks and WINNER channel is presented in this master thesis. It focuses on the implementation of the LTE downlink in the physical layer. In the transmitter, functionalities like Turbo coding, rate-matching, MIMO (up to 4×4) and OFDM are realized. In the receiver, ideal and non-ideal receiver algorithms are supported, including two types of channel estimation, linear equalizer, Turbo decoding, PMI calculation and HARQ functionality. The results, such as raw bit error rate, bit error rate, block error rate and user throughput are studied in the simulator for different system settings.


title: A Survey of Technologies in Internet of Things

abstract: Internet of Things (IoT) is a fast-growing innovation that will greatly change the way humans live. It can be thought of as the next big step in Internet technology. What really enable IoT to be a possibility are the various technologies that build it up. The IoT architecture mainly requires two types of technologies: data acquisition technologies and networking technologies. Many technologies are currently present that aim to serve as components to the IoT paradigm. This paper aims to categorize the various technologies present that are commonly used by Internet of Things.


title: Social Media Monitoring in Real Life with Blogmeter Platform

abstract: A social media monitoring platform used by business clients has to face interesting and sometimes unexpected issues arising from real texts processing, in particular dealing with the task of sentiment analysis of word-of-mouth communication. In this paper we describe some of the solutions adopted by BlogMeter, a proprietary listening platform that helps agencies and brands to discover what is said online about brands, people, topics and companies. We present some real life case studies, some of the linguistic resources used in the semantic annotation pipeline, and we suggest some topics for future investigations.


title: Linear interpolation revitalized

abstract: We present a simple, original method to improve piecewise-linear interpolation with uniform knots: we shift the sampling knots by a fixed amount, while enforcing the interpolation property. We determine the theoretical optimal shift that maximizes the quality of our shifted linear interpolation. Surprisingly enough, this optimal value is nonzero and close to 1/5. We confirm our theoretical findings by performing several experiments: a cumulative rotation experiment and a zoom experiment. Both show a significant increase of the quality of the shifted method with respect to the standard one. We also observe that, in these results, we get a quality that is similar to that of the computationally more costly "high-quality" cubic convolution.


title: Leveraging Billions of Faces to Overcome Performance Barriers in Unconstrained Face Recognition

abstract: We employ the face recognition technology developed in house at face.com to a well accepted benchmark and show that without any tuning we are able to considerably surpass state of the art results. Much of the improvement is concentrated in the high-valued performance point of zero false positive matches, where the obtained recall rate almost doubles the best reported result to date. We discuss the various components and innovations of our system that enable this significant performance gap. These components include extensive utilization of an accurate 3D reconstructed shape model dealing with challenges arising from pose and illumination. In addition, discriminative models based on billions of faces are used in order to overcome aging and facial expression as well as low light and overexposure. Finally, we identify a challenging set of identification queries that might provide useful focus for future research.


title: MIDeA: a multi-parallel intrusion detection architecture

abstract: Network intrusion detection systems are faced with the challenge of identifying diverse attacks, in extremely high speed networks. For this reason, they must operate at multi-Gigabit speeds, while performing highly-complex per-packet and per-flow data processing. In this paper, we present a multi-parallel intrusion detection architecture tailored for high speed networks. To cope with the increased processing throughput requirements, our system parallelizes network traffic processing and analysis at three levels, using multi-queue NICs, multiple CPUs, and multiple GPUs. The proposed design avoids locking, optimizes data transfers between the different processing units, and speeds up data processing by mapping different operations to the processing units where they are best suited. Our experimental evaluation shows that our prototype implementation based on commodity off-the-shelf equipment can reach processing speeds of up to 5.2 Gbit/s with zero packet loss when analyzing traffic in a real network, whereas the pattern matching engine alone reaches speeds of up to 70 Gbit/s, which is an almost four times improvement over prior solutions that use specialized hardware.


title: Pseudoscience and Extraordinary Claims of the Paranormal: A Critical Thinker's Toolkit

abstract: About the Author. Preface. Acknowledgments. Part I: Introduction. 1. The Continuum Mysteriosum. Making Sense out of Mysteries: The Continuum Mysteriosum. Extraordinary, Nonparanormal Mysteries. An Invitation. 2. Why Study These Things? The Dangers of Unexamined Paranormal Claims. The Risk of Paranormal Passion and the Critical Thinker's Toolkit. Eight Reality-Checks: The Critical Thinker's Toolkit. Part II: The Critical Thinker's Toolkit. Evaluate Support for a Claim. 3. Reality Check: Are the Sources Credible? Astrology. Questionable Sources. The Question of Authority. When Experts Get It Wrong. An Invitation to Question. 4. Reality Check: Is the Logic Valid and Sound? Basic Logic. Informal Logical Fallacies. Premises, Logic, and Hypothesis Testing. 5. Reality Check: Are Claims Based on Observation (Scientific Tests and Theories)? Observations. Tests. Theories. Sagan's Balance and the FEDS Standard. Science and Alternative Hypotheses. Consider Alternative Explanations. 6. Reality Checking for Oddities of Nature and the World of Numbers. Probability Estimates and Bias. Math Ignorance. Coincidences. The Clumpiness of Randomness. The Law of Very Large Numbers. Science and Chance. Summary: Psychic Bias. 7. Reality Checking for Perceptual Error and Trickery. Top-Down Processes and Perception. Manipulations of Magicians and Psychics: The Cold Reading Toolkit. Hypnotic Suggestion Enhancers. 8. Reality Checking for Memory Errors. Memory Myths. What Is Memory? Memory Errors. Deja vu. The Deja vu Reality Check. 9. Reality Checking for the Placebo Effect. What are Placebos? Weak and Strong Placebos. How Placebos Work. Placebos and Superstitious Beliefs. Placebo Controversies. Placebos and Performance. 10. Reality Checking for Sensory Anomalies and Hallucinations. Sensory Phenomena. Migraines. Tunnel Experiences. Hallucinations. Hallucinations and the Critical Thinker's Toolkit. Part III: The Paranormal Files. 11. Spiritualism and the Survival Hypothesis. History of the Spiritualist Movement. Impact of Spiritualism. Spiritualism Today. Research on Life after Death. Research on Channeling and Mediums. Conclusion. 12. Parapsychology. The Language of Parapsychology. Research on PSI. Conclusion: The State of PSI Research. 13. Energy Treatments and Complementary and Alternative Medicine (CAM). Contemporary Views of Energy: The Scientific Perspective. Concepts of Energy in Children and Western History: Vitalism. Chinese History and Energy: The Yin-Yang School. Chinese Energy Treatments. Western Energy Treatments. Conclusion. 14. Supernatural Cures and Faith Healing. The Varieties of Healing Experience. The Evidence. Conclusion. 15. Creationism, Intelligent Design, and God. The Great Debate. Things Great and Small. 16. The Reality Checkup: Using your Toolkit. The Reality Checkup. How to Carry on a Civilized Discussion about the Paranormal. Parting Words: Pandora's Challenge. Appendix A. Complementary and Alternative Medicine. Appendix B. Critical Thinking and Paranormal Resources. Appendix C. Susan Blackmore on Paranormal Research. Name Index. Subject Index.


title: Comparison of Fully Automated Urine Sediment Analyzers H800-FUS100 and Labumat-Urised with Manual Microscopy

abstract: Background: Recent technical developments have focused on the full automation of urinalyses, however the manual microscopic analysis of urine sediment is considered the reference method. The aim of this study was to compare the performances of the LabUMat-UriSed and the H800-FUS100 with manual microscopy, and with each other. Methods: The urine sediments of 332 urine samples were examined by these two devices (LabUMatUriSed, H800-FUS100) and manual microscopy. Results: The reproducibility of the analyzers, UriSed and Fus100 (4.1– 28.5% and 4.7–21.2%, respectively), was better than that with manual microscopy (8.5–33.3%). The UriSed was more sensitive for leukocytes (82%), while the Fus100 was more sensitive for erythrocyte cell counting (73%). There were moderate correlations between manual microscopy and the two devices, UriSed and Fus100, for erythrocyte (r = 0.496 and 0.498, respectively) and leukocyte (r = 0.597 and 0.599, respectively) cell counting however the correlation between the two devices was much better for erythrocyte (r = 0.643) and for leukocyte (r = 0.767) cell counting. Conclusion: It can be concluded that these two devices showed similar performances. They were time-saving and standardized techniques, especially for reducing preanalytical errors such as the study time, centrifugation, and specimen volume for sedimentary analysis; however, the automated systems are still inadequate for classifying the cells that are present in pathological urine specimens. J. Clin. Lab. Anal. 27:312–316, 2013. C � 2013 Wiley Periodicals, Inc.


title: Application of text classification and clustering of Twitter data for business analytics

abstract: In the recent years, social networks in business are gaining unprecedented popularity because of their potential for business growth. Companies can know more about consumers' sentiments towards their products and services, and use it to better understand the market and improve their brand. Thus, companies regularly reinvent their marketing strategies and campaigns to fit consumers' preferences. Social analysis harnesses and utilizes the vast volume of data in social networks to mine critical data for strategic decision making. It uses machine learning techniques and tools in determining patterns and trends to gain actionable insights. This paper selected a popular food brand to evaluate a given stream of customer comments on Twitter. Several metrics in classification and clustering of data were used for analysis. A Twitter API is used to collect twitter corpus and feed it to a Binary Tree classifier that will discover the polarity lexicon of English tweets, whether positive or negative. A k-means clustering technique is used to group together similar words in tweets in order to discover certain business value. This paper attempts to discuss the technical and business perspectives of text mining analysis of Twitter data and recommends appropriate future opportunities in developing this emerging field.


title: Multilingual Semantic Role Labeling

abstract: This paper describes our contribution to the semantic role labeling task (SRL-only) of the CoNLL-2009 shared task in the closed challenge (Hajic et al., 2009). Our system consists of a pipeline of independent, local classifiers that identify the predicate sense, the arguments of the predicates, and the argument labels. Using these local models, we carried out a beam search to generate a pool of candidates. We then reranked the candidates using a joint learning approach that combines the local models and proposition features. ::: ::: To address the multilingual nature of the data, we implemented a feature selection procedure that systematically explored the feature space, yielding significant gains over a standard set of features. Our system achieved the second best semantic score overall with an average labeled semantic F1 of 80.31. It obtained the best F1 score on the Chinese and German data and the second best one on English.


title: Combining gradient and albedo data for rotation invariant classification of 3D surface texture

abstract: We present a new texture classification scheme which is invariant to surface-rotation. Many texture classification approaches have been presented in the past that are image-rotation invariant. However, image rotation is not necessarily the same as surface rotation. We have therefore developed a classifier that uses invariants that are derived from surface properties rather than image properties. Previously we developed a scheme that used surface gradient (normal) fields estimated using photometric stereo. In this paper we augment these data with albedo information and also employ an additional feature set: the radial spectrum. We used 30 real textures to test the new classifier. A classification accuracy of 91% was achieved when albedo and gradient 1D polar and radial features were combined. The best performance was also achieved by using 2D albedo and gradient spectra. The classification accuracy is 99%.


title: Enhancing Learning with ICT at Primary Level.

abstract: This study looks at three primary schools which have been successful in integrating ICT into the teaching and learning process. It confirms that a range of organisational features are correlated with successful implementation (eg, a sound learning environment, headteacher commitment, good forward planning etc) and goes on to argue that staff commitment plays a pivotal role in the process. This commitment can, however, be compromised by technological change (in this case from CD-ROM and LAN-based learning to WAN and Web-based learning) if the process is not properly managed.


title: Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array

abstract: Recent advances in neuroscience together with nanoscale electronic device technology have resulted in huge interests in realizing brain-like computing hardwares using emerging nanoscale memory devices as synaptic elements. Although there has been experimental work that demonstrated the operation of nanoscale synaptic element at the single device level, network level studies have been limited to simulations. In this work, we demonstrate, using experiments, array level associative learning using phase change synaptic devices connected in a grid like configuration similar to the organization of the biological brain. Implementing Hebbian learning with phase change memory cells, the synaptic grid was able to store presented patterns and recall missing patterns in an associative brain-like fashion. We found that the system is robust to device variations, and large variations in cell resistance states can be accommodated by increasing the number of training epochs. We illustrated the tradeoff between variation tolerance of the network and the overall energy consumption, and found that energy consumption is decreased significantly for lower variation tolerance.


title: Early Cancer Detection in Blood Vessels Using Mobile Nanosensors

abstract: In this paper, we propose using mobile nanosensors (MNSs) for early stage anomaly detection. For concreteness, we focus on the detection of cancer cells located in a particular region of a blood vessel. These cancer cells produce and emit special molecules, so-called biomarkers, which are symptomatic for the presence of anomaly, into the cardiovascular system. Detection of cancer biomarkers with conventional blood tests is difficult in the early stages of a cancer due to the very low concentration of the biomarkers in the samples taken. However, close to the cancer cells, the concentration of the cancer biomarkers is high. Hence, detection is possible if a sensor with the ability to detect these biomarkers is placed in the vicinity of the cancer cells. Therefore, in this paper, we study the use of MNSs that are injected at a suitable injection site and can move through the blood vessels of the cardiovascular system, which potentially contain cancer cells. These MNSs can be activated by the biomarkers close to the cancer cells, where the biomarker concentration is sufficiently high. Eventually, the MNSs are collected by a fusion center (FC), where their activation levels are read and exploited to declare the presence of anomaly. We analytically derive the biomarker concentration in a network of interconnected blood vessels as well as the probability mass function of the MNSs’ activation levels and validate the obtained results via particle-based simulations. Then, we derive the optimal decision rule for the FC regarding the presence of anomaly assuming that the entire network is known at the FC. Finally, for the FC, we propose a simple sum detector that does not require knowledge of the network topology. Our simulations reveal that while the optimal detector achieves a higher performance than the sum detector, both proposed detectors significantly outperform a benchmark scheme that uses fixed nanosensors at the FC.


title: Main-memory foreign key joins on advanced processors: design and re-evaluations for OLAP workloads

abstract: The hash join algorithm family is one of the leading techniques for equi-join performance evaluation. OLAP systems borrow this line of research to efficiently implement foreign key joins between dimension tables and big fact tables. From data warehouse schema and workload feature perspective, the hash join algorithm can be further simplified with multidimensional mapping, and the foreign key join algorithms can be evaluated from multiple perspectives instead of single performance perspective. In this paper, we introduce the surrogate key index oriented foreign key join as schema-conscious and OLAP workload customized design foreign key join to comprehensively evaluate how state-of-the-art join algorithms perform in OLAP workloads. Our experiments and analysis gave the following insights: (1) customized foreign key join algorithm for OLAP workload can make join performance step forward than general-purpose hash joins; (2) each join algorithm shows strong and weak performance regions dominated by the cache locality ratio of input_size/cache_size with a fine-grained micro join benchmark; (3) the simple hardware-oblivious shared hash table join outperforms complex hardware-conscious radix partitioning hash join in most benchmark cases; (4) the customized foreign key join algorithm with surrogate key index simplified the algorithm complexity for hardware accelerators and make it easy to be implemented for different hardware accelerators. Overall, we argue that improving join performance is a systematic work opposite to merely hardware-conscious algorithm optimizations, and the OLAP domain knowledge enables surrogate key index to be effective for foreign key joins in data warehousing workloads for both CPU and hardware accelerators.


Coordination, Communication, and Competition in eSports: A Comparative Analysis of Teams in Two Action Games


title: Analysis of EEG signals and its application to neuromarketing

abstract: Marketing and promotions of various consumer products through advertisement campaign is a well known practice to increase the sales and awareness amongst the consumers. This essentially leads to increase in profit to a manufacturing unit. Re-production of products usually depends on the various facts including consumption in the market, reviewer's comments, ratings, etc. However, knowing consumer preference for decision making and behavior prediction for effective utilization of a product using unconscious processes is called "Neuromarketing". This field is emerging fast due to its inherent potential. Therefore, research work in this direction is highly demanded, yet not reached a satisfactory level. In this paper, we propose a predictive modeling framework to understand consumer choice towards E-commerce products in terms of "likes" and "dislikes" by analyzing EEG signals. The EEG signals of volunteers with varying age and gender were recorded while they browsed through various consumer products. The experiments were performed on the dataset comprised of various consumer products. The accuracy of choice prediction was recorded using a user-independent testing approach with the help of Hidden Markov Model (HMM) classifier. We have observed that the prediction results are promising and the framework can be used for better business model.


title: Perspectives on Ontology Learning

abstract: Perspectives on Ontology Learning brings together researchers and practitioners from different communities − natural language processing, machine learning, and the semantic web − in order to give an interdisciplinary overview of recent advances in ontology learning. Starting with a comprehensive introduction to the theoretical foundations of ontology learning methods, the edited volume presents the state-of-the-tart in automated knowledge acquisition and maintenance. It outlines future challenges in this area with a special focus on technologies suitable for pushing the boundaries beyond the creation of simple taxonomical structures, as well as on problems specifically related to knowledge modeling and representation using the Web Ontology Language. Perspectives on Ontology Learning is designed for researchers in the field of semantic technologies and developers of knowledge-based applications. It covers various aspects of ontology learning including ontology quality, user interaction, scalability, knowledge acquisition from heterogeneous sources, as well as the integration with ontology engineering methodologies.


title: AOL's Named Entity Resolver: Solving Disambiguation via Document Strongly Connected Components and Ad-Hoc Edges Construction

abstract: Named Entity Disambiguation is the task of disambiguating named entity mentions in unstructured text and linking them to their corresponding entries in a large knowledge base such as Freebase. Practically, each text match in a given document should be mapped to the correct entity out of the corresponding entities in the knowledge base or none of them if no correct entity is found (Empty Entry). The case of an empty entry makes the problem at hand more complex, but by solving it, one can successfully cope with missing and erroneous data as well as unknown entities. In this work we present AOL's Named Entity Resolver which was designed to handle real life scenarios including empty entries. As part of the automated news analysis platform, it processes over 500K news articles a day, entities from each article are extracted and disambiguated. According to our experiments, AOL's resolver shows much better results in disambiguating entities mapped to Wikipedia or Freebase compared to industry leading products.


title: Digital Game-Based Learning in high school Computer Science education: Impact on educational effectiveness and student motivation

abstract: The aim of this study was to assess the learning effectiveness and motivational appeal of a computer game for learning computer memory concepts, which was designed according to the curricular objectives and the subject matter of the Greek high school Computer Science (CS) curriculum, as compared to a similar application, encompassing identical learning objectives and content but lacking the gaming aspect. The study also investigated potential gender differences in the game's learning effectiveness and motivational appeal. The sample was 88 students, who were randomly assigned to two groups, one of which used the gaming application (Group A, N=47) and the other one the non-gaming one (Group B, N=41). A Computer Memory Knowledge Test (CMKT) was used as the pretest and posttest. Students were also observed during the interventions. Furthermore, after the interventions, students' views on the application they had used were elicited through a feedback questionnaire. Data analyses showed that the gaming approach was both more effective in promoting students' knowledge of computer memory concepts and more motivational than the non-gaming approach. Despite boys' greater involvement with, liking of and experience in computer gaming, and their greater initial computer memory knowledge, the learning gains that boys and girls achieved through the use of the game did not differ significantly, and the game was found to be equally motivational for boys and girls. The results suggest that within high school CS, educational computer games can be exploited as effective and motivational learning environments, regardless of students' gender.


title: Signal Acquisition Using Surface EMG and Circuit Design Considerations for Robotic Prosthesis

abstract: Electromyography (EMG) is the subject which deals with the detection, analysis and utilization of electrical signals emanating from skeletal muscles. The field of electromyography is studied in Biomedical Engineering. And prosthesis using electromyography is achieved under Biomechatronics [1]. The electric signal produced during muscle activation, known as the myoelectric signal, is produced from small electrical currents generated by the exchange of ions across the muscle membranes and detected with the help of electrodes. Electromyography is used to evaluate and record the electrical activity produced by muscles of a human body. The instrument from which we obtain the EMG signal is known as electromyograph and the resultant record obtained is known as electromyogram [2].


title: Direct Optimization of Ranking Measures

abstract: Web page ranking and collaborative filtering require the optimization of sophisticated performance measures. Current Support Vector approaches are unable to optimize them directly and focus on pairwise comparisons instead. We present a new approach which allows direct optimization of the relevant loss functions. This is achieved via structured estimation in Hilbert spaces. It is most related to Max-Margin-Markov networks optimization of multivariate performance measures. Key to our approach is that during training the ranking problem can be viewed as a linear assignment problem, which can be solved by the Hungarian Marriage algorithm. At test time, a sort operation is sufficient, as our algorithm assigns a relevance score to every (document, query) pair. Experiments show that the our algorithm is fast and that it works very well.


title: Wireless based Smart Parking System using Zigbee

abstract: One of main issues of developing big parking space for shopping complexes, office complexes ::: and other types of building that requires large parking space is to notify the visitors of occupied and nonoccupied ::: parking space. Most of the visitors might spending up to 30 to 45 minutes just to find an empty ::: parking space. In most recent technology, some parking lot system offered a system that could ::: automatically count when the car entering the empty car space and blocking an infrared signal thus ::: notify the system to count for it. However, this type of sensors actually has an increase of budgeting in ::: order to install and to be maintained. In this project, we have developed a unique solution by providing ::: cost effective solution by using Zigbee technology in parking lot system technology. Instead of using and ::: maintain cable that need to be installed at the ceiling of the parking lot, we developed a system that use ::: wireless technology of Zigbee and it could notify the visitors of empty and non-empty parking lot.


title: A collaborative model of feedback in human-computer interaction

abstract: Feedback plays an important role in human-computer interaction. It provides the user with evidence of closure, thus satisfying the communication expectations that users have when engaging in a dialogue. In this paper we present a model identifying five feedback states that must be communicated to the user to fulfill the communication expectations of a dialogue. The model is based on a linguistics theory of conversation, but is applied to a graphical user interface. An experiment is described in which we test users expectations and their behavior when those expectations are not met. The model subsumes some of the temporal requirements for feedback previously reported in the human-computer interaction literature.


The effects of an IT-related curriculum based on hybrid-style problem-based learning on career decision-making self-efficacy of women’s University students in Korea


title: Experiences inside the Ubiquitous Oulu Smart City

abstract: The UrBan Interactions (UBI) research program, coordinated by the University of Oulu, has created a middleware layer on top of the panOULU wireless network and opened it up to ubiquitous-computing researchers, offering opportunities to enhance and facilitate communication between citizens and the government.


title: Hybrid dual actuator unit: A design of a variable stiffness actuator based on an adjustable moment arm mechanism

abstract: For tasks requiring robot-environment interaction, stiffness control is important to ensure both stable contact motion and collision safety. The variable stiffness approach has been used to address this problem. We propose a hybrid dual actuator unit (HDAU) which is a novel variable stiffness unit design. The HDAU is composed of a hybrid control module based on an adjustable moment arm mechanism and a drive module with two motors. By controlling the relative motion of gears in the hybrid control module, position and stiffness can be simultaneously controlled for the same joint. The HDAU provides a wide range of joint stiffness due to nonlinearity obtained from the adjustable moment arm. The joint stiffness can be kept constant independent of the passive deflection angle of the output shaft. Furthermore, stable interaction can also be achieved because the joint stiffness is indirectly adjusted by position control of the hybrid control module. The characteristics of the HDAU are analyzed in this study. We show by experiment that the HDAU can provide a wide range of stiffness variation and rapid response for stiffness change.


title: Large-scale detection of non-technical losses in imbalanced data sets

abstract: Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. Detecting NTLs requires costly on-site inspections. Accurate prediction of NTLs for customers using machine learning is therefore crucial. To date, related research largely ignore that the two classes of regular and non-regular customers are highly imbalanced, that NTL proportions may change and mostly consider small data sets, often not allowing to deploy the results in production. In this paper, we present a comprehensive approach to assess three NTL detection models for different NTL proportions in large real world data sets of 100Ks of customers: Boolean rules, fuzzy logic and Support Vector Machine. This work has resulted in appreciable results that are about to be deployed in a leading industry solution. We believe that the considerations and observations made in this contribution are necessary for future smart meter research in order to report their effectiveness on imbalanced and large real world data sets.


title: Optimality of human teachers for robot learners

abstract: In this paper we address the question of how closely everyday human teachers match a theoretically optimal teacher. We present two experiments in which subjects teach a concept to our robot in a supervised fashion. In the first experiment we give subjects no instructions on teaching and observe how they teach naturally as compared to an optimal strategy. We find that people are suboptimal in several dimensions. In the second experiment we try to elicit the optimal teaching strategy. People can teach much faster using the optimal teaching strategy, however certain parts of the strategy are more intuitive than others.


title: Modeling of a stair-climbing wheelchair mechanism with high single-step capability

abstract: In the field of providing mobility for the elderly and disabled, the aspect of dealing with stairs continues largely unresolved. This paper focuses on presenting the development of a stair-climbing wheelchair mechanism with high single-step capability. The mechanism is based on front and rear wheel clusters connected to the base (chair) via powered linkages so as to permit both autonomous stair ascent and descent in the forward direction, and high single-step functionality for such as direct entry to and from a van. Primary considerations were inherent stability, provision of a mechanism that is physically no larger than a standard powered wheelchair, aesthetics, and being based on readily available low-cost components.


title: Data filtering for scalable high-dimensional k-NN search on multicore systems

abstract: K Nearest Neighbors (k-NN) search is a widely used category of algorithms with applications in domains such as computer vision and machine learning. With the rapidly increasing amount of data available, and their high dimensionality, k-NN algorithms scale poorly on multicore systems because they hit a memory wall. In this paper, we propose a novel data filtering strategy, named Subspace Clustering for Filtering (SCF), for k-NN search algorithms on multicore platforms. By excluding unlikely features in k-NN search, this strategy can reduce memory footprint as well as computation. Experimental results on four k-NN algorithms show that SCF can improve their performance on two modern multicore platforms with insignificant loss of search precision.


title: Motivation in video games: a literature review

abstract: Video gaming is a firmly established leisure pursuit, which continues to grow in popularity. This paper is an examination of what motivates people to play computer games, and the relevance of such factors to the positive and negative aspects of computer gaming. When all of an individual’s motivations to play video games are for the pursuit of ‘fun’, it is said that an intrinsic motivation is the most prevalent motivation. However, the primary motivation for playing video games among periodic gamers is different from the primary motivation of regular gamers: periodic gamers are driven by extrinsic motivation, whereas regular gamers are driven by intrinsic motivation. The pursuit of a challenge is the prevalent motivation reported by regular gamers of both genders. The Theory of Flow Experience, and the Attribution Theory have contributed to the understanding of why games may provide a safe medium, in which to learn about the consequences of actions through experience. Computer games may facilitate the development of self-monitoring and coping mechanisms. If the avoidance or escape from other activities is the primary motivation for playing video games, there tends to be an increased risk of engaging in addiction-related behaviours. This paper reports on the findings of previous research (into the motivations for playing computer games), and on industry reports containing data relating to gamer motivations. The aim is to build a picture of what motivates people to play computer games, and how motivation is associated with the main positive and negative aspects of computer gaming.


title: Localized deformation of SiCAl composites

abstract: Abstract Tensile tests of SiC-6061 Al composites containing various volume fractions of whiskers or particles (20, 5 and 0 vol.%) showed that for samples containing a high volume fraction (20 vol.%) the fracture process was very localized, i.e. a very narrow neck. As the volume fraction of whiskers or particles decreased, the deformed region spread out. One might expect that the microstructure should correspond to the macroscale changes. In the highly deformed region the dislocation density is expected to be higher, in the less deformed regions the dislocation density should be lower, and if the deformation is very localized, then the high dislocation density should also be limited to a very narrow region. Overall, there is good agreement between the microstructure (dislocation density) change and the macroscale deformation of SiCAl composite tensile samples. The mechanism proposed to account for this change in deformation behavior as a function of volume fraction of SiC in aluminum is related to the expansion of the plastic zone (due to differences in thermal coefficients of expansion between SiC and aluminum) when the external stress is applied. Also, the localized deformation is related to localized clusters of SiC particles. There is a cooperative effect which leads to a region of very localized plastic deformation.


title: Large-scale distributed non-negative sparse coding and sparse dictionary learning

abstract: We consider the problem of building compact, unsupervised representations of large, high-dimensional, non-negative data using sparse coding and dictionary learning schemes, with an emphasis on executing the algorithm in a Map-Reduce environment. The proposed algorithms may be seen as parallel optimization procedures for constructing sparse non-negative factorizations of large, sparse matrices. Our approach alternates between a parallel sparse coding phase implemented using greedy or convex (l1) regularized risk minimization procedures, and a sequential dictionary learning phase where we solve a set of l0 optimization problems exactly. These two-fold sparsity constraints lead to better statistical performance on text analysis tasks and at the same time make it possible to implement each iteration in a single Map-Reduce job. We detail our implementations and optimizations that lead to the ability to factor matrices with more than 100 million rows and billions of non-zero entries in just a few hours on a small commodity cluster.


title: New model of semantic similarity measuring in wordnet

abstract: This paper presents a new model of measuring semantic similarity in the taxonomy of WordNet. The model takes the path length between two concepts and IC value of each concept as its metric, furthermore, the weight of two metrics can be adapted artificially. In order to evaluate our model, traditional and widely used datasets are used. Firstly, coefficients of correlation between human ratings of similarity and six computational models are calculated, the result shows our new model outperforms their homologues. Then, the distribution graphs of similarity value of 65 word pairs are discussed our model having no faulted zone more centralized than other five methods. So our model can make up the insufficient of other methods which only using one metric(path length or IC value) in their model.


title: Evaluation of entity resolution approaches on real-world match problems

abstract: Despite the huge amount of recent research efforts on entity resolution (matching) there has not yet been a comparative evaluation on the relative effectiveness and efficiency of alternate approaches. We therefore present such an evaluation of existing implementations on challenging real-world match tasks. We consider approaches both with and without using machine learning to find suitable parameterization and combination of similarity functions. In addition to approaches from the research community we also consider a state-of-the-art commercial entity resolution implementation. Our results indicate significant quality and efficiency differences between different approaches. We also find that some challenging resolution tasks such as matching product entities from online shops are not sufficiently solved with conventional approaches based on the similarity of attribute values.


title: Hybridization of intelligent techniques and ARIMA models for time series prediction

abstract: Traditionally, the autoregressive moving average (ARMA) model has been one of the most widely used linear models in time series prediction. Recent research activities in forecasting with artificial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional ARMA structure. These linear models and ANNs are often compared with mixed conclusions in terms of the superiority in forecasting performance. In this paper we propose a hybridization of intelligent techniques such as ANNs, fuzzy systems and evolutionary algorithms, so that the final hybrid ARIMA-ANN model could outperform the prediction accuracy of those models when used separately. More specifically, we propose the use of fuzzy rules to elicit the order of the ARMA or ARIMA model, without the intervention of a human expert, and the use of a hybrid ARIMA-ANN model that combines the advantages of the easy-to-use and relatively easy-to-tune ARIMA models, and the computational power of ANNs.


title: Nanometer‐scale recording on chalcogenide films with an atomic force microscope

abstract: A nanometer‐scale recording technique has been demonstrated on an amorphous GeSb2Te4 film with an atomic force microscope (AFM). Data are recorded by locally changing the electrical property of the film with a conductive AFM probe. The conductance of the film is able to be increased more than one hundred times by applying a pulse voltage between the probe and the film. The recorded data are read by detecting the change of the conductance with the probe. The simultaneous measurement of the topographic and conductance images with the AFM shows that the surface topography of the recorded regions is not changed during the recording process. The smallest recorded region is 10 nm in diameter, which corresponds to a data storage density of 1 Tbit/cm2.


SURVEY : DEALING NON-FUNCTIONAL REQUIREMENTS AT ARCHITECTURE LEVEL


title: The Keywords Spotting with Context for Multi -Oriented Chinese Scene Text

abstract: Scene text spotting is currently a popular research topic in the computer vision community. However, it is a challenging task due to the variations of texts and clutter backgrounds. In some situation, especially in the natural scene, it is not necessary to understand the whole text information in an image if we have acquired the key information. In this paper, we propose a bottom-to-up framework for keywords spotting and context extraction for multi-oriented Chinese in scene images. The proposed framework includes three modules which are Chinese character detection, keywords spotting and context extractor. We adopt the object detection methods to detect and recognize the Chinese text simultaneously in character-level. The geometric relationship between keywords and their context is analyzed, which is robust to multi-oriented text. The proposed framework classifies the valuable images, extracts possible keywords context and achieves a good performance in the CTW dataset.


title: Actor-Critic Policy Optimization in Partially Observable Multiagent Environments

abstract: Optimization of parameterized policies for reinforcement learning (RL) is an important and challenging problem in artificial intelligence. Among the most common approaches are algorithms based on gradient ascent of a score function representing discounted return. In this paper, we examine the role of these policy gradient and actor-critic algorithms in partially-observable multiagent environments. We show several candidate policy update rules and relate them to a foundation of regret minimization and multiagent learning techniques for the one-shot and tabular cases, leading to previously unknown convergence guarantees. We apply our method to model-free multiagent reinforcement learning in adversarial sequential decision problems (zero-sum imperfect information games), using RL-style function approximation. We evaluate on commonly used benchmark Poker domains, showing performance against fixed policies and empirical convergence to approximate Nash equilibria in self-play with rates similar to or better than a baseline model-free algorithm for zero sum games, without any domain-specific state space reductions.


title: “ Found in Translation ” : Predicting Outcomes of Complex Organic Chemistry Reactions using Neural Sequence-to-Sequence Models †

abstract: There is an intuitive analogy of an organic chemist's understanding of a compound and a language speaker's understanding of a word. Consequently, it is possible to introduce the basic concepts and analyze potential impacts of linguistic analysis to the world of organic chemistry. In this work, we cast the reaction prediction task as a translation problem by introducing a template-free sequence-to-sequence model, trained end-to-end and fully data-driven. We propose a novel way of tokenization, which is arbitrarily extensible with reaction information. With this approach, we demonstrate results superior to the state-of-the-art solution by a significant margin on the top-1 accuracy. Specifically, our approach achieves an accuracy of 80.1% without relying on auxiliary knowledge such as reaction templates. Also, 66.4% accuracy is reached on a larger and noisier dataset.


title: Semantics of context-free languages

abstract: “Meaning” may be assigned to a string in a context-free language by defining “attributes” of the symbols in a derivation tree for that string. The attributes can be defined by functions associated with each production in the grammar. This paper examines the implications of this process when some of the attributes are “synthesized”, i.e., defined solely in terms of attributes of thedescendants of the corresponding nonterminal symbol, while other attributes are “inherited”, i.e., defined in terms of attributes of theancestors of the nonterminal symbol. An algorithm is given which detects when such semantic rules could possibly lead to circular definition of some attributes. An example is given of a simple programming language defined with both inherited and synthesized attributes, and the method of definition is compared to other techniques for formal specification of semantics which have appeared in the literature.


title: Automatic Synonym Discovery with Knowledge Bases

abstract: Recognizing entity synonyms from text has become a crucial task in many entity-leveraging applications. However, discovering entity synonyms from domain-specific text corpora (e.g., news articles, scientific papers) is rather challenging. Current systems take an entity name string as input to find out other names that are synonymous, ignoring the fact that often times a name string can refer to multiple entities (e.g., "apple" could refer to both Apple Inc. and the fruit apple). Moreover, most existing methods require training data manually created by domain experts to construct supervised-learning systems. In this paper, we study the problem of automatic synonym discovery with knowledge bases, that is, identifying synonyms for knowledge base entities in a given domain-specific corpus. The manually-curated synonyms for each entity stored in a knowledge base not only form a set of name strings to disambiguate the meaning for each other, but also can serve as "distant" supervision to help determine important features for the task. We propose a novel framework, called DPE, to integrate two kinds of mutually-complementing signals for synonym discovery, i.e., distributional features based on corpus-level statistics and textual patterns based on local contexts. In particular, DPE jointly optimizes the two kinds of signals in conjunction with distant supervision, so that they can mutually enhance each other in the training stage. At the inference stage, both signals will be utilized to discover synonyms for the given entities. Experimental results prove the effectiveness of the proposed framework.


Simple Heuristics That Make Us Smart


title: Wavelet based new hybrid image denoising

abstract: In the modern age, the transmission of digital images is one of the major processes of communication system. Meanwhile, images are often corrupted by the noise. In many image processing applications the reconstruction of high quality image is an essential fact. To achieve this, we proposed a wavelet based hybrid image denoising. In which, after wavelet decomposition of a noisy image both the approximation and the detail coefficients are incorporated into modification. Approximation coefficients are processed by the Cloud Model (CM) and estimated by the fuzzy mean without omitting the contribution of an individual. The detail parts undergo sub band based adaptive modified firm thresholding. Finally, the image is treated with an Adaptive Wiener filter, to further improve its visual quality. Evaluation indicates that our proposed algorithm outperforms over wavelet based Weighted High Frequency Kernel (WHFC) and median methods in terms of Peak Signal to Noise Ratio (PSNR).


title: Landslide susceptibility mapping at 1:1M scale over France

abstract: (1) CNRS University of Strasbourg, School and Observatory of Earth Sciences, Strasbourg, France (jeanphilippe.malet@eost.u-strasbg.fr), (2) CNRS University of Strasbourg, Laboratory Image, Ville et Environnement, Strasbourg, France, (3) Institute for Environment and Sustainability, Joint Research Centre, European Commission, Ispra, Italy, (4) BGR, Federal Institute for Geosciences and Natural Resources, Hannover, Germany, (5) BRGM, Bureau des Recherches Geologiques et Minieres, Orleans, France


title: Revisiting IS business value research: what we already know, what we still need to know, and how we can get there

abstract: The business value of investments in Information Systems (IS) has been, and is predicted to remain, one of the major research topics for IS researchers. While the vast majority of research papers on IS business value find empirical evidence in favour of both the operational and strategic relevance of IS, the fundamental question of the causal relationship between IS investments and business value remains partly unexplained. Three research tasks are essential requisites on the path towards addressing this epistemological question: the synthesis of existing knowledge, the identification of a lack of knowledge and the proposition of paths for closing the knowledge gaps. This paper considers each of these tasks. Research findings include that correlations between IS investments and productivity vary widely among companies and that the mismeasurement of IS investment impact may be rooted in delayed effects. Key limitations of current research are based on the ambiguity and fuzziness of IS business value, the neglected disaggregation of IS investments, and the unexplained process of creating internal and competitive value. Addressing the limitations we suggest research paths, such as the identification of synergy opportunities of IS assets, and the explanation of relationships between IS innovation and change in IS capabilities.


title: Performance Analysis and Algorithm Selection for Reliable Multicast in IEEE 802.11aa Wireless LAN

abstract: Legacy IEEE 802.11 does not efficiently support multicast transmissions. In order to cope with the increasing demand for multicast, mainly required to deliver multimedia traffic, the IEEE 802.11aa Task Group has recently standardized new mechanisms to allow efficient and robust transmission of multicast flows in Wireless LAN. However, the standard allows the use of different mechanisms for this purpose, and leaves open the choice of which one to use for a given scenario. In this paper, we present an analytical model to evaluate the performance of the mechanisms included in the 802.11aa standard and compare their performance. Our analysis shows that there is no absolute winner out of these mechanisms, and performance strongly depends on the scenario. Building on our model, we then propose a novel algorithm that selects the best multicast mechanism to use as a function of the scenario conditions. Our results are validated by extensive simulations.


title: Genomic Medicine: New Frontiers and New Challenges

abstract: BACKGROUND ::: The practice of personalized medicine has made large strides since the introduction of high-throughput technologies and the vast improvements in computational biotechnology. The personalized-medicine approach to cancer management holds promise for earlier disease detection, accurate prediction of prognosis, and better treatment options; however, the early experience with personalized medicine has revealed important concerns that need to be addressed before research findings can be translated to the bedside. ::: ::: ::: CONTENT ::: We discuss several emerging "practical" or "focused" applications of personalized medicine. Molecular testing can have an important positive impact on health and disease management in a number of ways, and the list of specific applications is evolving. This list includes improvements in risk assessment, disease prevention, identification of new disease-related mutations, accurate disease classification based on molecular signatures, selection of patients for enrollment in clinical trials, and development of new targeted therapies, especially for metastatic tumors that are refractory to treatment. Several challenges remain to be addressed before genomics information can be applied successfully in the routine clinical management of cancers. Further improvements and investigations are needed in data interpretation, extraction of actionable items, cost-effectiveness, how to account for patient heterogeneity and ethnic variation, and how to handle the risk of "incidental findings" in genetic testing. ::: ::: ::: SUMMARY ::: It is now clear that personalized medicine will not immediately provide a permanent solution for patient management and that further refinement in the applications of personalized medicine will be needed to address and focus on specific issues.


title: Incremental quantitative verification for Markov decision processes

abstract: Quantitative verification techniques provide an effective means of computing performance and reliability properties for a wide range of systems. However, the computation required can be expensive, particularly if it has to be performed multiple times, for example to determine optimal system parameters. We present efficient incremental techniques for quantitative verification of Markov decision processes, which are able to re-use results from previous verification runs, based on a decomposition of the model into its strongly connected components (SCCs). We also show how this SCC-based approach can be further optimised to improve verification speed and how it can be combined with symbolic data structures to offer better scalability. We illustrate the effectiveness of the approach on a selection of large case studies.


title: Plasma and brain pharmacokinetic profile of cannabidiol (CBD), cannabidivarine (CBDV), Δ9-tetrahydrocannabivarin (THCV) and cannabigerol (CBG) in rats and mice following oral and intraperitoneal administration and CBD action on obsessive–compulsive behaviour

abstract: RATIONALE ::: Phytocannabinoids are useful therapeutics for multiple applications including treatments of constipation, malaria, rheumatism, alleviation of intraocular pressure, emesis, anxiety and some neurological and neurodegenerative disorders. Consistent with these medicinal properties, extracted cannabinoids have recently gained much interest in research, and some are currently in advanced stages of clinical testing. Other constituents of Cannabis sativa, the hemp plant, however, remain relatively unexplored in vivo. These include cannabidiol (CBD), cannabidivarine (CBDV), Δ(9)-tetrahydrocannabivarin (Δ(9)-THCV) and cannabigerol (CBG). ::: ::: ::: OBJECTIVES AND METHODS ::: We here determined pharmacokinetic profiles of the above phytocannabinoids after acute single-dose intraperitoneal and oral administration in mice and rats. The pharmacodynamic-pharmacokinetic relationship of CBD (120 mg/kg, ip and oral) was further assessed using a marble burying test in mice. ::: ::: ::: RESULTS ::: All phytocannabinoids readily penetrated the blood-brain barrier and solutol, despite producing moderate behavioural anomalies, led to higher brain penetration than cremophor after oral, but not intraperitoneal exposure. In mice, cremophor-based intraperitoneal administration always attained higher plasma and brain concentrations, independent of substance given. In rats, oral administration offered higher brain concentrations for CBD (120 mg/kg) and CBDV (60 mg/kg), but not for Δ(9)-THCV (30 mg/kg) and CBG (120 mg/kg), for which the intraperitoneal route was more effective. CBD inhibited obsessive-compulsive behaviour in a time-dependent manner matching its pharmacokinetic profile. ::: ::: ::: CONCLUSIONS ::: These data provide important information on the brain and plasma exposure of new phytocannabinoids and guidance for the most efficacious administration route and time points for determination of drug effects under in vivo conditions.


title: The DEXMART hand: Mechatronic design and experimental evaluation of synergy-based control for human-like grasping

abstract: This paper summarizes recent activities carried out for the development of an innovative anthropomorphic robotic hand called the DEXMART Hand. The main goal of this research is to face the problems that affect current robotic hands by introducing suitable design solutions aimed at achieving simplification and cost reduction while possibly enhancing robustness and performance. While certain aspects of the DEXMART Hand development have been presented in previous papers, this paper is the first to give a comprehensive description of the final hand version and its use to replicate human-like grasping. In this paper, particular emphasis is placed on the kinematics of the fingers and of the thumb, the wrist architecture, the dimensioning of the actuation system, and the final implementation of the position, force and tactile sensors. The paper focuses also on how these solutions have been integrated into the mechanical structure of this innovative robotic hand to enable precise force and displacement control of the whole system. ::: ::: Another important aspect is the lack of suitable control tools that severely limits the development of robotic hand applications. To address this issue, a new method for the observation of human hand behavior during interaction with common day-to-day objects by means of a 3D computer vision system is presented in this work together with a strategy for mapping human hand postures to the robotic hand. A simple control strategy based on postural synergies has been used to reduce the complexity of the grasp planning problem. As a preliminary evaluation of the DEXMART Hand's capabilities, this approach has been adopted in this paper to simplify and speed up the transfer of human actions to the robotic hand, showing its effectiveness in reproducing human-like grasping.


title: Synthetic Graphene Oxide Leaf for Solar Desalination with Zero Liquid Discharge

abstract: Water vapor generation through sunlight harvesting and heat localization by carbon-based porous thin film materials holds great promise for sustainable, energy-efficient desalination and water treatment. However, the applicability of such materials in a high-salinity environment emphasizing zero-liquid-discharge brine disposal has not been studied. This paper reports the characterization and evaporation performance of a nature-inspired synthetic leaf made of graphene oxide (GO) thin film material, which exhibited broadband light absorption and excellent stability in high-salinity water. Under 0.82-sun illumination (825 W/m2), a GO leaf floating on water generated steam at a rate of 1.1 L per m2 per hour (LMH) with a light-to-vapor energy conversion efficiency of 54%, while a GO leaf lifted above water in a tree-like configuration generated steam at a rate of 2.0 LMH with an energy efficiency of 78%. The evaporation rate increased with increasing light intensity and decreased with increasing salinity. Duri...


title: Toward A Service Platform for Developing Smart Contracts on Blockchain in BDD and TDD Styles

abstract: In recent years, Blockchain technology has been highly valued, and the related applications have begun to be developed in large numbers. A smart contract is a software component encompass business logics and transactions that run on a blockchain. Thus, verifying whether the contract logics fully reflect the business requirements are one of the most important software engineering issues in blockchain application development. Currently, developing smart contracts is still a challenging task even for experienced programmers due to the lacking of an integrated tool for developing and testing. In response to this challenge, this paper presents a service platform that supports BDD-style (Behavior-Driven Development) smart contract development, testing, and deployment for the Ethereum-based blockchains. This platform focuses on providing and resolving the cross-cutting concerns across the life-cycle of smart contract development. The feasibility of this platform is shown by demonstrating how an application scenario, namely, loyalty points exchange, can be implemented using the proposed platform. Our experiences indicate that the burdens of developers when developing smart contracts can be effectively reduced and thus increases the quality of contracts.


title: The Costs of Self-Driving Cars: Reconciling Freedom and Privacy with Tort Liability in Autonomous Vehicle Regulation

abstract: TABLE OF CONTENTS Introduction I. Freedom and Privacy A. Freedom: Discretionary Vehicles B. Privacy: Communicative Vehicles C. The Intersection of Freedom and Privacy II. Liability Shifts A. Discretionary-Uncommunicative Vehicles B. Discretionary-Communicative Vehicles C. Nondiscretionary-Uncommunicative Vehicle D. Nondiscretionary-Communicative Vehicles E. Mixed Systems III. Reconciliation INTRODUCTION Imagine the night of your wedding anniversary ten years from now: your car, driving itself, heads over to the babysitter's house while you and your spouse get dressed, returns in time to chauffeur you downtown, and then parks while you head into the restaurant for dinner. Sound fanciful? Maybe so, but the Center for Automotive Research predicts that the first commercially available, fully autonomous vehicles (1) could come to a dealership near you as early as 2019. (2) California, Florida, Michigan, and Nevada have already passed laws that allow the testing of autonomous vehicles (AVs) on public roads, with more states currently considering similar legislation. (3) The stakes could hardly be higher: over ten million car crashes occur in this country each year, (4) many of which result in tragic injuries and expensive lawsuits that burden the nation's hospitals and court systems. Predictions that self-driving cars may be able to prevent many of these accidents have led some commentators to wholeheartedly endorse the impending arrival of AVs onto our nation's roadways. (5) Not everyone views the arrival of AV technology quite so positively. Serious concerns about self-driving cars have also been raised, largely focusing on the impact these vehicles could have on three key issues: freedom, privacy, and liability. (6) Though the automobile has stood as a symbol of freedom and personal autonomy for generations, (7) some fear that legal and economic pressures might eventually restrict the frequency and scope of human driving. (8) While both Nevada and California currently require that self-driving cars cede operational authority to human users whenever a human user requests control, (9) it is hard to predict whether this rule will be preserved if AV technology becomes more commonplace. Regulators have been even more concerned with the threat self-driving cars pose to their users' privacy. For example, California demands that the "manufacturer of the autonomous technology installed on a vehicle shall provide a written disclosure to the purchaser of an autonomous vehicle that describes what information is collected by the autonomous technology equipped on the vehicle." (10) Other measures might have the unintended consequence of diminishing user privacy: California also requires that all AVs preserve detailed records of the thirty seconds leading up to an accident. (11) Though intended to help determine fault in the event of an accident, this regulation could open the door to more continuous and invasive monitoring of AV behavior in the future. Some have even suggested that AV technology's potential to infringe on privacy is so grave that self-driving cars should be prohibited altogether. (12) California's decision to use the sensors installed on AVs to determine fault raises another concern: who should be held liable for crashes involving AVs? Though manufacturers, insurers, news outlets, and academics have all posed this question, they have not found easy answers. While some academics assert that the manufacturers of AVs should be held liable for their crashes under a products liability model, (13) others claim that products liability would strangle the introduction of self-driving cars and advocate for "strict liability to autonomous car owners." (14) And though California originally stipulated that "the conversion of vehicles originally manufactured by a third party shall control issues of liability arising from the operation of the autonomous vehicle," the legislature struck this provision on reconsideration. …


title: Sorption of pesticide endosulfan by electrodialysis membranes

abstract: Abstract Endosulfan (ES) is a micropollutant found in reverse osmosis concentrates from water reuse applications. Electrodialysis (ED) can remove and recover charged solutes from such concentrates. While polar compounds cannot normally be removed, their fate in ED is important as they can contribute to membrane fouling/poisoning and be released during cleaning. High adsorption of ES to ED membranes was observed. Consequently, the influence of solution pH and presence of humic acid (HA) on sorption mechanisms of ES to ion-exchange membranes during batch sorption isotherm and ED experiments were investigated systematically. ES-membrane partition coefficients (log K AEM/CEM ) quantified through sorption isotherm experiments suggested that ES sorption was resultant of membrane catalysed ES degradation, hydrogen bonding and cation–π interactions between ES and membrane functional groups. ES sorption at pH 7 (550 μg/cm 3 ) was greater than sorption at pH 11 (306 μg/cm 3 ) due to alkaline hydrolysed ES and resultant decrease in bonding capacity with the membranes at high pH. The presence of HA reduced sorption at pH 7 (471 μg/cm 3 ) and 11 (307 μg/cm 3 ) due to HA competitive sorption. Partial membrane desorption was noted in isotherm (≤20%) desorption experiments and was dependent on the initial mass sorbed, solvent pH and resultant membrane interactions.


title: Comparison of CORE network emulation platforms

abstract: CORE (Common Open Research Emulator) is a network emulation tool that uses virtualization provided by FreeBSD jails, Linux OpenVZ containers, and Linux namespaces containers. Here the modular architecture is described that makes supporting these three platforms possible, and the relative performance of each is compared. Initial work in integrating this tool with higher-fidelity link- and physical- layer emulation is also described.


title: Neurocognitive mechanisms of cognitive control: The role of prefrontal cortex in action selection, response inhibition, performance monitoring, and reward-based learning

abstract: Convergent evidence highlights the differential contributions of various regions of the prefrontal cortex in the service of cognitive control, but little is understood about how the brain determines and communicates the need to recruit cognitive control, and how such signals instigate the implementation of appropriate performance adjustments. Here we review recent progress from cognitive neuroscience in examining some of the main constituent processes of cognitive control as involved in dynamic decision making: goal-directed action selection, response activation and inhibition, performance monitoring, and reward-based learning. Medial frontal cortex is found to be involved in performance monitoring: evaluating outcome vis-a-vis expectancy, and detecting performance errors or conflicting response tendencies. Lateral and orbitofrontal divisions of prefrontal cortex are involved in subsequently implementing appropriate adjustments.


title: Performance of database workloads on shared-memory systems with out-of-order processors

abstract: Database applications such as online transaction processing (OLTP) and decision support systems (DSS) constitute the largest and fastest-growing segment of the market for multiprocessor servers. However, most current system designs have been optimized to perform well on scientific and engineering workloads. Given the radically different behavior of database workloads (especially OLTP), it is important to re-evaluate key system design decisions in the context of this important class of applications.This paper examines the behavior of database workloads on shared-memory multiprocessors with aggressive out-of-order processors, and considers simple optimizations that can provide further performance improvements. Our study is based on detailed simulations of the Oracle commercial database engine. The results show that the combination of out-of-order execution and multiple instruction issue is indeed effective in improving performance of database workloads, providing gains of 1.5 and 2.6 times over an in-order single-issue processor for OLTP and DSS, respectively. In addition, speculative techniques enable optimized implementations of memory consistency models that significantly improve the performance of stricter consistency models, bringing the performance to within 10--15% of the performance of more relaxed models.The second part of our study focuses on the more challenging OLTP workload. We show that an instruction stream buffer is effective in reducing the remaining instruction stalls in OLTP, providing a 17% reduction in execution time (approaching a perfect instruction cache to within 15%). Furthermore, our characterization shows that a large fraction of the data communication misses in OLTP exhibit migratory behavior; our preliminary results show that software prefetch and writeback/flush hints can be used for this data to further reduce execution time by 12%.


title: LightNet: A Versatile, Standalone Matlab-based Environment for Deep Learning

abstract: LightNet is a lightweight, versatile and purely Matlab-based deep learning framework. The aim of the design is to provide an easy-to-understand, easy-to-use and efficient computational platform for deep learning research. The implemented framework supports major deep learning architectures such as Multilayer Perceptron Networks (MLP), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The framework also supports both CPU and GPU for computation and the switch between them is straightforward. Different applications in computer vision, natural language processing and robotics are demonstrated as experiments.


title: Coordinated Interactions between Hippocampal Ripples and Cortical Spindles during Slow-Wave Sleep

abstract: Abstract Sleep is characterized by a structured combination of neuronal oscillations. In the hippocampus, slow-wave sleep (SWS) is marked by high-frequency network oscillations (∼200 Hz "ripples"), whereas neocortical SWS activity is organized into low-frequency delta (1–4 Hz) and spindle (7–14 Hz) oscillations. While these types of hippocampal and cortical oscillations have been studied extensively in isolation, the relationships between them remain unknown. Here, we demonstrate the existence of temporal correlations between hippocampal ripples and cortical spindles that are also reflected in the correlated activity of single neurons within these brain structures. Spindle–ripple episodes may thus constitute an important mechanism of cortico-hippocampal communication during sleep. This coactivation of hippocampal and neocortical pathways may be important for the process of memory consolidation, during which memories are gradually translated from short-term hippocampal to longer-term neocortical stores.


title: Review of Solid-State Transformer Technologies and Their Application in Power Distribution Systems

abstract: The solid-state transformer (SST), which has been regarded as one of the 10 most emerging technologies by Massachusetts Institute of Technology (MIT) Technology Review in 2010, has gained increasing importance in the future power distribution system. This paper presents a systematical technology review essential for the development and application of SST in the distribution system. The state-of-the-art technologies of four critical areas are reviewed, including high-voltage power devices, high-power and high-frequency transformers, ac/ac converter topologies, and applications of SST in the distribution system. In addition, future research directions are presented. It is concluded that the SST is an emerging technology for the future distribution system.


title: Combination therapy with finasteride and low-dose dutasteride in the treatment of androgenetic alopecia

abstract: We report on a 47-year-old man who was initially treated with finasteride for androgenetic alopecia. Despite continuous treatment, after year 4 his hair density was not as good as at year 2, and low-dose dutasteride at 0.5 mg/week was added to the finasteride therapy. This resulted in a dramatic increase in his hair density, demonstrating that combined therapy with finasteride and dutasteride can improve hair density in patients already taking finasteride.


title: Cellular and Molecular Mechanisms Underlying Perturbed Energy Metabolism and Neuronal Degeneration in Alzheimer's and Parkinson's Diseases

abstract: Synaptic degeneration and death of nerve cells are defining features of Alzheimer's disease (AD) and Parkinson's disease (PD), the two most prevalent age-related neurodegenerative disorders. In AD, neurons in the hippocampus and basal forebrain (brain regions that subserve learning and memory functions) are selectively vulnerable. In PD dopamine-producing neurons in the substantia nigra-striatum (brain regions that control body movements) selectively degenerate. Studies of postmortem brain tissue from AD and PD patients have provided evidence for increased levels of oxidative stress, mitochondrial dysfunction and impaired glucose uptake in vulnerable neuronal populations. Studies of animal and cell culture models of AD and PD suggest that increased levels of oxidative stress (membrane lipid peroxidation, in particular) may disrupt neuronal energy metabolism and ion homeostasis, by impairing the function of membrane ion-motive ATPases and glucose and glutamate transporters. Such oxidative and metabolic compromise may there-by render neurons vulnerable to excitotoxicity and apoptosis. Studies of the pathogenic mechanisms of AD-linked mutations in amyloid precursor protein (APP) and presenilins strongly support central roles for perturbed cellular calcium homeostasis and aberrant proteolytic processing of APP as pivotal events that lead to metabolic compromise in neurons. Specific molecular "players" in the neurodegenerative processes in AD and PD are being identified and include Par-4 and caspases (bad guys) and neurotrophic factors and stress proteins (good guys). Interestingly, while studies continue to elucidate cellular and molecular events occurring in the brain in AD and PD, recent data suggest that both AD and PD can manifest systemic alterations in energy metabolism (e.g., increased insulin resistance and dysregulation of glucose metabolism). Emerging evidence that dietary restriction can forestall the development of AD and PD is consistent with a major "metabolic" component to these disorders, and provides optimism that these devastating brain disorders of aging may be largely preventable.


title: RF-PUF: IoT security enhancement through authentication of wireless nodes using in-situ machine learning

abstract: Physical unclonable functions (PUF) in silicon exploit die-to-die manufacturing variations during fabrication for uniquely identifying each die. Since it is practically a hard problem to recreate exact silicon features across dies, a PUF-based authentication system is robust, secure and cost-effective, as long as bias removal and error correction are taken into account. In this work, we utilize the effects of inherent process variation on analog and radio-frequency (RF) properties of multiple wireless transmitters (Tx) in a sensor network, and detect the features at the receiver (Rx) using a deep neural network based framework. The proposed mechanism/ framework, called RF-PUF, harnesses already-existing RF communication hardware and does not require any additional PUF-generation circuitry in the Tx for practical implementation. Simulation results indicate that the RF-PUF framework can distinguish up to 10000 transmitters (with standard foundry defined variations for a 65 nm process, leading to non-idealities such as LO offset and I-Q imbalance) under varying channel conditions, with a probability of false detection < 10−3.


title: Hierarchical Multi-label Conditional Random Fields for Aspect-Oriented Opinion Mining

abstract: A common feature of many online review sites is the use of an overall rating that summarizes the opinions expressed in a review. Unfortunately, these document-level ratings do not provide any information about the opinions contained in the review that concern a specific aspect e.g., cleanliness of the product being reviewed e.g., a hotel. In this paper we study the finer-grained problem of aspect-oriented opinion mining at the sentence level, which consists of predicting, for all sentences in the review, whether the sentence expresses a positive, neutral, or negative opinion or no opinion at all about a specific aspect of the product. For this task we propose a set of increasingly powerful models based on conditional random fields CRFs, including a hierarchical multi-label CRFs scheme that jointly models the overall opinion expressed in the review and the set of aspect-specific opinions expressed in each of its sentences. We evaluate the proposed models against a dataset of hotel reviews which we here make publicly available in which the set of aspects and the opinions expressed concerning them are manually annotated at the sentence level. We find that both hierarchical and multi-label factors lead to improved predictions of aspect-oriented opinions.


title: Review on Contactless Power Transfer for Electric Vehicle Charging

abstract: For the past few years the feasibility of contactless power transfer (CPT) is being explored extensively as a future solution for charging electric vehicles (EVs). Studies report that the main obstacles in CPT are low power efficiency, misalignment tolerance, cost, range and charging time anxiety. This paper presents a review based on existing literature of the CPT systems for EV charging. Different cases of CPT technologies, their principle of operation and equivalent circuit based analysis is carried out. A discussion on compensation strategies and their effectiveness are reviewed and discussed. The design of coil systems for some city electric cars has been referenced in general. At the end recommendations and conclusions are made based on the study and analysis of the information available in literature.


title: A multi-one-class dynamic classifier for adaptive digitization of document streams

abstract: In this paper, we present a new dynamic classifier design based on a set of one-class independent SVM for image data stream categorization. Dynamic or continuous learning and classification has been recently investigated to deal with different situations, like online learning of fixed concepts, learning in non-stationary environments (concept drift) or learning from imbalanced data. Most of solutions are not able to deal at the same time with many of these specificities. Particularly, adding new concepts, merging or splitting concepts are most of the time considered as less important and are consequently less studied, whereas they present a high interest for stream-based document image classification. To deal with that kind of data, we explore a learning and classification scheme based on one-class SVM classifiers that we call mOC-iSVM (multi-one-class incremental SVM). Even if one-class classifiers are suffering from a lack of discriminative power, they have, as a counterpart, a lot of interesting properties coming from their independent modeling. The experiments presented in the paper show the theoretical feasibility on different benchmarks considering addition of new classes. Experiments also demonstrate that the mOC-iSVM model can be efficiently used for tasks dedicated to documents classification (by image quality and image content) in a context of streams, handling many typical scenarii for concepts extension, drift, split and merge.


title: Multimodal sparse and low-rank subspace clustering

abstract: Abstract In this paper, we propose multimodal extensions of the recently introduced sparse subspace clustering (SSC) and low-rank representation (LRR) based subspace clustering algorithms for clustering data lying in a union of subspaces. Given multimodal data, our method simultaneously clusters data in the individual modalities according to their subspaces. In our formulation, we exploit the self expressiveness property of each sample in its respective modality and enforce the common representation across the modalities. We modify our model so that it is robust to noise. Furthermore, we kernelize the proposed algorithms to handle nonlinearities in data. The optimization problems are solved efficiently using the alternative direction method of multiplier (ADMM). Experiments on face clustering indicate the proposed method performs favorably compared to state-of-the-art subspace clustering methods.


title: An Edge-Focused Model for Distributed Streaming Data Applications

abstract: This paper presents techniques for the description and management of distributed streaming data applications. The proposed solution provides an abstract description language and operational graph model for supporting collections of autonomous components functioning as distributed systems. Using our approach, applications supporting millions of devices can be easily modeled using abstract configurations. Through our models applications can implemented and managed through socalled server-less application orchestration.


title: Graph Convolutional Matrix Completion

abstract: We consider matrix completion for recommender systems from the point of view of link prediction on graphs. Interaction data such as movie ratings can be represented by a bipartite user-item graph with labeled edges denoting observed ratings. Building on recent progress in deep learning on graph-structured data, we propose a graph auto-encoder framework based on differentiable message passing on the bipartite interaction graph. Our model shows competitive performance on standard collaborative filtering benchmarks. In settings where complimentary feature information or structured data such as a social network is available, our framework outperforms recent state-of-the-art methods.


title: Distinguishing "benign" from "malignant early repolarization": the value of the ST-segment morphology.

abstract: BACKGROUND ::: Means for distinguishing the very common "benign early repolarization" from the very rare but malignant form are needed. Recently, the presence of early repolarization with "horizontal ST segment" was found to predict arrhythmic death during long-term follow-up in a large population study. We therefore speculated that the combination of "J waves with horizontal ST segment" would correlate with a history of idiopathic ventricular fibrillation (VF) better than the mere presence of J waves. ::: ::: ::: OBJECTIVES ::: To determine whether the morphology of the ST segment adds diagnostic value to the mere presence of J waves in a case-control series of idiopathic VF. ::: ::: ::: METHODS ::: We reanalyzed our case-control study showing that the presence of J waves strongly correlates with a history of idiopathic VF among 45 patients with this disorder, 124 controls matched for age and gender ("matched-control" group), and 121 young athletes. This time we focused only on those patients with J waves and graded their ST-segment morphology as either "horizontal" or "ascending" according to predefined criteria. ::: ::: ::: RESULTS ::: The presence of J waves was associated with a history of idiopathic VF with an odds ratio of 4.0 (95% confidence intervals = 2.0-7.9), but having both J waves and horizontal ST segment yielded an odds ratio of 13.8 (95% confidence intervals = 5.1-37.2) for having idiopathic VF. ::: ::: ::: CONCLUSIONS ::: We report, for the first time, that the combination of J waves with horizontal/descending ST segment improved our ability to distinguish patients with idiopathic VF from controls matched by gender and age.


title: Large-area microlens arrays fabricated on flexible polycarbonate sheets via single-step laser interference ablation

abstract: A single-step method for the fabrication of large-area microlens arrays on flexible polycarbonate sheets is described. On areas of approximately 1 cm2, 17 million to 120 million microlenses ranging in size from sub-micrometer to several micrometers are fabricated via deep-UV pulsed laser interference ablation. The uniformity and surface quality of fabricated microlens arrays are examined and confirmed through atomic force microscopy and scanning electron microscopy. Optical imaging performance of the microlenses, and their use in massively parallel, pulsed laser nanofabrication on silicon is demonstrated. The microlens arrays can be fabricated in a matter of seconds, suggesting the potential for fast and low-cost production on flexible plastic substrates.


title: Relationship between humor and proposed punishment for crimes: Beware of humorous people

abstract: Abstract It was hypothesized that appreciation of the structural basis and of sexual content in humor is related to punitiveness in different ways. Sixty-eight male subjects served as lay judges and stipulated what would be the just minimum and maximum punishment of committing one of 25 criminal acts and filled in scales relating to humor appreciation, conservatism, dogmatism, and intolerance of ambiguity. Funniness of incongruity-resolution correlated positively with minimum and maximum punishment. Aversiveness of nonsense humor correlated positively with minimum punishment only. Appreciation of sexual content in humor correlates negatively with the extent of demanded punishment for rape but not for other sex-related crimes. Personality scales were predictive of the extent of punishment but to a lesser extent than humor appreciation.


title: Design and Implementation of an Efficient Image Retrieval System using Spatial Feature Reduction

abstract: Image segmentation is a very emerging and important area due to a large number of real life applications. Bag-of-features (BoFs) model is the one of the most successful algorithm used for the image classification. BoF methods are based on order-less collections of quantized local image descriptors; they discard spatial information and are therefore conceptually and computationally simpler than many alternative methods.Bag-of-features algorithm is used for the feature extraction from the image. BoFs model has several advantages like,scalability,simplicity, and generality. But BoFs have some disadvantages too and some of them are Time and Accuracy of the image classification process. In this paper Bag-of-features model is extended by using spatial pooling to improve the time and accuracy of the image classification model. In proposed method first the system is trained by creating the database of the image feature for the evaluation process and then the evolution of the features is done for the input image by using clustering algorithm. In the proposed method KKN algorithm is used to find out the similar features from the database. Spatial pooling is then used to improve the performance of the existing system.


3D Online Path Planning in a Continuous Gaussian Process Occupancy Map


title: Object Detection Using the Statistics of Parts

abstract: In this paper we describe a trainable object detector and its instantiations for detecting faces and cars at any size, location, and pose. To cope with variation in object orientation, the detector uses multiple classifiers, each spanning a different range of orientation. Each of these classifiers determines whether the object is present at a specified size within a fixed-size image window. To find the object at any location and size, these classifiers scan the image exhaustively. ::: ::: Each classifier is based on the statistics of localized parts. Each part is a transform from a subset of wavelet coefficients to a discrete set of values. Such parts are designed to capture various combinations of locality in space, frequency, and orientation. In building each classifier, we gathered the class-conditional statistics of these part values from representative samples of object and non-object images. We trained each classifier to minimize classification error on the training set by using Adaboost with Confidence-Weighted Predictions (Shapire and Singer, 1999). In detection, each classifier computes the part values within the image window and looks up their associated class-conditional probabilities. The classifier then makes a decision by applying a likelihood ratio test. For efficiency, the classifier evaluates this likelihood ratio in stages. At each stage, the classifier compares the partial likelihood ratio to a threshold and makes a decision about whether to cease evaluation—labeling the input as non-object—or to continue further evaluation. The detector orders these stages of evaluation from a low-resolution to a high-resolution search of the image. Our trainable object detector achieves reliable and efficient detection of human faces and passenger cars with out-of-plane rotation.


title: Highlights of Thirty-Year Experience of CO2 Laser Use at the Florence (Italy) Department of Dermatology

abstract: The CO(2) laser has been used extensively in dermatological surgery over the past 30 years and is now recognised as the gold standard for soft tissue vaporization. Considering that the continuous wave CO(2) laser delivery system and the newer "superpulsed" and scanned CO(2) systems have progressively changed our practice and patient satisfaction, a long range documentation can be useful. Our experience has demonstrated that the use of CO(2) laser involves a reduced healing time, an infrequent need for anaesthesia, reduced thermal damage, less bleeding, less inflammation, the possibility of intra-operative histologic and/or cytologic examination, and easy access to anatomically difficult areas. Immediate side effects have been pain, erythema, edema, typically see with older methods, using higher power. The percentage of after-treatment keloids and hypertrophic scars observed was very low (~1%) especially upon the usage of lower parameters. The recurrence of viral lesions (condylomas and warts) have been not more frequent than those due to other techniques. Tumor recurrence is minor compared with radiotherapy or surgery. This method is a valid alternative to surgery and/or diathermocoagulation for microsurgery of soft tissues. Our results are at times not consistent with those published in the literature, stressing the concept that multicentric studies that harmonization methodology and the patient selection are vital.