title: A wide band low profile dual-pol “Thumbtack” array

abstract: A dual-pol array of 15×15 “Thumbtack” elements was built and tested for 0.5–2.0 GHz demonstration. The array is 3 in. (7.6 cm) thick, a bout 1/8 wavelength at the low end of the band, characterized by a high impedance (~300 ohms) aperture, matching to free space's wave impedance (377 ohms) directly without a long flare as in a Vivaldi notch. The array has a square lattice of 3 in. (7.6 cm) spacing, and the “Thumbtack” element has a coincident and stationary phase center. The thin and planar structure produces ver y low X-pol, typically 30 dB down at bore-sight, and 15 dB at 45° of axis along the diagonal cut. The array was designed fo r applications that require na nosecond rise time, ultra low X-pol and non-dispersive phase response. These unique features make it possible to produce sharp images for IED detection, FOP EN SAR, see-through-the wa ll radars, a nd other EW missions.


title: OpenAirInterface Traffic Generator (OTG): A Realistic Traffic Generation Tool for Emerging Application Scenarios

abstract: Traffic generation represents one of the main challenge in modeling and simulating the application and network load. In this work, we present a tool, called OpenAirInterface Traffic Generator (OTG), for the generation of realistic application traffic that can be used for testing and evaluating the performance of emerging networking architectures. In addition to the traffic of conventional applications, OTG is capable of accurately emulating the traffic of new application scenarios such as online gaming and machine-type communication. To highlight the capability and new features of the tool, the one-way delay of OpenArena online gaming application in the presence of the background traffic is analyzed over the LTE network using OpenAirInterface emulation platform.


title: Fast Kernels for String and Tree Matching

abstract: In this paper we present a new algorithm suitable for matching discrete objects such as strings and trees in linear time, thus obviating dynamic programming with quadratic time complexity. Furthermore, prediction cost in many cases can be reduced to linear cost in the length of the sequence to be classified, regardless of the number of support vectors. This improvement on the currently available algorithms makes string kernels a viable alternative for the practitioner.


Social Signal Detection in Spontaneous Dialogue Using Bidirectional LSTM-CTC.


title: A New Scheme for Scoring Phrases in Unsupervised Keyphrase Extraction

abstract: Many unsupervised methods for keyphrase extraction typically compute a score for each word in a document based on various measures such as tf-idf or the PageRank score computed from the word graph built from the text document. The final score of a candidate phrase is then calculated by summing up the scores of its constituent words. A potential problem with the sum up scoring scheme is that the length of a phrase highly impacts its score. To reduce this impact and extract keyphrases of varied lengths, we propose a new scheme for scoring phrases which calculates the final score using the average of the scores of individual words weighted by the frequency of the phrase in the document. We show experimentally that the unsupervised approaches that use this new scheme outperform their counterparts that use the sum up scheme to score phrases.


title: Defining Open Source Software Project Success

abstract: Information systems success is one of the most widely used dependent variables in information systems research. In this paper, we identify a range of measures that can be used to assess the success of open source software (OSS) projects. We identify measures based on a review of the literature, a consideration of the OSS development process and an analysis of the opinions of OSS developers. For each measure, we provide examples of how they might be used in a study of OSS development.


title: PPMF: A Patient-based Predictive Modeling Framework for Early ICU Mortality Prediction

abstract: To date, developing a good model for early intensive care unit (ICU) mortality prediction is still challenging. This paper presents a patient based predictive modeling framework (PPMF) to improve the performance of ICU mortality prediction using data collected during the first 48 hours of ICU admission. PPMF consists of three main components verifying three related research hypotheses. The first component captures dynamic changes of patients status in the ICU using their time series data (e.g., vital signs and laboratory tests). The second component is a local approximation algorithm that classifies patients based on their similarities. The third component is a Gradient Decent wrapper that updates feature weights according to the classification feedback. Experiments using data from MIMICIII show that PPMF significantly outperforms: (1) the severity score systems, namely SASP III, APACHE IV, and MPM0III, (2) the aggregation based classifiers that utilize summarized time series, and (3) baseline feature selection methods.


title: The Power of Complementary Regularizers: Image Recovery via Transform Learning and Low-Rank Modeling

abstract: Recent works on adaptive sparse and on low-rank signal modeling have demonstrated their usefulness in various image / video processing applications. Patch-based methods exploit local patch sparsity, whereas other works apply low-rankness of grouped patches to exploit image non-local structures. However, using either approach alone usually limits performance in image reconstruction or recovery applications. In this work, we propose a simultaneous sparsity and low-rank model, dubbed STROLLR, to better represent natural images. In order to fully utilize both the local and non-local image properties, we develop an image restoration framework using a transform learning scheme with joint low-rank regularization. The approach owes some of its computational efficiency and good performance to the use of transform learning for adaptive sparse representation rather than the popular synthesis dictionary learning algorithms, which involve approximation of NP-hard sparse coding and expensive learning steps. We demonstrate the proposed framework in various applications to image denoising, inpainting, and compressed sensing based magnetic resonance imaging. Results show promising performance compared to state-of-the-art competing methods.


Toward Survivable Intrusion-Tolerant Open-Source SCADA


Modeling and toolpath generation for consumer-level 3d printing


title: Environment perception using grid occupancy estimation with belief functions

abstract: Grid map offers a useful representation of the perceived world for mobile robotics navigation. It will play a major role for the safety (obstacle avoidance) of next generations of terrestrial vehicles, as well as for future autonomous navigation systems. In a grid map, the occupancy state of each cell represents a small piece of information of the surrounding area of the vehicle. The state of each cell must be estimated from sensors measurements and classified in order to get a complete and precise perception of the dynamic environment where the vehicle moves. So far, the estimation and the grid map updating have been done using fusion techniques based on the probabilistic framework, or on the classical belief function framework thanks to an inverse model of the sensors and Dempster-Shafer rule of combination. Recently we have shown that PCR6 rule (Proportional Conflict Redistribution rule #6) proposed in DSmT (Dezert-Smarandache Theory) did improve substantially the quality of grid map with respect to other techniques, especially when the quality of available information is low, and when the sources of information appear as conflicting. In this paper, we go further and we analyze the performance of the improved version of PCR6 with Zhang's degree of intersection. We will show through different realistic scenarios (based on a LIDAR sensor) the benefit of using this new rule of combination in a practical application.


title: MPR: Multi-Objective Pairwise Ranking

abstract: The recommendation challenge can be posed as the problem of predicting either item ratings or item rankings. The latter approach has proven more effective. Pairwise learning-to-rank techniques have been relatively successful. Hence, they are popularly used for learning recommender model parameters such as those in collaborative filtering (CF) models. The model parameters are learned by optimizing close smooth approximations of the non-smooth information retrieval (IR) metrics such as Mean Area Under ROC curve (AUC). Targeted campaigns are an alternative to item recommendations for increasing conversion. The user ranking task is referred to as audience retrieval. It is used in targeted campaigns to rank push campaign recipients based on their potential to convert. In this work, we consider the task of efficiently learning a ranking model that provides item recommendations and user rankings simultaneously. We adopt pairwise learning for this task. We refer to our approach as multi-objective pairwise ranking (MPR). We describe our approach and use experiments to evaluate its performance.


title: CNN transfer learning for the automated diagnosis of celiac disease

abstract: In this work, four well known convolutional neural networks (CNNs) that were pretrained on the ImageNet database are applied for the computer assisted diagnosis of celiac disease based on endoscopic images of the duodenum. The images are classified using three different transfer learning strategies and a experimental setup specifically adapted for the classification of endoscopic imagery. The CNNs are either used as fixed feature extractors without any fine-tuning to our endoscopic celiac disease image database or they are fine-tuned by training either all layers of the CNN or by fine-tuning only the fully connected layers. Classification is performed by the CNN SoftMax classifier as well as linear support vector machines. The CNN results are compared with the results of four state-of-the-art image representations. We will show that fine-tuning all the layers of the nets achieves the best results and outperforms the comparison approaches.


title: Formative assessment: a critical review

abstract: This paper covers six interrelated issues in formative assessment (aka, ‘assessment for learning’). The issues concern the definition of formative assessment, the claims commonly made for its effectiveness, the limited attention given to domain considerations in its conceptualisation, the under‐representation of measurement principles in that conceptualisation, the teacher‐support demands formative assessment entails, and the impact of the larger educational system. The paper concludes that the term, ‘formative assessment’, does not yet represent a well‐defined set of artefacts or practices. Although research suggests that the general practices associated with formative assessment can facilitate learning, existing definitions admit such a wide variety of implementations that effects should be expected to vary widely from one implementation and student population to the next. In addition, the magnitude of commonly made quantitative claims for effectiveness is suspect, deriving from untraceable, flawed, dated, or unpublished sources. To realise maximum benefit from formative assessment, new development should focus on conceptualising well‐specified approaches built around process and methodology rooted within specific content domains. Those conceptualisations should incorporate fundamental measurement principles that encourage teachers and students to recognise the inferential nature of assessment. The conceptualisations should also allow for the substantial time and professional support needed if the vast majority of teachers are to become proficient users of formative assessment. Finally, for greatest benefit, formative approaches should be conceptualised as part of a comprehensive system in which all components work together to facilitate learning.


title: The MemSQL Query Optimizer: A modern optimizer for real-time analytics in a distributed database

abstract: Real-time analytics on massive datasets has become a very common need in many enterprises. These applications require not only rapid data ingest, but also quick answers to analytical queries operating on the latest data. MemSQL is a distributed SQL database designed to exploit memory-optimized, scale-out architecture to enable real-time transactional and analytical workloads which are fast, highly concurrent, and extremely scalable. Many analytical queries in MemSQL's customer workloads are complex queries involving joins, aggregations, sub-queries, etc. over star and snowflake schemas, often ad-hoc or produced interactively by business intelligence tools. These queries often require latencies of seconds or less, and therefore require the optimizer to not only produce a high quality distributed execution plan, but also produce it fast enough so that optimization time does not become a bottleneck. ::: ::: In this paper, we describe the architecture of the MemSQL Query Optimizer and the design choices and innovations which enable it quickly produce highly efficient execution plans for complex distributed queries. We discuss how query rewrite decisions oblivious of distribution cost can lead to poor distributed execution plans, and argue that to choose high-quality plans in a distributed database, the optimizer needs to be distribution-aware in choosing join plans, applying query rewrites, and costing plans. We discuss methods to make join enumeration faster and more effective, such as a rewrite-based approach to exploit bushy joins in queries involving multiple star schemas without sacrificing optimization time. We demonstrate the effectiveness of the MemSQL optimizer over queries from the TPC-H benchmark and a real customer workload.


title: License plate Localization from vehicle images:An edge based multistage Approach

abstract: Automatic license plate recognition (ALPR) system for vehicles is a challenging area of research due to its importance to a wide range of commercial applications. The first and the most important stage for any ALPR system is the localization of the license plate within the image captured by a camera. Variety of techniques has already been reported for localization of license plate and recognition of license number thereafter. But most of the works seem to be applicable for a very controlled environment. In the current work, we have concentrated on localization of license plate regions from true color still snapshots captured in a very realistic situation. The technique is based on a novel multi-stage approach for analysis of vertical edge gradients from contrast stretched gray-scale images. The technique successfully localizes the actual license plates in 89.2% images.


title: Scribbler: Controlling Deep Image Synthesis with Sketch and Color

abstract: Several recent works have used deep convolutional networks to generate realistic imagery. These methods sidestep the traditional computer graphics rendering pipeline and instead generate imagery at the pixel level by learning from large collections of photos (e.g. faces or bedrooms). However, these methods are of limited utility because it is difficult for a user to control what the network produces. In this paper, we propose a deep adversarial image synthesis architecture that is conditioned on sketched boundaries and sparse color strokes to generate realistic cars, bedrooms, or faces. We demonstrate a sketch based image synthesis system which allows users to scribble over the sketch to indicate preferred color for objects. Our network can then generate convincing images that satisfy both the color and the sketch constraints of user. The network is feed-forward which allows users to see the effect of their edits in real time. We compare to recent work on sketch to image synthesis and show that our approach generates more realistic, diverse, and controllable outputs. The architecture is also effective at user-guided colorization of grayscale images.


title: Mobile Social Commerce Implementation

abstract: When mobile commerce meets social commerce, e-commerce platforms as we know them are transformed into powerful tools that not only support an organization's business strategy, but also provide its customers with the features and convenience they have come to expect from online transactions. We call this mobile social commerce (ms-commerce). This paper sheds light on this new concept by showing how mobile and social capabilities can enhance existing e-commerce business models. An implementation approach for ms-commerce is then presented, guided by a feasible commercial scenario. We finally discuss our ms-commerce implementation approach and compare it for the two leading mobile platforms, namely iOS and Android.


title: Ion-Induced Hydrogel Formation and Nematic Ordering of Nanocrystalline Cellulose Suspensions.

abstract: Nanocrystalline cellulose (NCC) is a promising material for formation of hydrogels and nematic liquid crystals. While salt addition is known to facilitate hydrogel formation, it remains unclear whether this originates from cationic bridging or charge screening effects. Herein, we demonstrate the effect of mono- and divalent salts on NCC gelation and nematic ordering. A strong correlation of NCC suspension zeta-potential and rheological behavior was found. Lower concentrations of divalent cations were needed to decrease NCC zeta-potential and form hydrogels. The same zeta-potentials and gel strengths were achieved at higher concentrations of monovalent salts. Salt-induced NCC aggregation is thus caused by intermolecular attractive forces rather than cationic bridging. Against excluded volume argumentation, salt addition was found to promote NCC nematic phase formation. Increased nematic ordering was observed in a transition regime of moderate salt addition before complete aggregation occurs. This regime is governed by an equilibrium of repulsive and attractive forces. Small angle neutron scattering suggests lateral orientation of NCC. Hence, NCC gelation and nematic ordering can be modulated via its zeta-potential by targeted salt addition.


title: Educational Video Game Design : A Review of the Literature

abstract: Much attention has been directed to the use of video games for learning in recent years, in part due to the staggering amounts of capital spent on games in the entertainment industry, but also because of their ability to captivate player attention and hold it for lengthy periods of time as players learn to master game complexities and accomplish objectives. This review of the literature on video game research focuses on publications analyzing educational game design, namely those that present design elements conducive to learning, the theoretical underpinnings of game design, and learning outcomes from video game play.


title: CatchUp!: capturing and replaying refactorings to support API evolution

abstract: Library developers who have to evolve a library to accommodate changing requirements often face a dilemma: Either they implement a clean, efficient solution but risk breaking client code, or they maintain compatibility with client code, but pay with increased design complexity and thus higher maintenance costs over time. We address this dilemma by presenting a lightweight approach for evolving application programming interfaces (APIs), which does not depend on version control or configuration management systems. Instead, we capture API refactoring actions as a developer evolves an API. Users of the API can then replay the refactorings to bring their client software components up to date. We present CatchUp!, an implementation of our approach that captures and replays refactoring actions within an integrated development environment semi-automatically. Our experiments suggest that our approach could be valuable in practice.


Implementing Full Device Cloning on the NVIDIA Jetson Platform


title: A SURVEY OF ALGORITHMS FOR DENSE SUBGRAPH DISCOVERY

abstract: In this chapter, we present a survey of algorithms for dense subgraph discovery.The problem of dense subgraph discovery is closely related to clustering though the two problems also have a number of differences. For example, the problem of clustering is largely concerned with that of finding a fixed partition in the data, whereas the problem of dense subgraph discovery defines these dense components in a much more flexible way. The problem of dense subgraph discovery may wither be defined over single or multiple graphs. We explore both cases. In the latter case, the problem is also closely related to the problem of the frequent subgraph discovery. This chapter will discuss and organize the literature on this topic effectively in order to make it much more accessible to the reader.


TOWARDS AN EDUCATION BEHAVIORAL INTENTION MODEL FOR E-LEARNING SYSTEMS : AN EXTENSION OF


title: Research on anti-money laundering based on core decision tree algorithm

abstract: This paper presents a core decision tree algorithm to identify money laundering activities. The clustering algorithm is the combination of BIRCH and K-means. In this method, decision tree of data mining technology is applied to anti-money-laundering filed after research of money laundering features. We select an appropriate identifying strategy to discover typical money laundering patterns and money laundering rules. Consequently, with the core decision tree algorithm, we can identify abnormal transaction data more effectively.


Mathematics Not Lack of Ability but More Choice : Individual and Gender Differences in Choice of Careers in Science


title: Measuring Systems Interoperability: Challenges and Opportunities

abstract: Abstract : Interoperability is the ability of systems, units, or forces to provide services to and accept services from other systems, units, or forces and to use the services exchanged to enable them to operate effectively together. In an April 1998 report to Congress, the Secretary of Defense noted that "joint operations have been hindered by the inability of forces to share critical information at the rate and at the locations demanded by modern warfare." Serious interoperability deficiencies have been perpetuated across all the services and have been identified in all recent, allied, joint, and combined operations and exercises. Interoperability is a broad and complex subject. Developing and applying precise measurements in an area as multidimensional and complex as interoperability is difficult. However, measuring, assessing, and reporting interoperability in a visible way is essential to setting the right priorities. Despite laudable case-by-case efforts, there is today no method for tracking interoperability on a comprehensive or systematic basis. This technical note presents best practices for measuring systems interoperability and assisting military planners in the acquisition, development, and implementation of command, control, communications, computers, and intelligence (C4I) systems that are interoperable. The Levels of Information Systems Interoperability (LISI) Model, although immature, provides a structured and systematic approach for assessing and measuring interoperability throughout the systems life cycle. In addition to exploring the many complex issues surrounding the state of interoperability for military applications, next steps for promoting a deeper understanding of interoperability and recommended measures that will promote systems interoperability are presented.


How People Explain Action (and Autonomous Intelligent Systems Should Too).


title: A 1.83 μW, 0.78 μVrms input referred noise neural recording front end

abstract: This paper describes a neural recording front end for both Local Field Potential (LFP) and Spike Potential (SP) recordings, which range from 0.1 Hz ~ 200 Hz and 200 Hz ~ 10 kHz, respectively. Based on the capacitively-coupled chopper instrumentation amplifier (CCIA) topology, a ripple reduction loop (RRL) is used to suppress the chopping ripple. A DC servo loop (DSL) that utilizes pseudo-feedback to achieve a very small unity gain bandwidth with reduced capacitor size while consuming only 12 nA is proposed. The proposed CCIA is implemented in a standard 0.18 μm CMOS process. Simulation results show that with a total power consumption of 1.525 μA from a 1.2 V supply, a NEF of 2.73 (LFP) and 2.6 (SP) can be achieved.


title: LIME: Live Intrinsic Material Estimation

abstract: We present the first end to end approach for real time material estimation for general object shapes with uniform material that only requires a single color image as input. In addition to Lambertian surface properties, our approach fully automatically computes the specular albedo, material shininess, and a foreground segmentation. We tackle this challenging and ill posed inverse rendering problem using recent advances in image to image translation techniques based on deep convolutional encoder decoder architectures. The underlying core representations of our approach are specular shading, diffuse shading and mirror images, which allow to learn the effective and accurate separation of diffuse and specular albedo. In addition, we propose a novel highly efficient perceptual rendering loss that mimics real world image formation and obtains intermediate results even during run time. The estimation of material parameters at real time frame rates enables exciting mixed reality applications, such as seamless illumination consistent integration of virtual objects into real world scenes, and virtual material cloning. We demonstrate our approach in a live setup, compare it to the state of the art, and demonstrate its effectiveness through quantitative and qualitative evaluation.


title: MEMS FOR CELLULAR FORCE MEASUREMENTS AND MOLECULAR DETECTION

abstract: MEMS technology and devices have proven their importance in facilitating single cell studies by providing quantitative information on cellular and sub-cellular levels. This paper reviews existing techniques for cellular and sub-cellular force measurement and molecular detection using MEMS-based devices. Literature on these techniques and sample devices is reviewed. The significance and limitations of various approaches are analyzed.


title: Attending Category Disentangled Global Context for Image Classification

abstract: In this paper, we propose a general framework for image classification using the attention mechanism and global context, which could incorporate with various network architectures to improve their performance. To investigate the capability of the global context, we compare four mathematical models and observe the global context encoded in the category disentangled conditional generative model could give more guidance as "know what is task irrelevant will also know what is relevant". Based on this observation, we define a novel Category Disentangled Global Context (CDGC) and devise a deep network to obtain it. By attending CDGC, the baseline networks could identify the objects of interest more accurately, thus improving the performance. We apply the framework to many different network architectures and compare with the state-of-the-art on four publicly available datasets. Extensive results validate the effectiveness and superiority of our approach. Code will be made public upon paper acceptance.


title: Solar powered unmanned aerial vehicle for continuous flight: Conceptual overview and optimization

abstract: An aircraft that is capable of continuous flight offers a new level of autonomous capacity for unmanned aerial vehicles. We present an overview of the components and concepts of a small scale unmanned aircraft that is capable of sustaining powered flight without a theoretical time limit. We then propose metrics that quantify the robustness of continuous flight achieved and optimization criteria to maximize these metrics. Finally, the criteria are applied to a fabricated and flight tested small scale high efficiency aircraft prototype to determine the optimal battery and photovoltaic array mass for robust continuous flight.


CWP-678 Image-guided full waveform inversion


title: Pre-operative diagnosis and successful surgery of a strangulated internal hernia through a defect in the falciform ligament: a case report

abstract: INTRODUCTION ::: Internal hernia within the falciform ligament is exceedingly rare. A literature search revealed only 14 cases of internal herniation of the small bowel through a congenital defect of the falciform ligament, most of which were found intra-operatively. ::: ::: ::: CASE PRESENTATION ::: A 77-year-old Japanese woman presented to our emergency department with sudden hematemesis, occurring at least four to five times over a 12-hour period. No ulcer or gastrointestinal bleeding was detected on gastroendoscopy. A 40mm mass in the inferior lobe of the right lung was found on a chest X-ray, and our patient's symptoms were therefore initially ascribed to aspirated blood from lung tumor-associated hemoptysis. However, our patient continued to show signs of severe abdominal pain and decreased urine output despite aggressive hydration, leading her examining physicians to search for a possibly severe, occult abdominal pathology. On emergent computed tomography imaging, we found an acute strangulated internal hernia within the falciform ligament. Diagnosis was made by helical computed tomography, permitting rapid surgical intervention. ::: ::: ::: CONCLUSIONS ::: Our findings on computed tomography imaging assisted with the pre-operative diagnosis and enabled us to make a rapid surgical intervention. Early diagnosis may help preclude significant strangulation with unnecessary resection.


title: Coordinated V-f and P-Q Control of Solar Photovoltaic Generators With MPPT and Battery Storage in Microgrids

abstract: The microgrid concept allows small distributed energy resources (DERs) to act in a coordinated manner to provide a necessary amount of active power and ancillary service when required. This paper proposes an approach of coordinated and integrated control of solar PV generators with the maximum power point tracking (MPPT) control and battery storage control to provide voltage and frequency (V-f) support to an islanded microgrid. Also, active and nonactive/reactive power (P-Q) control with solar PV, MPPT and battery storage is proposed for the grid connected mode. The control strategies show effective coordination between inverter V-f (or P-Q) control, MPPT control, and energy storage charging and discharging control. The paper also shows an effective coordination among participating microresources while considering the case of changing irradiance and battery state of charge (SOC) constraint. The simulation studies are carried out with the IEEE 13-bus feeder test system in grid connected and islanded microgrid modes. The results clearly verify the effectiveness of proposed control methods. The simulations are carried out in Matlab and Simpowersystems.


title: SC-FDMA-based resource allocation and power control scheme for D2D communication using LTE-A uplink resource

abstract: Device-to-device (D2D) communication-enabled cellular networks allow cellular devices to directly communicate with each other without any evolved NodeB (eNB). D2D communication aims to improve the spectral efficiency and increases the overall system capacity. For future mobile networks, intelligent radio resource allocation and power control schemes are required to accommodate the increasing number of cellular devices and their growing demand of data traffic. In this paper, a combined resource allocation and power control scheme for D2D communication is proposed. In the proposed scheme, D2D communication reuses the uplink (UL) resources of conventional cellular user equipments (CUEs); therefore, we have adopted single-carrier frequency division multiple access (SC-FDMA) as UL transmission scheme. The proposed scheme uses fractional frequency reuse (FFR)-based architecture to efficiently allocate the resources and mitigate the interference between CUEs and D2D user equipments (DUEs). In order to guarantee the user fairness, the proposed scheme uses the well-known proportional fair (PF) scheduling algorithm for resource allocation. We have also proposed an intelligent power control scheme which provides equal opportunity to both CUEs and DUEs to achieve a certain minimum signal-to-interference and noise ratio (SINR) value. The performance evaluation results show that the proposed scheme significantly improves the overall cell capacity and achieves low peak-to-average power ratio (PAPR).


Neural User Response Generator: Fake News Detection with Collective User Intelligence


title: Learning to Listen, Read, and Follow: Score Following as a Reinforcement Learning Game

abstract: Score following is the process of tracking a musical performance (audio) with respect to a known symbolic representation (a score). We start this paper by formulating score following as a multimodal Markov Decision Process, the mathematical foundation for sequential decision making. Given this formal definition, we address the score following task with state-of-the-art deep reinforcement learning (RL) algorithms such as synchronous advantage actor critic (A2C). In particular, we design multimodal RL agents that simultaneously learn to listen to music, read the scores from images of sheet music, and follow the audio along in the sheet, in an end-to-end fashion. All this behavior is learned entirely from scratch, based on a weak and potentially delayed reward signal that indicates to the agent how close it is to the correct position in the score. Besides discussing the theoretical advantages of this learning paradigm, we show in experiments that it is in fact superior compared to previously proposed methods for score following in raw sheet music images.


title: On Evaluation of Embodied Navigation Agents

abstract: Skillful mobile operation in three-dimensional environments is a primary topic of study in Artificial Intelligence. The past two years have seen a surge of creative work on navigation. This creative output has produced a plethora of sometimes incompatible task definitions and evaluation protocols. To coordinate ongoing and future research in this area, we have convened a working group to study empirical methodology in navigation research. The present document summarizes the consensus recommendations of this working group. We discuss different problem statements and the role of generalization, present evaluation measures, and provide standard scenarios that can be used for benchmarking.


title: Energy efficient virtual machine placement algorithm with balanced and improved resource utilization in a data center

abstract: Powerful data centers are the essential supporting infrastructure for mobile, ubiquitous, and cognitive computing, which are the most popular computing paradigms to utilize all kinds of physical resources and provide various services. To ensure the high quality of services, the performance and cost of a data center is a critical factor. In this paper, we investigate the issue of increasing the resource utilization of data centers to improve their performance and lower the cost. It is an efficient way to increase resource utilization via resource sharing. Technically, server virtualization provides the opportunity to share resources in data centers. However, it also introduces other problems, the primary problem being virtual machine placement (VMP), which is to choose a proper physical machine (PM) to deploy virtual machines (VMs) in runtime. We study the virtual machine placement problem with the target of minimizing the total energy consumption by the running of PMs, which is also an indication of resource utilization and the cost of a data center. Due to the multiple dimensionality of physical resources, there always exists a waste of resources, which results from the imbalanced use of multi-dimensional resources. To characterize the multi-dimensional resource usage states of PMs, we present a multi-dimensional space partition model. Based on this model, we then propose a virtual machine placement algorithm EAGLE, which can balance the utilization of multi-dimensional resources, reduce the number of running PMs, and thus lower the energy consumption. We also evaluate our proposed balanced algorithm EAGLE via extensive simulations and experiments on real traces. Experimental results show, over the long run, that EAGLE can save as much as 15% more energy than the first fit algorithm.


title: Effects of coke oven emissions and benzo[a]pyrene on blood pressure and electrocardiogram in coke oven workers

abstract: OBJECTIVE ::: To evaluate the effects of occupational exposures to coke oven emissions (COEs) and benzo[a]pyrene (B[a]P) on the prevalence of hypertension and abnormal electrocardiogram (ECG) in coke oven workers. ::: ::: ::: METHODS ::: We included 880 coke oven workers and 710 oxygen employees in the exposed and control groups, respectively. Blood pressure (BP), ECG, blood lipid levels, and glucose levels of all subjects were measured. COE and B[a]P concentrations at the bottom, side, and top of the oven and control plants were estimated by weighing and high-performance liquid chromatography. ::: ::: ::: RESULTS ::: The COE concentration at the top and side was higher than that at the bottom (P < 0.05). The levels of B[a]P at the top and side significantly exceeded the limit value. Abnormal BP, ECG, the detection ratio of hypertension and left ventricular high voltage were significantly greater in the exposed group than in the control group (P < 0.05). The logistic regression analysis results revealed that age and B[a]P exposure were risk factors for hypertension in coke oven workers (P < 0.05) and both were risk factors for abnormal ECG (P < 0.05). Moreover, B[a]P exposure, age, and gender were risk factors for impaired fasting glucose in coke oven workers (P < 0.05). ::: ::: ::: CONCLUSIONS ::: B[a]P and COE exposures are risk factors for hypertension and abnormal ECG in coke oven workers.


title: An operating system architecture for network processors

abstract: Network devices have become significantly more complex in recent years, with the most sophisticated current devices incorporating one or more general-purpose CPUs as part of their hardware. The need for such processing capability is motivated by the desire to move greater amounts of functionality, of ever-increasing complexity, from the host CPU to the network device itself. A significant challenge in doing so is managing the complexity of the software running on the network device. We believe that the complexity of this software has reached the point where it is now on a par with many general-purpose systems, and thus requires the same management infrastructure - an operating system for network processors. In this paper we describe an architecture for such an OS, presenting the features most relevant to network processors and describing similarities to and differences from a general- purpose OS. We present a prototype implementation using an SMP system as a virtual network processor, and show how our prototype was used to evaluate a novel user-space interface to a network device.


title: Genetic, hormonal and metabolic aspects of PCOS: an update

abstract: Polycystic ovary syndrome (PCOS) is a complex endocrine disorder affecting 5–10 % of women of reproductive age. It generally manifests with oligo/anovulatory cycles, hirsutism and polycystic ovaries, together with a considerable prevalence of insulin resistance. Although the aetiology of the syndrome is not completely understood yet, PCOS is considered a multifactorial disorder with various genetic, endocrine and environmental abnormalities. Moreover, PCOS patients have a higher risk of metabolic and cardiovascular diseases and their related morbidity, if compared to the general population.


title: Online Topic-aware Influence Maximization Queries

abstract: Influence maximization is the key algorithmic problem behind viral marketing: it requires to identify a set of influential users in a social network, who, when convinced to adopt a product, shall influence other users in the network, leading to a large number of adoptions. Although real world users evidently have di↵erent degrees of interest and authoritativeness on di↵erent topics, the bulk of the literature on influence maximization is topic-blind, in the sense that it treats all items as they were the same. In this paper we study Topic-aware Influence Maximization (TIM) queries: given a directed social graph, where the arcs are associated with a topic-dependent user-to-user social influence strength, and given a budget k, the problem requires to find a set of k users (named seed set) that we shall target in a viral marketing campaign for a given new item (described as a distribution over topics) in order to maximize its adoption. Our goal is to answer such queries in milliseconds, thus enabling online social influence analytics, what-if simulation, and marketing decision making. The main challenge here is the enormous number of potential queries: any possible distribution over the topic space (i.e., any possible item) induces a di↵erent probabilistic graph, and thus a di↵erent instance of the standard influence maximization problem, for which eciency and scalability are still unsolved problems. Given these computational challenges, we propose to build an index over pre-computed solutions for a limited number of possible queries. Our proposal, INFLEX, employs a treebased index for similarity search with Bregman divergences, to eciently retrieve a good-enough set of neighbor points for the query item. Then it performs rank aggregation on their seed sets to produce the final answer to the query. Experimental results on real data show that INFLEX can provide in few milliseconds a solution very similar (Kendall⌧ distance < 0.1) to the one produced by the best known o✏ine computation (which usually takes several days).


title: Assistive Text Reading from Natural Scene for Blind Persons

abstract: Text information serves as an understandable and comprehensive indicator, which plays a significant role in navigation and recognition in our daily lives. It is very difficult to access this valuable information for blind or visually impaired persons, in particular, in unfamiliar environments. With the development of computer vision technology and smart mobile applications, many assistive systems are developed to help blind or visually impaired persons in their daily lives. This chapter focuses on the methods of text reading from natural scene as well as their applications to assist people who are visually impaired. With the research work on accessibility for the disabled, the assistive text reading technique for the blind is implemented in mobile platform, such as smart phone, tablet, and other wearable device. The popularity and interconnection of mobile devices would provide more low-cost and convenient assistance for blind or visually impaired persons.


title: 60, Part 3, pp.627^641 Multivariate Bayesian variable selection and prediction

abstract: The multivariate regression model is considered with p regressors. A latent vector with p binary entries serves to identify one of two types of regression coefficients: those close to 0 and those not. Specializing our general distributional setting to the linear model with Gaussian errors and using natural conjugate prior distributions, we derive the marginal posterior distribution of the binary latent vector. Fast algorithms aid its direct computation, and in high dimensions these are supplemented by a Markov chain Monte Carlo approach to sampling from the known posterior distribution. Problems with hundreds of regressor variables become quite feasible. We give a simple method of assigning the hyperparameters of the prior distribution. The posterior predictive distribution is derived and the approach illustrated on compositional analysis of data involving three sugars with 160 near infra-red absorbances as regressors.


title: DMFA-SR: Deeper Membership and Friendship Awareness for Social Recommendation

abstract: The existing social recommendation models mostly utilize various explicit user-generated information. Although there exist a few studies adopting the implicit relationship between users for social recommendation, however, these studies do not consider the deeper social relationship, nor simultaneously take into account two or more deeper relationships between users from different angles. To this end, we propose a new deeper membership and friendship awareness for social recommendation. Specifically, we first calculate the deeper membership similarity between users utilizing the improved Jaccard similarity coefficient and the deeper friendship similarity between users using the proposed two-hop random walk algorithm. Second, the deeper membership similarity and the deeper friendship similarity are combined in a unified way to form a comprehensive deeper social relation similarity. Third, we adopt the matrix factorization method incorporating the deeper membership and the deeper friendship between users as a regularization term for social recommendation, and the corresponding comprehensive deeper social relationship similarity is regarded as the regularization parameter. Experiments on two real-world datasets demonstrate the superiority of the proposed recommendation model.


title: Thingi10K: A Dataset of 10,000 3D-Printing Models

abstract: Empirically validating new 3D-printing related algorithms and implementations requires testing data representative of inputs encountered \emph{in the wild}. An ideal benchmarking dataset should not only draw from the same distribution of shapes people print in terms of class (e.g., toys, mechanisms, jewelry), representation type (e.g., triangle soup meshes) and complexity (e.g., number of facets), but should also capture problems and artifacts endemic to 3D printing models (e.g., self-intersections, non-manifoldness). We observe that the contextual and geometric characteristics of 3D printing models differ significantly from those used for computer graphics applications, not to mention standard models (e.g., Stanford bunny, Armadillo, Fertility). We present a new dataset of 10,000 models collected from an online 3D printing model-sharing database. Via analysis of both geometric (e.g., triangle aspect ratios, manifoldness) and contextual (e.g., licenses, tags, classes) characteristics, we demonstrate that this dataset represents a more concise summary of real-world models used for 3D printing compared to existing datasets. To facilitate future research endeavors, we also present an online query interface to select subsets of the dataset according to project-specific characteristics. The complete dataset and per-model statistical data are freely available to the public.


title: Automatic Text Summarization

abstract: Automatic Text Summarization is a Natural Language Processing task which has experienced great development in recent years, mostly due to the rapid growth of the Internet. Therefore, we need methods and tools that help users to manage large amounts of information. Text Summarization aims to condense the information contained in one or more documents and present it in a more concise way, can be very useful for this purpose. It is the creation of a shortened version of a text by a computer program. The product of this procedure still contains the most important points of the original text.


title: Topical treatments for cutaneous warts

abstract: Background ::: Viral warts are a common skin condition, which can range in severity from a minor nuisance that resolve spontaneously to a troublesome, chronic condition. Many different topical treatments are available. ::: ::: Objectives ::: To evaluate the efficacy of local treatments for cutaneous non-genital warts in healthy, immunocompetent adults and children. ::: ::: Search methods ::: We updated our searches of the following databases to May 2011: the Cochrane Skin Group Specialised Register, CENTRAL in The Cochrane Library, MEDLINE (from 2005), EMBASE (from 2010), AMED (from 1985), LILACS (from 1982), and CINAHL (from 1981). We searched reference lists of articles and online trials registries for ongoing trials. ::: ::: Selection criteria ::: Randomised controlled trials (RCTs) of topical treatments for cutaneous non-genital warts. ::: ::: Data collection and analysis ::: Two authors independently selected trials and extracted data; a third author resolved any disagreements. ::: ::: Main results ::: We included 85 trials involving a total of 8815 randomised participants (26 new studies were included in this update). There was a wide range of different treatments and a variety of trial designs. Many of the studies were judged to be at high risk of bias in one or more areas of trial design. ::: ::: Trials of salicylic acid (SA) versus placebo showed that the former significantly increased the chance of clearance of warts at all sites (RR (risk ratio) 1.56, 95% CI (confidence interval) 1.20 to 2.03). Subgroup analysis for different sites, hands (RR 2.67, 95% CI 1.43 to 5.01) and feet (RR 1.29, 95% CI 1.07 to 1.55), suggested it might be more effective for hands than feet. ::: ::: A meta-analysis of cryotherapy versus placebo for warts at all sites favoured neither intervention nor control (RR 1.45, 95% CI 0.65 to 3.23). Subgroup analysis for different sites, hands (RR 2.63, 95% CI 0.43 to 15.94) and feet (RR 0.90, 95% CI 0.26 to 3.07), again suggested better outcomes for hands than feet. One trial showed cryotherapy to be better than both placebo and SA, but only for hand warts. ::: ::: There was no significant difference in cure rates between cryotherapy at 2-, 3-, and 4-weekly intervals. ::: ::: Aggressive cryotherapy appeared more effective than gentle cryotherapy (RR 1.90, 95% CI 1.15 to 3.15), but with increased adverse effects. ::: ::: Meta-analysis did not demonstrate a significant difference in effectiveness between cryotherapy and SA at all sites (RR 1.23, 95% CI 0.88 to 1.71) or in subgroup analyses for hands and feet. ::: ::: Two trials with 328 participants showed that SA and cryotherapy combined appeared more effective than SA alone (RR 1.24, 95% CI 1.07 to 1.43). ::: ::: The benefit of intralesional bleomycin remains uncertain as the evidence was inconsistent. The most informative trial with 31 participants showed no significant difference in cure rate between bleomycin and saline injections (RR 1.28, 95% CI 0.92 to 1.78). ::: ::: Dinitrochlorobenzene was more than twice as effective as placebo in 2 trials with 80 participants (RR 2.12, 95% CI 1.38 to 3.26). ::: ::: Two trials of clear duct tape with 193 participants demonstrated no advantage over placebo (RR 1.43, 95% CI 0.51 to 4.05). ::: ::: We could not combine data from trials of the following treatments: intralesional 5-fluorouracil, topical zinc, silver nitrate (which demonstrated possible beneficial effects), topical 5-fluorouracil, pulsed dye laser, photodynamic therapy, 80% phenol, 5% imiquimod cream, intralesional antigen, and topical alpha-lactalbumin-oleic acid (which showed no advantage over placebo). ::: ::: We did not identify any RCTs that evaluated surgery (curettage, excision), formaldehyde, podophyllotoxin, cantharidin, diphencyprone, or squaric acid dibutylester. ::: ::: Authors' conclusions ::: Data from two new trials comparing SA and cryotherapy have allowed a better appraisal of their effectiveness. The evidence remains more consistent for SA, but only shows a modest therapeutic effect. Overall, trials comparing cryotherapy with placebo showed no significant difference in effectiveness, but the same was also true for trials comparing cryotherapy with SA. Only one trial showed cryotherapy to be better than both SA and placebo, and this was only for hand warts. Adverse effects, such as pain, blistering, and scarring, were not consistently reported but are probably more common with cryotherapy. ::: ::: None of the other reviewed treatments appeared safer or more effective than SA and cryotherapy. Two trials of clear duct tape demonstrated no advantage over placebo. Dinitrochlorobenzene (and possibly other similar contact sensitisers) may be useful for the treatment of refractory warts.


title: Distance measures for layout-based document image retrieval

abstract: Most methods for document image retrieval rely solely on text information to find similar documents. This paper describes a way to use layout information for document image retrieval instead. A new class of distance measures is introduced for documents with Manhattan layouts, based on a two-step procedure: First, the distances between the blocks of two layouts are calculated. Then, the blocks of one layout are assigned to the blocks of the other layout in a matching step. Different block distances and matching methods are compared and evaluated using the publicly available MARG database. On this dataset, the layout type can be determined successfully in 92.6% of the cases using the best distance measure in a nearest neighbor classifier. The experiments show that the best distance measure for this task is the overlapping area combined with the Manhattan distance of the corner points as block distance together with the minimum weight edge cover matching.


title: Graph-based Data Integration and Business Intelligence with BIIIG

abstract: We demonstrate BIIIG (Business Intelligence with Integrated Instance Graphs), a new system for graph-based data integration and analysis. It aims at improving business analytics compared to traditional OLAP approaches by comprehensively tracking relationships between entities and making them available for analysis. BIIIG supports a largely automatic data integration pipeline for metadata and instance data. Metadata from heterogeneous sources are integrated in a so-called Unified Metadata Graph (UMG) while instance data is combined in a single integrated instance graph (IIG). A unique feature of BIIIG is the concept of business transaction graphs, which are derived from the IIG and which reflect all steps involved in a specific business process. Queries and analysis tasks can refer to the entire instance graph or sets of business transaction graphs. In the demonstration, we perform all data integration steps and present analytic queries including pattern matching and graph-based aggregation of business measures.


title: Line matching leveraged by point correspondences

abstract: A novel method for line matching is proposed. The basic idea is to use tentative point correspondences, which can be easily obtained by keypoint matching methods, to significantly improve line matching performance, even when the point correspondences are severely contaminated by outliers. When matching a pair of image lines, a group of corresponding points that may be coplanar with these lines in 3D space is firstly obtained from all corresponding image points in the local neighborhoods of these lines. Then given such a group of corresponding points, the similarity between this pair of lines is calculated based on an affine invariant from one line and two points. The similarity is defined on the basis of median statistic in order to handle the problem of inevitable incorrect correspondences in the group of point correspondences. Furthermore, the relationship of rotation between the reference and query images is estimated from all corresponding points to filter out those pairs of lines which are obviously impossible to be matches, hence speeding up the matching process as well as further improving its robustness. Extensive experiments on real images demonstrate the good performance of the proposed method as well as its superiority to the state-of-the-art methods.


title: Frequency Response Acquisition of a Digital Radio Transceiver Using USRP Module and GNU Radio Software

abstract: The objective of this paper is to show a low-cost and an easy deployable technique that allows to acquire the frequency response of a transceiver with different configurations between two receiver daughter boards: BasicRx and LFRX, and two transmitter daughter boards: BasicTx and LFTX, at a frequency range from 1 up to 30 MHz using the low-cost hardware module USRP (Universal Software Radio Peripheral) to implement the Software Defined Radio (SDR) concept based on license-free GNU Radio software. This system avoids the need of an expensive Frequency Response Analyzer (FRA). The transceiver amplitude frequency response gives a correction parameter using any transceiver configuration in order to acquire any external electrical circuit amplitude frequency response, acoustic signature of a room, failure detection on transformers coils, etc. The frequency range depends on the transmitter and receiver boards used. It also aims to show a good type of signal to be transmitted, one technique of measuring the received signals in a semi-automatic way and the constraints in the phase frequency response using this system.


title: Venture: a higher-order probabilistic programming platform with programmable inference

abstract: We describe Venture, an interactive virtual machine for probabilistic programming that aims to be sufficiently expressive, extensible, and efficient for general-purpose use. Like Church, probabilistic models and inference problems in Venture are specified via a Turing-complete, higher-order probabilistic language descended from Lisp. Unlike Church, Venture also provides a compositional language for custom inference strategies built out of scalable exact and approximate techniques. We also describe four key aspects of Venture's implementation that build on ideas from probabilistic graphical models. First, we describe the stochastic procedure interface (SPI) that specifies and encapsulates primitive random variables. The SPI supports custom control flow, higher-order probabilistic procedures, partially exchangeable sequences and ``likelihood-free'' stochastic simulators. It also supports external models that do inference over latent variables hidden from Venture. Second, we describe probabilistic execution traces (PETs), which represent execution histories of Venture programs. PETs capture conditional dependencies, existential dependencies and exchangeable coupling. Third, we describe partitions of execution histories called scaffolds that factor global inference problems into coherent sub-problems. Finally, we describe a family of stochastic regeneration algorithms for efficiently modifying PET fragments contained within scaffolds. Stochastic regeneration linear runtime scaling in cases where many previous approaches scaled quadratically. We show how to use stochastic regeneration and the SPI to implement general-purpose inference strategies such as Metropolis-Hastings, Gibbs sampling, and blocked proposals based on particle Markov chain Monte Carlo and mean-field variational inference techniques.


Parallel Computer Architecture: A Hardware/Software Approach


Applying COM-B to medication adherence


title: Cartoon textures

abstract: In this paper we present a method for creating novel animations from a library of existing two-dimensional cartoon data. Drawing inspiration from the idea of video textures, sequences of similar-looking cartoon data are combined into a user-directed sequence. Starting with a small amount of cartoon data, we employ a method of nonlinear dimensionality reduction to discover a lower-dimensional structure of the data. The user selects a start and end frame and the system traverses this lower-dimensional manifold to re-sequence the data into a new animation. The system can automatically detect when a new sequence has visual discontinuities and may require additional source material.


title: Developing a Dark Web collection and infrastructure for computational and social sciences

abstract: In recent years, there have been numerous studies from a variety of perspectives analyzing the Internet presence of hate and extremist groups. Yet the websites and forums of extremist and terrorist groups have long remained an underutilized resource for terrorism researchers due to their ephemeral nature and access and analysis problems. The purpose of the Dark Web archive is to provide a research infrastructure for use by social scientists, computer and information scientists, policy and security analysts, and others studying a wide range of social and organizational phenomena and computational problems. The Dark Web Forum Portal provides web enabled access to critical international jihadist and other extremist web forums. The focus of this paper is on the significant extensions to previous work including: increasing the scope of data collection, adding an incremental spidering component for regular data updates; enhancing the searching and browsing functions; enhancing multilingual machine-translation for Arabic, French, German and Russian; and advanced Social Network Analysis. A case study on identifying active participants is shown at the end.


title: Gait phase prediction for lower limb exoskeleton robots

abstract: Robust gait phase prediction is crucial to exoskeleton robots, as it detects the intention of users and improves the lag of motion signals. Therefore, this paper predicts gait phases from two perspectives, including one perspective of spatial features and the other of spatio-temporal features. We employ two machine learning models from the two perspectives to predict. One is support vector machine (SVM) optimized by particle swarm optimization (PSO) algorithm, and it only focuses on joint information. Another is nonlinear autoregressive models with external inputs (NARX), and it utilizes previous data to predict present status. As for input, four goniometers built into the exoskeleton robot are used to collect hip and knee joint angles during the walking process. To obtain gait phases, a multi-pressure sensor network composed of three force sensitive resistors (FSRs) is set up and four gait phases, including heel-contact, foot-flat, heel-off and toe-high, are determined according to plantar pressure distribution. Experimental results show that both SVM and NARX are capable of predicting gait phases. Specifically, NARX outperforms SVM in terms of accuracy, since it uses FSRs data to correct the wrong predictions. Consequently, it is better to predict gait phases based on space and time dimensions simultaneously.


title: TAGME: on-the-fly annotation of short text fragments (by wikipedia entities)

abstract: We designed and implemented TAGME, a system that is able to efficiently and judiciously augment a plain-text with pertinent hyperlinks to Wikipedia pages. The specialty of TAGME with respect to known systems [5,8] is that it may annotate texts which are short and poorly composed, such as snippets of search-engine results, tweets, news, etc.. This annotation is extremely informative, so any task that is currently addressed using the bag-of-words paradigm could benefit from using this annotation to draw upon (the millions of) Wikipedia pages and their inter-relations.


title: 3D-Parallel Coordinates: Visualization for time varying multidimensional data

abstract: Parallel coordinates can visualize multidimensional data efficiently, during to its shortage in displaying time-varying data, we present a new method to add time dimension, which can extend parallel coordinates into 3D space(3D-Parallel Coordinates) consisted of attribute, range and time dimension. So that time-varying multidimensional data can be displayed as polygonal line cluster for recording and analyzing. A technique called clipping shade is taken to highlight datasets around the current time, and results reveal that 3D-Parallel Coordinates can effectively analyze attribute's time-varying character.


title: Vietnam License Plate Recognition System based on Edge Detection and Neural Networks

abstract: This paper proposed an improved Automatic License Plate Recognition (ALPR) system for all types of Vietnam license plates (LP), which consists of three modules: license plate location (LPL), characters segmentation and characters recognition. In the LPL module, we have combined edge detection, image subtraction, mathematical morphology, radon transform, interpolation and specific characteristics of Vietnam LP to locate exact LP region. In the characters segmentation module, we have used peak-to-valley method and statistical parameters of Vietnam LP to segment characters & numbers in one-row & two-row types of Vietnam LP. In the characters recognition module, we have used a Multilayer Perceptron (MLP) neural network and back-propagation (BP) algorithm to recognize characters & numbers of Vietnam LP, two MLP networks used independent for characters & numbers and MLP has trained with noises in the training task. Character & number images processed by the pre-processing task, which obtained high quality of character & number images for the using network task to improve accuracy of the system. We have implemented our ALPR system on 700 Vietnam vehicle images taken from actual system with different conditions such as lightening conditions (night & day), license angles, illumination, size and type, colors and reflected light. Our method is more effective than some existing methods and efficiency of computing time & accuracy is improved and very satisfied for all types of Vietnam LP and Vietnam environment.


title: Minimizing Computation in Convolutional Neural Networks

abstract: Convolutional Neural Networks (CNNs) have been successfully used for many computer vision applications. It would be beneficial to these applications if the computational workload of CNNs could be reduced. In this work we analyze the linear algebraic properties of CNNs and propose an algorithmic modification to reduce their computational workload. An up to a 47% reduction can be achieved without any change in the image recognition results or the addition of any hardware accelerators.


title: Event detection over twitter social media streams

abstract: In recent years, microblogs have become an important source for reporting real-world events. A real-world occurrence reported in microblogs is also called a social event. Social events may hold critical materials that describe the situations during a crisis. In real applications, such as crisis management and decision making, monitoring the critical events over social streams will enable watch officers to analyze a whole situation that is a composite event, and make the right decision based on the detailed contexts such as what is happening, where an event is happening, and who are involved. Although there has been significant research effort on detecting a target event in social networks based on a single source, in crisis, we often want to analyze the composite events contributed by different social users. So far, the problem of integrating ambiguous views from different users is not well investigated. To address this issue, we propose a novel framework to detect composite social events over streams, which fully exploits the information of social data over multiple dimensions. Specifically, we first propose a graphical model called location-time constrained topic (LTT) to capture the content, time, and location of social messages. Using LTT, a social message is represented as a probability distribution over a set of topics by inference, and the similarity between two messages is measured by the distance between their distributions. Then, the events are identified by conducting efficient similarity joins over social media streams. To accelerate the similarity join, we also propose a variable dimensional extendible hash over social streams. We have conducted extensive experiments to prove the high effectiveness and efficiency of the proposed approach.


title: Robust current observer design for DC-DC converters

abstract: In recent years several fast response controllers have been proposed for DC-DC converters. The majority of them rely on a cascade control structure involving the inductor's current in the control function as its faster dynamics, respect to output voltage, allows a significant increase in the regulation performance; the drawback is the need of a proper and expensive current sensor. In this paper a simple and robust formulation for the design of a current observer, based on the output voltage measurement is presented. The formulation is common for the three standard DC-DC converters and the resulting observer is robust to load variation. The observers have been tested in a cascade PI control structure for buck, boost and buck-boost converters and the numerical results show the good performance of the approach.


title: Learning pattern classification tasks with imbalanced data sets

abstract: This chapter is concerned with the class imbalance problem, which has been recognised as a crucial problem in machine learning and data mining. The problem occurs when there are significantly fewer training instances of one class compared to another class.


title: Affordances, effectivities, and assisted imitation: Caregivers and the directing of attention

abstract: We focus on how infants' discovery of a range of affordances and effectivities contributes to participating in a new activity. We emphasize how caregivers bracket ongoing actions with gestures that direct the infant's attention to perceptual information embodied in action sequences. Such supervised learning narrows the search space and enhances the speed of engaging adeptly in a new activity and provides a basis for achieving a common understanding of ongoing events. These caregiver practices during assisted imitation may illuminate how automata might detect and learn new affordances for action by observing and interacting with other intelligent agents.


title: Graphical SLAM - a self-correcting map

abstract: We describe an approach to simultaneous localization and mapping, SLAM. This approach has the highly desirable property of robustness to data association errors. Another important advantage of our algorithm is that non-linearities are computed exactly, so that global constraints can be imposed even if they result in large shifts to the map. We represent the map as a graph and use the graph to find an efficient map update algorithm. We also show how topological consistency can be imposed on the map, such as, closing a loop. The algorithm has been implemented on an outdoor robot and we have experimental validation of our ideas. We also explain how the graph can be simplified leading to linear approximations of sections of the map. This reduction gives us a natural way to connect local map patches into a much larger global map.


title: Learning Shared Latent Structure for Image Synthesis and Robotic Imitation

abstract: We propose an algorithm that uses Gaussian process regression to learn common hidden structure shared between corresponding sets of heterogenous observations. The observation spaces are linked via a single, reduced-dimensionality latent variable space. We present results from two datasets demonstrating the algorithms's ability to synthesize novel data from learned correspondences. We first show that the method can learn the nonlinear mapping between corresponding views of objects, filling in missing data as needed to synthesize novel views. We then show that the method can learn a mapping between human degrees of freedom and robotic degrees of freedom for a humanoid robot, allowing robotic imitation of human poses from motion capture data.


title: A unified architecture for scalable and replicable autonomous shuttles in a smart city

abstract: Connected and automated driving vehicles have become a present and near future reality. There are already a slowly increasing number of trucks that do autonomous test drives on highways. Level 3 Highway Chauffeur applications are expected to be on the market as early as 2020 and Level 4 Highway Pilot applications are expected to be available around 2025. There is also a drive towards smarter cities with smarter mobility choices that include the use of smaller, lower speed automated driving vehicles. This paper proposes a unified basic computing, sensing, communication and actuation architecture and scalable and replicable automated driving control systems built upon that architecture. A passenger sedan and a small electric vehicle are used to illustrate the application of the proposed unified architecture to these two different sized vehicles. Their automation along with a scalable control algorithm for path following are used for presenting the scalability and replicability of the unified architecture proposed in this paper.


title: Development of a smart wheelchair for people with disabilities

abstract: The intelligent power wheelchair (iChair) is designed to assist people with mobility, sensory, and cognitive impairment lead a higher quality, more independent lifestyle. The iChair includes a power wheelchair (PW), laptop computer, laptop mount, multi-modal input platform, and a custom plastic enclosure for the environmental sensors, made with a 3D printer. We have developed the configuration of sensors to facilitate scientific observation, while maintaining the flexibility to mount the system on almost any power wheelchair, and remain easily removed for maintenance or travel. The first scientific observations have been used to compile ACCESS Reports that quantify a location or event's level of accessibility. If barriers exist we collect a 3D point cloud to be used as evidence and to make recommendations on how to remedy the problem. The iChair will serve a wide variety of disability types by incorporating several input methods, voice, touch, proximity switch, and head tracking camera. The HD camera and 3D scanner have been mounted in such a way as to provide reliable data with the precision necessary to detect obstacles, build 3D maps, follow guides, anticipate events, and provide navigational assistance. We evaluate the human factors in the current prototype to ensure that the technology will be accepted by those it is designed to serve, and propose a wheelchair skills test for future trial participants.


title: Stochastic Variational Inference

abstract: We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions. We develop this technique for a large class of probabilistic models and we demonstrate it with two probabilistic topic models, latent Dirichlet allocation and the hierarchical Dirichlet process topic model. Using stochastic variational inference, we analyze several large collections of documents: 300K articles from Nature, 1.8M articles from The New York Times, and 3.8M articles from Wikipedia. Stochastic inference can easily handle data sets of this size and outperforms traditional variational inference, which can only handle a smaller subset. (We also show that the Bayesian nonparametric topic model outperforms its parametric counterpart.) Stochastic variational inference lets us apply complex Bayesian models to massive data sets.


title: Putaminal and thalamic hemorrhage in ethnic chinese living in Hong Kong.

abstract: BACKGROUND ::: Hemorrhagic stroke is very common in the Chinese population, and it is one of the leading causes of mortality in Chinese communities. The risk factors to explain this high incidence are unknown. It is the purpose of this study to look into the features of hemorrhagic stroke in the Hong Kong Chinese. ::: ::: ::: METHODS ::: We conducted a prospective hospital-based study in which 60 consecutive Chinese patients with computed tomography diagnosis of putaminal or thalamic hemorrhage were included. Their demographic and clinical data were collected and analyzed. ::: ::: ::: RESULTS ::: Two major findings evolved from the present study. (1) Unlike the Western studies, the majority of our patients were about a decade younger; (2) 50% of the patients had previously diagnosed hypertension, but only 20% of these patients were compliant with their antihypertensive medication. Our results also suggested that low admission Glasgow Coma Scale scores, large hematoma size, and the presence of intraventricular blood were associated with poor outcomes. ::: ::: ::: CONCLUSIONS ::: This study concludes that hemorrhagic stroke is indeed a serious health problem in Hong Kong. Simple measures, such as improvement of health education and the primary care system in the management of hypertension, would help to reduce the incidence of hemorrhagic stroke.


Using distributed Scrum for supporting online community : A qualitative descriptive study of students’ perceptions


title: Dose–response relationship between hand-transmitted vibration and hand-arm vibration syndrome in a tropical environment

abstract: Objectives The dose–response relationship for handtransmitted vibration has been investigated extensively in temperate environments. Since the clinical features of hand-arm vibration syndrome (HAVS) differ between the temperate and tropical environment, we conducted this study to investigate the dose–response relationship of HAVS in a tropical environment. Methods A total of 173 male construction, forestry and automobile manufacturing plant workers in Malaysia were recruited into this study between August 2011 and 2012. The participants were interviewed for history of vibration exposure and HAVS symptoms, followed by hand functions evaluation and vibration measurement. Three types of vibration doses—lifetime vibration dose (LVD), total operating time (TOT) and cumulative exposure index (CEI)—were calculated and its log values were regressed against the symptoms of HAVS. The correlation between each vibration exposure dose and the hand function evaluation results was obtained. Results The adjusted prevalence ratio for finger tingling and numbness was 3.34 (95% CI 1.27 to 8.98) for subjects with lnLVD≥20 ln m 2 s −4 against those <16 ln m 2 s −4 . Similar dose–response pattern was found for CEI but not for TOT. No subject reported white finger. The prevalence of finger coldness did not increase with any of the vibration doses. Vibrotactile perception thresholds correlated moderately with lnLVD and lnCEI. Conclusions The dose–response relationship of HAVS in a tropical environment is valid for finger tingling and numbness. The LVD and CEI are more useful than TOT when evaluating the dose–response pattern of a heterogeneous group of vibratory tools workers.


title: Using Dynamic Programming for Solving Variational Problems in Vision

abstract: Dynamic programming is discussed as an approach to solving variational problems in vision. Dynamic programming ensures global optimality of the solution, is numerically stable, and allows for hard constraints to be enforced on the behavior of the solution within a natural and straightforward structure. As a specific example of the approach's efficacy, applying dynamic programming to the energy-minimizing active contours is described. The optimization problem is set up as a discrete multistage decision process and is solved by a time-delayed discrete dynamic programming algorithm. A parallel procedure for decreasing computational costs is discussed. >


Arithmetic Coding for Data Compression


title: You're Hired! An Examination of Crowdsourcing Incentive Models in Human Resource Tasks

abstract: human resource tasks, such as screening a large number of job candidates, are labor-intensive and rely on subjective evaluation, making them excellent candidates for crowdsourcing. We conduct several experiments using the Amazon Mechanical Turk platform to conduct resume reviews. We then apply several incentive-based models and examine their effects. Next, we assess the accuracy measures of our incentive models against a gold standard and ascertain which incentives provide the best results. We find that some incentives actually encourage quality if the task is designed appropriately.


title: The Lack of A Priori Distinctions Between Learning Algorithms

abstract: This is the first of two papers that use off-training set (OTS) error to investigate the assumption-free relationship between learning algorithms. This first paper discusses the senses in which there are no a priori distinctions between learning algorithms. (The second paper discusses the senses in which there are such distinctions.) In this first paper it is shown, loosely speaking, that for any two algorithms A and B, there are “as many” targets (or priors over targets) for which A has lower expected OTS error than B as vice versa, for loss functions like zero-one loss. In particular, this is true if A is cross-validation and B is “anti-cross-validation” (choose the learning algorithm with largest cross-validation error). This paper ends with a discussion of the implications of these results for computational learning theory. It is shown that one cannot say: if empirical misclassification rate is low, the Vapnik-Chervonenkis dimension of your generalizer is small, and the training set is large, then with high probability your OTS error is small. Other implications for “membership queries” algorithms and “punting” algorithms are also discussed.


title: An approximation method for Type reduction of an Interval Type-2 fuzzy set based on α-cuts

abstract: This paper shows a proposal for Type-reduction of an Interval Type-2 fuzzy set composed from α-cuts done over its primary membership functions. The definition of available Type-reduction methods for Interval Type-2 fuzzy sets are based on an homogeneous subdivision of the universe of discourse, so we propose an approximation algorithm for Type-reduction of an Interval type-2 fuzzy set through its primary α-cuts. Some definitions about the α-cut of a Type-2 fuzzy set are provided and used for computing the centroid of an Interval Type-2 fuzzy set through a mapping of its membership function, instead of its universe of discourse.


title: Real-time visual odometry from dense RGB-D images

abstract: We present an energy-based approach to visual odometry from RGB-D images of a Microsoft Kinect camera. To this end we propose an energy function which aims at finding the best rigid body motion to map one RGB-D image into another one, assuming a static scene filmed by a moving camera. We then propose a linearization of the energy function which leads to a 6×6 normal equation for the twist coordinates representing the rigid body motion. To allow for larger motions, we solve this equation in a coarse-to-fine scheme. Extensive quantitative analysis on recently proposed benchmark datasets shows that the proposed solution is faster than a state-of-the-art implementation of the iterative closest point (ICP) algorithm by two orders of magnitude. While ICP is more robust to large camera motion, the proposed method gives better results in the regime of small displacements which are often the case in camera tracking applications.


title: Multi-Document Summarization via the Minimum Dominating Set

abstract: Multi-document summarization has been an important problem in information retrieval. It aims to distill the most important information from a set of documents to generate a compressed summary. Given a sentence graph generated from a set of documents where vertices represent sentences and edges indicate that the corresponding vertices are similar, the extracted summary can be described using the idea of graph domination. In this paper, we propose a new principled and versatile framework for multi-document summarization using the minimum dominating set. We show that four well-known summarization tasks including generic, query-focused, update, and comparative summarization can be modeled as different variations derived from the proposed framework. Approximation algorithms for performing summarization are also proposed and empirical experiments are conducted to demonstrate the effectiveness of our proposed framework.


title: Mutation in the CAV3 gene causes partial caveolin-3 deficiency and hyperCKemia.

abstract: Mutations in the caveolin-3 (CAV3) gene are associated with autosomal dominant limb-girdle muscular dystrophy (LGMD1C). The authors report a novel sporadic mutation in the CAV3 gene in two unrelated children with persistent elevated levels of serum creatine kinase (hyperCKemia) without muscle weakness. Immunohistochemistry and quantitative immunoblot analysis of caveolin-3 showed reduced expression of the protein in muscle fibers. Our data indicate that a partial caveolin-3 deficiency should be considered in the differential diagnosis of idiopathic hyperCKemia.


title: Multi-view Machines

abstract: With rapidly growing amount of data available on the web, it becomes increasingly likely to obtain data from different perspectives for multi-view learning. Some successive examples of web applications include recommendation and target advertising. Specifically, to predict whether a user will click an ad in a query context, there are available features extracted from user profile, ad information and query description, and each of them can only capture part of the task signals from a particular aspect/view. Different views provide complementary information to learn a practical model for these applications. Therefore, an effective integration of the multi-view information is critical to facilitate the learning performance. In this paper, we propose a general predictor, named multi-view machines (MVMs), that can effectively explore the full-order interactions between features from multiple views. A joint factorization is applied for the interaction parameters which makes parameter estimation more accurate under sparsity and renders the model with the capacity to avoid overfitting. Moreover, MVMs can work in conjunction with different loss functions for a variety of machine learning tasks. The advantages of MVMs are illustrated through comparison with other methods for multi-view prediction, including support vector machines (SVMs), support tensor machines (STMs) and factorization machines (FMs). A stochastic gradient descent method and a distributed implementation on Spark are presented to learn the MVM model. Through empirical studies on two real-world web application datasets, we demonstrate the effectiveness of MVMs on modeling feature interactions in multi-view data. A 3.51\% accuracy improvement is shown on MVMs over FMs for the problem of movie rating prediction, and 0.57\% for ad click prediction.


title: A General Technique to Train Language Models on Language Models

abstract: We show that under certain conditions, a language model can be trained on the basis of a second language model. The main instance of the technique trains a finite automaton on the basis of a probabilistic context-free grammar, such that the Kullback-Leibler distance between grammar and trained automaton is provably minimal. This is a substantial generalization of an existing algorithm to train an n-gram model on the basis of a probabilistic context-free grammar.


title: An interactive flower image recognition system

abstract: In this paper, we present an interactive system for recognizing flower images taken by digital cameras. The proposed system provides an interactive interface allowing each user to draw an appropriate bounding window that contains the interested flower region. Then, a flower boundary tracing method is developed to extract the flower region as accurately as possible. In addition to the color and shape features of the whole flower region, the color and shape features of the pistil/stamen area will also be used to represent the flower characteristics more precisely. Experiments conducted on two distinct databases consisting of 24 species and 102 species have shown that our proposed system outperforms other approaches in terms of the recognition rate.


title: Processable aqueous dispersions of graphene nanosheets

abstract: Graphene sheets offer extraordinary electronic, thermal and mechanical properties and are expected to find a variety of applications. A prerequisite for exploiting most proposed applications for graphene is the availability of processable graphene sheets in large quantities. The direct dispersion of hydrophobic graphite or graphene sheets in water without the assistance of dispersing agents has generally been considered to be an insurmountable challenge. Here we report that chemically converted graphene sheets obtained from graphite can readily form stable aqueous colloids through electrostatic stabilization. This discovery has enabled us to develop a facile approach to large-scale production of aqueous graphene dispersions without the need for polymeric or surfactant stabilizers. Our findings make it possible to process graphene materials using low-cost solution processing techniques, opening up enormous opportunities to use this unique carbon nanostructure for many technological applications.


title: A simple method for mass production of potato microtubers using a bioreactor system

abstract: A simple protocol for mass propagation of potato microtubers was developed using an automated low-cost bioreactor system. Microtubers of potato were induced by a two-step culture method. In the first step (step A), the stock plants were inoculated in the bioreactor for growth and multiplication of plantlets, After four weeks, the medium was replaced with a new one to proceed to step B for microtuber induction. Comparative studies between solid and bioreactor culture (continuous immersion [with net or without net] and temporary immersion in liquid medium using ebb and flood) revealed that shoot multiplication and growth were more efficient in continuous immersion (with net) bioreactor. We also studied the effect of inoculation density on potato micropropagation during bioreactor culture and maximum responses were recorded when there were 50 nodal explants per bioreactor. After shoot proliferation, the culture medium was replaced with one containing a higher concentration of sucrose, with or without 6-benzylaminopurine (BAP) and kept under dark conditions. The analysis of tuber classification according to size showed that addition of BAP in the culture medium influenced the formation of microtubers larger than 1.1 g. It has also been observed that there is a strong influence of medium renewal on individual microtuber growth during bioreactor culture of potato. The results indicate that our system could be applied for mass propagation of potato tubers at low cost.


title: Improving the lenet with batch normalization and online hard example mining for digits recognition

abstract: Nowadays, applications based on digits recognition and characters recognition have become much more reliable thanks to the rapid development of the DNN(deep neural network) architecture and constantly increasing the efficiency to the computing resources. A lot of methods have been proposed to improve the performance of DNNs, such as the ReLU (Rectified Linear Unit) which is a widely used alternative of the sigmoid activation function, Local Response Normalization and Batch Normalization. What's more, some methods, OHEM (Online Hard Sample Mining) for instance, aims to augment the capability of generalization of DNNs in certain scenarios. In this paper, we choose the LeNet architecture as our baseline model, which is known to work well on digit classification tasks. Starting from this baseline model, our goal is to investigate its drawbacks and enhance it by applying three different strategies including: adding activation layers right after each convolutional layer, applying batch normalization and, finally, the online hard example mining is adopted. By combining these methods together, we boost the performance of the baseline model from 98.106% to 98.720%.


title: Space-Time Block Codes from Orthogonal Designs

abstract: We introduce space-time block coding, a new paradigm for communication over Rayleigh fading channels using multiple transmit antennas. Data is encoded using a space-time block code and the encoded data is split into n streams which are simultaneously transmitted using n transmit antennas. The received signal at each receive antenna is a linear superposition of the n transmitted signals perturbed by noise. Maximum-likelihood decoding is achieved in a simple way through decoupling of the signals transmitted from different antennas rather than joint detection. This uses the orthogonal structure of the space-time block code and gives a maximum-likelihood decoding algorithm which is based only on linear processing at the receiver. Space-time block codes are designed to achieve the maximum diversity order for a given number of transmit and receive antennas subject to the constraint of having a simple decoding algorithm. The classical mathematical framework of orthogonal designs is applied to construct space-time block codes. It is shown that space-time block codes constructed in this way only exist for few sporadic values of n. Subsequently, a generalization of orthogonal designs is shown to provide space-time block codes for both real and complex constellations for any number of transmit antennas. These codes achieve the maximum possible transmission rate for any number of transmit antennas using any arbitrary real constellation such as PAM. For an arbitrary complex constellation such as PSK and QAM, space-time block codes are designed that achieve 1/2 of the maximum possible transmission rate for any number of transmit antennas. For the specific cases of two, three, and four transmit antennas, space-time block codes are designed that achieve, respectively, all, 3/4, and 3/4 of maximum possible transmission rate using arbitrary complex constellations. The best tradeoff between the decoding delay and the number of transmit antennas is also computed and it is shown that many of the codes presented here are optimal in this sense as well.


title: CMOS implementation of a new high speed, glitch-free 5-2 compressor for fast arithmetic operations

abstract: This paper presents a new high speed, low power 5-2 compressor which is constructed according to a sensible combination of pass transistor logics and static logics. The 5-2 compressor is designed based on a new truth table that is obtained by performing some changes on its conventional truth table. So simple structures are obtained in which capacitances of middle stages are decreased. Therefore, a reduction of power dissipation and a reduction of overall latency are achieved. Also, the input and internal driving problems have been decreased, considerably. Because of similar paths from inputs to the outputs, there will be no need for extra buffers in low latency paths to equalize the delays. Therefore, the power dissipation will be decreased and the output waveforms will be glitch-free. Furthermore, utilizing voltage full swing logics in these architectures has enhanced the speed of cascaded operations. The total latency and power dissipation of the proposed 5-2 compressor are about 302 ps and 248.62 μw, respectively, which is simulated by HSPICE using TSMC 0.18 μm CMOS technology.


title: The Future of Big Data Is ... JavaScript?

abstract: JavaScript has already made serious inroads as an integrated technology stack for building user-facing applications. In this article, the authors explore the idea of using JavaScript in Big Data platforms. Could the future be ... JavaScript everywhere?


title: The Cauchy problem for the system of equations describing migration motivated by regional economic disparity

abstract: We prove that there exists a unique global solution to the Cauchy problem for the system of nonlinear integro-partial differential equations describing migration motivated by regional economic disparity.


title: The Internet Archive and the socio-technical construction of historical facts

abstract: ABSTRACTThis article analyses the socio-technical epistemic processes behind the construction of historical facts by the Internet Archive Wayback Machine (IAWM). Grounded in theoretical debates in ...


title: Colour Feature Extraction Techniques of Fruits: A Survey

abstract: There is a great significance of agriculture in India. Fruit grading can improve the revenue of fruits. In the automated fruit grading system the most important feature is its colour. So for any automated fruit grading system one should have the idea of colour space and segmentation needs to be performed. As colour feature extraction has a vital role in grading, here a review of various colour feature extraction techniques in detail is provided.


abstract: pages 151–160, Dublin, Ireland, August 23-29 2014. A Probabilistic Model for Learning Multi-Prototype Word Embeddings Fei Tian†, Hanjun Dai∗, Jiang Bian‡, Bin Gao‡, Rui Zhang?, Enhong Chen†, Tie-Yan Liu‡ †University of Science and Technology of China, Hefei, P.R.China ∗Fudan University, Shanghai, P.R.China ‡Microsoft Research, Building 2, No. 5 Danling Street, Beijing, P.R.China ?Sun Yat-Sen University, Guangzhou, P.R.China †tianfei@mail.ustc.edu.cn, †cheneh@ustc.edu.cn, ∗daihanjun@gmail.com, ‡{jibian, bingao, tyliu}@microsoft.com, ?rayz0620@hotmail.com Abstract


title: Multi – Detector Row CT Systems and Image-Reconstruction Techniques 1

abstract: The introduction in 1998 of multi–detector row computed tomography (CT) by the major CT vendors was a milestone with regard to increased scan speed, improved z-axis spatial resolution, and better utilization of the available x-ray power. In this review, the general technical principles of multi–detector row CT are reviewed as they apply to the established four- and eight-section systems, the most recent 16-section scanners, and future generations of multi–detector row CT systems. Clinical examples are used to demonstrate both the potential and the limitations of the different scanner types. When necessary, standard single-section CT is referred to as a common basis and starting point for further developments. Another focus is the increasingly important topic of patient radiation exposure, successful dose management, and strategies for dose reduction. Finally, the evolutionary steps from traditional single-section spiral image-reconstruction algorithms to the most recent approaches toward multisection spir...