title: A Research on Iron Loss of IPMSM With a Fractional Number of Slot Per Pole

abstract: In this paper, we investigated rotor iron loss of interior permanent magnet synchronous machine (IPMSM), which has distributed armature windings. In order to study the iron loss with the effect of slot-pole combination, two machines for high-speed operation such as electric vehicle were designed. One is fractional slot winding and the other is conventional one. In the analysis, we developed a new iron loss model of electrical machines for high-speed operation. The calculated iron loss was compared with the experimental data. It was clarified that the proposed method can estimate iron loss effectively at high-speed operation. Based on this newly proposed method, we analyzed iron loss of the two machines according to their driving conditions. From the analysis results, it was shown that rotor iron loss of machine employing fractional slot-winding is definitely large at load condition. In addition, it is interesting that the ratio (rotor iron loss/stator iron loss) becomes larger as the speed of the machines increase if the number of slot per pole is fractional.


Source Code Authorship Attribution


title: A test of central coherence theory: linguistic processing in high-functioning adults with autism or Asperger syndrome: is local coherence impaired?

abstract: Central coherence theory (Frith, U., 1989. Autism: Explaining the Enigma. Blackwell, Oxford.) is addressed by exploring linguistic processing in normally intelligent adults with either autism or Asperger syndrome, to test whether local coherence is impaired. Local coherence is the ability to make contextually meaningful connections between linguistic information in short-term or working memory. Experiment 1 demonstrated that individuals with an autism spectrum condition were less likely to use the sentence context spontaneously to provide the context-appropriate pronunciation of a homograph. Experiment 2 presented scenarios which had a situation and outcome which only cohered if a bridging inference was drawn. The clinical groups were less likely to select the most coherent (bridging) inference from competing alternatives. Experiment 3 demonstrated that individuals with an autism spectrum condition were less able to use context to interpret an auditorily presented ambiguous sentence. The findings from Experiments 2 and 3 suggest that individuals with autism or Asperger syndrome have a difficulty in achieving local coherence, while the evidence from Experiment 1 suggests a preference not to strive for coherence. Taken together, these results suggest that individuals with an autism spectrum condition are impaired in achieving local coherence, and they have a preference not to strive for coherence unless instructed to do so, or unless they make a conscious decision to do so. Moreover, the three experiments correlate with one another, which suggests that central coherence may be a unitary force in these different tasks. Of the two clinical groups, the autism group had the greater difficulty in achieving coherence. Possible explanations for the clinical groups' difficulty are explored.


title: Experimental addition of Eleutherococcus senticosus and probiotic to the canine diet

abstract: There is a current trend to support pet health through the addition of natural supplements to their diet, taking into account the high incidence of medical conditions related to their immune system and gastrointestinal tract. This study investigates effects of the plant Eleutherococcus senticosus as a dietary additive on faecal microbiota, faecal characteristics, blood serum biochemistry and selected parameters of cellular immunity in healthy dogs. A combination of the plant with the canine-derived probiotic strain Lactobacillus fermentum CCM 7421 was also evaluated. Thirty-two dogs were devided into 4 treatment groups; receiving no additive (control), dry root extract of E. senticosus (8 mg/kg of body weight), probiotic strain (108 CFU/mL, 0.1 mL/kg bw) and the combination of both additives. The trial lasted 49 days with 14 days supplementation period. Results confirm no antimicrobial effect of the plant on the probiotic abundance either in vitro (cultivation test) or in vivo. The numbers of clostridia, lactic acid bacteria and Gram-negative bacteria as well as the concentration of serum total protein, triglyceride, glucose and aspartate aminotransferase were significantly altered according to the treatment group. Leukocyte phagocytosis was significantly stimulated by the addition of probiotic while application of plant alone led to a significant decrease.


title: Firmware modification attacks on programmable logic controllers

abstract: Abstract Recent attacks on industrial control systems, such as the highly publicized Stuxnet malware, have intensified a “race to the bottom” where lower-level attacks have a tactical advantage. Programmable logic controller (PLC) firmware, which provides a software-driven interface between system inputs and physical outputs, can be easily modified at the user level. Efforts directed at protecting against firmware modification are hindered by the lack of foundational research about attack development and implementation. This paper examines the vulnerability of PLCs to intentional firmware modifications in order to obtain a better understanding of the threats posed by PLC firmware modification attacks and the feasibility of these attacks. A general firmware analysis methodology is presented, and a proof-of-concept experiment is used to demonstrate how legitimate firmware can be updated and uploaded to an Allen-Bradley ControlLogix L61 PLC.


title: A Survey of Conventional and Artificial Intelligence / Learning based Resource Allocation and Interference Mitigation Schemes in D2D Enabled Networks

abstract: 5th generation networks are envisioned to provide seamless and ubiquitous connection to 1000-fold more devices and is believed to provide ultra-low latency and higher data rates up to tens of Gbps. Different technologies enabling these requirements are being developed including mmWave communications, Massive MIMO and beamforming, Device to Device (D2D) communications and Heterogeneous Networks. D2D communication is a promising technology to enable applications requiring high bandwidth such as online streaming and online gaming etc. It can also provide ultra- low latencies required for applications like vehicle to vehicle communication for autonomous driving. D2D communication can provide higher data rates with high energy efficiency and spectral efficiency compared to conventional communication. The performance benefits of D2D communication can be best achieved when D2D users reuses the spectrum being utilized by the conventional cellular users. This spectrum sharing in a multi-tier heterogeneous network will introduce complex interference among D2D users and cellular users which needs to be resolved. Motivated by limited number of surveys for interference mitigation and resource allocation in D2D enabled heterogeneous networks, we have surveyed different conventional and artificial intelligence based interference mitigation and resource allocation schemes developed in recent years. Our contribution lies in the analysis of conventional interference mitigation techniques and their shortcomings. Finally, the strengths of AI based techniques are determined and open research challenges deduced from the recent research are presented.


title: Comment on"All-optical machine learning using diffractive deep neural networks"

abstract: Lin et al. (Reports, 7 September 2018, p. 1004) reported a remarkable proposal that employs a passive, strictly linear optical setup to perform pattern classifications. But interpreting the multilayer diffractive setup as a deep neural network and advocating it as an all-optical deep learning framework are not well justified and represent a mischaracterization of the system by overlooking its defining characteristics of perfect linearity and strict passivity.


title: ROSCC: An Efficient Remote Sensing Observation-Sharing Method Based on Cloud Computing for Soil Moisture Mapping in Precision Agriculture

abstract: The inversion of remote sensing images is crucial for soil moisture mapping in precision agriculture. However, the large size of remote sensing images complicates their management. Therefore, this study proposes a remote sensing observation sharing method based on cloud computing (ROSCC) to enhance remote sensing observation storage, processing, and service capability. The ROSCC framework consists of a cloud computing-enabled sensor observation service, web processing service tier, and a distributed database tier. Using MongoDB as the distributed database and Apache Hadoop as the cloud computing service, this study achieves a high-throughput method for remote sensing observation storage and distribution. The map, reduced algorithms and the table structure design in distributed databases are then explained. Along the Yangtze River, the longest river in China, Hubei Province was selected as the study area to test the proposed framework. Using GF-1 as a data source, an experiment was performed to enhance earth observation data (EOD) storage and achieve large-scale soil moisture mapping. The proposed ROSCC can be applied to enhance EOD sharing in cloud computing context, so as to achieve soil moisture mapping via the modified perpendicular drought index in an efficient way to better serve precision agriculture.


title: Ship of opportunity launch and recovery system for REMUS 600 AUV's

abstract: A new autonomous underwater vehicle (AUV) launch and recovery system has been built that is adaptable enough to allow integration to most vessels equipped with an A-frame or deck crane. The system was developed to allow an AUV to be lifted either horizontally or vertically depending on vehicle configuration or vessel geometry.


title: Neural Network Ensembles, Cross Validation, and Active Learning

abstract: Learning of continuous valued functions using neural network ensembles (committees) can give improved accuracy, reliable estimation of the generalization error, and active learning. The ambiguity is defined as the variation of the output of ensemble members averaged over unlabeled data, so it quantifies the disagreement among the networks. It is discussed how to use the ambiguity in combination with cross-validation to give a reliable estimate of the ensemble generalization error, and how this type of ensemble cross-validation can sometimes improve performance. It is shown how to estimate the optimal weights of the ensemble members using unlabeled data. By a generalization of query by committee, it is finally shown how the ambiguity can be used to select new training data to be labeled in an active learning scheme.


title: A new self-oscillation loop for MEMS vibratory gyroscopes

abstract: In this paper we analyze self-oscillation loop for MEMS vibratory gyroscopes, present analytical equations for system design, and propose a new self-oscillation loop to improve stability of the drive control system and the recovery speed of oscillation amplitude after a shock.


title: Local Probabilistic Models for Link Prediction

abstract: One of the core tasks in social network analysis is to predict the formation of links (i.e. various types of relationships) over time. Previous research has generally represented the social network in the form of a graph and has leveraged topological and semantic measures of similarity between two nodes to evaluate the probability of link formation. Here we introduce a novel local probabilistic graphical model method that can scale to large graphs to estimate the joint co-occurrence probability of two nodes. Such a probability measure captures information that is not captured by either topological measures or measures of semantic similarity, which are the dominant measures used for link prediction. We demonstrate the effectiveness of the co-occurrence probability feature by using it both in isolation and in combination with other topological and semantic features for predicting co-authorship collaborations on real datasets.


title: Practical limitations for deployment of LoRa gateways

abstract: In this paper, we discuss the upcoming network stack LoRaWAN and its underlying modulation technology LoRa, which is an implementation of a LPWAN (Low Power Wide Area Network) and consider practical limitations that can occur when restricted Machine-to-Machine (M2M) data agreements for the connection of LoRa gateways to a cellular network are involved. We provide a theoretical estimate of the maximum possible communication range of a LoRa link depending on output power and spreading factor and illustrate a LoRa-transceiver's contrary behaviour of energy consumption and range versus the achievable data rate by means of current consumption and RF output power measurement results for different combinations of modulation parameters. Our analysis of the data overhead introduced by LoRa gateways identifies that the amount of generated backhaul network traffic can cause substantial costs for IoT service providers in case of cellular-connected gateways or limit the number of supported sensor nodes.


title: Analyzing deception, evolvability, and behavioral rarity in evolutionary robotics

abstract: A common aim across evolutionary search is to skillfully navigate complex search spaces, which requires search algorithms that exploit search space structure. This paper focuses on evolutionary robotics (ER) in particular, wherein controllers for robots are evolved to produce complex behavior. One productive approach for probing search space structure is to analyze properties of fitness landscapes; however, this paper argues that ER may require a fresh perspective for landscape analysis, because ER often goes beyond the black-box setting, i.e. evaluations provide useful information about how robots behave, beyond scalar performance heuristics. Indeed, some ER algorithms explicitly exploit such behavioral information, e.g. to follow gradients of behavioral novelty rather than to climb gradients of increasing performance. Thus well-motivated behavior-aware metrics may aid probing search-space structure in ER. In particular, this paper argues that behavioral conceptions of deception, evolvability, and rarity may help to understand ER landscapes, and seeks to quantify and explore them within a common ER benchmark task. To help this investigation, an expressive but limited encoding is designed, such that the behavior of all possible individuals in the domain can be precomputed. The result is an efficient platform for experimentation that facilitates (1) probing exact quantifications of deception, evolvability, and rarity in the chosen domain, and (2) the ability to efficiently drive search through idealistic ground-truth measures. The results help develop intuitions and suggest possible new ER algorithms. The hope is that the extensible open-source framework enables quick experimentation and idea generation, aiding brainstorming of new search algorithms and measures.


title: Early Identification of Vulnerable Software Components via Ensemble Learning

abstract: Software components, which are vulnerable to being exploited, need to be identified and patched. Employing any prevention techniques designed for the purpose of detecting vulnerable software components in early stages can reduce the expenses associated with the software testing process significantly and thus help building a more reliable and robust software system. Although previous studies have demonstrated the effectiveness of adapting prediction techniques in vulnerability detection, the feasibility of those techniques is limited mainly because of insufficient training data sets. This paper proposes a prediction technique targeting at early identification of potentially vulnerable software components. In the proposed scheme, the potentially vulnerable components are viewed as mislabeled data that may contain true but not yet observed vulnerabilities. The proposed hybrid technique combines the supports vector machine algorithm and ensemble learning strategy to better identify potential vulnerable components. The proposed vulnerability detection scheme is evaluated using some Java Android applications. The results demonstrated that the proposed hybrid technique could identify potentially vulnerable classes with high precision and relatively acceptable accuracy and recall.


title: Language independent semantic kernels for short-text classification

abstract: Short-text classification is increasingly used in a wide range of applications. However, it still remains a challenging problem due to the insufficient nature of word occurrences in short-text documents, although some recently developed methods which exploit syntactic or semantic information have enhanced performance in short-text classification. The language-dependency problem, however, caused by the heavy use of grammatical tags and lexical databases, is considered the major drawback of the previous methods when they are applied to applications in diverse languages. In this article, we propose a novel kernel, called language independent semantic (LIS) kernel, which is able to effectively compute the similarity between short-text documents without using grammatical tags and lexical databases. From the experiment results on English and Korean datasets, it is shown that the LIS kernel has better performance than several existing kernels.


title: Recent astronomy highlights from the Odin satellite

abstract: Abstract Astronomy highlights, mainly from the third year of Odin observations – time shared 50/50% with aeronomy – are presented: the very low O 2 abundance limits achieved, the highly pressure broadened absorption lines of H 2 O, H 2 18 O, and CO (5 → 4) in the atmosphere of Mars, the high precision H 2 O and H 2 18 O observations of comets, the detections of NH 3 and H 2 O around the C-rich star IRC+10216 (CW Leo) and of H 2 O around the O-rich star W Hya, NH 3 and H 2 O observations of infall/outflow interactions, observations of H 2 O, H 2 18 O, H 2 17 O as well as NH 3 and 15 NH 3 in multiple absorptions towards Sgr B2, and in emission towards Orion KL, the H 2 O detection of several new outflows in the DR21 W75S region. We also discuss the results of deconvolution of high S / N H 2 O, CO and 13 CO (5 → 4) maps of the Orion KL region to 40″ resolution (the beam size of the Herschel telescope) and the first results from our ongoing “spectral scan” of Orion KL in bands around 555 and 570 GHz. Finally, a search for primordial molecules is presented.


title: Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur's entropy

abstract: The objective of image segmentation is to extract meaningful objects. A meaningful segmentation selects the proper threshold values to optimize a criterion using entropy. The conventional multilevel thresholding methods are efficient for bi-level thresholding. However, they are computationally expensive when extended to multilevel thresholding since they exhaustively search the optimal thresholds to optimize the objective functions. To overcome this problem, two successful swarm-intelligence-based global optimization algorithms, cuckoo search (CS) algorithm and wind driven optimization (WDO) for multilevel thresholding using Kapur's entropy has been employed. For this purpose, best solution as fitness function is achieved through CS and WDO algorithm using Kapur's entropy for optimal multilevel thresholding. A new approach of CS and WDO algorithm is used for selection of optimal threshold value. This algorithm is used to obtain the best solution or best fitness value from the initial random threshold values, and to evaluate the quality of a solution, correlation function is used. Experimental results have been examined on standard set of satellite images using various numbers of thresholds. The results based on Kapur's entropy reveal that CS, ELR-CS and WDO method can be accurately and efficiently used in multilevel thresholding problem.


title: The Language of Deceivers: Linguistic Features of Crowdfunding Scams

abstract: Crowdfunding sites with recent explosive growth are equally attractive platforms for swindlers or scammers. Though the growing number of articles on crowdfunding scams indicate that the fraud threats are accelerating, there has been little knowledge on the scamming practices and patterns. The key contribution of this research is to discover the hidden clues in the text by exploring linguistic features to distinguish scam campaigns from non-scams. Our results indicate that by providing less information and writing more carefully (and less informally), scammers deliberately try to deceive people; (i) they use less number of words, verbs, and sentences in their campaign pages. (ii) scammers make less typographical errors, 4.5-4.7 times lower than non-scammers.(iii) Expressivity of scams is 2.6-8.5 times lower as well.


title: Providing open access to PhD theses: visibility and citation benefits

abstract: Purpose ::: ::: ::: ::: ::: The purpose of this paper is to report the benefits of scientific communication model of open access (OA) repositories to e-theses. Specifically, the study focusses on the benefits of accessibility, dissemination, visibility and impact of PhD e-theses deposited in repositories. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The study analysed a random sample of 125 PhD e-theses deposited in an OA repository, specifically in the institutional repository (IR) of the University of Salamanca (Gredos). It tracked the sample for analysing on one side the visits and downloads of the e-theses, whose data are obtained from the statistics module of the repository, and on the other side the data citation provided by Google Scholar Citation on these e-theses. To analyse the differences between the indicators (visits, downloads and citations) in different knowledge areas, the Kurskall-Wallis test has been used. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The results indicate that OA IRs become an advantageous channel of scientific communication to grey literature like dissertations and PhD theses, because it increases visibility and use and also produces a significant citation rate. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: The paper uses metrics that are used in IR to measure the visibility and impact of a type of grey literature that is very difficult to track because it is unpublished. The dissemination of the grey literature through OA repositories makes it possible. The value of the work lies in the empirical evidence obtained from the analysed PhD e-theses of Gredos repository.


title: Design of a Novel Wideband and Dual-Polarized MagnetoElectric Dipole Antenna

abstract: A wideband antenna with ±45° dual polarization is proposed. The antenna is mainly comprised of four printed trapezoidal metal walls standing on a ground plane that forms a 3-D open square aperture cavity structure with tapered cut on the four wall edges. The metal walls are fed by two orthogonally positioned (±45°) feeding networks, forming two pairs of dipole arms. Because of the excited magnetic-electric resonances, wide bandwidth can be achieved. The proposed antenna is fabricated, and the measured results show that it has an impedance bandwidth of 56% (1.62–2.87 GHz) for VSWR < 1.5. In addition, isolation of larger than 30 dB is also exhibited across the bandwidth between the two feeding ports. Because the proposed antenna has a hollow inside structure, it is a good candidate for multiband sharing aperture antenna element. Furthermore, it has a simplified fabrication process and is suitable for outdoor base station antennas that require VSWR < 1.5.


title: Exposure to ideologically diverse news and opinion on Facebook

abstract: Exposure to news, opinion and civic information increasingly occurs through social media. How do these online networks influence exposure to perspectives that cut across ideological lines? Using de-identified data, we examined how 10.1 million U.S. Facebook users interact with socially shared news. We directly measured ideological homophily in friend networks, and examine the extent to which heterogeneous friends could potentially expose individuals to cross-cutting content. We then quantified the extent to which individuals encounter comparatively more or less diverse content while interacting via Facebook’s algorithmically ranked News Feed, and further studied users’ choices to click through to ideologically discordant content. Compared to algorithmic ranking, individuals’ choices about what to consume had a stronger effect limiting exposure to cross-cutting content.


title: Photos Don't Have Me, But How Do You Know Me?: Analyzing and Predicting Users on Instagram

abstract: This paper presents a comprehensive analysis on social media use and engagement by age and gender on Instagram. We define five user age groups (from 10s to 50s) and two user gender groups (males and females), and compare them based on three aspects: activity, image object, and tag. We especially excluded the information that indicates human (e.g., selfies, faces, body) for each aspect in order to examine whether users are still identifiable without that information. Our study results indicate that each user group exhibits unique characteristics and the features from each aspect can be used to develop user classification models (82% for gender and 43% for age classification) without relying on the information that specifically indicates age and gender.


title: Representation Stability as a Regularizer for Improved Text Analytics Transfer Learning

abstract: Although neural networks are well suited for sequential transfer learning tasks, the catastrophic forgetting problem hinders proper integration of prior knowledge. In this work, we propose a solution to this problem by using a multi-task objective based on the idea of distillation and a mechanism that directly penalizes forgetting at the shared representation layer during the knowledge integration phase of training. We demonstrate our approach on a Twitter domain sentiment analysis task with sequential knowledge transfer from four related tasks. We show that our technique outperforms networks fine-tuned to the target task. Additionally, we show both through empirical evidence and examples that it does not forget useful knowledge from the source task that is forgotten during standard fine-tuning. Surprisingly, we find that first distilling a human made rule based sentiment engine into a recurrent neural network and then integrating the knowledge with the target task data leads to a substantial gain in generalization performance. Our experiments demonstrate the power of multi-source transfer techniques in practical text analytics problems when paired with distillation. In particular, for the SemEval 2016 Task 4 Subtask A (Nakov et al., 2016) dataset we surpass the state of the art established during the competition with a comparatively simple model architecture that is not even competitive when trained on only the labeled task specific data.


title: A new method for perspective correction of document images

abstract: In this paper we propose a method for perspective distortion correction of rectangular documents. This scheme ::: exploits the orthogonality of the document edges, allowing to recover the aspect ratio of the original document. ::: The results obtained after correcting the perspective of several document images captured with a mobile phone ::: are compared with those achieved by digitizing the same documents with several scanner models.


title: Symbolic execution with mixed concrete-symbolic solving

abstract: Symbolic execution is a powerful static program analysis technique that has been used for the automated generation of test inputs. Directed Automated Random Testing (DART) is a dynamic variant of symbolic execution that initially uses random values to execute a program and collects symbolic path conditions during the execution. These conditions are then used to produce new inputs to execute the program along different paths. It has been argued that DART can handle situations where classical static symbolic execution fails due to incompleteness in decision procedures and its inability to handle external library calls. We propose here a technique that mitigates these previous limitations of classical symbolic execution. The proposed technique splits the generated path conditions into (a) constraints that can be solved by a decision procedure and (b) complex non-linear constraints with uninterpreted functions to represent external library calls. The solutions generated from the decision procedure are used to simplify the complex constraints and the resulting path conditions are checked again for satisfiability. We also present heuristics that can further improve our technique. We show how our technique can enable classical symbolic execution to cover paths that other dynamic symbolic execution approaches cannot cover. Our method has been implemented within the Symbolic PathFinder tool and has been applied to several examples, including two from the NASA domain.


title: From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification

abstract: We propose sparsemax, a new activation function similar to the traditional softmax, but able to output sparse probabilities. After deriving its properties, we show how its Jacobian can be efficiently computed, enabling its use in a network trained with backpropagation. Then, we propose a new smooth and convex loss function which is the sparsemax analogue of the logistic loss. We reveal an unexpected connection between this new loss and the Huber classification loss. We obtain promising empirical results in multi-label classification problems and in attention-based neural networks for natural language inference. For the latter, we achieve a similar performance as the traditional softmax, but with a selective, more compact, attention focus.


title: Multifocal planes head-mounted displays.

abstract: Stereoscopic head-mounted displays (HMD's) provide an effective capability to create dynamic virtual environments. For a user of such environments, virtual objects would be displayed ideally at the appropriate distances, and natural concordant accommodation and convergence would be provided. Under such image display conditions, the user perceives these objects as if they were objects in a real environment. Current HMD technology requires convergent eye movements. However, it is currently limited by fixed visual accommodation, which is inconsistent with real-world vision. A prototype multiplanar volumetric projection display based on a stack of laminated planes was built for medical visualization as discussed in a paper presented at a 1999 Advanced Research Projects Agency workshop (Sullivan, Advanced Research Projects Agency, Arlington, Va., 1999). We show how such technology can be engineered to create a set of virtual planes appropriately configured in visual space to suppress conflicts of convergence and accommodation in HMD's. Although some scanning mechanism could be employed to create a set of desirable planes from a two-dimensional conventional display, multiplanar technology accomplishes such function with no moving parts. Based on optical principles and human vision, we present a comprehensive investigation of the engineering specification of multiplanar technology for integration in HMD's. Using selected human visual acuity and stereoacuity criteria, we show that the display requires at most 27 equally spaced planes, which is within the capability of current research and development display devices, located within a maximal 26-mm-wide stack. We further show that the necessary in-plane resolution is of the order of 5 microm.


title: Geochemistry of ferromanganese crusts and nodules from the South Tasman Rise, southeast of Australia

abstract: Abstract Eighteen samples of ferromanganese oxide crusts up to 15 cm thick, and nodules, have been dredged or cored from elevated parts of the seafloor south of Tasmania. Ferromanganese deposits overlie a variety of basement rocks. Nodules are dominantly ellipsoidal and mononucleate, and range up to 13 cm in diameter. Surface textures for both crusts and nodules range from smooth to rough, gritty or botryoidal. Typically internally laminated, the ferromanganese deposits are dominantly ferruginous vernadite, while quartz, plagioclase, fluorapatite, phillipsite, goethite, and, in five samples todorokite, are minor phases. Both crusts and nodules show enrichment of Fe, Co and Pb and depletion of Mn, Ni, Cu and Zn, to be expected with a vernadite-dominated mineralogy. Cobalt and Pb content increase with decreasing water depth, with highest values in the range 1–2 km. The mineralogy, chemistry and physical appearance of the deposits indicate a hydrogenetic origin, probably in an environment swept by strong, erosive, and oxygenated bottom currents. The samples analyzed are similar in composition to seamount and ridge crusts and nodules from elsewhere in the Southwest Pacific. They are markedly different to those described from the North Pacific and, in particular, are generally much lower in Co. One Co-rich crust from a seamount on the South Tasman Rise (water depth 1700 m) does not fit in the distribution of Co in ferromanganese deposits of the southern Pacific, and may reflect a local zone of oxygen depletion associated with the subtropical convergence. Its significant enrichment in Co (0.8–1.0%) suggests a detailed survey may be warranted to fully assess the economic potential of this material.


title: Measuring Epistemic Curiosity and Its Diversive and Specific Components

abstract: A questionnaire constructed to assess epistemic curiosity (EC) and perceptual curiosity (PC) curiosity was administered to 739 undergraduates (546 women, 193 men) ranging in age from 18 to 65. The study participants also responded to the trait anxiety, anger, depression, and curiosity scales of the State-Trait Personality Inventory (STPI; Spielberger et al., 1979) and selected subscales of the Sensation Seeking (SSS; Zuckerman, Kolin, Price, & Zoob, 1964) and Novelty Experiencing (NES; Pearson, 1970) scales. Factor analyses of the curiosity items with oblique rotation identified EC and PC factors with clear simple structure. Subsequent analyses of the EC items provided the basis for developing an EC scale, with Diversive and Specific Curiosity subscales. Moderately high correlations of the EC scale and subscales with other measures of curiosity provided strong evidence of convergent validity. Divergent validity was demonstrated by minimal correlations with trait anxiety and the sensation-seeking measures,...


title: A mobile manipulator

abstract: This paper describes a mobile manipulator that uses its wheels for manipulation as well as locomotion. This robot, named the mobipulator, looks like a small car with four independently powered wheels, none of them steered. It is designed to manipulate paper and other objects on the surface of a desk. The wheels are used for locomotion or for manipulation, switching functions dynamically as the task demands. So far we have preliminary demonstrations of a variety of motions, and performance data for the task of moving a sheet of paper in a square while maintaining constant orientation.


title: TRIPLE-NEGATIVE OR HER2-POSITIVE STATUS PREDICTS HIGHER RATES OF LOCOREGIONAL RECURRENCE IN NODE-POSITIVE BREAST CANCER PATIENTS AFTER MASTECTOMY

abstract: Purpose To evaluate the prognostic value of determining estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor 2 (HER2) expression in node-positive breast cancer patients treated with mastectomy. Methods and Materials The records of 835 node-positive breast cancer patients who had undergone mastectomy between January 2000 and December 2004 were analyzed retrospectively. Of these, 764 patients (91.5%) received chemotherapy; 68 of 398 patients (20.9%) with T1–2N1 disease and 352 of 437 patients (80.5%) with T3–4 or N2–3 disease received postoperative radiotherapy. Patients were classified into four subgroups according to hormone receptor (Rec+ or Rec-) and HER2 expression profiles: Rec-/HER2- (triple negative; n = 141), Rec-/HER2+ ( n = 99), Rec+/HER2+ ( n = 157), and Rec+/HER2- ( n = 438). The endpoints were the duration of locoregional recurrence–free survival, distant metastasis–free survival, disease-free survival, and overall survival. Results Patients with triple-negative, Rec-/HER2+, and Rec+/HER2+ expression profiles had a significantly lower 5-year locoregional recurrence-free survival than those with Rec+/HER2- profiles (86.5% vs. 93.6%, p = 0.002). Compared with those with Rec+/HER2+ and Rec+/HER2- profiles, patients with Rec-/HER2- and Rec-/HER2+ profiles had significantly lower 5-year distant metastasis–free survival (69.1% vs. 78.5%, p = 0.000), lower disease-free survival (66.6% vs. 75.6%, p = 0.000), and lower overall survival (71.4% vs. 84.2%, p = 0.000). Triple-negative or Rec-/HER2+ breast cancers had an increased likelihood of relapse and death within the first 3 years after treatment. Conclusions Triple-negative and HER2-positive profiles are useful markers of prognosis for locoregional recurrence and survival in node-positive breast cancer patients treated with mastectomy.


title: The evolution of gene duplications: classifying and distinguishing between models

abstract: Gene duplications and their subsequent divergence play an important part in the evolution of novel gene functions. Several models for the emergence, maintenance and evolution of gene copies have been proposed. However, a clear consensus on how gene duplications are fixed and maintained in genomes is lacking. Here, we present a comprehensive classification of the models that are relevant to all stages of the evolution of gene duplications. Each model predicts a unique combination of evolutionary dynamics and functional properties. Setting out these predictions is an important step towards identifying the main mechanisms that are involved in the evolution of gene duplications.


title: FlowFixer: Using BFECC for Fluid Simulation

abstract: Back and Forth Error Compensation and Correction (BFECC) was recently developed for interface computation by using the level set method. We show that it can be applied to reduce dissipation and diffusion encountered in various advection steps in uid simulation such as velocity, smoke density and image advections. BFECC can be implemented easily on top of the r st order upwinding or semi-Lagrangian integration of advection equations, while providing second order accuracy both in space and time. When applied to level set evolution, BFECC reduces volume loss signi cantly . We combine these techniques with variable density projection and show that they yield a realistic animations of two-phase ows. We demonstrate the bene ts of this approach on the image advection and on the simulation of smoke, of bubbles in water, and of a highly dynamic interaction between water, a solid, and air.


title: Facial Micro-Expression Detection in Hi-Speed Video Based on Facial Action Coding System (FACS)

abstract: SUMMARY Facial micro-expressions are fast and subtle facial motions that are considered as one of the most useful external signs for detecting hidden emotional changes in a person. However, they are not easy to detect and measure as they appear only for a short time, with small muscle contraction in the facial areas where salient features are not available. We propose a new computer vision method for detecting and measuring timing characteristics of facial micro-expressions. The core of this method is based on a descriptor that combines pre-processing masks, histograms and concatenation of spatial-temporal gradient vectors. Presented 3D gradient histogram descriptor is able to detect and measure the timing characteristics of the fast and subtle changes of the facial skin surface. This method is specifically designed for analysis of videos recorded using a hi-speed 200fps camera. Final classification of micro expressions is done by using a k-meanclassifier and a votingprocedure. The Facial Action CodingSystem was utilized to annotate the appearance and dynamics of the expressions in our new hi-speed micro-expressions video database. The efficiency of the


title: Class Diagram Extraction from Textual Requirements Using NLP Techniques

abstract: A new method for translating software requirements to object-oriented model is proposed. Software requirements specified in a natural language using scenario like format will be transformed into class diagrams of Unified Modeling Language using specification transformation rule. The transformation rule is based on a grammatical analysis, more specifically syntactical analysis, of requirements specification. This paper proposes a new method for translating software requirements specified using natural language to formal specification (in this context is executable and translatable Unified Modeling Language Class Diagram). Requirements specification written in a scenario like format will be transformed into class diagram's components the automation of class generation from natural language requirements is highly challenging. This paper proposes a method and a tool to facilitate requirements analysis process and class diagram extraction from textual requirements supporting natural language processing techniques. Requirements engineers analyze requirements manually to come out with analysis artifacts such as class diagram.


title: The effects of transparency on trust in and acceptance of a content-based art recommender

abstract: The increasing availability of (digital) cultural heritage artefacts offers great potential for increased access to art content, but also necessitates tools to help users deal with such abundance of information. User-adaptive art recommender systems aim to present their users with art content tailored to their interests. These systems try to adapt to the user based on feedback from the user on which artworks he or she finds interesting. Users need to be able to depend on the system to competently adapt to their feedback and find the artworks that are most interesting to them. This paper investigates the influence of transparency on user trust in and acceptance of content-based recommender systems. A between-subject experiment (N = 60) evaluated interaction with three versions of a content-based art recommender in the cultural heritage domain. This recommender system provides users with artworks that are of interest to them, based on their ratings of other artworks. Version 1 was not transparent, version 2 explained to the user why a recommendation had been made and version 3 showed a rating of how certain the system was that a recommendation would be of interest to the user. Results show that explaining to the user why a recommendation was made increased acceptance of the recommendations. Trust in the system itself was not improved by transparency. Showing how certain the system was of a recommendation did not influence trust and acceptance. A number of guidelines for design of recommender systems in the cultural heritage domain have been derived from the study’s results.


title: Vehicles overtaking detection using RGB-D data

abstract: Outstanding breakthrough in traffic video surveillance has been made with the development of computer vision techniques. However, there are still some problems to be solved due to the limitations of two-dimensional (2D) video data. With the popularity of three-dimensional (3D) cameras, high-quality color and depth data can be obtained simultaneously in real time through the video stream. In this paper, we propose a vehicles overtaking detection method using RGB-D data captured by the Kinect device in simulated traffic scenes. Vehicles are detected and tracked with a robust traffic scene understanding on RGB-D data. The depth data is utilized to recognize vehicles overtaking by analyzing the posture change of vehicles in different scenes. The principle of vehicles overtaking detection is to fit the line of vehicle side in the coordinate system and calculate the angle between the vehicle side and the road orientation. As a consequence, the posture change of the vehicle can be recognized. The proposed method is evaluated using simulation experiments which show that our work has good performance in vehicles overtaking detection and tracking. HighlightsA robust vehicle tracking algorithm is proposed by utilizing RGB-D cameras.Better semantic understanding of vehicles overtaking is achieved by depth data.An efficient vehicle tracking algorithm for overtaking judgment is implemented.


title: Compiling for reconfigurable computing: A survey

abstract: Reconfigurable computing platforms offer the promise of substantially accelerating computations through the concurrent nature of hardware structures and the ability of these architectures for hardware customization. Effectively programming such reconfigurable architectures, however, is an extremely cumbersome and error-prone process, as it requires programmers to assume the role of hardware designers while mastering hardware description languages, thus limiting the acceptance and dissemination of this promising technology. To address this problem, researchers have developed numerous approaches at both the programming languages as well as the compilation levels, to offer high-level programming abstractions that would allow programmers to easily map applications to reconfigurable architectures. This survey describes the major research efforts on compilation techniques for reconfigurable computing architectures. The survey focuses on efforts that map computations written in imperative programming languages to reconfigurable architectures and identifies the main compilation and synthesis techniques used in this mapping.


Sentiment Classification of News Articles


title: Review: Comparison of QRS detection algorithms

abstract: An Electrocardiogram (ECG) is graphical representation of the electrical movement of human heart and used in diagnosis of various heart diseases. The primary function of ECG analysis is correct detection of QRS complex and other ECG characteristics [1]. In this review paper, researchers provide a comparative study between four popular algorithms: namely Window pair algorithm, Dynamic Plosion Index algorithm, KNN algorithm, Slope vector waveform algorithm [2-5] against three assessment criteria 1) Cross Validation, 2) Threshold and 3) Robustness to noise in order to obtain a fast, robust and highly accurate QRS detection algorithm which helps in the development of a more robust clinical instrument by making the front end signal processing more effective [6].


title: Hierarchical Exploration of Volumes Using Multilevel Segmentation of the Intensity-Gradient Histograms

abstract: Visual exploration of volumetric datasets to discover the embedded features and spatial structures is a challenging and tedious task. In this paper we present a semi-automatic approach to this problem that works by visually segmenting the intensity-gradient 2D histogram of a volumetric dataset into an exploration hierarchy. Our approach mimics user exploration behavior by analyzing the histogram with the normalized-cut multilevel segmentation technique. Unlike previous work in this area, our technique segments the histogram into a reasonable set of intuitive components that are mutually exclusive and collectively exhaustive. We use information-theoretic measures of the volumetric data segments to guide the exploration. This provides a data-driven coarse-to-fine hierarchy for a user to interactively navigate the volume in a meaningful manner.


title: Misinformation : Challenges and Future Directions Conference or Workshop Item

abstract: Misinformation has become a common part of our digital media environments and it is compromising the ability of our societies to form informed opinions. It generates misperceptions, which have affected the decision making processes in many domains, including economy, health, environment, and elections, among others. Misinformation and its generation, propagation, impact, and management is being studied through a variety of lenses (computer science, social science, journalism, psychology, etc.) since it widely affects multiple aspects of society. In this paper we analyse the phenomenon of misinformation from a technological point of view. We study the current socio-technical advancements towards addressing the problem, identify some of the key limitations of current technologies, and propose some ideas to target such limitations. The goal of this position paper is to reflect on the current state of the art and to stimulate discussions on the future design and development of algorithms, methodologies, and applications.


title: Statistical prediction of task execution times through analytic benchmarking for scheduling in a heterogeneous environment

abstract: In this paper a method for estimating task execution times is presented, in order to facilitate dynamic scheduling in a heterogeneous metacomputing environment. Execution time is treated as a random variable and is statistically estimated from past observations. This method predicts the execution time as a function of several parameters of the input data, and does not require any direct information about the algorithms used by the tasks or the architecture of the machines. Techniques based upon the concept of analytic benchmarking/code profiling are used to accurately determine the performance differences between machines, allowing observations to be shared between machines. Experimental results using real data are presented.


title: An auto exposure control algorithm based on lane recognition for on-board camera

abstract: In order to obtain the accurate exposure time in real-time for an on-board camera in different urban environments, the proposed algorithm divides captured image into 5 ×5 sub-areas, calculates each sub-area's average brightness value to get a histogram, analyzes the peak value distribution in histogram to determine what environments the automobile is in. According to the environment the automobile works in, the algorithm includes two modes: normal lit condition and high-contrast lit condition. It executes the appropriate exposure adjustment mechanism for two modes by analyzing their brightness distribution in different environments. To avoid the interference of other factors like the sky, the algorithm marks the road surface be the regions of interest in real time. Besides, the optimization goal is not mid-tone at all but the maximum difference between lane and background in the region of interest. The experimental results show that the algorithm can rapidly and stably switch exposure mode when the automobile is traveling in different road conditions, and it can get accurate exposure time in both modes fast and accurately.


title: Noninvasive cuff'less estimation of blood pressure using Photoplethysmography without electrocardiograph measurement

abstract: The paper presents a novel approach of measuring blood pressure using Photoplethysmography (PPG). It is a non-invasive, cuffless and painless technique that deploys infrared light to detect small variation in blood volume in the tissues with each cardiac cycle. Few specific features (viz. systolic upstroke time (ST), diastolic time (DT) and the time delay between the systolic and diastolic peak (T1)) of the waveform obtained via this technique were examined and correlated with the arterial blood pressure in 22 subjects of two age groups i) 18–25 years ii) 26–50 years. It was observed that there is a good correlation of blood pressure (both systolic blood pressure (SBP) and diastolic blood pressure (DBP)) with diastolic time and also with the time delay between systolic and diastolic peak.


title: Employing Morphological Structures and Sememes for Chinese Event Extraction

abstract: Current Chinese event extract ion systems suffer much from the low recall due to unknown triggers. To resolve this problem, this paper firstly introduces morphological structures to better represent the compositional semantics inside Chinese triggers and then proposes a mechanism to automatically identify the head morpheme (either verb or noun) as the governing sememe of a trigger. Finally, it proposes a mechanism of combining the morphological structures and sememes of Chinese words to infer unknown triggers to improve the recall of the Chinese event extraction system. Evaluation on the ACE 2005 Chinese corpus justifies the effectiveness of our approach over a state-of-the-art system.


title: Leveraging Image Representation of Network Traffic Data and Transfer Learning in Botnet Detection

abstract: The advancements in the Internet has enabled connecting more devices into this technology every day. The emergence of the Internet of Things has aggregated this growth. Lack of security in an IoT world makes these devices hot targets for cyber criminals to perform their malicious actions. One of these actions is the Botnet attack, which is one of the main destructive threats that has been evolving since 2003 into different forms. This attack is a serious threat to the security and privacy of information. Its scalability, structure, strength, and strategy are also under successive development, and that it has survived for decades. A bot is defined as a software application that executes a number of automated tasks (simple but structurally repetitive) over the Internet. Several bots make a botnet that infects a number of devices and communicates with their controller called the botmaster to get their instructions. A botnet executes tasks with a rate that would be impossible to be done by a human being. Nowadays, the activities of bots are concealed in between the normal web flows and occupy more than half of all web traffic. The largest use of bots is in web spidering (web crawler), in which an automated script fetches, analyzes, and files information from web servers. They also contribute to other attacks, such as distributed denial of service (DDoS), SPAM, identity theft, phishing, and espionage. A number of botnet detection techniques have been proposed, such as honeynet-based and Intrusion Detection System (IDS)-based. These techniques are not effective anymore due to the constant update of the bots and their evasion mechanisms. Recently, botnet detection techniques based upon machine/deep learning have been proposed that are more capable in comparison to their previously mentioned counterparts. In this work, we propose a deep learning-based engine for botnet detection to be utilized in the IoT and the wearable devices. In this system, the normal and botnet network traffic data are transformed into image before being given into a deep convolutional neural network, named DenseNet with and without considering transfer learning. The system is implemented using Python programming language and the CTU-13 Dataset is used for evaluation in one study. According to our simulation results, using transfer learning can improve the accuracy from 33.41% up to 99.98%. In addition, two other classifiers of Support Vector Machine (SVM) and logistic regression have been used. They showed an accuracy of 83.15% and 78.56%, respectively. In another study, we evaluate our system by an in-house live normal dataset and a solely botnet dataset. Similarly, the system performed very well in data classification in these studies. To examine the capability of our system for real-time applications, we measure the system training and testing times. According to our examination, it takes 0.004868 milliseconds to process each packet from the network traffic data during testing.


title: Withdrawing life-sustaining treatment: ethical considerations.

abstract: In the community of caregivers, there is a general consensus that some heroic measures are not obligatory in certain circumstances that are defined by professional norms. For example, cardiopulmonary resuscitation in terminal cancer patients is not endorsed because of its violation of the dignity of the irremediably ill, and its unproductive cost to society. Moving back from this extreme, the availability and effectiveness of life-prolonging treatments, such as ventilators, dialysis, and implantable mechanical hearts, moves into a domain where the boundary limit of the obligation to preserve life is less clearly defined. When the continuing intervention of caregivers is essential to the prolongation of life, but the outcome and quality of residual life has deteriorated far below everyone's expectations when the treatment was initiated, caregivers are morally troubled as their treatments prolong the process of dying. Uncertainty or disputation about the prognosis raises the voltage of the fear and potential remorse that is a normal condition of care and support at the end of life. Unilateral decisions and overruling of objections should be avoided when possible, and reinforced by legal or ethical authorities when necessary. An ethics consultant, especially one skilled and experienced in management of end-of-life issues, can be a helpful negotiator and guide. The transition to palliative support should include the discontinuation of all unnecessary monitoring devices and tubes. Monitors should be turned off allowing families to direct their attention to the patient. Removing the monitor relieves family members from painful suspense and confusion. Removing the endotracheal tube sometimes allows conscious patients to talk to their loved ones, ending a silence forced on them by their treatment. If interventions are seen as masking the natural dying process, removing them should not be troubling. Their absence gives moral clarity to the elemental moments of closure at the end of life, no longer masked by futile contrivance. Withdrawal of life-sustaining treatment is a process that "merits the same meticulous preparation and expectation of quality that clinicians provide when they perform other procedures to initiate life support". Families and patients should never feel abandoned during this process and attention should be devoted to communicating that care is not being withdrawn. The family needs to be prepared for what the dying process may look like. Assure them that all energy is now being directed toward the comfort of the patient including sedation as required if signs of suffering are observed. Easing death, like easing birth, can be one of the most fulfilling contributions one can make to reduce the suffering and enrich the lives of patients and their families. Neglecting this part of the duty to provide appropriate care brings moral anguish to all participants in the peculiar circumstances that have come to surround death in the ICUs of developed countries. It is helpful to accept the inevitable reality that death is, in Shakespeare's words, a "necessary end" to all mortal life, and to recognize that defying death with technology can sometimes become an unnatural and degrading activity, however well motivated. The withdrawal of life-sustaining treatment, when conducted expertly, is a shared human experience that can be gratifying, although difficult for all concerned.


Visual Exploration of Spatial and Temporal Variations of Tweet Topic Popularity


title: Mindfulness training modifies subsystems of attention

abstract: Mindfulness is defined as paying attention in the present moment. We investigate the hypothesis that mindfulness training may alter or enhance specific aspects of attention. We examined three functionally and neuroanatomically distinct but overlapping attentional subsystems: alerting, orienting, and conflict monitoring. Functioning of each subsystem was indexed by performance on the Attention Network Test (ANT; Fan, McCandliss, Sommer, Raz, & Posner, 2002). Two types of mindfulness training (MT) programs were examined, and behavioral testing was conducted on participants before (Time 1) and after (Time 2) training. One training group consisted of individuals naive to mindfulness techniques who participated in an 8-week mindfulness-based stress reduction (MBSR) course that emphasized the development of concentrative meditation skills. The other training group consisted of individuals experienced in concentrative meditation techniques who participated in a 1-month intensive mindfulness retreat. Performance of these groups was compared with that of control participants who were meditation naive and received no MT. At Time 1, the participants in the retreat group demonstrated improved conflict monitoring performance relative to those in the MBSR and control groups. At Time 2, the participants in the MBSR course demonstrated significantly improved orienting in comparison with the control and retreat participants. In contrast, the participants in the retreat group demonstrated altered performance on the alerting component, with improvements in exogenous stimulus detection in comparison with the control and MBSR participants. The groups did not differ in conflict monitoring performance at Time 2. These results suggest that mindfulness training may improve attention-related behavioral responses by enhancing functioning of specific subcomponents of attention. Whereas participation in the MBSR course improved the ability to endogenously orient attention, retreat participation appeared to allow for the development and emergence of receptive attentional skills, which improved exogenous alerting-related process.


title: Implications of probabilistic data modeling for mining association rules

abstract: Mining association rules is an important technique for discovering meaningful patterns in transaction databases. In the current literature, the properties of algorithms to mine association rules are discussed in great detail. We present a simple probabilistic framework for transaction data which can be used to simulate transaction data when no associations are present. We use such data and a real-world grocery database to explore the behavior of confidence and lift, two popular interest measures used for rule mining. The results show that confidence is systematically influenced by the frequency of the items in the left-hand-side of rules and that lift performs poorly to filter random noise in transaction data. The probabilistic data modeling approach presented in this paper not only is a valuable framework to analyze interest measures but also provides a starting point for further research to develop new interest measures which are based on statistical tests and geared towards the specific properties of transaction data.


title: Superpixels and supervoxels in an energy optimization framework

abstract: Many methods for object recognition, segmentation, etc., rely on a tessellation of an image into "superpixels". A superpixel is an image patch which is better aligned with intensity edges than a rectangular patch. Superpixels can be extracted with any segmentation algorithm, however, most of them produce highly irregular superpixels, with widely varying sizes and shapes. A more regular space tessellation may be desired. We formulate the superpixel partitioning problem in an energy minimization framework, and optimize with graph cuts. Our energy function explicitly encourages regular superpixels. We explore variations of the basic energy, which allow a trade-off between a less regular tessellation but more accurate boundaries or better efficiency. Our advantage over previous work is computational efficiency, principled optimization, and applicability to 3D "supervoxel" segmentation. We achieve high boundary recall on images and spatial coherence on video. We also show that compact superpixels improve accuracy on a simple application of salient object segmentation.


title: A Plant Recognition Approach Using Shape and Color Features in Leaf Images

abstract: Recognizing plants is a vital problem especially for biologists, chemists, and environmentalists. Plant recognition can be performed by human experts manually but it is a time consuming and low-efficiency process. Automation of plant recognition is an important process for the fields working with plants. This paper presents an approach for plant recognition using leaf images. Shape and color features extracted from leaf images are used with k-Nearest Neighbor, Support Vector Machines, Naive Bayes, and Random Forest classification algorithms to recognize plant types. The presented approach is tested on 1897 leaf images and 32 kinds of leaves. The results demonstrated that success rate of plant recognition can be improved up to 96% with Random Forest method when both shape and color features are used.


title: Time-Delay Observables for Koopman: Theory and Applications

abstract: Nonlinear dynamical systems are ubiquitous in science and engineering, yet analysis and prediction of these systems remains a challenge. Koopman operator theory circumvents some of these issues by considering the dynamics in the space of observable functions on the state, in which the dynamics are intrinsically linear and thus amenable to standard techniques from numerical analysis and linear algebra. However, practical issues remain with this approach, as the space of observables is infinite-dimensional and selecting a subspace of functions in which to accurately represent the system is a nontrivial task. In this work we consider time-delay observables to represent nonlinear dynamics in the Koopman operator framework. We prove the surprising result that Koopman operators for different systems admit universal (system-independent) representations in these coordinates, and give analytic expressions for these representations. In addition, we show that for certain systems a restricted class of these observables form an optimal finite-dimensional basis for representing the Koopman operator, and that the analytic representation of the Koopman operator in these coordinates coincides with results computed by the dynamic mode decomposition. We provide numerical examples to complement our results. In addition to being theoretically interesting, these results have implications for a number of linearization algorithms for dynamical systems.


title: Megalomastia: histological, histochemical and immunohistochemical study

abstract: Megalomastia is a rare entity characterized by an uncommon enlargement of both breasts. Unilateral megalomastia is extremely rare. The purpose of this study was to collect information concerning the history of patients with this condition and to investigate its histology in order to outline the profile of this peculiar entity. Fifty cases of megalomastia were studied. In 41 data concerning the history of the patients was complete; there were 32 juvenile, 7 gravid and 2 adult type cases. All three unilateral megalomastias were in the juvenile group. A family history of megalomastia was frequently present; gravid megalomastia was more closely connected with a maternal familial history. A case of simultaneous megalomastia in monozygotic twins is included. The final size achieved by the breasts was independent of the type of megalomastia, the rapidity of breast development and the body weight of the patients. It was greater in breasts containing abundant adipose tissue and less in fibrous breasts. In all cases of megalomastia associated with pregnancy the breasts had lost the ability to produce milk. The main histological feature in all cases was severe damage and destruction of the lobular units associated with extensive fibrosis. In some breasts of all three types of megalomastia ramified new ducts named "juvenile units" had developed and had proceeded to atrophy. Immunohistochemistry revealed that the epithelium of these units was negative for oestrogen and positive to progesterone receptors. A biphasic pathological appearance, consisting of atrophic lobular units and "juvenile units", is diagnostic of megalomastia.


title: Using wise interventions to motivate deliberate practice.

abstract: Deliberate practice leads to world-class excellence across domains. In the current investigation, we examined whether psychologically “wise” interventions targeting expectancies and values—stock antecedents of ordinary effortful behaviors—could motivate nonexperts to engage in deliberate practice and improve their achievement. As a preliminary, we developed and validated a novel task measure of deliberate practice and confirmed its association with (a) expectancy-value beliefs, and (b) achievement in the nonexpert setting (Study 1). Next, across 4 longitudinal, randomized-controlled, field experiments, we intervened. Among lower-achievers, wise deliberate practice interventions improved math performance for 5th and 6th graders (Study 2), end-of-semester grades for undergraduates (Study 3), and end-of-quarter grades for 6th graders (Study 4); the same pattern of results emerged in end-of-quarter grades for 7th graders (Study 5). Following the intervention, expectancy-value beliefs and deliberate practice improved for 1 month (Study 4), but not 4 (Study 5). Treatment proved beneficial over and above 2 control conditions: 1 that taught standard study skills (Studies 2 and 3), and 1 that discussed deep interests, generalized motivation, and high achievement (Studies 4 and 5). Collectively, these findings provide preliminary support for the heretofore untested hypothesis that deliberate practice submits to the same laws that govern typical forms of effortful behavior, and that wise interventions that tap into these laws can spur short-term gains in adaptive beliefs, deliberate practice, and objectively measured achievement. (PsycINFO Database Record (c) 2016 APA, all rights reserved)


title: Detecting Semantic Parts on Partially Occluded Objects

abstract: In this paper, we address the task of detecting semantic parts on partially occluded objects. We consider a scenario where the model is trained using non-occluded images but tested on occluded images. The motivation is that there are infinite number of occlusion patterns in real world, which cannot be fully covered in the training data. So the models should be inherently robust and adaptive to occlusions instead of fitting / learning the occlusion patterns in the training data. Our approach detects semantic parts by accumulating the confidence of local visual cues. Specifically, the method uses a simple voting method, based on log-likelihood ratio tests and spatial constraints, to combine the evidence of local cues. These cues are called visual concepts, which are derived by clustering the internal states of deep networks. We evaluate our voting scheme on the VehicleSemanticPart dataset with dense part annotations. We randomly place two, three or four irrelevant objects onto the target object to generate testing images with various occlusions. Experiments show that our algorithm outperforms several competitors in semantic part detection when occlusions are present.


title: I Know What You See: Power Side-Channel Attack on Convolutional Neural Network Accelerators

abstract: Deep learning has become the de-facto computational paradigm for various kinds of perception problems, including many privacy-sensitive applications such as online medical image analysis. No doubt to say, the data privacy of these deep learning systems is a serious concern. Different from previous research focusing on exploiting privacy leakage from deep learning models, in this paper, we present the first attack on the implementation of deep learning models. To be specific, we perform the attack on an FPGA-based convolutional neural network accelerator and we manage to recover the input image from the collected power traces without knowing the detailed parameters in the neural network. For the MNIST dataset, our power side-channel attack is able to achieve up to 89% recognition accuracy.


title: A Bayesian Mixture Model for Across-Site Heterogeneities in the Amino-Acid Replacement Process

abstract: Most current models of sequence evolution assume that all sites of a protein evolve under the same substitution process, characterized by a 20 3 20 substitution matrix. Here, we propose to relax this assumption by developing a Bayesian mixture model that allows the amino-acid replacement pattern at different sites of a protein alignment to be described by distinct substitution processes. Our model, named CAT, assumes the existence of distinct processes (or classes) differing by their equilibrium frequencies over the 20 residues. Through the use of a Dirichlet process prior, the total number of classes and their respective amino-acid profiles, as well as the affiliations of each site to a given class, are all free variables of the model. In this way, the CAT model is able to adapt to the complexity actually present in the data, and it yields an estimate of the substitutional heterogeneity through the posterior mean number of classes. We show that a significant level of heterogeneity is present in the substitution patterns of proteins, and that the standard one-matrix model fails to account for this heterogeneity. By evaluating the Bayes factor, we demonstrate that the standard model is outperformed by CAT on all of the data sets which we analyzed. Altogether, these results suggest that the complexity of the pattern of substitution of real sequences is better captured by the CAT model, offering the possibility of studying its impact on phylogenetic reconstruction and its connections with structure-function determinants.


title: Sybot: an adaptive and mobile spectrum survey system for wifi networks

abstract: Information on site-specific spectrum characteristics is essential to evaluate and improve the performance of wireless networks. However, it is usually very costly to obtain accurate spectrum-condition information in heterogeneous wireless environments. This paper presents a novel spectrum-survey system, called Sybot (Spectrum survey robot), that guides network engineers to efficiently monitor the spectrum condition (e.g., RSS) of WiFi networks. Sybot effectively controls mobility and employs three disparate monitoring techniques - complete, selective, and diagnostic - that help produce and maintain an accurate spectrum-condition map for challenging indoor WiFi networks. By adaptively triggering the most suitable of the three techniques, Sybot captures spatio-temporal changes in spectrum condition. Moreover, based on the monitoring results, Sybot automatically determines several key survey parameters, such as site-specific measurement time and space granularities. Sybot has been prototyped with a commodity IEEE 802.11 router and Linux OS, and experimentally evaluated, demonstrating its ability to generate accurate spectrum-condition maps while reducing the measurement effort (space, time) by more than 56%.


title: Achieving High Performance Oxide TFT-Based Inverters by Use of Dual-Gate Configurations With Floating and Biased Secondary Gates

abstract: A dual-gate thin-film transistor (TFT) has two gate electrodes. In inverted, staggered TFTs, the primary gate is usually the bottom gate (BG) and the TFT's threshold voltage (VTH) can be adjusted by applying a constant bias to the secondary gate, the top gate (TG), while scanning the BG. Dual-gate TFTs are being used as the load TFT in oxide semiconductor-based inverters, where the TG is biased positively to shift the VTH negatively and, thereby improving the inverter's output swing. Here, we show that the same can be achieved by the use of a floating TG. Furthermore, we show that the noise margin of the inverter can also be controlled by adding a biased TG to the driving TFT.


title: In situ process monitoring of a thermoplastic-based fibre composite using optical fibre sensors

abstract: This paper reports the use of optical fibres to obtain real-time information during the processing of thermoplastic-based glass fibre-reinforced polypropylene. The technique used to monitor the melting and solidification process in this study is based on monitoring the modulation of the refractive index of the polymer matrix. Before embedding the sensor in the composite laminate, the cladding layer of the optical fibre was removed to expose the optical fibre core thus allowing intimate contact with the host polymer matrix. As the matrix undergoes melting and solidification cycles, the refractive index of the polymer at the sensor–host interface changes which in turn varies the amount of light transmitted through the optical core. This signal intensity modulation was monitored during the composite processing cycle. Differential scanning calorimetry was carried out to provide a reference for evaluating the validity of the optical fibre data.


title: Almost-polynomial ratio ETH-hardness of approximating densest k-subgraph

abstract: In the Densest $k$-Subgraph problem, given an undirected graph $G$ and an integer $k$, the goal is to find a subgraph of $G$ on $k$ vertices that contains maximum number of edges. Even though the state-of-the-art algorithm for the problem achieves only $O(n^{1/4 + \varepsilon})$ approximation ratio (Bhaskara et al., 2010), previous attempts at proving hardness of approximation, including those under average case assumptions, fail to achieve a polynomial ratio; the best ratios ruled out under any worst case assumption and any average case assumption are only any constant (Raghavendra and Steurer, 2010) and $2^{\Omega(\log^{2/3} n)}$ (Alon et al., 2011) respectively. In this work, we show, assuming the exponential time hypothesis (ETH), that there is no polynomial-time algorithm that approximates Densest $k$-Subgraph to within $n^{1/(\log \log n)^c}$ factor of the optimum, where $c>0$ is a universal constant independent of $n$. In addition, our result has"perfect completeness", meaning that we prove that it is ETH-hard to even distinguish between the case in which $G$ contains a $k$-clique and the case in which every induced $k$-subgraph of $G$ has density at most $1/n^{-1/(\log \log n)^c}$ in polynomial time. Moreover, if we make a stronger assumption that there is some constant $\varepsilon>0$ such that no subexponential-time algorithm can distinguish between a satisfiable 3SAT formula and one which is only $(1 - \varepsilon)$-satisfiable (also known as Gap-ETH), then the ratio above can be improved to $n^{f(n)}$ for any function $f$ whose limit is zero as $n$ goes to infinity (i.e. $f \in o(1)$).


title: Detection of high variability in gene expression from single-cell RNA-seq profiling

abstract: BackgroundThe advancement of the next-generation sequencing technology enables mapping gene expression at the single-cell level, capable of tracking cell heterogeneity and determination of cell subpopulations using single-cell RNA sequencing (scRNA-seq). Unlike the objectives of conventional RNA-seq where differential expression analysis is the integral component, the most important goal of scRNA-seq is to identify highly variable genes across a population of cells, to account for the discrete nature of single-cell gene expression and uniqueness of sequencing library preparation protocol for single-cell sequencing. However, there is lack of generic expression variation model for different scRNA-seq data sets. Hence, the objective of this study is to develop a gene expression variation model (GEVM), utilizing the relationship between coefficient of variation (CV) and average expression level to address the over-dispersion of single-cell data, and its corresponding statistical significance to quantify the variably expressed genes (VEGs).ResultsWe have built a simulation framework that generated scRNA-seq data with different number of cells, model parameters, and variation levels. We implemented our GEVM and demonstrated the robustness by using a set of simulated scRNA-seq data under different conditions. We evaluated the regression robustness using root-mean-square error (RMSE) and assessed the parameter estimation process by varying initial model parameters that deviated from homogeneous cell population. We also applied the GEVM on real scRNA-seq data to test the performance under distinct cases.ConclusionsIn this paper, we proposed a gene expression variation model that can be used to determine significant variably expressed genes. Applying the model to the simulated single-cell data, we observed robust parameter estimation under different conditions with minimal root mean square errors. We also examined the model on two distinct scRNA-seq data sets using different single-cell protocols and determined the VEGs. Obtaining VEGs allowed us to observe possible subpopulations, providing further evidences of cell heterogeneity. With the GEVM, we can easily find out significant variably expressed genes in different scRNA-seq data sets.


title: Noname manuscript No. (will be inserted by the editor) Conceptual Metaphor Theory Meets the Data: A Corpus-based Human Annotation Study

abstract: Metaphor makes our thoughts more vivid and fills our communication with richer imagery. Furthermore, according to the conceptual metaphor theory (CMT) of Lakoff and Johnson (Metaphors we live by. University of Chicago Press, Chicago, 1980), metaphor also plays an important structural role in the organization and processing of conceptual knowledge. According to this account, the phenomenon of metaphor is not restricted to similarity-based extensions of meanings of individual words, but instead involves activating fixed mappings that reconceptualize one whole area of experience in terms of another. CMT produced a significant resonance in the fields of philosophy, linguistics, cognitive science and artificial intelligence and still underlies a large proportion of modern research on metaphor. However, there has to date been no comprehensive corpus-based study of conceptual metaphor, which would provide an empirical basis for evaluating the CMT using real-world linguistic data. The annotation scheme and the empirical study we present in this paper is a step towards filling this gap. We test our annotation procedure in an experimental setting involving multiple annotators and estimate their agreement on the task. The goal of the study is to investigate (1) how intuitive the conceptual metaphor explanation of linguistic metaphors is for human annotators and whether it is possible to consistently annotate interconceptual mappings; (2) what are the main difficulties that the annotators experience during the annotation process; (3) whether one conceptual metaphor is sufficient to explain a linguistic metaphor or whether a chain of conceptual metaphors is needed. The resulting corpus annotated for conceptual mappings provides a new, valuable dataset for linguistic, computational and cognitive experiments on metaphor.


title: Advances in Mediation Analysis: A Survey and Synthesis of New Developments

abstract: Mediation processes are fundamental to many classic and emerging theoretical paradigms within psychology. Innovative methods continue to be developed to address the diverse needs of researchers studying such indirect effects. This review provides a survey and synthesis of four areas of active methodological research: (a) mediation analysis for longitudinal data, (b) causal inference for indirect effects, (c) mediation analysis for discrete and nonnormal variables, and (d) mediation assessment in multilevel designs. The aim of this review is to aid in the dissemination of developments in these four areas and suggest directions for future research.


title: RIO: A Pervasive RFID-based Touch Gesture Interface

abstract: In this paper, we design and develop RIO, a novel battery-free touch sensing user interface (UI) primitive for future IoT and smart spaces. RIO enables UIs to be constructed using off-the-shelf RFID readers and tags, and provides a unique approach to designing smart IoT spaces. With RIO, any surface can be turned into a touch-aware surface by simply attaching RFID tags to them. RIO also supports custom-designed RFID tags, and thus allows specially customized UIs to be easily deployed into a real-world environment. RIO is built using the technique of impedance tracking: when a human finger touches the surface of an RFID tag, the impedance of the antenna changes. This change manifests as a change in the phase of the RFID backscattered signal, and is used by RIO to track fine-grained touch movement over both off-the shelf and custom built tags. We study this impedance behavior in-depth and show how RIO is a reliable UI primitive that is robust even within a multi-tag environment. We leverage this primitive to build a prototype of RIO that can continuously locate a finger during a swipe movement to within 3 mm of its actual position. We also show how custom-design RFID tags can be built and used with RIO, and provide two example applications that demonstrate its real-world use.


title: Treatment technologies for urban solid biowaste to create value products: a review with focus on low- and middle-income settings

abstract: Treatment of biowaste, the predominant waste fraction in low- and middle-income settings, offers public health, environmental and economic benefits by converting waste into a hygienic product, diverting it from disposal sites, and providing a source of income. This article presents a comprehensive overview of 13 biowaste treatment technologies, grouped into four categories: (1) direct use (direct land application, direct animal feed, direct combustion), (2) biological treatment (composting, vermicomposting, black soldier fly treatment, anaerobic digestion, fermentation), (3) physico-chemical treatment (transesterification, densification), and (4) thermo-chemical treatment (pyrolysis, liquefaction, gasification). Based on a literature review and expert consultation, the main feedstock requirements, process conditions and treatment products are summarized, and the challenges and trends, particularly regarding the applicability of each technology in the urban low- and middle-income context, are critically discussed. An analysis of the scientific articles published from 2005 to 2015 reveals substantial differences in the amount and type of research published for each technology, a fact that can partly be explained with the development stage of the technologies. Overall, publications from case studies and field research seem disproportionately underrepresented for all technologies. One may argue that this reflects the main task of researchers—to conduct fundamental research for enhanced process understanding—but it may also be a result of the traditional embedding of the waste sector in the discipline of engineering science, where socio-economic and management aspects are seldom object of the research. More unbiased, well-structured and reproducible evidence from case studies at scale could foster the knowledge transfer to practitioners and enhance the exchange between academia, policy and practice.


title: Support Vector Machine Active Learning Algorithms with Query-by-Committee versus Closest-to-Hyperplane Selection

abstract: This paper investigates and evaluates support vector machine active learning algorithms for use with imbalanced datasets, which commonly arise in many applications such as information extraction applications. Algorithms based on closest-to-hyperplane selection and query-by-committee selection are combined with methods for addressing imbalance such as positive amplification based on prevalence statistics from initial random samples. Three algorithms (ClosestPA, QBagPA, and QBoostPA) are presented and carefully evaluated on datasets for text classification and relation extraction. The ClosestPA algorithm is shown to consistently outperform the other two in a variety of ways and insights are provided as to why this is the case.


title: Structured Optimal Transport

abstract: Optimal Transport has recently gained interest in machine learning for applications ranging from domain adaptation, sentence similarities to deep learning. Yet, its ability to capture frequently occurring structure beyond the "ground metric" is limited. In this work, we develop a nonlinear generalization of (discrete) optimal transport that is able to reflect much additional structure. We demonstrate how to leverage the geometry of this new model for fast algorithms, and explore connections and properties. Illustrative experiments highlight the benefit of the induced structured couplings for tasks in domain adaptation and natural language processing.


Fault Tolerant Control for a 4-Wheel Skid Steering Mobile Robot


title: The coalitional switch-off game of service providers

abstract: This paper studies a significant problem in green networking called switching off base stations in case of cooperating service providers by means of stochastic geometric and coalitional game tools. The coalitional game herein considered is played by service providers who cooperate in switching off base stations. When they cooperate, any mobile is associated to the nearest BS of any service provider. Given a Poisson point process deployment model of nodes over an area and switching off base stations with some probability, it is proved that the distribution of signal to interference plus noise ratio remains unchanged while the transmission power is increased up to preserving the quality of service. The coalitional game behavior of a typical player is called to be \emph{hedonic} if the gain of any player depends solely on the members of the coalition to which the player belongs, thus, the coalitions form as a result of the preferences of the players over their possible coalitions' set. We also introduce a novel concept which is called the Nash-stable core containing those gain allocation methods that result in Nash-stable partitions. By this way, we always guarantee Nash stability. We study the non-emptiness of the Nash-stable core. Assuming the choice of a coalition is performed only by one player in a point of time, we prove that the Nash-stable core is non-empty when a player chooses its coalition in its turn, the player gains zero utility if the chosen coalition is visited before by itself.


title: POSTER: UAFChecker: Scalable Static Detection of Use-After-Free Vulnerabilities

abstract: Use-after-free vulnerabilities are gaining more and more attentions in recent years, since they are commonly exploited in applications like browsers, and exposed in abundant security updates, e.g., from Microsoft, Google or Mozilla. This kind of vulnerabilities are triggered by dereferencing a dangling pointer, and may introduce high risks into the system once they are exploited. In this paper, we propose a comprehensive solution called UAFChecker to detect use-after-free vulnerabilities in source code. Our solution utilizes classical static analysis techniques, including taint analysis and symbolic execution, to make an inter-procedural analysis to find as many use-after-free vulnerabilities as possible, with a low false negative rate and a low false positive rate. We implement a prototype of UAFChecker based on the compiler framework LLVM. We then use the Juliet Test Suite to evaluate UAFChecker's capability of detecting use-after-free vulnerabilities. Results show that UAFChecker is able to identify most use-after-free vulnerabilities in the Juliet Test Suite. We also test UAFChecker against two open source applications, and successfully find out all known use-after-free vulnerabilities in them.


title: The KGRAM Abstract Machine for Knowledge Graph Querying

abstract: In this paper we present the KGRAM abstract machine dedicated to querying knowledge graphs. It is the result of an abstraction process we performed to reach a generic solution to the problem of querying graphs in various models. We identified high level abstract primitives that constitute the expressions of the query language and the interfaces of KGRAM for both its data structures and its operations.


title: Learning Sparse Feature Representations for Music Annotation and Retrieval

abstract: We present a data-processing pipeline based on sparse feature learning and describe its applications to music annotation and retrieval. Content-based music annotation and retrieval systems process audio starting with features. While commonly used features, such as MFCC, are handcrafted to extract characteristics of the audio in a succinct way, there is increasing interest in learning features automatically from data using unsupervised algorithms. We describe a systemic approach applying feature-learning algorithms to music data, in particular, focusing on a highdimensional sparse-feature representation. Our experiments show that, using only a linear classifier, the newly learned features produce results on the CAL500 dataset comparable to state-of-the-art music annotation and retrieval systems.


title: Practical Control Flow Integrity and Randomization for Binary Executables

abstract: Control Flow Integrity (CFI) provides a strong protection against modern control-flow hijacking attacks. However, performance and compatibility issues limit its adoption. We propose a new practical and realistic protection method called CCFIR (Compact Control Flow Integrity and Randomization), which addresses the main barriers to CFI adoption. CCFIR collects all legal targets of indirect control-transfer instructions, puts them into a dedicated "Springboard section" in a random order, and then limits indirect transfers to flow only to them. Using the Springboard section for targets, CCFIR can validate a target more simply and faster than traditional CFI, and provide support for on-site target-randomization as well as better compatibility. Based on these approaches, CCFIR can stop control-flow hijacking attacks including ROP and return-into-libc. Results show that ROP gadgets are all eliminated. We observe that with the wide deployment of ASLR, Windows/x86 PE executables contain enough information in relocation tables which CCFIR can use to find all legal instructions and jump targets reliably, without source code or symbol information. We evaluate our prototype implementation on common web browsers and the SPEC CPU2000 suite: CCFIR protects large applications such as GCC and Firefox completely automatically, and has low performance overhead of about 3.6%/8.6% (average/max) using SPECint2000. Experiments on real-world exploits also show that CCFIR-hardened versions of IE6, Firefox 3.6 and other applications are protected effectively.


title: End-to-end Neural Coreference Resolution

abstract: We introduce the first end-to-end coreference resolution model and show that it significantly outperforms all previous work without using a syntactic parser or hand-engineered mention detector. The key idea is to directly consider all spans in a document as potential mentions and learn distributions over possible antecedents for each. The model computes span embeddings that combine context-dependent boundary representations with a head-finding attention mechanism. It is trained to maximize the marginal likelihood of gold antecedent spans from coreference clusters and is factored to enable aggressive pruning of potential mentions. Experiments demonstrate state-of-the-art performance, with a gain of 1.5 F1 on the OntoNotes benchmark and by 3.1 F1 using a 5-model ensemble, despite the fact that this is the first approach to be successfully trained with no external resources.


title: on Factoring Polynomials Over Finite Fields: A Survey

abstract: This survey reviews several algorithms for the factorization of univariate polynomials over finite fields. We emphasize the main ideas of the methods and provide an up-to-date bibliography of the problem.


title: Integrated MIMO Slot Antenna on Laptop Computer for Eight-Band LTE/WWAN Operation

abstract: This paper proposes a two-element multi-input multi-output (MIMO) open-slot antenna implemented on the display ground plane of a laptop computer for eight-band long-term evolution/wireless wide-area network operations. The metal surroundings of the antennas have been well integrated as a part of the radiation structure. In the single-element open-slot antenna, the nearby hinge slot (which is bounded by two ground planes and two hinges) is relatively large as compared with the open slot itself and acts as a good radiator. In the MIMO antenna consisting of two open-slot elements, a T slot is embedded in the display ground plane and is connected to the hinge slot. The T and hinge slots when connected behave as a radiator; whereas, the T slot itself functions as an isolation element. With the isolation element, simulated isolations between the two elements of the MIMO antenna are raised from 8.3–11.2 to 15–17.1 dB in 698–960 MHz and from 12.1–21 to 15.9–26.7 dB in 1710–2690 MHz. Measured isolations with the isolation element in the desired low- and high-frequency ranges are 17.6–18.8 and 15.2–23.5 dB, respectively. Measured and simulated efficiencies for the two-element MIMO antenna with either element excited are both larger than 50% in the desired operating frequency bands.


title: High current planar transformer for very high efficiency isolated boost dc-dc converters

abstract: This paper presents a design and optimization of a high current planar transformer for very high efficiency dc-dc isolated boost converters. The analysis considers different winding arrangements, including very high copper thickness windings. The analysis is focused on the winding ac-resistance and transformer leakage inductance. Design and optimization procedures are validated based on an experimental prototype of a 6 kW dc-dc isolated full bridge boost converter developed on fully planar magnetics. The prototype is rated at 30-80 V 0-80 A on the low voltage side and 700-800 V on the high voltage side with a peak efficiency of 97.8% at 80 V 3.5 kW. Results highlights that thick copper windings can provide good performance at low switching frequencies due to the high transformer filling factor. PCB windings can also provide very high efficiency if stacked in parallel utilizing the transformer winding window in an optimal way.