title: Prevention of Alzheimer's Disease Pathology by Cannabinoids: Neuroprotection Mediated by Blockade of Microglial Activation

abstract: Alzheimer's disease (AD) is characterized by enhanced beta-amyloid peptide (betaA) deposition along with glial activation in senile plaques, selective neuronal loss, and cognitive deficits. Cannabinoids are neuroprotective agents against excitotoxicity in vitro and acute brain damage in vivo. This background prompted us to study the localization, expression, and function of cannabinoid receptors in AD and the possible protective role of cannabinoids after betaA treatment, both in vivo and in vitro. Here, we show that senile plaques in AD patients express cannabinoid receptors CB1 and CB2, together with markers of microglial activation, and that CB1-positive neurons, present in high numbers in control cases, are greatly reduced in areas of microglial activation. In pharmacological experiments, we found that G-protein coupling and CB1 receptor protein expression are markedly decreased in AD brains. Additionally, in AD brains, protein nitration is increased, and, more specifically, CB1 and CB2 proteins show enhanced nitration. Intracerebroventricular administration of the synthetic cannabinoid WIN55,212-2 to rats prevent betaA-induced microglial activation, cognitive impairment, and loss of neuronal markers. Cannabinoids (HU-210, WIN55,212-2, and JWH-133) block betaA-induced activation of cultured microglial cells, as judged by mitochondrial activity, cell morphology, and tumor necrosis factor-alpha release; these effects are independent of the antioxidant action of cannabinoid compounds and are also exerted by a CB2-selective agonist. Moreover, cannabinoids abrogate microglia-mediated neurotoxicity after betaA addition to rat cortical cocultures. Our results indicate that cannabinoid receptors are important in the pathology of AD and that cannabinoids succeed in preventing the neurodegenerative process occurring in the disease.


title: Constructing PM2.5 Map Based on Mobile PM2.5 Sensor and Cloud Platform

abstract: In recent years, air quality and environmental protection are getting more and more concern in public, much people are pay attention to air quality of their surroundings. As one of air pollution PM2.5 will be affected to human body directly, it was invisible to the naked eye. According to many epidemiological studies have demonstrated the health hazards of PM2.5, including early death, bronchitis, asthma, cardiovascular disease, lung cancer, and etc. Most of the current PM2.5 Sensor is fixed, and it cannot true reflection of the air quality of people surroundings. Thus, we want to implement Mobile PM2.5 Sensor (MPM) to detect air quality of user surroundings. At the same time, the MPM will send the air quality data to user's smart phone, then send user's GPS location and sensor data to cloud server to construct PM2.5 map by using Google Map API, anyone can get the current air quality information of the region of PM2.5 concentration through PM2.5 maps.


YNU-HPCC at IJCNLP-2017 Task 1: Chinese Grammatical Error Diagnosis Using a Bi-directional LSTM-CRF Model


title: Prediction as a candidate for learning deep hierarchical models of data

abstract: Recent findings [HOT06] have made possible the learning of deep layered hierarchical representations of data mimicking the brains working. It is hoped that this paradigm will unlock some of the power of the brain and lead to advances towards true AI. In this thesis I implement and evaluate state-of-the-art deep learning models and using these as building blocks I investigate the hypothesis that predicting the time-to-time sensory input is a good learning objective. I introduce the Predictive Encoder (PE) and show that a simple non-regularized learning rule, minimizing prediction error on natural video patches leads to receptive fields similar to those found in Macaque monkey visual area V1. I scale this model to video of natural scenes by introducing the Convolutional Predictive Encoder (CPE) and show similar results. Both models can be used in deep architectures as a deep learning module.


title: Reasoning about Reasoning by Nested Conditioning: Modeling Theory of Mind with Probabilistic Programs

abstract: A wide range of human reasoning patterns can be explained as conditioning in probabilistic models; however, conditioning has traditionally been viewed as an operation applied to such models, not represented in such models. We describe how probabilistic programs can explicitly represent conditioning as part of a model. This enables us to describe reasoning about others' reasoning using nested conditioning. Much of human reasoning is about the beliefs, desires, and intentions of other people; we use probabilistic programs to formalize these inferences in a way that captures the flexibility and inherent uncertainty of reasoning about other agents. We express examples from game theory, artificial intelligence, and linguistics as recursive probabilistic programs and illustrate how this representation language makes it easy to explore new directions in each of these fields. We discuss the algorithmic challenges posed by these kinds of models and describe how dynamic programming techniques can help address these challenges.


title: Text-based question routing for question answering communities via deep learning

abstract: Online Communities for Question Answering (CQA) such as Quora and Stack Overflow face the challenge of providing sufficient answers for the questions asked by users. The exponential growing rate of the unanswered questions compromises the effectiveness of the CQA frameworks as knowledge sharing platforms. The main reason for this issue is the inefficient routing of the questions to the potential answerers, who are the field experts and interested users. This paper proposes the deep-learning-based technique QR-DSSM to increase the accuracy of the question routing process. This technique uses deep semantic similarity model (DSSM) to extract semantic similarity features using deep neural networks and use the features to rank users' profiles. QR-DSSM maps the asked questions and the profiles of the users into a latent semantic space where the ability to answer is measured using the cosine similarity between the questions and the profiles of the users. QR-DSSM experiments outperformed the baseline models such as LDA, SVM, and Rank-SVM techniques and achieved an MRR score of 0.1737.


title: Who reads research articles? An altmetrics analysis of Mendeley user categories

abstract: Little detailed information is known about who reads research articles and the contexts in which research articles are read. Using data about people who register in Mendeley as readers of articles, this article explores different types of users of Clinical Medicine, Engineering and Technology, Social Science, Physics, and Chemistry articles inside and outside academia. The majority of readers for all disciplines were PhD students, postgraduates, and postdocs but other types of academics were also represented. In addition, many Clinical Medicine articles were read by medical professionals. The highest correlations between citations and Mendeley readership counts were found for types of users who often authored academic articles, except for associate professors in some sub-disciplines. This suggests that Mendeley readership can reflect usage similar to traditional citation impact if the data are restricted to readers who are also authors without the delay of impact measured by citation counts. At the same time, Mendeley statistics can also reveal the hidden impact of some research articles, such as educational value for nonauthor users inside academia or the impact of research articles on practice for readers outside academia.


title: No reference color image contrast and quality measures

abstract: No-reference (NR) image quality assessment is essential in evaluating the performance of image enhancement and retrieval algorithms. Much effort has been made in recent years to develop objective NR grayscale and color image quality metrics that correlate with perceived quality evaluations. Unfortunately, only limited success has been achieved and most existing NR quality assessment is feasible only when prior knowledge about the types of image distortion is available. This paper present: a) a new NR contrast based grayscale image contrast measure: Root Mean Enhancement (RME); b) a NR color RME contrast measure CRME which explores the three dimensional contrast relationships of the RGB color channels; c) a NR color quality measure Color Quality Enhancement (CQE) which is based on the linear combination of colorfulness, sharpness and contrast. Computer simulations demonstrate that each measure has its own advantages: the CRME measure is fast and suitable for real time processing of low contrast images; the CQE measure can be used for a wider variety of distorted images. The effectiveness of the presented measures is demonstrated by using the TID2008 database. Experimental results also show strong correlations between the presented measures and Mean Opinion Score (MOS)1.


title: An Efficient and Fast Li-Ion Battery Charging System Using Energy Harvesting or Conventional Sources

abstract: This paper presents a multi-input battery charging system that is capable of increasing the charging efficiency of lithium-ion (Li-ion) batteries. The proposed battery charging system consists of three main building blocks: a pulse charger, a step-down dc–dc converter, and a power path controller. The pulse charger allows charging via a wall outlet or an energy harvesting system. It implements charge techniques that increase the battery charge efficiency of a Li-ion battery. The power path controller (PPC) functions as a power monitor and selects the optimal path for charging either via an energy harvesting system or an ac adapter. The step-down dc–dc converter provides an initial supply voltage to start up the energy harvesting system. The integrated circuit design is implemented on a CMOS 0.18 μm technology process. Experimental results verify that the proposed pulse charger reduces the charging time of 100 mAh and 45 mAh Li-ion batteries respectively by 37.35% and 15.56% and improves the charge efficiency by 3.15% and 3.27% when compared to the benchmark constant current-constant voltage charging technique. The step-down dc–dc converter has a maximum efficiency of 90% and the operation of the PPC is also verified by charging the battery via a thermoelectric energy harvesting system.


title: A Low Voltage Delta-Sigma Fractional Frequency Divider for Multi-band WSN Frequency Synthesizers

abstract: A 1 V low voltage delta-sigma fractional-N frequency divider for multi-band (780/868/915 MHz and 2.4 GHz) WSN frequency synthesizers is presented. The frequency divider consists of a dual-modulus prescaler, a pulse-swallow counter and a delta-sigma modulator. The high-speed and low-voltage phase-switching dual- modulus prescaler is used in the frequency divider. Low threshold voltage transistors are applied to overcome low voltage supply and forward phase-switching technique is adopted to prevent glitches. The modified delta- sigma modulator with long output sequence length and less spurs is adopted to minimize the fractional spurs. The frequency divider is designed in 0.18 µm TSMC RF CMOS technology under 1 V supply instead of the standard 1.8 V supply. The total chip area is 1190 µm × 485 µm including I/O pads. The post simulation results show the frequency divider operates normally over a wide range of 1.3-5.0 GHz and the core circuit (without test buffers) consumes 2.3 mW. Copyright © 2013 IFSA.


Chapter 12: Game Data Mining


title: Secure E-voting with blind signature

abstract: With a rapid growth in computer networks, many people can access the network through the Internet and therefore an electronic voting can be a viable alternative for conducting an election. An electronic voting system must attempt to achieve at least the same level of security as ordinary elections. We have developed an electronic voting system, E-voting for a general election. The E-voting system employs cryptographic techniques to overcome the security issues in the election process. In this system, voter's privacy is guaranteed by using a blind signature for confidentiality and voter's digital signature for voter's authentication. E-voting is implemented by employing Java socket technology and BouncyCastle cryptography provider. The provider, which is an open source library, is used to provide the secure communication channel. The voter's private key for digital signature is protected by using password-based encryption with SHA and Twofish-CBC algorithm so that only valid voter can use it.


title: Multidetector computerized tomographic fistulography in the evaluation of congenital branchial cleft fistulae and sinuses.

abstract: OBJECTIVE ::: The aim of this study was to primarily investigate the usefulness of computerized tomographic (CT) fistulography in the diagnosis and management of branchial cleft fistulae and sinuses. ::: ::: ::: STUDY DESIGN ::: Fifteen patients with confirmed branchial fistulae or sinuses who had undergone CT fistulography were included. The diagnoses were confirmed by clinical, radiologic, or histopathologic examinations. The internal openings, distribution, and neighboring relationship of the lesions presented by CT fistulography were analyzed to evaluate the usefulness in comparison with x-ray fistulography. ::: ::: ::: RESULTS ::: Nine patients were diagnosed with first branchial fistulae or sinuses, 2 with second branchial fistulae, and 4 with third or fourth branchial fistulae. The presence and location of the lesions could be seen on x-ray fistulography. The distribution of the lesions, internal openings, and neighboring relationship with parotid gland, carotid sheath, and submandibular gland could be clearly demonstrated on CT cross-sectional or volume-rendering images. ::: ::: ::: CONCLUSIONS ::: CT fistulography could provide valuable information and benefit surgical planning by demonstrating the courses of branchial anomalies in detail.


title: Detecting Flying Objects Using a Single Moving Camera

abstract: We propose an approach for detecting flying objects such as Unmanned Aerial Vehicles (UAVs) and aircrafts when they occupy a small portion of the field of view, possibly moving against complex backgrounds, and are filmed by a camera that itself moves. We argue that solving such a difficult problem requires combining both appearance and motion cues. To this end we propose a regression-based approach for object-centric motion stabilization of image patches that allows us to achieve effective classification on spatio-temporal image cubes and outperform state-of-the-art techniques. As this problem has not yet been extensively studied, no test datasets are publicly available. We therefore built our own, both for UAVs and aircrafts, and will make them publicly available so they can be used to benchmark future flying object detection and collision avoidance algorithms.


title: Online multiscale dynamic topic models

abstract: We propose an online topic model for sequentially analyzing the time evolution of topics in document collections. Topics naturally evolve with multiple timescales. For example, some words may be used consistently over one hundred years, while other words emerge and disappear over periods of a few days. Thus, in the proposed model, current topic-specific distributions over words are assumed to be generated based on the multiscale word distributions of the previous epoch. Considering both the long-timescale dependency as well as the short-timescale dependency yields a more robust model. We derive efficient online inference procedures based on a stochastic EM algorithm, in which the model is sequentially updated using newly obtained data; this means that past data are not required to make the inference. We demonstrate the effectiveness of the proposed method in terms of predictive performance and computational efficiency by examining collections of real documents with timestamps.


title: ROPdefender: a detection tool to defend against return-oriented programming attacks

abstract: Modern runtime attacks increasingly make use of the powerful return-oriented programming (ROP) attack techniques and principles such as recent attacks on Apple iPhone and Acrobat products to name some. These attacks even work under the presence of modern memory protection mechanisms such as data execution prevention (DEP). In this paper, we present our tool, ROPdefender, that dynamically detects conventional ROP attacks (that are based on return instructions). In contrast to existing solutions, ROPdefender can be immediately deployed by end-users, since it does not rely on side information (e.g., source code or debugging information) which are rarely provided in practice. Currently, our tool adds a runtime overhead of 2x which is comparable to similar instrumentation-based tools.


title: Dark silicon and the end of multicore scaling

abstract: Since 2005, processor designers have increased core counts to exploit Moore's Law scaling, rather than focusing on single-core performance. The failure of Dennard scaling, to which the shift to multicore parts is partially a response, may soon limit multicore scaling just as single-core scaling has been curtailed. This paper models multicore scaling limits by combining device scaling, single-core scaling, and multicore scaling to measure the speedup potential for a set of parallel workloads for the next five technology generations. For device scaling, we use both the ITRS projections and a set of more conservative device scaling parameters. To model single-core scaling, we combine measurements from over 150 processors to derive Pareto-optimal frontiers for area/performance and power/performance. Finally, to model multicore scaling, we build a detailed performance model of upper-bound performance and lower-bound core power. The multicore designs we study include single-threaded CPU-like and massively threaded GPU-like multicore chip organizations with symmetric, asymmetric, dynamic, and composed topologies. The study shows that regardless of chip organization and topology, multicore scaling is power limited to a degree not widely appreciated by the computing community. Even at 22 nm (just one year from now), 21% of a fixed-size chip must be powered off, and at 8 nm, this number grows to more than 50%. Through 2024, only 7.9x average speedup is possible across commonly used parallel workloads, leaving a nearly 24-fold gap from a target of doubled performance per generation.


title: A convex, smooth and invertible contact model for trajectory optimization

abstract: Trajectory optimization is done most efficiently when an inverse dynamics model is available. Here we develop the first model of contact dynamics defined in both the forward and inverse directions. The contact impulse is the solution to a convex optimization problem: minimize kinetic energy in contact space subject to non-penetration and friction-cone constraints. We use a custom interior-point method to make the optimization problem unconstrained; this is key to defining the forward and inverse dynamics in a consistent way. The resulting model has a parameter which sets the amount of contact smoothing, facilitating continuation methods for optimization. We implemented the proposed contact solver in our new physics engine (MuJoCo). A full Newton step of trajectory optimization for a 3D walking gait takes only 160 msec, on a 12-core PC.


title: An Evaluation on Feature Selection for Text Clustering

abstract: Feature selection methods have been successfully applied to text categorization but seldom applied to text clustering due to the unavailability of class label information. In this paper, we first give empirical evidence that feature selection methods can improve the efficiency and performance of text clustering algorithm. Then we propose a new feature selection method called "Term Contribution (TC)" and perform a comparative study on a variety of feature selection methods for text clustering, including Document Frequency (DF), Term Strength (TS), Entropy-based (En), Information Gain (IG) and χ2 statistic (CHI). Finally, we propose an "Iterative Feature Selection (IF)" method that addresses the unavailability of label problem by utilizing effective supervised feature selection method to iteratively select features and perform clustering. Detailed experimental results on Web Directory data are provided in the paper.


title: DCT${^2}$Gen: A Versatile TCP Traffic Generator for Data Centers

abstract: Only little is publicly known about traffic in non-educational data centers. Recent studies made some knowledge available, which gives us the opportunity to create more realistic traffic models for data center research. We used this knowledge to create the first publicly available traffic generator that produces realistic traffic between hosts in data centers of arbitrary size. We characterize traffic by using six probability distribution functions and concentrate on the generation of traffic on flow-level. The distribution functions are described as step functions, which makes our generator highly configurable to generate traffic for different kinds of data centers. Moreover, in data centers, traffic between hosts in the same rack and hosts in different racks have different properties. We model this phenomenon, making our generated traffic very realistic. We carefully evaluated our approach and conclude that it reproduces these characteristics with high accuracy.


title: Management of xml documents without schema in relational database systems

abstract: Abstract Many applications deal with highly flexible XML documents from different sources, which makes it difficult to define their structure by a fixed schema or a DTD. Therefore, it is necessary to explore ways to cope with such XML documents. The paper analyzes different storage and retrieval methods for schemaless XML documents using the capabilities of relational systems. We compare our experiences with the implementation of an XML-to-relational mapping with an LOB implementation in a commercial RDBMS. The paper concludes with a vision of how different storage methods could converge towards a common high-level XML-API for databases.


title: Evaluation of X2-handover performance based on RSRP measurement with Friis path loss using network simulator version 3 (NS-3)

abstract: Long term evolution (LTE) is the next generation after UMTS system which developed by 3GPP release 8. LTE as the fourth generation provides a high transmission speed and high mobility to the user. In this paper we concern about the mobility performance. In order to see the mobility performance, handover scenario is chosen. Handover scenario is based on lena-x2-handover-measure script which provided in network simulator (ns-3). The ns-3 is used to simulate the x2-handover using lena-x2-handover-measure. Lena-x2-handover-measure instantiates one UE attached to serving eNodeB and moves toward neighbor eNodeB. The focus is on the impact of A2 and A4 event based on measurement report known as reference signal receive power (RSRP) and reference signal receive quality (RSRQ) on x2-handover performance in different parameters such as velocity, distance and eNodeB transmitted power. Receive power is calculated using Friis path loss equation and later it will be compared with the simulation result.


title: Application of structured light imaging for high resolution mapping of underwater archaeological sites

abstract: This paper presents results from recent work using structured light laser profile imaging to create high resolution bathymetric maps of underwater archaeological sites. Documenting the texture and structure of submerged sites is a difficult task and many applicable acoustic and photographic mapping techniques have recently emerged. This effort was completed to evaluate laser profile imaging in comparison to stereo imaging and high frequency multibeam mapping. A ROV mounted camera and inclined 532 nm sheet laser were used to create profiles of the bottom that were then merged into maps using platform navigation data. These initial results show very promising resolution in comparison to multibeam and stereo reconstructions, particularly in low contrast scenes. At the test sites shown here there were no significant complications related to scattering or attenuation of the laser sheet by the water. The resulting terrain was gridded at 0.25 cm and shows overall centimeter level definition. The largest source of error was related to the calibration of the laser and camera geometry. Results from three small areas show the highest resolution 3D models of a submerged archaeological site to date and demonstrate that laser imaging will be a viable method for accurate three dimensional site mapping and documentation.


ANSI/IEEE 1471 and Systems Engineering •


title: Development of the Mental, Emotional, and Bodily Toughness Inventory in Collegiate Athletes and Nonathletes

abstract: Abstract Context: The assessment of an individual's mental toughness would assist clinicians in enhancing an individual's performance, improving compliance with the rehabilitation program, and improving the individual treatment program. However, no sound measure of mental toughness exists. Objective: To develop a new measure of mental toughness, the Mental, Emotional, and Bodily Toughness Inventory (MeBTough). Design: Participants were invited to complete a 45-item questionnaire. Setting: University research laboratory. Patients or Other Participants: A total of 261 undergraduate students were recruited to complete the questionnaire. Main Outcome Measure(s): The Rasch-calibrated item difficulties, fit statistics, and persons' mental toughness ability estimates were examined for model-data fit of the MeBTough. Results: Forty-three of the 45 items had good model-data fit with acceptable fit statistics. Results indicated that the distribution of items was fittingly targeted to the people and the collapsed ra...


title: A Prototype Electrical Actuator for Aircraft Flaps

abstract: This paper considers the electrical actuation of aircraft wing surfaces, with particular emphasis on flap systems. It discusses existing hydraulic and electrohydraulic systems and proposes an electrical alternative, examining the potential system benefits in terms of increased functionality, maintenance, and life-cycle costs. This paper then progresses to describe a full-scale actuation demonstrator of the flap system, including the high-speed electrical drive, step-down gearbox, and flaps. Detailed descriptions of the fault-tolerant motor, power electronics, control architecture, and position sensor systems are given, along with a range of test results, demonstrating the system in operation.


title: Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life

abstract: Two studies examined violent video game effects on aggression-related variables. Study 1 found that real-life violent video game play was positively related to aggressive behavior and delinquency. The relation was stronger for individuals who are characteristically aggressive and for men. Academic achievement was negatively related to overall amount of time spent playing video games. In Study 2, laboratory exposure to a graphically violent video game increased aggressive thoughts and behavior. In both studies, men had a more hostile view of the world than did women. The results from both studies are consistent with the General Affective Aggression Model, which predicts that exposure to violent video games will increase aggressive behavior in both the short term (e.g., laboratory aggression) and the long term (e.g., delinquency). Language: en


— Technical Report — Analyzing and Detecting Flash-based Malware using Lightweight MultiPath Exploration


Analysing the agglomerative hierarchical Clustering Algorithm for Categorical Attributes


title: Classification and management of acute wounds and open fractures

abstract: Abstract Open fractures and traumatic wounds represent a surgical challenge due to the risk of infection that can, at the extreme threaten life and limb but more commonly limit options for reconstruction. The treatment of these wounds requires removal of contamination and non-viable tissue in a way that minimizes further tissue damage, because healthy tissue in the wound bed is essential for microbial eradication. In more high-energy and complex wounds, particularly those associated with fractures, treatment requires a combined approach from both orthopaedic and plastic surgical teams. Initial surgical treatment is an urgent priority and must involve senior surgeons from both specialities. This combined approach can maximize potential for reconstruction and rehabilitation while minimizing risk of complications like infection and amputation. However, this combined approach requires significant resources and therefore these injuries should be managed in specialist centres appropriately staffed and resourced.


title: Copper chromite decorated on nitrogen-doped graphene aerogel as an efficient catalyst for thermal decomposition of ammonium perchlorate particles

abstract: In this study, CuCr2O4 spinel (CCO) nanoparticles decorated on three-dimensional graphene networks were synthesized using hydrothermal method followed by a calcination process. The as-prepared material was characterized by different analysis methods and used for catalytic thermal decomposition of ammonium perchlorate particles (AP). For this purpose, CuCr2O4@GA/AP composites were fabricated by solvent/non-solvent (composite processing) and simple mixing methods. The catalytic effect of the as-prepared composites was investigated by differential scanning calorimetric and thermogravimetric (TG) analysis techniques. Owing to the synergistic effect of the spinel-structured copper chromite nanoparticles, high surface area of graphene aerogel and composite processing of AP, the high-temperature decomposition of AP in the presence of 4 mass% CuCr2O4@GA nanocomposite prepared by solvent/non-solvent method was reduced from 432 to 323 °C and the heat released (∆H) from decomposition of AP was increased from 590 to 1760 J g−1.


title: Handling Incomplete Heterogeneous Data using VAEs

abstract: Variational autoencoders (VAEs), as well as other generative models, have been shown to be efficient and accurate to capture the latent structure of vast amounts of complex high-dimensional data. However, existing VAEs can still not directly handle data that are heterogenous (mixed continuous and discrete) or incomplete (with missing data at random), which is indeed common in real-world applications. ::: In this paper, we propose a general framework to design VAEs, suitable for fitting incomplete heterogenous data. The proposed HI-VAE includes likelihood models for real-valued, positive real valued, interval, categorical, ordinal and count data, and allows to estimate (and potentially impute) missing data accurately. Furthermore, HI-VAE presents competitive predictive performance in supervised tasks, outperforming supervised models when trained on incomplete data.


title: The Essential Guide to Realizing 5G-Connected UAVs with Massive MIMO

abstract: What will it take for drones, and the whole associated ecosystem, to take off? Arguably, infallible C&C channels for safe and autonomous flying, and high-throughput links for multi-purpose live video streaming. And indeed, meeting these aspirations may entail full cellular support, provided through 5G-and-beyond hardware and software upgrades by both mobile operators and manufacturers of these UAVs. In this article, we vouch for massive MIMO as the key building block to realize 5G-connected UAVs. Through the sheer evidence of 3GPP-compliant simulations, we demonstrate how massive MIMO can be enhanced by complementary network-based and UAV-based solutions, resulting in consistent UAV C&C support, large UAV uplink data rates, and harmonious coexistence with legacy ground users.


title: A dynamic network architecture

abstract: Network software is a critical component of any distributed system. Because of its complexity, network software is commonly layered into a hierarchy of protocols, or more generally, into a protocol graph . Typical protocol graphs—including those standardized in the ISO and TCP/IP network architectures—share three important properties; the protocol graph is simple, the nodes of the graph (protocols) encapsulate complex functionality, and the topology of the graph is relatively static. This paper describes a new way to organize network software that differs from conventional architectures in all three of these properties. In our approach, the protocol graph is complex, individual protocols encapsulate a single function, and the topology of the graph is dynamic. The main contribution of this paper is to describe the ideas behind our new architecture, illustrate the advantages of using the architecture, and demonstrate that the architecture results in efficient network software.


title: Automated scenario generation: Toward tailored and optimized military training in virtual environments

abstract: Scenario-based training exemplifies the learning-by-doing approach to human performance improvement. In this paper, we enumerate the advantages of incorporating automated scenario generation technologies into the traditional scenario development pipeline. An automated scenario generator is a system that creates training scenarios from scratch, augmenting human authoring to rapidly develop new scenarios, providing a richer diversity of tailored training opportunities, and delivering training scenarios on demand. We introduce a combinatorial optimization approach to scenario generation to deliver the requisite diversity and quality of scenarios while tailoring the scenarios to a particular learner's needs and abilities. We propose a set of evaluation metrics appropriate to scenario generation technologies and present preliminary evidence for the suitability of our approach compared to other scenario generation approaches.


title: Electric Load Forecasting

abstract: This article illustrates the application of a nonlinear system identification technique to the problem of STLF. Five NARX models are estimated using fixed-size LS-SVM, and two of the models are later modified into AR-NARX structures following the exploration of the residuals. The forecasting performance, assessed for different load series, is satisfactory. The MSE levels on the test data are below 3% in most cases. The models estimated with fixed-size LS-SVM give better results than a linear model estimated with the same variables and also better than a standard LS-SVM in dual space estimated using only the last 1000 data points. Furthermore, the good performance of the fixed-size LS-SVM is obtained based on a subset of M = 1000 initial support vectors, representing a small fraction of the available sample. Further research on a more dedicated definition of the initial input variables (for example, incorporation of external variables to reflect industrial activity, use of explicit seasonal information) might lead to further improvements and the extension toward other types of load series.


title: Hardware-assisted on-demand hypervisor activation for efficient security critical code execution on mobile devices

abstract: As more and more mobile applications need to run security critical codes (SCCs) for secure transactions and critical information handling, the demand for a Trusted Execution Environment (TEE) to ensure safe execution of SCCs is rapidly escalating. Although a number of studies have implemented TEEs using TrustZone or hypervisors and have evinced the effectiveness in terms of security, they face major challenges when considering deployment in mobile devices. TrustZone-based approaches bloat the TCB of the system as they must increase the code base size of the most privileged software. Hypervisor-based approaches incur performance overhead on mobile devices that are already suffering from resource restrictions. ::: ::: To alleviate these problems, in this paper, we propose a hybrid approach that utilizes both TrustZone and a hypervisor. Our approach basically implements a TEE using a hypervisor, while mitigating performance overhead by activating the hypervisor only when the TEE is demanded by SCCs. This scheme, called on-demand hypervisor activation, has been efficiently and securely implemented by leveraging the memory protection capability of TrustZone. We have implemented and experimented our system with real world applications. The results show that our system can successfully protect SCCs without any noticeable delay (< 100 µs), while limiting the overhead increase due to our hypervisor during its hibernation near 0 %.


title: Conceptions of narcissism and the DSM-5 pathological personality traits.

abstract: The Diagnostic and Statistical Manual of Mental Disorders–Fifth Edition (DSM-5) features two conceptions of Narcissistic Personality Disorder (NPD), one based on the retained DSM-IV’s categorical diagnosis and the other based on a model that blends impairments in personality functioning with a specific trait profile intended to recapture DSM-IV NPD. Nevertheless, the broader literature contains a richer array of potential conceptualizations of narcissism, including distinguishable perspectives from psychiatric nosology, clinical observation and theory, and social/personality psychology. This raises questions about the most advantageous pattern of traits to use to reflect various conceptions of narcissistic pathology via the Personality Inventory for the DSM-5 (PID-5). In this study, we examine the associations of the Personality Disorder Questionnaire–Narcissistic Personality Disorder scale, Narcissistic Personality Inventory–16, and the Pathological Narcissism Inventory and the PID-5 dimensions and facet...


title: Risk Management in Total System Ship Design

abstract: Ships are being designed with an increased emphasis on reduced life cycle costs, obtained through means such as reduced crew size, increased automation, and adoption of commercial practices. Ship cost is closely related to the likelihood and consequence of future events, or risk. Ship designers must have tools to assess and manage risks to obtain cost-effective designs. Risk assumptions were built into prescriptive standards, but performance standards are now being used. Ships systems built to varying degrees of acceptable risk are not cost-effective. Underdesigns and overdesigns will result, and the ship ends up only as strong as its weakest link. The authors propose that the top-down risk management methodology that is currently used for commercial ships be considered for use by naval ship designers in conjunction with both the traditional ship design spiral and the total systems ship design concept. The IMO-endorsed formal safety assessment (FSA) methodology was designed for marine systems. By adopting the FSA approach the Navy will benefit from existing lessons-learned, and will have a smoother transition into the application of commercial standards when the ship is built.


title: A comparative study of artificial intelligence and human doctors for the purpose of triage and diagnosis

abstract: Online symptom checkers have significant potential to improve patient care, however their reliability and accuracy remain variable. We hypothesised that an artificial intelligence (AI) powered triage and diagnostic system would compare favourably with human doctors with respect to triage and diagnostic accuracy. We performed a prospective validation study of the accuracy and safety of an AI powered triage and diagnostic system. Identical cases were evaluated by both an AI system and human doctors. Differential diagnoses and triage outcomes were evaluated by an independent judge, who was blinded from knowing the source (AI system or human doctor) of the outcomes. Independently of these cases, vignettes from publicly available resources were also assessed to provide a benchmark to previous studies and the diagnostic component of the MRCGP exam. Overall we found that the Babylon AI powered Triage and Diagnostic System was able to identify the condition modelled by a clinical vignette with accuracy comparable to human doctors (in terms of precision and recall). In addition, we found that the triage advice recommended by the AI System was, on average, safer than that of human doctors, when compared to the ranges of acceptable triage provided by independent expert judges, with only a minimal reduction in appropriateness.


title: Inverse Reinforcement Learning through policy gradient minimization

abstract: Inverse Reinforcement Learning (IRL) deals with the problem of recovering the reward function optimized by an expert given a set of demonstrations of the expert's policy. Most IRL algorithms need to repeatedly compute the optimal policy for different reward functions. This paper proposes a new IRL approach that allows to recover the reward function without the need of solving any "direct" RL problem. The idea is to find the reward function that minimizes the gradient of a parameterized representation of the expert's policy. In particular, when the reward function can be represented as a linear combination of some basis functions, we will show that the aforementioned optimization problem can be efficiently solved. We present an empirical evaluation of the proposed approach on a multidimensional version of the Linear-Quadratic Regulator (LQR) both in the case where the parameters of the expert's policy are known and in the (more realistic) case where the parameters of the expert's policy need to be inferred from the expert's demonstrations. Finally, the algorithm is compared against the state-of-the-art on the mountain car domain, where the expert's policy is unknown.


title: Semantic Role Labeling Using Dependency Trees

abstract: In this paper, a novel semantic role labeler based on dependency trees is developed. This is accomplished by formulating the semantic role labeling as a classification problem of dependency relations into one of several semantic roles. A dependency tree is created from a constituency parse of an input sentence. The dependency tree is then linearized into a sequence of dependency relations. A number of features are extracted for each dependency relation using a predefined linguistic context. Finally, the features are input to a set of one-versus-all support vector machine (SVM) classifiers to determine the corresponding semantic role label. We report results on CoNLL2004 shared task data using the representation and scoring scheme adopted for that task.


title: Phytochemical biopesticides: some recent developments

abstract: Synthetic pesticides in general, are highly toxic, persistent and their harmful residues contaminate crops, food commodities and pollute soils and groundwater. They adversely affect non-target organisms like pollinators, fish, birds, animals, and their excessive use results in increased resistance in pests. Phytochemical biopesticides on the other hand are less toxic, least persistent, environment friendly and safe to humans and non target organisms. Several phytochemical biopesticides like azadirachtin, nicotine, pyrethrins, rotenone, veratrum, annonins, rocaglamides, isobutylamides etc. have been successfully commercilalized in the past. In this review pesticidal products based on Madhuca indica (Mahua), Sapindus mukorossi (soapnut), Curcuma longa (turmeric), Pongamia glabra (karanja), Eupatorium adenophorum (Crofton weed), Tagetes erecta (marigold), Rheum emodi (Himalayan Rhubarb) and essential oil bearing plants have been discussed. Natural insecticide synergists derived from Anethum sowa and their semisynthetic derivatives have been used to prolong efficacy and counter resistance in insect pests.


title: DIVE: A Mixed-Initiative System Supporting Integrated Data Exploration Workflows

abstract: Generating knowledge from data is an increasingly important activity. This process of data exploration consists of multiple tasks: data ingestion, visualization, statistical analysis, and storytelling. Though these tasks are complementary, analysts often execute them in separate tools. Moreover, these tools have steep learning curves due to their reliance on manual query specification. Here, we describe the design and implementation of DIVE, a web-based system that integrates state-of-the-art data exploration features into a single tool. DIVE contributes a mixed-initiative interaction scheme that combines recommendation with point-and-click manual specification, and a consistent visual language that unifies different stages of the data exploration workflow. In a controlled user study with 67 professional data scientists, we find that DIVE users were significantly more successful and faster than Excel users at completing predefined data visualization and analysis tasks.


Real Time Vehicle Locking and Tracking System using GSM and GPS Technology-An Anti-theft System


title: A Survey of HTTP Caching Implementations on the Open Semantic Web

abstract: Scalability of the data access architecture in the Semantic Web is dependent on the establishment of caching mechanisms to take the load off of servers. Unfortunately, there is a chicken and egg problem here: Research, implementation, and evaluation of caching infrastructure is uninteresting as long as data providers do not publish relevant metadata. And publishing metadata is useless as long as there is no infrastructure that uses it. ::: ::: We show by means of a survey of live RDF data sources that caching metadata is prevalent enough already to be used in some cases. On the other hand, they are not commonly used even on relatively static data, and when they are given, they are very conservatively set. We point out future directions and give recommendations for the enhanced use of caching in the Semantic Web.


title: Financial fraud detection using social media crowdsourcing

abstract: Detecting financial fraud is a daunting challenge for banks and credit card companies due to massive amount of transaction data and wide diversity of user behaviors [1]. In recent years social media has demonstrated the capability of crowdsourcing in a broad range of applications, e.g., disseminating breaking news, launching marketing campaigns, and tracking the flu [2], [3], [4]. Inspired by these novel applications, this paper explores the benefits of social media crowdsourcing, in particularly the tweets, re-tweets and comments from Twitter online social network for effectively detecting financial fraud events.


title: A New Log Gabor Approach for Text Detection from Video

abstract: Text information embedded in video frames play an important role in content-based multimedia indexing and retrieval, as a result the automatic detection of texts from videos has gained wide attention in recent years. However,large variations in size, color, orientation and background complexity makes video text detection and extraction a challenging problem. In this paper an effective method for text detection and localization approach based on Log Gabor filter and Block Eigen map analysis is proposed. Log Gabor filter response is used to identify candidate text regions and Block wise Eigen map analysis is used to classify pixels as true text pixels. Finally,a heuristic based on gray scale pixel co-occurrence to eliminate false positives from the frame is used. Experimental results show the promising overall performance of the proposed method against the best features reported in the literature for the standard databases.


title: Utilize Old Coordinates: Faster Doubly Stochastic Gradients for Kernel Methods

abstract: To address the scalability issue of kernel methods, random features are commonly used for kernel approximation (Rahimi and Recht, 2007). They map the input data to a randomized low-dimensional feature space and apply fast linear learning algorithms on it. However, to achieve high precision results, one might still need a large number of random features, which is infeasible in large-scale applications. Dai et al. (2014) address this issue by recomputing the random features of small batches in each iteration instead of pre-generating for the whole dataset and keeping them in the memory. The algorithm increases the number of random features linearly with iterations, which can reduce the approximation error to arbitrarily small. A drawback of this approach is that the large number of random features slows down the prediction and gradient evaluation after several iterations. We propose two algorithms to remedy this situation by "utilizing" old random features instead of adding new features in certain iterations. By checking the expected descent amount, the proposed algorithm selects "important" old features to update. The resulting procedure is surprisingly simple without enhancing the complexity of the original algorithm but effective in practice. We conduct empirical studies on both medium and large-scale datasets, such as ImageNet, to demonstrate the power of the proposed algorithms.


Design Criteria for Reconstruction Filter-Optimized Sample and Hold Circuits


title: Locomotion of free-swimming ghost knifefish: anal fin kinematics during four behaviors

abstract: a b s t r a c t The maneuverability demonstrated by the weakly electric ghost knifefish (Apteronotus albifrons) is a result of its highly flexible ribbon-like anal fin, which extends nearly three-quarters the length of its body and is composed of approximately 150 individual fin rays. To understand how movement of the anal fin controls locomotion we examined kinematics of the whole fin, as well as selected individual fin rays, during four locomotor behaviors executed by free-swimming ghost knifefish: forward swimming, backward swimming, heave (vertical) motion, and hovering. We used high-speed video (1000 fps) to examine the motion of the entire anal fin and we measured the three-dimensional curvature of four adjacent fin rays in the middle of the fin during each behavior to determine how individual fin rays bend along their length during swimming. Canonical discriminant analysis separated all four behaviors on anal fin kinematic variables and showed that forward and backward swimming behaviors contrasted the most: forward behaviors exhibited a large anterior wavelength and posterior amplitude while during backward locomotion the anal fin exhibited both a large posterior wavelength and anterior amplitude. Heave and hover behaviors were defined by similar kinematic variables; however, for each variable, the mean values for heave motions were generally greater than for hovering. Individual fin rays in the middle of the anal fin curved substantially along their length during swimming, and the magnitude of this curvature was nearly twice the previously measured maximum curvature for ray-finned fish fin rays during locomotion. Fin rays were often curved into the direction of motion, indicating active control of fin ray curvature, and not just passive bending in response to fluid loading.


title: Interactive Visual Exploration of a Large Spatio-temporal Dataset: Reflections on a Geovisualization Mashup.

abstract: Exploratory visual analysis is useful for the preliminary investigation of large structured, multifaceted spatio-temporal datasets. This process requires the selection and aggregation of records by time, space and attribute, the ability to transform data and the flexibility to apply appropriate visual encodings and interactions. We propose an approach inspired by geographical 'mashups' in which freely-available functionality and data are loosely but flexibly combined using de facto exchange standards. Our case study combines MySQL, PHP and the LandSerf GIS to allow Google Earth to be used for visual synthesis and interaction with encodings described in KML. This approach is applied to the exploration of a log of 1.42 million requests made of a mobile directory service. Novel combinations of interaction and visual encoding are developed including spatial 'tag clouds', 'tag maps', 'data dials' and multi-scale density surfaces. Four aspects of the approach are informally evaluated: the visual encodings employed, their success in the visual exploration of the dataset, the specific tools used and the 'mashup' approach. Preliminary findings will be beneficial to others considering using mashups for visualization. The specific techniques developed may be more widely applied to offer insights into the structure of multifarious spatio-temporal data of the type explored here. Extracting structure, meaning and insight from large, multifaceted, spatio-temporal datasets is a challenging task and such data are consequently underused in many application domains (1). This paper considers how the increasingly used 'mashup' approach to software and data integration can be applied to exploratory visualisation in order to address this underuse. In doing so, wider issues for the use of mashups in visualisation are identified. Exploratory visual analysis that proceeds in an iterative fashion can be a highly effective means of preliminary investigation (2,3). Such analyses require the means to select, inspect and modify visual encodings, with data transformed, filtered, sampled and aggregated according to time-, space- and attribute-based criteria. As the exploration proceeds, flexibility is needed to generate new samples, derivatives (such as alternate spatial aggregations), filterings and visual encodings in response to the insights and informal hypothesis gained and to integrate these with ancillary data. This process can be likened to Shneiderman's (4) "visual information-seeking mantra: overview first, zoom and filter, then details-on-demand". Off-the-shelf geographic information systems (GIS) provide some of the functionality, particularly for spatial processing, but less flexibility for geovisualization. The alternative of developing tools from scratch with the required flexibility is difficult, time-consuming and requires skills not possessed by many engaged in


title: First-Language Acquisition in Adolescence: Evidence for a Critical Period for Verbal Language Development

abstract: It has been hypothesized that there is a critical period for first-language acquisition that extends into late childhood and possibly until puberty. The hypothesis is difficult to test directly because cases of linguistic deprivation during childhood are fortunately rare. We present here the case of E.M., a young man who has been profoundly deaf since birth and grew up in a rural area where he received no formal education and had no contact with the deaf community. At the age of 15, E.M. was fitted with hearing aids that corrected his hearing loss to 35 dB, and he began to learn verbal Spanish. We describe his language development over the 4-year period since his acquisition of hearing aids and conclude that he demonstrates severe deficits in verbal comprehension and production that support the critical period hypothesis.


title: Logical depth and physical complexity

abstract: Some mathematical and natural objects (a random sequence, a sequence of zeros, a perfect crystal, a gas) are intuitively trivial, while others (e.g. the human body, the digits of π) contain internal evidence of a nontrivial causal history. We formalize this distinction by defining an object’s “logical depth” as the time required by a standard universal Turing machine to generate it from an input that is algorithmically random (i.e. Martin-Lof random). This definition of depth is shown to be reasonably machineindependent, as well as obeying a slow-growth law: deep objects cannot be quickly produced from shallow ones by any deterministic process, nor with much probability by a probabilistic process, but can be produced slowly. Next we apply depth to the physical problem of “self-organization,” inquiring in particular under what conditions (e.g. noise, irreversibility, spatial and other symmetries of the initial conditions and equations of motion) statistical-mechanical model systems can imitate computers well enough to undergo unbounded increase of depth in the limit of infinite space and time.


title: Automated light probes from capture to render for Peter Rabbit

abstract: We created an efficient pipeline for automated, HDR light probes for the hybrid live-action / animated feature film Peter Rabbit. A specially developed "360°" spherical camera allows on-set acquisition at more positions and in less time than traditional techniques. Reduced capture time, drastically simplified stitching and a custom multiple-exposure raw to HDR process minimizes artefacts in the resulting images. A semi-automated system recovers clipped radiance in direct sunlight using surfaces with known properties. By recording capture location and orientation and combining with other scene data we produce automated rendering setups using the light probes for illumination and projection onto 3d render geometry.


Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control


title: Efficient and Available In-Memory KV-Store with Hybrid Erasure Coding and Replication

abstract: In-memory key/value store (KV-store) is a key building block for many systems like databases and large websites. Two key requirements for such systems are efficiency and availability, which demand a KV-store to continuously handle millions of requests per second. A common approach to availability is using replication, such as primary-backup (PBR), which, however, requires M+1 times memory to tolerate M failures. This renders scarce memory unable to handle useful user jobs. This article makes the first case of building highly available in-memory KV-store by integrating erasure coding to achieve memory efficiency, while not notably degrading performance. A main challenge is that an in-memory KV-store has much scattered metadata. A single KV put may cause excessive coding operations and parity updates due to excessive small updates to metadata. Our approach, namely Cocytus, addresses this challenge by using a hybrid scheme that leverages PBR for small-sized and scattered data (e.g., metadata and key), while only applying erasure coding to relatively large data (e.g., value). To mitigate well-known issues like lengthy recovery of erasure coding, Cocytus uses an online recovery scheme by leveraging the replicated metadata information to continuously serve KV requests. To further demonstrate the usefulness of Cocytus, we have built a transaction layer by using Cocytus as a fast and reliable storage layer to store database records and transaction logs. We have integrated the design of Cocytus to Memcached and extend it to support in-memory transactions. Evaluation using YCSB with different KV configurations shows that Cocytus incurs low overhead for latency and throughput, can tolerate node failures with fast online recovery, while saving 33% to 46% memory compared to PBR when tolerating two failures. A further evaluation using the SmallBank OLTP benchmark shows that in-memory transactions can run atop Cocytus with high throughput, low latency, and low abort rate and recover fast from consecutive failures.


title: pp. X–XX ASYMPTOTIC BEHAVIOR OF A DIFFUSIVE SCHEME SOLVING THE INVISCID ONE-DIMENSIONAL PRESSURELESS GASES SYSTEM

abstract: In this work, we discuss some numerical properties of the viscous numerical scheme introduced in [Boudin, Mathiaud, NMPDE 2012] to solve the one-dimensional pressureless gases system, and study in particular, from a computational viewpoint, its asymptotic behavior when the viscosity parameter used in the scheme becomes smaller.


title: Robust joint discriminative feature learning for visual tracking

abstract: Because of the complementarity of multiple visual cues (features) in appearance modeling, many tracking algorithms attempt to fuse multiple features to improve the tracking performance from two aspects: increasing the representation accuracy against appearance variations and enhancing the discriminability between the tracked target and its background. Since both these two aspects simultaneously contribute to the success of a visual tracker, how to fully unleash the capabilities of multiple features from these two aspects in appearance modeling is a key issue for feature fusion-based visual tracking. To address this problem, different from other feature fusion-based trackers which consider one of these two aspects only, this paper proposes an unified feature learning framework which simultaneously exploits both the representation capability and the discriminability of multiple features for visual tracking. In particular, the proposed feature learning framework is capable of: 1) learning robust features by separating out corrupted features for accurate feature representation, 2) seamlessly imposing the discriminabiltiy of multiple visual cues into feature learning, and 3) fusing features by exploiting their shared and feature-specific discriminative information. Extensive experiment results on challenging videos show that the the proposed tracker performs favourably against other ten state-of-the-art trackers.


title: Why is scheduling difficult? a CSP perspective

abstract: Interest in Constraint Satisfaction Problems (CSP) continues to grow, fueled by both their uniform problem representation, i.e., constraint graph, and conceptually clear problem solver, i.e., heuristically guided variable and value ordering. With the advent of interval constraints, e.g, temporal and spatial, and their associated consistency techniques, and the availability of Constraint Language Programming (CLP) it has become possible to explore complex problems such as planning and scheduling. This paper explores how a sequence of successively more complex scheduling problems can be modeled as a CSP, and the relevance of existing CSP problem solving heuristics. A number of problems arise with the CSP paradigm in modeling and solving scheduling problems: 1) Scheduling is an optimization problem in a very large combinatorial space. Therefore a good solution must be found as quickly as possible. 2) The existence of alternative process plans introduces disjunctive constraints in the constraint graph. 3) In most scheduling problems there exists a plethora of constraints. It is often the case that the problem is infeasible, requiring that one or more constraints be relaxed in order to find a solution. 4) Scheduling, and resource allocation problems in general, have constraint graphs in which many variables are tightly coupled (by capacity constraints) restricting the assignment of the same value to a single variable.


A Tree Search Algorithm for the Manufacturer ’ s Pallet Loading Problem


Nearest-Neighbor and Clustering based Anomaly Detection Algorithms for RapidMiner


Developing competitive advantages: Successful export SMEs in Argentina, Chile and Colombia


title: epSICAR: An Emerging Patterns based approach to sequential, interleaved and Concurrent Activity Recognition

abstract: Recognizing human activities from sensor readings has recently attracted much research interest in pervasive computing. This task is particularly challenging because human activities are often performed in not only a simple (i.e., sequential), but also a complex (i.e., interleaved and concurrent) manner in real life. In this paper, we propose a novel Emerging Patterns based approach to Sequential, Interleaved and Concurrent Activity Recognition (epSICAR). We exploit Emerging Patterns as powerful discriminators to differentiate activities. Different from other learning-based models built upon the training dataset for complex activities, we build our activity models by mining a set of Emerging Patterns from the sequential activity trace only and apply these models in recognizing sequential, interleaved and concurrent activities. We conduct our empirical studies in a real smart home, and the evaluation results demonstrate that with a time slice of 15 seconds, we achieve an accuracy of 90.96% for sequential activity, 87.98% for interleaved activity and 78.58% for concurrent activity.


title: New Ranking Algorithms For Parsing And Tagging: Kernels Over Discrete Structures, And The Voted Perceptron

abstract: This paper introduces new learning algorithms for natural language processing based on the perceptron algorithm. We show how the algorithms can be efficiently applied to exponential sized representations of parse trees, such as the "all subtrees" (DOP) representation described by (Bod 1998), or a representation tracking all sub-fragments of a tagged sentence. We give experimental results showing significant improvements on two tasks: parsing Wall Street Journal text, and named-entity extraction from web data.


title: Effective elliptic arc selection from connected edge points

abstract: Extracting edge points from an image and fitting ellipses to them is a fundamental technique for computer vision applications. However, since the extracted edge points sometimes contain non-elliptic arcs such as line segments, it is a very difficult to extract only elliptic arcs from them. In this paper, we propose a new method for extracting elliptic arcs from a spatially connected point sequence. We first fit an ellipse to an input point sequence and segment the sequence into partial arcs at the intersection points of the fitted ellipse. Next, we compute residuals of the fitted ellipse for all input points and select elliptic arcs among the segmented arcs by checking the curvatures of the residual graph. Then, we fit an ellipse to the selected arcs and repeat the above process until the selected arcs do not change. By using simulated data and real images, we compare the performance of our method with existing methods and show the efficiency of our proposed method.


CMOS on-chip stacked Marchand balun for millimeter-wave applications


title: Transcend: Detecting Concept Drift in Malware Classification Models

abstract: Building machine learning models of malware behavior is widely accepted as a panacea towards effective malware classification. A crucial requirement for building sustainable learning models, though, is to train on a wide variety of malware samples. Unfortunately, malware evolves rapidly and it thus becomes hard—if not impossible—to generalize learning models to reflect future, previously-unseen behaviors. Consequently, most malware classifiers become unsustainable in the long run, becoming rapidly antiquated as malware continues to evolve. In this work, we propose Transcend, a framework to identify aging classification models in vivo during deployment, much before the machine learning model’s performance starts to degrade. This is a significant departure from conventional approaches that retrain aging models retrospectively when poor performance is observed. Our approach uses a statistical comparison of samples seen during deployment with those used to train the model, thereby building metrics for prediction quality. We show how Transcend can be used to identify concept drift based on two separate case studies on Android andWindows malware, raising a red flag before the model starts making consistently poor decisions due to out-of-date training.


title: Bengali Handwriting Recognition and Conversion to Editable Text

abstract: Handwriting recognition has been a very active field of research in the past few years in different sectors. Analyzing and deducing information from various handwritings can help to tackle many ongoing issues. Though extensive work has been done for English handwritings, any progress can hardly be seen in other languages like Bengali. Hence in this paper, a system has been proposed that takes a scanned image of Bengali handwritten text as its input and after processing it gives an editable version of that text. The system consists of many phases which mainly conducts image processing, machine learning by training the neural network and lastly identification of the Bengali characters. Several data and algorithms have been used to produce a thorough and accurate result.


title: Distributed Intelligent MEMS: A Survey and a Real-Time Programming Framework

abstract: In recent years, distributed intelligent microelectromechanical systems (DiMEMSs) have appeared as a new form of distributed embedded systems. DiMEMSs contain thousands or millions of removable autonomous devices, which will collaborate with each other to achieve the final target of the whole system. Programming such systems is becoming an extremely difficult problem. The difficulty is due not only to their inherent nature of distributed collaboration, mobility, large scale, and limited resources of their devices (e.g., in terms of energy, memory, communication, and computation) but also to the requirements of real-time control and tolerance for uncertainties such as inaccurate actuation and unreliable communications. As a result, existing programming languages for traditional distributed and embedded systems are not suitable for DiMEMSs. In this article, we first introduce the origin and characteristics of DiMEMSs and then survey typical implementations of DiMEMSs and related research hotspots. Finally, we propose a real-time programming framework that can be used to design new real-time programming languages for DiMEMSs. The framework is composed of three layers: a real-time programming model layer, a compilation layer, and a runtime system layer. The design challenges and requirements of these layers are investigated. The framework is then discussed in further detail and suggestions for future research are given.


title: FDA-OFDM for integrated navigation, sensing, and communication systems

abstract: Integrated wireless communications, radar sensing, and smart navigation is becoming increasingly necessary owing to their similar finite radio frequency (RF) electromagnetic spectrum resource [1]. By integration of various disciplines, a multitude of new and more intelligent services, such as smart city and ambient assistant living, can become available. For instance, using a joint waveform for both radar and communication applications, the occupied spectrum would be used in a more efficiently way and both applications could be operated simultaneously. Such an integrated radar and communication systems has been reported in multiple papers [2]–[9]. An early example of integrated radar and communication system is the National Aeronautics and Space Administration Space Shuttle Orbiter [10]. Another example is the intelligent transportation system [11], which allows the intelligent vehicles to autonomously sense the driving environment and cooperatively exchange the information data. Furthermore, the proposals for integrating cognitive radar and cognitive radio for efficient spectrum sharing are also suggested [12]. Many applications would possibly benefit from the availability of integrated navigation, sensing, and communications, which advantages can be summarized as follows [1]: (i) System cost is reduced and resource utilization is improved; (ii) The advantages of radar, such as high power and strong directivity, can be utilized to improve the communication quality and increase the effective sensing range; (iii) System automation level is enhanced.


title: Towards an IP/GSE-Only Signalling Framework for DVB Transmission Systems

abstract: An Internet Protocol/Generic Stream Encapsulation (IP/GSE) signalling framework is proposed to replace the current MPEG-2 Transport Stream (TS) Table-based signalling system and to enable smoother convergence of Digital Video Broadcasting (DVB) transmission systems and IP networks. GSE suitability is explained and a review of IP-based signalling techniques is presented in order to select the optimal candidate methods for this architecture.


title: ARPKI: Attack Resilient Public-Key Infrastructure

abstract: We present ARPKI, a public-key infrastructure that ensures that certificate-related operations, such as certificate issuance, update, revocation, and validation, are transparent and accountable. ARPKI is the first such infrastructure that systematically takes into account requirements identified by previous research. Moreover, ARPKI is co-designed with a formal model, and we verify its core security property using the Tamarin prover. We present a proof-of-concept implementation providing all features required for deployment. ARPKI efficiently handles the certification process with low overhead and without incurring additional latency to TLS. ARPKI offers extremely strong security guarantees, where compromising n-1 trusted signing and verifying entities is insufficient to launch an impersonation attack. Moreover, it deters misbehavior as all its operations are publicly visible.


title: A review of on-board integrated chargers for electric vehicles

abstract: Electric vehicles (EVs) use grid power to charge their batteries. Because the battery is charged only when the car is parked -except for regeneration at braking-, using the on-board traction system components to form an integrated charging device is made possible. This paper presents a review of on-board integrated EV chargers and design criteria. It is aimed at presenting a state of the art on the integrated chargers to researchers, designers, and engineers. A classified list of around 70 research articles is also appended for a quick reference.


title: Column-Parallel Correlated Multiple Sampling Circuits for CMOS Image Sensors and Their Noise Reduction Effects

abstract: For low-noise complementary metal-oxide-semiconductor (CMOS) image sensors, the reduction of pixel source follower noises is becoming very important. Column-parallel high-gain readout circuits are useful for low-noise CMOS image sensors. This paper presents column-parallel high-gain signal readout circuits, correlated multiple sampling (CMS) circuits and their noise reduction effects. In the CMS, the gain of the noise cancelling is controlled by the number of samplings. It has a similar effect to that of an amplified CDS for the thermal noise but is a little more effective for 1/f and RTS noises. Two types of the CMS with simple integration and folding integration are proposed. In the folding integration, the output signal swing is suppressed by a negative feedback using a comparator and one-bit D-to-A converter. The CMS circuit using the folding integration technique allows to realize a very low-noise level while maintaining a wide dynamic range. The noise reduction effects of their circuits have been investigated with a noise analysis and an implementation of a 1Mpixel pinned photodiode CMOS image sensor. Using 16 samplings, dynamic range of 59.4 dB and noise level of 1.9 e- for the simple integration CMS and 75 dB and 2.2 e- for the folding integration CMS, respectively, are obtained.


title: Resolving Language and Vision Ambiguities Together: Joint Segmentation&Prepositional Attachment Resolution in Captioned Scenes

abstract: We present an approach to simultaneously perform semantic segmentation and prepositional phrase attachment resolution for captioned images. Some ambiguities in language cannot be resolved without simultaneously reasoning about an associated image. If we consider the sentence "I shot an elephant in my pajamas", looking at language alone (and not using common sense), it is unclear if it is the person or the elephant wearing the pajamas or both. Our approach produces a diverse set of plausible hypotheses for both semantic segmentation and prepositional phrase attachment resolution that are then jointly reranked to select the most consistent pair. We show that our semantic segmentation and prepositional phrase attachment resolution modules have complementary strengths, and that joint reasoning produces more accurate results than any module operating in isolation. Multiple hypotheses are also shown to be crucial to improved multiple-module reasoning. Our vision and language approach significantly outperforms the Stanford Parser (De Marneffe et al., 2006) by 17.91% (28.69% relative) and 12.83% (25.28% relative) in two different experiments. We also make small improvements over DeepLab-CRF (Chen et al., 2015).


title: — An Exploratory Case Study

abstract: The technical debt metaphor is increasingly being used to describe the effect of delaying certain software maintenance tasks on software projects. Practitioners understand intuitively how technical debt can turn into a serious problem if it is left unattended. However, it remains unknown how serious the problem is and whether explicit measurement and management of technical debt is useful. In this paper, we explore the effect of technical debt by tracking a single delayed maintenance task in a real software project throughout its lifecycle and simulate how explicit technical debt management might have changed project outcomes. The results from this study demonstrate how and to what extent technical debt affects software projects. The study also sheds light on the research methodologies that can be used to investigate the technical debt management problem.


title: Discovering Descriptive Tile Trees by Mining Optimal Geometric Subtiles

abstract: When analysing binary data, the ease at which one can interpret results is very important. Many existing methods, however, discover either models that are difficult to read, or return so many results interpretation becomes impossible. Here, we study a fully automated approach for mining easily interpretable models for binary data. We model data hierarchically with noisy tiles-rectangles with significantly different density than their parent tile. To identify good trees, we employ the Minimum Description Length principle. ::: We propose STIJL, a greedy any-time algorithm for mining good tile trees from binary data. Iteratively, it finds the locally optimal addition to the current tree, allowing overlap with tiles of the same parent. A major result of this paper is that we find the optimal tile in only $\Theta(NM\min(N, M))$ time. STIJL can either be employed as a top-$k$ miner, or by MDL we can identify the tree that describes the data best. ::: Experiments show we find succinct models that accurately summarise the data, and, by their hierarchical property are easily interpretable.


title: Knowledge Graph Embedding via Dynamic Mapping Matrix

abstract: Knowledge graphs are useful resources for numerous AI applications, but they are far from completeness. Previous work such as TransE, TransH and TransR/CTransR regard a relation as translation from head entity to tail entity and the CTransR achieves state-of-the-art performance. In this paper, we propose a more fine-grained model named TransD, which is an improvement of TransR/CTransR. In TransD, we use two vectors to represent a named symbol object (entity and relation). The first one represents the meaning of a(n) entity (relation), the other one is used to construct mapping matrix dynamically. Compared with TransR/CTransR, TransD not only considers the diversity of relations, but also entities. TransD has less parameters and has no matrix-vector multiplication operations, which makes it can be applied on large scale graphs. In Experiments, we evaluate our model on two typical tasks including triplets classification and link prediction. Evaluation results show that our approach outperforms state-of-the-art methods.


title: Conservative treatment of fractures of the thoracolumbar spine

abstract: We reviewed 48 patients with thoracolumbar fractures treated conservatively between 1988 and 1999. The average follow-up was 77.5 (31–137) months and average patient age (23 women, 25 men) was 46 (18–76) years. Twenty-nine patients suffered a fall from a height and 13 patients were injured in traffic accidents. Thirty-two patients had compression-type fractures and 16 burst-type fractures. There were no neurological deficits. Twenty-nine patients were treated by orthosis, 13 by body cast and six by bed rest. In addition to pain and functional scoring, we measured a number of radiographic parameters at the time of admission and at latest follow-up and compared the values. In patients with compression fractures there were significant changes in scoliosis angle and wedging index (p<0.05). The mean pain score was 1.66 and mean functional score 1.03. In patients with burst fractures, vertebral index, wedging index and height loss increased after treatment (p<0.05). The mean pain score was 1.26 and functional score 0.93. Compression fractures with kyphosis angle <30° are supposed to be stable and can be treated conservatively. If the kyphosis angle is more than 30°, magnetic resonance imaging (MRI) should be performed, and if the posterior ligamentous complex is damaged, surgery should be considered. In burst fractures, MRI should always be performed and conservative treatment should only be considered if there is no neurological deficit and the ligaments are intact.


title: Personal Visualization and Personal Visual Analytics

abstract: Data surrounds each and every one of us in our daily lives, ranging from exercise logs, to archives of our interactions with others on social media, to online resources pertaining to our hobbies. There is enormous potential for us to use these data to understand ourselves better and make positive changes in our lives. Visualization (Vis) and visual analytics (VA) offer substantial opportunities to help individuals gain insights about themselves, their communities and their interests; however, designing tools to support data analysis in non-professional life brings a unique set of research and design challenges. We investigate the requirements and research directions required to take full advantage of Vis and VA in a personal context. We develop a taxonomy of design dimensions to provide a coherent vocabulary for discussing personal visualization and personal visual analytics. By identifying and exploring clusters in the design space, we discuss challenges and share perspectives on future research. This work brings together research that was previously scattered across disciplines. Our goal is to call research attention to this space and engage researchers to explore the enabling techniques and technology that will support people to better understand data relevant to their personal lives, interests, and needs.


title: Text image deblurring via two-tone prior

abstract: General natural image deblurring methods do not work well for document images. We exploit a two-tone prior to steer the intermediate latent image towards a piece-wise constant image with only two distinct gray levels. This prior is helpful for the process of kernel estimation to overcome undesirable local minima, and it is not too restrictive to deblur text images with complex backgrounds. Our kernel estimation method comprises two stages, where we first employ contrast-enhancing two-tone prior and then use intermediate-value inhibition regularizer. The resulting optimization formulation is solved by half-quadratic splitting and alternating minimization techniques. The experimental results show that the proposed method is capable of achieving accurate results and compares well with the state-of-the-art.


Multilingualism in the Design of Degree Programmes: the Case of Higher School of Economics


title: Towards a swarm of agile micro quadrotors

abstract: We describe a prototype 75 g micro quadrotor with onboard attitude estimation and control that operates autonomously with an external localization system. The motivation for designing quadrotors at this scale comes from two observations. First, the agility of the robot increases with a reduction in size, a fact that is supported by experimental results in this paper. Second, smaller robots are able to operate in tight formations in constrained, indoor environments. We describe the hardware and software used to operate the vehicle as well our dynamic model. We also discuss the aerodynamics of vertical flight and the contribution of ground effect to the vehicle performance. Finally, we discuss architecture and algorithms to coordinate a team of these quadrotors, and provide experimental results for a team of 20 micro quadrotors.


title: Fog computing: Security issues, solutions and robust practices

abstract: Fog Computing (FC) has extended the services of cloud computing to the edge of the network. It inherits some of the characteristics from cloud computing but FC also have some distinguished features such as geo-distribution, location awareness and low latency. Along with the inherited characteristics, it also inherits the issues and problems of cloud computing like energy efficiency, resource management and security issues. This paper presents the critical analysis of the fog architecture with respect to security. The state of the art work done since 2012 is critical analyzed on the bases of security techniques and security threats. We grouped the existing security techniques on the bases of security goals achieved by each. It will provide a clear and comprehensive distinction between the security areas explored and those, which still need researchers' attention.


title: Origin-Destination Flow Data Smoothing and Mapping

abstract: This paper presents a new approach to flow mapping that extracts inherent patterns from massive geographic mobility data and constructs effective visual representations of the data for the understanding of complex flow trends. This approach involves a new method for origin-destination flow density estimation and a new method for flow map generalization, which together can remove spurious data variance, normalize flows with control population, and detect high-level patterns that are not discernable with existing approaches. The approach achieves three main objectives in addressing the challenges for analyzing and mapping massive flow data. First, it removes the effect of size differences among spatial units via kernel-based density estimation, which produces a measurement of flow volume between each pair of origin and destination. Second, it extracts major flow patterns in massive flow data through a new flow sampling method, which filters out duplicate information in the smoothed flows. Third, it enables effective flow mapping and allows intuitive perception of flow patterns among origins and destinations without bundling or altering flow paths. The approach can work with both point-based flow data (such as taxi trips with GPS locations) and area-based flow data (such as county-to-county migration). Moreover, the approach can be used to detect and compare flow patterns at different scales or in relatively sparse flow datasets, such as migration for each age group. We evaluate and demonstrate the new approach with case studies of U.S. migration data and experiments with synthetic data.


title: A user-centric machine learning framework for cyber security operations center

abstract: To assure cyber security of an enterprise, typically SIEM (Security Information and Event Management) system is in place to normalize security events from different preventive technologies and flag alerts. Analysts in the security operation center (SOC) investigate the alerts to decide if it is truly malicious or not. However, generally the number of alerts is overwhelming with majority of them being false positive and exceeding the SOC's capacity to handle all alerts. Because of this, potential malicious attacks and compromised hosts may be missed. Machine learning is a viable approach to reduce the false positive rate and improve the productivity of SOC analysts. In this paper, we develop a user-centric machine learning framework for the cyber security operation center in real enterprise environment. We discuss the typical data sources in SOC, their work flow, and how to leverage and process these data sets to build an effective machine learning system. The paper is targeted towards two groups of readers. The first group is data scientists or machine learning researchers who do not have cyber security domain knowledge but want to build machine learning systems for security operations center. The second group of audiences are those cyber security practitioners who have deep knowledge and expertise in cyber security, but do not have machine learning experiences and wish to build one by themselves. Throughout the paper, we use the system we built in the Symantec SOC production environment as an example to demonstrate the complete steps from data collection, label creation, feature engineering, machine learning algorithm selection, model performance evaluations, to risk score generation.


title: Robotic pets in the lives of preschool children

abstract: This study examined preschool children's reasoning about and behavioral interactions with one of the most advanced robotic pets currently on the retail market, Sony's robotic dog AIBO. Eighty children, equally divided between two age groups, 34-50 months and 58-74 months, participated in individual sessions that included play with and an interview about two artifacts: AIBO and a stuffed dog. Results showed similarities in children's reasoning about the two artifacts, but differences in their behavioral interactions. Discussion focuses on how robotic pets, as representative of an emerging technological genre in HCI, may be (a) blurring foundational ontological categories, and (b) impacting children's social and moral development. More broadly, results inform on our understanding of the human-robotic relationship.


title: Quantitative Data Analysis in Finance

abstract: Quantitative tools have been widely adopted in order to extract the massive information from a variety of financial data. Mathematics, statistics and computers algorithms have never been so important to financial practitioners in history. Investment banks develop equilibrium models to evaluate financial instruments; mutual funds applied time series to identify the risks in their portfolio; and hedge funds hope to extract market signals and statistical arbitrage from noisy market data. The rise of quantitative finance in the last decade relies on the development of computer techniques that make processing large datasets possible. As more data is available at a higher frequency, more researches in quantitative finance have switched to the microstructures of financial market. High frequency data is a typical example of big data that is characterized by the 3V’s: velocity, variety and volume. In addition, the signal-to-noise ratio in financial time series is usually very small. High frequency datasets are more likely to be exposed to extreme values, jumps and errors than the low frequency ones. Specific data processing techniques and quantitative models are elaborately designed to extract information from financial data efficiently. In this chapter, we present the quantitative data analysis approaches in finance. First, we review the development of quantitative finance in the past decade. Then we discuss the characteristics of high frequency data and the challenges it brings. The quantitative data analysis consists of two basic steps: (i) data cleaning and aggregating; (ii) data modeling. We review the mathematics tools and computing technologies behind the two steps. The valuable information extracted from raw data is represented by a group of statistics. The most widely used statistics in finance are expected return and volatility, which are the fundamentals of modern portfolio theory. We further introduce some simple portfolio optimization strategies as an example of the application of financial data analysis. Big data has already changed financial industry fundamentally; while quantitative tools for addressing massive financial data still have a long way to go. Adoptions of advanced statistics, information theory, machine learning and faster computing algorithms are inevitable in order to predict complicated financial markets. These topics are briefly discussed in the later part of this chapter.


title: Calibration of the Computer Science and Applications, Inc. accelerometer

abstract: ABSTRACTPurpose:We established accelerometer count ranges for the Computer Science and Applications, Inc. (CSA) activity monitor corresponding to commonly employed MET categories.Methods:Data were obtained from 50 adults (25 males, 25 females) during treadmill exercise at three different speeds (4.8


title: Microalgae as a raw material for biofuels production

abstract: Biofuels demand is unquestionable in order to reduce gaseous emissions (fossil CO(2), nitrogen and sulfur oxides) and their purported greenhouse, climatic changes and global warming effects, to face the frequent oil supply crises, as a way to help non-fossil fuel producer countries to reduce energy dependence, contributing to security of supply, promoting environmental sustainability and meeting the EU target of at least of 10% biofuels in the transport sector by 2020. Biodiesel is usually produced from oleaginous crops, such as rapeseed, soybean, sunflower and palm. However, the use of microalgae can be a suitable alternative feedstock for next generation biofuels because certain species contain high amounts of oil, which could be extracted, processed and refined into transportation fuels, using currently available technology; they have fast growth rate, permit the use of non-arable land and non-potable water, use far less water and do not displace food crops cultures; their production is not seasonal and they can be harvested daily. The screening of microalgae (Chlorella vulgaris, Spirulina maxima, Nannochloropsis sp., Neochloris oleabundans, Scenedesmus obliquus and Dunaliella tertiolecta) was done in order to choose the best one(s), in terms of quantity and quality as oil source for biofuel production. Neochloris oleabundans (fresh water microalga) and Nannochloropsis sp. (marine microalga) proved to be suitable as raw materials for biofuel production, due to their high oil content (29.0 and 28.7%, respectively). Both microalgae, when grown under nitrogen shortage, show a great increase (approximately 50%) in oil quantity. If the purpose is to produce biodiesel only from one species, Scenedesmus obliquus presents the most adequate fatty acid profile, namely in terms of linolenic and other polyunsaturated fatty acids. However, the microalgae Neochloris oleabundans, Nannochloropsis sp. and Dunaliella tertiolecta can also be used if associated with other microalgal oils and/or vegetable oils.


title: Efficient smile detection by Extreme Learning Machine

abstract: Smile detection is a specialized task in facial expression analysis with applications such as photo selection, user experience analysis, and patient monitoring. As one of the most important and informative expressions, smile conveys the underlying emotion status such as joy, happiness, and satisfaction. In this paper, an efficient smile detection approach is proposed based on Extreme Learning Machine (ELM). The faces are first detected and a holistic flow-based face registration is applied which does not need any manual labeling or key point detection. Then ELM is used to train the classifier. The proposed smile detector is tested with different feature descriptors on publicly available databases including real-world face images. The comparisons against benchmark classifiers including Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) suggest that the proposed ELM based smile detector in general performs better and is very efficient. Compared to state-of-the-art smile detector, the proposed method achieves competitive results without preprocessing and manual registration.


title: AI for game production

abstract: A number of changes are occurring in the field of computer game development: persistent online games, digital distribution platforms and portals, social and mobile games, and the emergence of new business models have pushed game development to put heavier emphasis on the live operation of games. Artificial intelligence has long been an important part of game development practices. The forces of change in the industry present an opportunity for Game AI to have new and profound impact on game production practices. Specifically, Game AI agents should act as “producers” responsible for managing a long-running set of live games, their player communities, and real-world context. We characterize a confluence of four major forces at play in the games industry today, together producing a wealth of data that opens unique research opportunities and challenges for Game AI in game production. We enumerate 12 new research areas spawned by these forces and steps toward how they can be addressed by data-driven Game AI Producers.


Computational Thinking Patterns


title: Trace elements and thyroid cancer

abstract: To evaluate the importance of trace amounts of elements in thyroid cancer etiology and diagnostics, instrumental neutron activation analysis has been used to estimate Ag, Co, Cr, Fe, Hg, I,Rb, Sb, Sc, Se, and Zn concentrations in malignant and benign thyroid nodules as well as in apparently intact paranodular thyroid tissue. Resected material from 135 patients was obtained from operations. Forty-five cancer cases were diagnosed and the rest were of benign nodules. The thyroid glands of 65 people, 53 male and 12 female, who died and unexpected death or committed suicide, were used as a control group. Trace element contents of the International Atomic Energy Agency reference material H-4 (animal muscle) were analysed simultaneously with the thyroid tissue in order to evaluate the accuracy of the obtained data. No dependence of trace element contents on sex and age (14-80 years) was found for normal thyroids. In paranodular tissue, the Ag, Co, Hg, I and Rb contents were much higher for malignant and benign nodules than they were for the standard. There was also a slight deficiency of Se in the nodules compared with the standard. This result supports the hypothesis that the direct toxic heavy metal influence on thyrocytes plays a major role in thyroid cancer etiology, provided that an adequate level of the defence mechanisms is absent. Iodine concentrations are 15 times lower, on average, in malignant compared with benign nodules. It is also shown that the radio between the iodine concentration in nodular and paranodular tissue can be used for in vivo thyroid cancer diagnostics.