title: Increasing rates of people identifying as transgender presenting to Endocrine Services in the Wellington region.

abstract: AIMS ::: Overseas clinics specialising in management of transgender people have noted a marked increase in the numbers of people requesting therapy in the last few years. No data has been presented for New Zealand. We therefore reviewed the number of transgender people seen in the Wellington Endocrine Service to assess if the pattern was similar and assess any potential problems for service delivery. ::: ::: ::: METHODS ::: Using hospital records, we reviewed the new appointments of people who were referred for advice on gender reassignment and seen in the Wellington Endocrine Service from 1990 to 2016. ::: ::: ::: RESULTS ::: In total, 438 people who identified as transgender attended the clinic at least once in this period. There has been a progressive increase in number of people identifying as transgender presenting to the clinic, particularly since 2010. In addition to increasing overall numbers, there has been in particular increase in referrals for people under age 30, as well as an increasing proportion of people requesting female-to-male (FtM) therapy so that it is now approaching the number of people requesting male-to-female therapy (MtF). ::: ::: ::: CONCLUSION ::: The pattern observed is comparable to changes reported overseas. These changes have practical consequences for the delivery of both secondary and primary level healthcare, requiring an increased focus on clinical coordination between the relevant medical services and their links to the primary services sector.


title: Chemical composition and variability of cork from Quercus suber L.

abstract: The chemical composition of cork from Quercus suber L. was determined for virgin cork and for reproduction cork. Average chemical composition of virgin cork is: ash 0.7%, total extractives 15.3%, suberin 38.6%, lignin 21.7% and polysaccharides 18.2%. The carbohydrate composition shows that glucose represents 50.6% of all monosaccharides, xylose 35.0%, arabinose 7.0% and galactose and mannose, respectively, 3.6% and 3.4%. Reproduction cork has a composition similar to virgin cork. For the cases studied, reproduction cork had however less extractives and a higher amount of suberin. Significant differences were found for the chemical composition of virgin cork in relation to location of extractives and polysaccharide content. The between-tree variability was large and a variation within the tree was also recorded.


title: How computer games affect CS (and other) students' school performance

abstract: Compulsive game playing, especially of the role-playing variety, risks failing grades and withdrawal of financial support from tuition-paying parents.


title: A factor graph approach to estimation and model predictive control on Unmanned Aerial Vehicles

abstract: In this paper, we present a factor graph framework to solve both estimation and deterministic optimal control problems, and apply it to an obstacle avoidance task on Unmanned Aerial Vehicles (UAVs). We show that factor graphs allow us to consistently use the same optimization method, system dynamics, uncertainty models and other internal and external parameters, which potentially improves the UAV performance as a whole. To this end, we extended the modeling capabilities of factor graphs to represent nonlinear dynamics using constraint factors. For inference, we reformulate Sequential Quadratic Programming as an optimization algorithm on a factor graph with nonlinear constraints. We demonstrate our framework on a simulated quadrotor in an obstacle avoidance application.


title: Cooperative Query Answer Authentication Scheme Over Anonymous Sensing Data

abstract: In cloud service over crowd-sensing data, the data owner (DO) publishes the sensing data through the cloud server, so that the user can obtain the information of interest on demand. But the cloud service providers (CSP) are often untrustworthy. The privacy and security concerns emerge over the authenticity of the query answer and the leakage of the DO identity. To solve these issues, many researchers study the query answer authentication scheme for cloud service system. The traditional technique is providing DO’s signature for the published data. But the signature would always reveal DO’s identity. To deal with this disadvantage, this paper proposes a cooperative query answer authentication scheme, based on the ring signature, the Merkle hash tree (MHT) and the non-repudiable service protocol. Through the cooperation among the entities in cloud service system, the proposed scheme could not only verify the query answer, but also protect the DO’s identity. First, it picks up the internal nodes of MHT to sign, as well as the root node. Thus, the verification computation complexity could be significantly reduced from $O({log}_{2}N)$ to $O({log}_{2}N^{0.5})$ in the best case. Then, it improves an existing ring signature to sign the selected nodes. Furthermore, the proposed scheme employs the non-repudiation protocol during the transmission of query answer and verification object to protect trading behavior between the CSP and users. The security and performance analysis prove the security and feasibility of the proposed scheme. Extensive experimental results demonstrate its superiority of verification efficiency and communication overhead.


Coreference Resolution : A Survey


title: Two-Dimensional Radiation Field Map of a Be-based Source

abstract: The aim of this project was to measure the profile of the radiation exiting an aperture of the Aquarium at the Source Testing Facility at the Division of Nuclear Physics at Lund University. So far, the outgoing beam of radiation at the aperture of the Aquarium has been assumed to be uniform. The radiation is produced by a Beryllium-based radioactive source and comes in the form of neutrons and γ-rays. Thermal neutrons were detected using a 3He proportional counter and γ-rays were detected using a Cerium-Activated Lanthanum Bromide scintillation detector. To form a two-dimensional map of the radiation, the detectors were placed at several different well-defined positions. The data obtained in the measurements that were performed showed that the intensity of radiation indeed varies across the different positions. The ultimate goal of the project to which this thesis contributes is to create a simulation framework that provides a map of the radiation profile at the aperture of the Aquarium. This will facilitate precision studies of newly developed detector responses in the future.


title: A foundation for representing and querying moving objects

abstract: Spatio-temporal databases deal with geometries changing over time. The goal of our work is to provide a DBMS data model and query language capable of handling such time-dependent geometries, including those changing continuously that describe moving objects . Two fundamental abstractions are moving point and moving region , describing objects for which only the time-dependent position, or position and extent, respectively, are of interest. We propose to present such time-dependent geometries as attribute data types with suitable operations, that is, to provide an abstract data type extension to a DBMS data model and query language. This paper presents a design of such a system of abstract data types. It turns out that besides the main types of interest, moving point and moving region, a relatively large number of auxiliary data types are needed. For example, one needs a line type to represent the projection of a moving point into the plane, or a “moving real” to represent the time-dependent distance of two points. It then becomes crucial to achieve (i) orthogonality in the design of the system, i.e., type constructors can be applied unifomly; (ii) genericity and consistency of operations, i.e., operations range over as many types as possible and behave consistently; and (iii) closure and consistency between structure and operations of nontemporal and related temporal types. Satisfying these goal leads to a simple and expressive system of abstract data types that may be integrated into a query language to yield a powerful language for querying spatio-temporal data, including moving objects. The paper formally defines the types and operations, offers detailed insight into the considerations that went into the design, and exemplifies the use of the abstract data types using SQL. The paper offers a precise and conceptually clean foundation for implementing a spatio-temporal DBMS extension.


title: Deep Learning for Wind Speed Forecasting in Northeastern Region of Brazil

abstract: Deep Learning is one of the latest approaches in the field of artificial neural networks. Since they were first proposed in mid-2006, Deep Learning models have obtained state-of-art results in some problems with classification and pattern recognition. However, such models have been little used in time series forecasting. This work aims to investigate the use of some of these architectures in this kind of problem, specifically in predicting the hourly average speed of winds in the Northeastern region of Brazil. The results showed that Deep Learning offers a good alternative for performing this task, overcoming some results of previous works.


title: Saccade Mirror 3: High-speed gaze controller with ultra wide gaze control range using triple rotational mirrors

abstract: This paper reports the prototype of a new optical high-speed gaze controller, named Saccade Mirror 3, that overcomes the limited gaze control range (∼60 deg) of our previously proposed high-speed gaze controller, called Saccade Mirror. Saccade Mirror 3 is based on three automated rotational mirrors, and this gaze control mechanism can achieve an ultrawide gaze control range of 360 deg in theory. A prototype was developed based on this mechanism. A gaze control range of over 260 degrees in the pan direction and a high-speed response of about 10 ms were confirmed. Furthermore, the prototype was applied to high-speed visual tracking coupled with a 1000 fps high-speed vision system. A visual tracking algorithm was developed for this mechanism, and high-speed tracking of a table tennis ball in play and a flying drone was successfully demonstrated.


title: Phase III Safety Evaluation of Cyclosporine 0.1 % Ophthalmic Emulsion Administered Twice Daily to Dry Eye Disease Patients for

abstract: OBJECTIVE ::: To evaluate cyclosporine 0.1% ophthalmic emulsion over a 1- to 3-year period in moderate to severe dry eye disease patients. ::: ::: ::: DESIGN ::: Nonrandomized, multicenter, open-label clinical trial extending 2 ophthalmic cyclosporine phase III clinical trials. ::: ::: ::: PARTICIPANTS ::: Four hundred twelve patients previously dosed for 6 to 12 months with cyclosporine 0.05% or 0.1% in prior phase III trials. ::: ::: ::: INTERVENTION ::: Patients instilled ophthalmic cyclosporine 0.1% twice daily into both eyes for up to 3 consecutive 12-month extension periods. ::: ::: ::: MAIN OUTCOME MEASURES ::: Corneal staining, Schirmer tests, and symptom severity assessments were conducted during the first 12-month extension, with a patient survey during the second 12-month extension. Biomicroscopy and visual acuity (VA) examinations, intraocular pressure (IOP) measurements, and adverse effects queries occurred at 6-month intervals. ::: ::: ::: RESULTS ::: Mean duration of treatment was 19.8 months. Improvements in objective and subjective measures of dry eye disease were modest, probably because of prior treatment with cyclosporine. Most survey respondents said their symptoms began to resolve in the first 3 months of cyclosporine treatment during the previous phase III clinical trials. At study exit, VA decreased in 12.6% (93/738) and increased in 5.4% (40/738) of eyes by > or =2 lines; severity of biomicroscopy findings increased in 3.4% (chemosis; 26/760), 7.2% (conjunctival hyperemia; 55/760), or 8.5% (tear film debris; 64/756) of eyes; and mean IOP increased 0.18 mmHg relative to baseline. The most common treatment-related adverse events were burning (10.9% of patients [45/412]), stinging (3.9% [16/412]), and conjunctival hyperemia (3.4% [14/412]). No serious treatment-related adverse events occurred. Most patients (95.2% [140/147]) said they would continue cyclosporine therapy; 97.9% (143/146) would recommend it to other dry eye patients. ::: ::: ::: CONCLUSIONS ::: Therapy of chronic dry eye disease with cyclosporine 0.1% ophthalmic emulsion for 1 to 3 years was safe, well tolerated, and not associated with systemic side effects. The results supplement the safety record of the commercially available cyclosporine 0.05% ophthalmic emulsion.


title: Personality traits, individual innovativeness and satisfaction with life

abstract: Abstract There is plenty of research on personality traits that explains its impact on human behaviors in different situations. However, there is sparse research available in the literature that explains how does personality traits affect innovativeness among individuals and satisfaction with life perceptions (subjective wellbeing). The current study proposes and empirically examines a conceptual model that addresses this important gap in the body of knowledge. Famous Big-Five personality traits theory is used to explain this phenomenon in this research. Data is collected from 613 students enrolled in different executive, master and PhD level programs in different universities of Pakistan. The study found positive influence of extraversion, agreeableness, conscientiousness, and openness to experience on individual innovativeness and satisfaction with life perceptions. Neuroticism is found to be negatively related to individual innovativeness and satisfaction with life perceptions. Finally, the study noted a positive association between individual innovativeness and perception with life. The applications and implications of this research are discussed in details.


title: X-View: Graph-Based Semantic Multiview Localization

abstract: Global registration of multi-view robot data is a challenging task. Appearance-based global localization approaches often fail under drastic view-point changes, as representations have limited view-point invariance. This work is based on the idea that human-made environments contain rich semantics which can be used to disambiguate global localization. Here, we present X-View, a Multi-View Semantic Global Localization system. X-View leverages semantic graph descriptor matching for global localization, enabling localization under drastically different view-points. While the approach is general in terms of the semantic input data, we present and evaluate an implementation on visual data. We demonstrate the system in experiments on the publicly available SYNTHIA dataset, and a realistic urban dataset recorded with a simulator. On these data, our findings show that X-View is able to globally localize aerial-to-ground, and ground-to-ground robot data of drastically different view-points. Our approach achieves an accuracy of up to 85 % on global localizations in the multi-view case, while the benchmarked traditional appearance based method reaches up to 50 %.


title: Spatial reconstruction of single-cell gene expression data

abstract: RNA-seq data from single cells are mapped to their location in complex tissues using gene expression atlases based on in situ hybridization.


title: Friend or Foe: Fine-Grained Categorization With Weak Supervision

abstract: Multi-instance learning (MIL) is widely acknowledged as a fundamental method to solve weakly supervised problems. While MIL is usually effective in standard weakly supervised object recognition tasks, in this paper, we investigate the applicability of MIL on an extreme case of weakly supervised learning on the task of fine-grained visual categorization, in which intra-class variance could be larger than inter-class due to the subtle differences between subordinate categories. For this challenging task, we propose a new method that generalizes the standard multi-instance learning framework, for which a novel multi-task co-localization algorithm is proposed to take advantage of the relationship among fine-grained categories and meanwhile performs as an effective initialization strategy for the non-convex multi-instance objective. The localization results also enable object-level domain-specific fine-tuning of deep neural networks, which significantly boosts the performance. Experimental results on three fine-grained datasets reveal the effectiveness of the proposed method, especially the importance of exploiting inter-class relationships between object categories in weakly supervised fine-grained recognition.


title: DGWC: Distributed and generic web crawler for online information extraction

abstract: Online information has become important data source to analyze the public opinion and behavior, which is significant for social management and business decision. Web crawler systems target at automatically download and parse web pages to extract expected online information. However, as the rapid increasing of web pages and the heterogeneous page structures, the performance and the rules of parsing have become two serious challenges to web crawler systems. In this paper, we propose a distributed and generic web crawler system (DGWC), in which spiders are scheduled to parallel access and parse web pages to improve performance, utilized a shared and memory based database. Furthermore, we package the spider program and the dependencies in a container called Docker to make the system easily horizontal scaling. Last but not the least, a statistics-based approach is proposed to extract the main text using supervised-learning classifier instead of parsing the page structures. Experimental results on real-world data validate the efficiency and effectiveness of DGWC.


title: Exploiting local features from deep networks for image retrieval

abstract: Deep convolutional neural networks have been successfully applied to image classification tasks. When these same networks have been applied to image retrieval, the assumption has been made that the last layers would give the best performance, as they do in classification. We show that for instance-level image retrieval, lower layers often perform better than the last layers in convolutional neural networks. We present an approach for extracting convolutional features from different layers of the networks, and adopt VLAD encoding to encode features into a single vector for each image. We investigate the effect of different layers and scales of input images on the performance of convolutional features using the recent deep networks OxfordNet and GoogLeNet. Experiments demonstrate that intermediate layers or higher layers with finer scales produce better results for image retrieval, compared to the last layer. When using compressed 128-D VLAD descriptors, our method obtains state-of-the-art results and outperforms other VLAD and CNN based approaches on two out of three test datasets. Our work provides guidance for transferring deep networks trained on image classification to image retrieval tasks.


title: Handwritten Digit Recognition Using K Nearest-Neighbor, Radial-Basis Function, and Backpropagation Neural Networks

abstract: Results of recent research suggest that carefully designed multiplayer neural networks with local receptive fields and shared weights may be unique in providing low error rates on handwritten digit recognition tasks. This study, however, demonstrates that these networks, radial basis function (RBF) networks, and k nearest-neighbor (kNN) classifiers, all provide similar low error rates on a large handwritten digit database. The backpropagation network is overall superior in memory usage and classification time but can provide false positive classifications when the input is not a digit. The backpropagation network also has the longest training time. The RBF classifier requires more memory and more classification time, but less training time. When high accuracy is warranted, the RBF classifier can generate a more effective confidence judgment for rejecting ambiguous inputs. The simple kNN classifier can also perform handwritten digit recognition, but requires a prohibitively large amount of memory and is much slower at classification. Nevertheless, the simplicity of the algorithm and fast training characteristics makes the kNN classifier an attractive candidate in hardware-assisted classification tasks. These results on a large, high input dimensional problem demonstrate that practical constraints including training time, memory usage, and classification time often constrain classifier selection more strongly than small differences in overall error rate.


title: Demonstration of 13.56-MHz class-E amplifier using a high-Voltage GaN power-HEMT

abstract: A 13.56-MHz class-E amplifier with a high-voltage GaN HEMT as the main switching device is demonstrated to show the possibility of using GaN HEMTs in high-frequency switching power applications such as RF power-supply applications. The 380-V/1.9-A GaN power HEMT was designed and fabricated for high-voltage power-electronics applications. The demonstrated circuit achieved the output power of 13.4 W and the power efficiency of 91% under a drain-peak voltage as high as 330 V. This result shows that high-voltage GaN devices are suitable for high-frequency switching applications under high dc input voltages of over 100 V.


title: The game-theoretic interaction index on social networks with applications to link prediction and community detection

abstract: Measuring similarity between nodes has been an issue of extensive research in the social network analysis literature. In this paper, we construct a new measure of similarity between nodes based on the game-theoretic interaction index (Grabisch and Roubens, 1997). Despite the fact that, in general, this index is computationally challenging, we show that in our network application it can be computed in polynomial time. We test our measure on two important problems, namely link prediction and community detection, given several real-life networks. We show that, for the majority of those networks, our measure outperforms other local similarity measures from the literature.


title: Mining Hindi-English Transliteration Pairs from Online Hindi Lyrics

abstract: This paper describes a method to mine Hindi-English transliteration pairs from online Hindi song lyrics. The technique is based on the observations that lyrics are transliterated word-by-word, maintaining the precise word order. The mining task is nevertheless challenging because the Hindi lyrics and its transliterations are usually available from different, often unrelated, websites. Therefore, it is a non-trivial task to match the Hindi lyrics to their transliterated counterparts. Moreover, there are various types of noise in lyrics data that needs to be appropriately handled before songs can be aligned at word level. The mined data of 30823 unique Hindi-English transliteration pairs with an accuracy of more than 92% is available publicly. Although the present work reports mining of Hindi-English word pairs, the same technique can be easily adapted for other languages for which song lyrics are available online in native and Roman scripts.


title: Mood, food, and obesity

abstract: Food is a potent natural reward and food intake is a complex process. Reward and gratification associated with food consumption leads to dopamine (DA) production, which in turn activates reward and pleasure centers in the brain. An individual will repeatedly eat a particular food to experience this positive feeling of gratification. This type of repetitive behavior of food intake leads to the activation of brain reward pathways that eventually overrides other signals of satiety and hunger. Thus, a gratification habit through a favorable food leads to overeating and morbid obesity. Overeating and obesity stems from many biological factors engaging both central and peripheral systems in a bi-directional manner involving mood and emotions. Emotional eating and altered mood can also lead to altered food choice and intake leading to overeating and obesity. Research findings from human and animal studies support a two-way link between three concepts, mood, food, and obesity. The focus of this article is to provide an overview of complex nature of food intake where various biological factors link mood, food intake, and brain signaling that engages both peripheral and central nervous system signaling pathways in a bi-directional manner in obesity.


title: A Hand-held Instrument to Maintain Steady Tissue Contact during Probe-Based Confocal Laser Endomicroscopy

abstract: Probe-based confocal laser endomicroscopy (pCLE) provides high-resolution in vivo imaging for intraoperative tissue characterization. Maintaining a desired contact force between target tissue and the pCLE probe is important for image consistency, allowing large area surveillance to be performed. A hand-held instrument that can provide a predetermined contact force to obtain consistent images has been developed. The main components of the instrument include a linear voice coil actuator, a donut load-cell, and a pCLE probe. In this paper, detailed mechanical design of the instrument is presented and system level modeling of closed-loop force control of the actuator is provided. The performance of the instrument has been evaluated in bench tests as well as in hand-held experiments. Results demonstrate that the instrument ensures a consistent predetermined contact force between pCLE probe tip and tissue. Furthermore, it compensates for both simulated physiological movement of the tissue and involuntary movements of the operator's hand. Using pCLE video feature tracking of large colonic crypts within the mucosal surface, the steadiness of the tissue images obtained using the instrument force control is demonstrated by confirming minimal crypt translation.


title: Understanding the influence of organizational change strategies on information technology and knowledge management strategies

abstract: Abstract While discussion about knowledge management often centers around how knowledge may best be codified into an explicit format for use in decision support or expert systems, some knowledge best serves the organization when it is kept in tacit form. We draw upon the resource-based view to identify how information technology can best be used during different types of strategic change. Specifically, we suggest that different change strategies focus on different combinations of tacit and explicit knowledge that make certain types of information technology more appropriate in some situations than in others.


title: A Hybrid Filter-Wrapper Approach for FeatureSelection

abstract: Feature selection is the task of selecting a small subset from original featuresthat can achieve maximum classification accuracy. This subset of features hassome very important benefits like, it reduces computational complexity of learningalgorithms, saves time, improve accuracy and the selected features can beinsightful for the people involved in problem domain. This makes feature selectionas an indispensable task in classification task.This dissertation presents a two phase approach for feature selection. In thefirst phase a filter method is used with “correlation coefficient” and “mutualinformation” as statistical measure of similarity. This phase helps in improvingthe classification performance by removing redundant and unimportantfeatures. A wrapper method is used in the second phase with the sequentialforward selection and sequential backward elimination. This phase helps in selectingrelevant feature subset that produce maximum accuracy according tothe underlying classifier. The Support Vector Machine (SVM) classifier (linearand nonlinear) is used to evaluate the classification accuracy of our approach.This empirical results of commonly used data sets from the University ofCalifornia, Irvine repository and microarray data sets showed that the proposedmethod performs better in terms of classification accuracy, number of selectedfeatures, and computational efficiency.7


title: Remote sensing image fusion using the curvelet transform

abstract: This paper presents an image fusion method suitable for pan-sharpening of multispectral (MS) bands, based on nonseparable multiresolution analysis (MRA). The low-resolution MS bands are resampled to the fine scale of the panchromatic (Pan) image and sharpened by injecting highpass directional details extracted from the high-resolution Pan image by means of the curvelet transform (CT). CT is a nonseparable MRA, whose basis functions are directional edges with progressively increasing resolution. The advantage of CT with respect to conventional separable MRA, either decimated or not, is twofold. Firstly, directional detail coefficients matching image edges may be preliminarily soft-thresholded to achieve a noise reduction that is better than that obtained in the separable wavelet domain. Secondly, modeling of the relationships between high-resolution detail coefficients of the MS bands and of the Pan image is more fitting, being accomplished in the directional multiresolution domain. Experiments are carried out on very-high-resolution MS+Pan images acquired by the QuickBird and Ikonos satellite systems. Fusion simulations on spatially degraded data, whose original MS bands are available for reference, show that the proposed curvelet-based fusion method performs slightly better than the state-of-the art. Fusion tests at the full scale reveal that an accurate and reliable Pan-sharpening, little affected by local inaccuracies even in the presence of complex and detailed urban landscapes, is achieved by the proposed method.


title: Bidirectional antagonistic variable stiffness actuation: Analysis, design & Implementation

abstract: The variable stiffness actuation concept is considered to provide a human-friendly robot technology. This paper examines a joint concept called the bidirectional antagonistic joint which is a extension of antagonistic joints. A new operating mode called the helping mode is introduced, which increases the joint load range. Although the joint can not be pretensioned in the helping mode, it is shown that a stiffness variation is possible, assuming a suitable torque-stiffness characteristic of the elastic elements. A methodology to design such characteristics is presented along with several example cases interpreted in a torque-stiffness plot. Furthermore, a stiffness adaptation control scheme which ensures mechanism safety is described. Finally, the design methodology and the control are evaluated on an implementation of a bidirectional antagonistic joint.


title: Mirage: Toward a Stealthier and Modular Malware Analysis Sandbox for Android

abstract: Nowadays, malware is affecting not only PCs but also mobile devices, which became pervasive in everyday life. Mobile devices can access and store personal information (e.g., location, photos, and messages) and thus are appealing to malware authors. One of the most promising approach to analyze malware is by monitoring its execution in a sandbox (i.e., via dynamic analysis). In particular, most malware sandboxing solutions for Android rely on an emulator, rather than a real device. This motivates malware authors to include runtime checks in order to detect whether the malware is running in a virtualized environment. In that case, the malicious app does not trigger the malicious payload. The presence of differences between real devices and Android emulators started an arms race between security researchers and malware authors, where the former want to hide these differences and the latter try to seek them out.


title: ALPHA: A hybrid self-adaptable hand for a social humanoid robot

abstract: This paper presents a novel design of a compact and light-weight robotic hand for a social humanoid robot. The proposed system is able to perform common hand gestures and self-adaptable grasps by mixing under-actuated and self-adaptable hand kinematics in a unique design. The hand answers the need for precise finger postures and sensor-less force feedback during gestures and for finger adaptation and autonomous force distribution during grasps. These are provided by a dual actuation system embodied within the palm and the fingers. Coexistence is ensured by compliant transmissions based on elastomer bars rather than classical tension springs, thanks to their high elastic coefficient at reduced sizes and strains. The proposed solution significantly reduces the weight and the size of the hand by using a reduced number of small actuators for gesturing and a single motor for grasping. The hand prototype (ALPHA) is realized to confirm the design feasibility and functional capabilities. It is controlled to provide safe human-robot interaction and preserve mechanical integrity in order to be embodied on a humanoid robot.


title: Data-driven quality monitoring and fault detection for multimode nonlinear processes

abstract: This paper addresses the problem of quality monitoring and fault detection in nonlinear processes which are working in different operating points. For such processes the statistical model which is obtained from process data is different from one operating point to another, due to nonlinearities and set-point changes. Therefore the classical methods for quality monitoring and fault detection, e.g. partial least squares (PLS), may not be suitable. To this end, a new approach is proposed based on the modeling of nonlinear process as a piecewise linear parameter varying system, considering the behavior of the plant in each operating point as linear time invariant with different parameters in each operating point. The expectation-maximization (EM) algorithm is used to model the process as a finite mixture of Gaussian components and based on the identified model a Bayesian inference strategy is developed to detect the faults which influence the product quality. Finally, the usefulness of the proposed method is demonstrated on a laboratory continuous stirred tank heater (CSTH) setup.


title: Unsupervised Segmentation Helps Supervised Learning of Character Tagging for Word Segmentation and Named Entity Recognition

abstract: This paper describes a novel character tagging approach to Chinese word segmentation and named entity recognition (NER) for our participation in Bakeoff-4.1 It integrates unsupervised segmentation and conditional random fields (CRFs) learning successfully, using similar character tags and feature templates for both word segmentation and NER. It ranks at the top in all closed tests of word segmentation and gives promising results for all closed and open NER tasks in the Bakeoff. Tag set selection and unsupervised segmentation play a critical role in this success.


title: Ambient intelligence - a state of the art from artificial intelligence perspective

abstract: Ambient Intelligence (AmI) deals with a new world where computing devices are spread everywhere (ubiquity), allowing the human being to interact in physical world environments in an intelligent and unobtrusive way. These environments should be aware of the needs of people, customizing requirements and forecasting behaviours. AmI environments may be so diverse, such as homes, offices, meeting rooms, schools, hospitals, control centers, transports, touristic attractions, stores, sport installations, music devices, etc. In the aims of Ambient Intelligence, research envisages to include more intelligence in the AmI environments, allowing a better support to the human being and the access to the essential knowledge to make better decisions when interacting with these environments. This paper can be seen as a State of the Art of Ambient Intelligence, according to an Artificial Intelligence (AI) perspective. We will define Ambient Intelligence; refer some of their prototype and systems; and to analyze how the main Artificial Intelligence areas can be applied.


title: An improved endpoint detector for isolated word recognition

abstract: Accurate location of the endpoints of an isolated word is important for reliable and robust word recognition. The endpoint detection problem is nontrivial for nonstationary backgrounds where artifacts (i.e., nonspeech events) may be introduced by the speaker, the recording environment, and the transmission system. Several techniques for the detection of the endpoints of isolated words recorded over a dialed-up telephone line were studied. The techniques were broadly classified as either explicit, implicit, or hybrid in concept. The explicit techniques for endpoint detection locate the endpoints prior to and independent of the recognition and decision stages of the system. For the implicit methods, the endpoints are determined solely by the recognition and decision stages of the system, i.e., there is no separate stage for endpoint detection. The hybrid techniques incorporate aspects from both the explicit and implicit methods. Investigations showed that the hybrid techniques consistently provided the best estimates for both of the word endpoints and, correspondingly, the highest recognition accuracy of the three classes studied. A hybrid end-point detector is proposed which gives a rejection rate of less than 0.5 percent, while providing recognition accuracy close to that obtained from hand-edited endpoints.


title: Information Structure and the Syntax-Phonology Interface

abstract: The article proposes a theory of grammar relating syntax, discourse semantics, and intonational prosody. The full range of English intonational tunes distinguished by Beckman and Pierrehumbert (1986) and their semantic interpretation in terms of focus and information structure are discussed, including “discontinuous” themes and rhemes. The theory extends an earlier account based on Combinatory Categorial Grammar, which directly pairs phonological and logical forms without intermediary representational levels.


title: Generalized extracellular molecule sensor platform for programming cellular behavior

abstract: Strategies for expanding the sensor space of designer receptors are urgently needed to tailor cell-based therapies to respond to any type of medically relevant molecules. Here, we describe a universal approach to designing receptor scaffolds that enables antibody-specific molecular input to activate JAK/STAT, MAPK, PLCG or PI3K/Akt signaling rewired to transgene expression driven by synthetic promoters. To demonstrate its scope, we equipped the GEMS (generalized extracellular molecule sensor) platform with antibody fragments targeting a synthetic azo dye, nicotine, a peptide tag and the PSA (prostate-specific antigen) biomarker, thereby covering inputs ranging from small molecules to proteins. These four GEMS devices provided robust signaling and transgene expression with high signal-to-noise ratios in response to their specific ligands. The sensitivity of the nicotine- and PSA-specific GEMS devices matched the clinically relevant concentration ranges, and PSA-specific GEMS were able to detect pathological PSA levels in the serum of patients diagnosed with prostate cancer.


title: Design of Millimeter-Wave Bandpass Filter Using Electric Coupling of Substrate Integrated Waveguide (SIW)

abstract: A millimeter-wave (mmW) bandpass filter (BPF) using substrate integrated waveguide (SIW) is proposed in this work. A BPF with three resonators is formed by etching slots on the top metal plane of the single SIW cavity. The filter is investigated with the theory of electric coupling mechanism. The design procedure and design curves of the coupling coefficient (K) and quality factor (Q) are given and discussed here. The extracted K and Q are used to determine the filter circuit dimensions. In order to prove the validity, a SIW BPF operating at 140 GHz is fabricated in a single circuit layer using low temperature co-fired ceramic (LTCC) technology. The measured insertion loss is 1.913 dB at 140 GHz with a fractional bandwidth of 13.03%. The measured results are in good agreement with simulated results in such high frequency.


title: Learning RAT Selection Game in 5G Heterogeneous Networks

abstract: In this letter, we consider the problem of RAT selection in a heterogeneous cellular network where each user chooses among multiple access technologies. The competition of the users is modeled as an incomplete information game where players are not aware other players’ actions. A RAT selection strategy is proposed and is shown that it converges to the Nash equilibrium of the game. Moreover, we consider a more practical case in which the users have no information about other users. For this case, we propose a Q-learning-based algorithm, which converges to the Nash equilibrium of the first problem.


title: Neurostimulation for Parkinson's Disease with Early Motor Complications

abstract: A B S T R AC T BACKGROUND Subthalamic stimulation reduces motor disability and improves quality of life in patients with advanced Parkinson’s disease who have severe levodopa-induced motor complications. We hypothesized that neurostimulation would be beneficial at an earlier stage of Parkinson’s disease. METHODS In this 2-year trial, we randomly assigned 251 patients with Parkinson’s disease and early motor complications (mean age, 52 years; mean duration of disease, 7.5 years) to undergo neurostimulation plus medical therapy or medical therapy alone. The primary end point was quality of life, as assessed with the use of the Parkinson’s Disease Questionnaire (PDQ-39) summary index (with scores ranging from 0 to 100 and higher scores indicating worse function). Major secondary outcomes included parkinsonian motor disability, activities of daily living, levodopa-induced motor com plications (as assessed with the use of the Unified Parkinson’s Disease Rating Scale, parts III, II, and IV, respectively), and time with good mobility and no dyskinesia. RESULTS For the primary outcome of quality of life, the mean score for the neurostimulation group improved by 7.8 points, and that for the medical-therapy group worsened by 0.2 points (between-group difference in mean change from baseline to 2 years, 8.0 points; P = 0.002). Neurostimulation was superior to medical therapy with respect to motor disability (P<0.001), activities of daily living (P<0.001), levodopa-induced motor complications (P<0.001), and time with good mobility and no dyskinesia (P = 0.01). Serious adverse events occurred in 54.8% of the patients in the neuro stimulation group and in 44.1% of those in the medical-therapy group. Serious adverse events related to surgical implantation or the neurostimulation device oc curred in 17.7% of patients. An expert panel confirmed that medical therapy was consistent with practice guidelines for 96.8% of the patients in the neurostimula tion group and for 94.5% of those in the medical-therapy group. CONCLUSIONS Subthalamic stimulation was superior to medical therapy in patients with Parkinson’s disease and early motor complications. (Funded by the German Ministry of Research and others; EARLYSTIM ClinicalTrials.gov number, NCT00354133.)


title: Convolutional recurrent neural networks for electrocardiogram classification

abstract: We propose two deep neural network architectures for classification of arbitrary-length electrocardiogram (ECG) recordings and evaluate them on the atrial fibrillation (AF) classification data set provided by the PhysioNet/CinC Challenge 2017. The first architecture is a deep convolutional neural network (CNN) with averaging-based feature aggregation across time. The second architecture combines convolutional layers for feature extraction with long-short term memory (LSTM) layers for temporal aggregation of features. As a key ingredient of our training procedure we introduce a simple data augmentation scheme for ECG data and demonstrate its effectiveness in the AF classification task at hand. The second architecture was found to outperform the first one, obtaining an $F_1$ score of $82.1$% on the hidden challenge testing set.


title: O uso da crioterapia no tratamento das disfunções temporomandibulares

abstract: ABSTRACT The purpose of this research was to perform an integrative review of scientific bibliographic production on the use of cryotherapy on temporomandibular disorders treatment, highlighting the techniques, duration, body area stimulated and frequency of application. Literature review was accomplished on Medline, LILACS, SciELO, Cochrane Library and IBECS databases. The descriptors used were: Cryotherapy, Cold Temperature, Induced Hypothermia, Heat Transference, Temporomandibular Joint, Temporomandibular Joint Disorders, Temporomandibular Joint Dysfunction Syndrome and their equivalents in Portuguese and Spanish. Articles that addressed the cryotherapy for the treatment of temporomandibular disorders, published in English, Spanish or Portuguese, between 1980 and 2013, were included. The following data were collected: technique, duration of application, area stimulated and frequency of application. Initially, 34 studies were found, but just 13 contemplated the selection criteria proposed. Data were tabulated and presented in chronological order. The decrease of body heat can be conducted through application of cold compresses, cold bags or vapocoolant sprays applied to the painful areas, or trigger points regions, or in masticatory muscles. The average time of application of the stimulus was 10 to 15 minutes for cold bags and about 10 seconds for vapocoolant sprays, repeated approximately 2 to 4 times per day, preceding muscle stretching techniques. The literature has no consensus about the intensity of the thermal stimulus.


title: Current-Pumped Battery Charger

abstract: A novel current-pumped battery charger (CPBC) is proposed in this paper to increase the Li-ion battery charging performance. A complete charging process, consisting of three subprocesses, namely: 1) the bulk current charging process; 2) the pulsed current charging process; and 3) the pulsed float charging process, can be automatically implemented by using the inherent characteristics of current-pumped phase-locked loop (CPLL). A design example for a 700-mA ldr h Li-ion battery is built to assess the CPBC's performance. In comparison with the conventional phase-locked battery charger, the battery available capacity and charging efficiency of the proposed CPBC are improved by about 6.9% and 1.5%, respectively. The results of the experiment show that a CPLL is really suitable for carrying out a Li-ion battery pulse charger.


title: Ownership structure, corporate governance, and fraud: Evidence from China

abstract: Abstract Our study examines whether ownership structure and boardroom characteristics have an effect on corporate financial fraud in China. The data come from the enforcement actions of the Chinese Securities Regulatory Commission (CSRC). The results from univariate analyses, where we compare fraud and no-fraud firms, show that ownership and board characteristics are important in explaining fraud. However, using a bivariate probit model with partial observability we demonstrate that boardroom characteristics are important, while the type of owner is less relevant. In particular, the proportion of outside directors, the number of board meetings, and the tenure of the chairman are associated with the incidence of fraud. Our findings have implications for the design of appropriate corporate governance systems for listed firms. Moreover, our results provide information that can inform policy debates within the CSRC.


title: A comparison of some soft computing methods for software fault prediction

abstract: Software fault prediction is implemented with ANN, SVM and ANFIS.First ANFIS implementation is applied to solve fault prediction problem.Parameters are discussed in neuro fuzzy approach.Experiments show that the application of ANFIS to the software fault prediction problem is highly reasonable. The main expectation from reliable software is the minimization of the number of failures that occur when the program runs. Determining whether software modules are prone to fault is important because doing so assists in identifying modules that require refactoring or detailed testing. Software fault prediction is a discipline that predicts the fault proneness of future modules by using essential prediction metrics and historical fault data. This study presents the first application of the Adaptive Neuro Fuzzy Inference System (ANFIS) for the software fault prediction problem. Moreover, Artificial Neural Network (ANN) and Support Vector Machine (SVM) methods, which were experienced previously, are built to discuss the performance of ANFIS. Data used in this study are collected from the PROMISE Software Engineering Repository, and McCabe metrics are selected because they comprehensively address the programming effort. ROC-AUC is used as a performance measure. The results achieved were 0.7795, 0.8685, and 0.8573 for the SVM, ANN and ANFIS methods, respectively.


title: Cooperative SLAM on small mobile robots

abstract: We present a method for simultaneous localization and mapping for robots. The focus of our work is to use multiple small mobile robots which have only limited sensing and computational resources. Our robots each uses a laser pointer and an event based vision sensor to compute distances to its surroundings. The acquired data is used to update an occupancy map which can be shared among many robots at the same time. Here we demonstrate initial results for our proof-of-concept implementation. It runs in real-time using a mobile robot platform with an event based vision sensor for data acquisition. Distance estimates to objects are transferred to a remote computer to build a map of the environment. The results of our work can be used in future technical implementations, and for further investigations into cooperative mapping.


title: RGB-D Scene Recognition with Object-to-Object Relation

abstract: A scene is usually abstract that consists of several less abstract entities such as objects or themes. It is very difficult to reason scenes from visual features due to the semantic gap between the abstract scenes and low-level visual features. Some alternative works recognize scenes with a two-step framework by representing images with intermediate representations of objects or themes. However, the object co-occurrences between scenes may lead to ambiguity for scene recognition. In this paper, we propose a framework to represent images with intermediate (object) representations with spatial layout, i.e., object-to-object relation (OOR) representation. In order to better capture the spatial information, the proposed OOR is adapted to RGB-D data. In the proposed framework, we first apply object detection technique on RGB and depth images separately. Then the detected results of both modalities are combined with a RGB-D proposal fusion process. Based on the detected results, we extract semantic feature OOR and regional convolutional neural network (CNN) features located by bounding boxes. Finally, different features are concatenated to feed to the classifier for scene recognition. The experimental results on SUN RGB-D and NYUD2 datasets illustrate the efficiency of the proposed method.


title: Data Wrangling: Making data useful again

abstract: Abstract Data analysis has become an everyday business and advancements of data management routines open up new opportunities. Nevertheless, transforming and assembling newly acquired data into a suitable form remains tedious. It is often stated, that data cleaning is a critical part of the overall process, but also consumes sublime amounts of time and resources. Data Wrangling is not only about transforming and cleaning procedures. Many other aspects like data quality, merging of different sources, reproducible processes, and managing data provenance have to be considered. Although various tools designed for specific tasks are available, software solutions accompanying the whole process are still rare. In this paper, some aspects of this first phase of most data driven projects, also known as data wrangling, data munging or janitorial work are described. Beginning with an overview on the topic and current problems, concrete common tasks as well as selected software solutions and techniques are discussed.


title: The nerve supply of the human auricle

abstract: Knowledge of the innervation of the outer ear is crucial for surgery in this region. The aim of this study was to describe the system of the auricular nerve supply. On 14 ears of seven cadavers the complete course of the nerve supply was exposed and categorized. A heterogeneous distribution of two cranial branchial nerves and two somatic cervical nerves was found. At the lateral as well as the medial surface the great auricular nerve prevails. No region with triple innervation was found. Clin. Anat. 15:35–37, 2002. © 2002 Wiley-Liss, Inc.


title: Estimating the selectivity of tf-idf based cosine similarity predicates

abstract: An increasing number of database applications today require sophisticated approximate string matching capabilities. Examples of such application areas include data integration and data cleaning. Cosine similarity has proven to be a robust metric for scoring the similarity between two strings, and it is increasingly being used in complex queries. An immediate challenge faced by current database optimizers is to find accurate and efficient methods for estimating the selectivity of cosine similarity predicates. To the best of our knowledge, there are no known methods for this problem. In this paper, we present the first approach for estimating the selectivity of tf.idf based cosine similarity predicates. We evaluate our approach on three different real datasets and show that our method often produces estimates that are within 40% of the actual selectivity.


A Novel Multimodal Approach for Studying the Dynamics of Curiosity in Group Learning


title: Approximate convex decomposition of polyhedra

abstract: Decomposition is a technique commonly used to partition complex models into simpler components. While decomposition into convex components results in pieces that are easy to process, such decompositions can be costly to construct and can result in representations with an unmanageable number of components. In this paper we explore an alternative partitioning strategy that decomposes a given model into “approximately convex” pieces that may provide similar benefits as convex components, while the resulting decomposition is both significantly smaller (typically by orders of magnitude) and can be computed more efficiently. Indeed, for many applications, an approximate convex decomposition (ACD) can more accurately represent the important structural features of the model by providing a mechanism for ignoring less significant features, such as surface texture. We describe a technique for computing ACDs of threedimensional polyhedral solids and surfaces of arbitrary genus. We provide results illustrating that our approach results in high quality decompositions with very few components and applications showing that comparable or better results can be obtained using ACD decompositions in place of exact convex decompositions (ECD) that are several orders of magnitude larger. CR Categories: I.3.5 [COMPUTER GRAPHICS]: Computational Geometry and Object Modeling—Geometric algorithms, languages, and systems


title: Biometrics: privacy's foe or privacy's friend?

abstract: From the INS to ATM's, both the public and private sectors are making extensive use of biometrics for human recognition. As this technology becomes more economically viable and technically perfected, and thus more commonplace, the field of biometrics will spark legal and policy concerns. Critics inevitably compare biometrics to Big Brother and the loss of individual privacy. The probiometric lobby generally stresses the greater security and improved service that the technology provides. Is biometrics privacy's friend or privacy's foe? This paper explores the various arguments for and against biometrics and contends that while biometrics may pose legitimate privacy concerns, these issues can be adequately addressed. In the final analysis, biometrics emerges as privacy's friend.


title: Characterizing Computer Systems' Workloads

abstract: The performance of any system cannot be determined without knowing the workload, that is, the set of requests presented to the system. Workload characterization is the process by which we produce models that are capable of describing and reproducing the behavior of a workload. Such models are imperative to any performance related studies such as capacity planning, workload balancing, performance prediction and system tuning. In this paper, we survey workload characterization techniques used for several types of computer systems. We identify significant issues and concerns encountered during the characterization process and propose an augmented methodology for workload characterization as a framework. We believe that the surveyed case studies, the described characterization techniques, and the proposed framework give a good introduction to the topic, assist in exploring the different options of characterization tools that can be adopted, and provide general guidelines for deriving a good workload model suitable as an input to performance studies.


title: Chemistry for peptide and protein PEGylation

abstract: Poly(ethylene glycol) (PEG) is a highly investigated polymer for the covalent modification of biological macromolecules and surfaces for many pharmaceutical and biotechnical applications. In the modification of biological macromolecules, peptides and proteins are of extreme importance. Reasons for PEGylation (i.e. the covalent attachment of PEG) of peptides and proteins are numerous and include shielding of antigenic and immunogenic epitopes, shielding receptor-mediated uptake by the reticuloendothelial system (RES), and preventing recognition and degradation by proteolytic enzymes. PEG conjugation also increases the apparent size of the polypeptide, thus reducing the renal filtration and altering biodistribution. An important aspect of PEGylation is the incorporation of various PEG functional groups that are used to attach the PEG to the peptide or protein. In this paper, we review PEG chemistry and methods of preparation with a particular focus on new (second-generation) PEG derivatives, reversible conjugation and PEG structures.


title: MPE and Partial Inversion in Lifted Probabilistic Variable Elimination

abstract: It is often convenient to represent probabilistic models in a first-order fashion, using logical atoms such as partners (X, Y) as random variables parameterized by logical variables. (de Salvo Braz, Amir, & Roth 2005), following (Poole 2003), give a lifted variable elimination algorithm (FOVE) for computing marginal probabilities from first-order probabilistic models (belief assessment, or BA). FOVE is lifted because it works directly at the first-order level, eliminating all the instantiations of a set of atoms in a single step, in some cases independently of the number of these instantiations. Previous work could treat only restricted potential functions. There, atoms' instantiations cannot constrain each other: predicates can appear at most once, or logical variables must not interact across atoms. In this paper, we present two contributions. The first one is a significantly more general lifted variable elimination algorithm, FOVE-P, that covers many cases where atoms share logical variables. The second contribution is to use FOVE-P for solving the Most Probable Explanation (MPE) problem, which consists of calculating the most probable assignment of the random variables in a model. The transition from BA to MPE is straightforward in propositional models, but the lifted first-order case is harder. We introduce the notion of lifted assignments, a distribution of values to a set of random variables rather than to each individual one. Lifted assignments are cheaper to compute while being as useful as regular assignments over that group. Both contributions advance the theoretical understanding of lifted probabilistic inference.


title: DART: directed automated random testing

abstract: We present a new tool, named DART, for automatically testing software that combines three main techniques: (1) automated extraction of the interface of a program with its external environment using static source-code parsing; (2) automatic generation of a test driver for this interface that performs random testing to simulate the most general environment the program can operate in; and (3) dynamic analysis of how the program behaves under random testing and automatic generation of new test inputs to direct systematically the execution along alternative program paths. Together, these three techniques constitute Directed Automated Random Testing, or DART for short. The main strength of DART is thus that testing can be performed completely automatically on any program that compiles -- there is no need to write any test driver or harness code. During testing, DART detects standard errors such as program crashes, assertion violations, and non-termination. Preliminary experiments to unit test several examples of C programs are very encouraging.


title: Automatic circuit sizing technique for the analog circuits with flexible TFTs considering process variation and bending effects

abstract: Flexible electronics are possible alternative for portable consumer applications with many advantages. However, the circuit design for flexible electronics is still challenging, especially for sensitive analog circuits. Significant parameter variations and bending effects of flexible TFTs further increase the difficulties for circuit designers. In this paper, an automatic circuit sizing technique is proposed for the analog circuits with flexible TFTs. The process variation and bending effects of flexible TFTs are considered simultaneously in the optimization flow. As shown in the experimental results, the proposed approach can further improve the design yield and significantly reduce the design overhead.


title: Complexity leadership theory: An interactive perspective on leading in complex adaptive systems

abstract: �Traditional, hierarchical views of leadership are less and less useful given the complexities of our modern world. Leadership theory must transition to new perspectives that account for the complex adaptive needs of organizations. In this paper, we propose that leadership (as opposed to leaders) can be seen as a complex dynamic process that emerges in the interactive “spaces between” people and ideas. That is, leadership is a dynamic that transcends the capabilities of individuals alone; it is the product of interaction, tension, and exchange rules governing changes in perceptions and understanding. We label this a dynamic of adaptive leadership, and we show how this dynamic provides important insights about the nature of leadership and its outcomes in organizational fields. We define a leadership event as a perceived segment of action whose meaning is created by the interactions of actors involved in producing it, and we present a set of innovative methods for capturing and analyzing these contextually driven processes. We provide theoretical and practical implications of these ideas for organizational behavior and organization and management theory.


title: Second to fourth digit ratio confirms aggressive tendencies in patients with boxers fractures

abstract: Abstract Background Upper limb second to fourth digit ratio (2D:4D) has been shown to be dependent on prenatal androgen exposure. A longer relative fourth digit to second digit is indicative of increased intrauterine testosterone exposure prenatally and the converse is also true for oestrogen exposure. The 2D:4D ratio has implications in the sporting, academic, financial and sexual arenas. The purpose of this study was to examine the association between smaller finger length ratios (2D:4D) and boxers fractures, in both men and women, by comparing the 2D:4D ratios in 150 boxers fractures and comparing them to matched controls. Boxers fractures are an injury classically incurred during acts of aggression and we postulated that this cohort of patients would have a smaller 2D:4D ratio in comparison to the normal population mean ratio. Methods One hundred and fifty radiographs from patients with boxers fractures secondary to aggressive actions were analysed and the 2D:4D ratio was calculated. A further 150 X-rays from patients not involved in aggressive activities were used as a control group and the 2D:4D ratio was calculated in the same manner. We then performed statistical analysis to compare the 2D:4D ratios between our two groups. Results As predicted, the 2D:4D in males was smaller than females in all of the groups. However, our results showed that those presenting with a boxers fracture due to an aggression related injury had a statistically significant smaller 2D:4D ratio when compared to the normal population. Conclusion Boxers fractures are injuries that typically occur from an aggressive act. It is well documented that a low 2D:4D ratio is reflective of an increased prenatal exposure to androgens, particularly testosterone. We have shown that boxers fractures are associated with a smaller 2D:4D ratio than the normal population, thus suggesting that persons exposed to high levels of prenatal androgens are more likely to exhibit aggressive tendencies in adulthood. Our results suggest that smaller digit ratios may predict a predisposition to acts of aggression, and as such result in an increased likelihood of sustaining an injury such as a boxers fracture. This relationship seems to be present independently of gender.


title: Stimulation of muscle protein synthesis by whey and caseinate ingestion after resistance exercise in elderly individuals

abstract: Sarcopenia is a well-known phenomenon in elderly individuals and resistance exercise together with sufficient amino acid (AA) availability has proved to be a counteractive implement. However, the source of AA and supplement timing require further investigation. The objective was to compare muscle protein synthesis (MPS) to intakes of whey and caseinate after heavy resistance exercise in healthy elderly individuals, and, furthermore, to compare the timing effect of caseinate intake. Twenty-four elderly men and women (mean ± SEM; 68 ± 1 years) were randomized to one of four groups: caseinate intake before exercise (CasPre), caseinate intake immediately after exercise (CasPost), whey intake immediately after exercise (Whey), or intake of a non-caloric control drink (Control). Muscle myofibrillar and collagen fractional synthesis rates (FSR) were measured by a primed continuous infusion of L-[1-(13) C]leucine using labeled proteins during a 6-h recovery period. No differences were observed in muscle myofibrillar and collagen FSR with Whey (0.09 ± 0.01%/h) compared with CasPost (0.09 ± 0.003%/h), and it did not differ between CasPre (0.10 ± 0.01%/h) and CasPost. MPS does not differ with whey and caseinate feeding immediately after heavy resistance exercise in elderly individuals, and MPS is similar with caseinate ingestion before and after exercise.


title: Trusting the Cloud: A PROV + RBAC Approach

abstract: This paper describes a provenance-based access control system for the cloud. The system relies on provenance (PROV-DM, PROV-O) and access-control models (RBAC) together with a distributed rule-based mechanism. The system allows rule propagation (in the cloud) and uses a central execution engine for enforcing security and trustworthiness. This paper provides a comparison between PROV-DM and PROV-O in terms of efficiency and expressiveness regarding Provenance graphs.


Preprocessing and Classification of Electrophoresis Gel Images Using Dynamic Time Warping


title: Bio-Inspired Optic Flow from Event-Based Neuromorphic Sensor Input

abstract: Computational models of visual processing often use frame-based image acquisition techniques to process a temporally changing stimulus. This approach is unlike biological mechanisms that are spike-based and independent of individual frames. The neuromorphic Dynamic Vision Sensor (DVS) [Lichtsteiner et al., 2008] provides a stream of independent visual events that indicate local illumination changes, resembling spiking neurons at a retinal level. We introduce a new approach for the modelling of cortical mechanisms of motion detection along the dorsal pathway using this type of representation. Our model combines filters with spatio-temporal tunings also found in visual cortex to yield spatio-temporal and direction specificity. We probe our model with recordings of test stimuli, articulated motion and ego-motion. We show how our approach robustly estimates optic flow and also demonstrate how this output can be used for classification purposes.


title: Unroofing and marsupialization vs. rhomboid excision and Limberg flap in pilonidal disease: a prospective, randomized, clinical trial.

abstract: BACKGROUND ::: The aim of the study was to compare clinical outcomes and quality of life in patients undergoing surgery for pilonidal disease with unroofing and marsupialization (UM) or rhomboid excision and Limberg flap (RELP) procedures. ::: ::: ::: METHODS ::: One hundred forty consecutive patients with pilonidal sinus were randomly assigned to receive either UM or RELP procedures. A specifically designed questionnaire was administered at three months to assess time from the operation until the patient was able to walk, return to daily activities, or sit without pain, time to return to work or school, and time to healing. Postoperative pain was assessed with a visual analog scale and the McGill Pain Questionnaire. Patients' quality of life was evaluated with the Cardiff Wound Impact Schedule (CWIS). Questionnaires were administered by a clinician blinded to treatment. ::: ::: ::: RESULTS ::: Compared with RELP, patients receiving UM had significantly shorter duration of operation and hospital stay, shorter time periods to walk, return to daily activities, or sit without pain and to return to work or school, and fewer complications. Time to final healing was significantly shorter and quality of life scores on the CWIS were higher in patients receiving RELP than in those receiving UM. Patients with UM had lower levels of pain one week after surgery. ::: ::: ::: CONCLUSION ::: The unroofing and marsupialization procedure provides more clinical benefits in the treatment of pilonidal disease than rhomboid excision and Limberg flap and should be considered the procedure of choice. However, it may be associated with more inconvenience in wound care and longer healing time than rhomboid excision and Lindberg flap.


title: The Evolution of Fun: Automatic Level Design Through Challenge Modeling

abstract: A generative system that creates levels for 2D platformer games is presented. The creation process is driven by generic models of challenge-based fun which are derived from existing theories of game design. These models are used as fitness functions in a genetic algorithm to produce new levels that maximize the amount of player fun, and the results are compared with existing levels from the classic video game Super Mario Bros. This technique is novel among systems for creating video game content as it does not follow a complex rulebased approach but instead generates output from simple and generic high-level descriptions of player enjoyment.


title: A Classification for Business Model Types in E-commerce

abstract: Internet business models have been widely discussed in literature and applied within the last decade. Nevertheless, a clear understanding of some e-commerce concepts does not exist yet. The classification of business models in e-commerce is one of these areas. The current research tries to fill this gap through a conceptual and qualitative study. Nine main e-commerce business model types are selected from literature and analyzed to define the criteria and their sub-criteria (characteristics). As a result three different classifications for business models are determined. This study can be used to improve the understanding of essential functions, relations and mechanisms of existing e-commerce business models.


title: Online advertising, behavioral targeting, and privacy

abstract: Studying how privacy regulation might impact economic activity on the advertising-supported Internet.


title: CNN-Webshell: Malicious Web Shell Detection with Convolutional Neural Network

abstract: Malicious web shell detection is one of the most important methods for protecting the network security. Most state of the art methods are based on malicious keywords matching, where the keywords are usually defined by the domain experts. So its effect depends on the domain experts and it is hard to detect new type of malicious web shells. This paper proposed a new malicious web shell detection approach based on 'word2vec' representation and convolutional neural network (CNN). Firstly, each word, separated from the HTTP requests, is represented as a vector by using the 'word2vec' tool. Next, a web request can be represented as a size-fixed matrix. Finally, a CNN-based model is designed to classify the malicious web shells and the normal ones. Experimental results showed that this approach achieves the best performance, comparing with several other classification methods. To the best of our knowledge, this is the first time that CNN has been applied to malicious web shell detection.


title: MVDepthNet: Real-Time Multiview Depth Estimation Neural Network

abstract: Although deep neural networks have been widely applied to computer vision problems, extending them into multiview depth estimation is non-trivial. In this paper, we present MVDepthNet, a convolutional network to solve the depth estimation problem given several image-pose pairs from a localized monocular camera in neighbor viewpoints. Multiview observations are encoded in a cost volume and then combined with the reference image to estimate the depth map using an encoder-decoder network. By encoding the information from multiview observations into the cost volume, our method achieves real-time performance and the flexibility of traditional methods that can be applied regardless of the camera intrinsic parameters and the number of images. Geometric data augmentation is used to train MVDepthNet. We further apply MVDepthNet in a monocular dense mapping system that continuously estimates depth maps using a single localized moving camera. Experiments show that our method can generate depth maps efficiently and precisely.


title: Ontology-based semantic recommendation for context-aware e-learning

abstract: Nowadays, e-learning systems are widely used for education and training in universities and companies because of their electronic course content access and virtual classroom participation. However, with the rapid increase of learning content on the Web, it will be time-consuming for learners to find contents they really want to and need to study. Aiming at enhancing the efficiency and effectiveness of learning, we propose an ontology-based approach for semantic content recommendation towards context-aware e-learning. The recommender takes knowledge about the learner (user context), knowledge about content, and knowledge about the domain being learned into consideration. Ontology is utilized to model and represent such kinds of knowledge. The recommendation consists of four steps: semantic relevance calculation, recommendation refining, learning path generation, and recommendation augmentation. As a result, a personalized, complete, and augmented learning program is suggested for the learner.


title: How Do Drivers Behave in a Highly Automated Car?

abstract: This paper outlines the results of a driving simulator study conducted for the European CityMobil project, which was designed to investigate the effect of a highly automated driving scenario on driver behaviour. Drivers’ response to a number of ‘critical’ scenarios was compared in manual driving with that in automated driving. Drivers were in full control of the vehicle and its manoeuvres in the manual driving condition, whilst control of the vehicle was transferred to an ‘automated system’ in the automated driving condition. Automated driving involved the engagement of lateral and longitudinal controllers, which kept the vehicle in the centre of the lane and at a speed of 40 mph, respectively. Drivers were required to regain control of the driving task if the automated system was unable to handle a critical situation. An auditory alarm forewarned drivers of an imminent collision in such critical situations. Drivers’ response to all critical events was found to be much later in the automated driving condition, compared to manual driving. This is thought to be because drivers’ situation awareness was reduced during automated driving, with response only produced after drivers heard the alarm. Alternatively, drivers may have relied too heavily on the system, waiting for the auditory alarm before responding in a critical situation. These results suggest that action must be taken when implementing fully automated driving to ensure that the driver is kept in the loop at all times and is able to respond in time and appropriately during critical situations.


title: Social (pragmatic) communication disorder: a research review of this new DSM-5 diagnostic category

abstract: Social (pragmatic) communication disorder (SCD) is a new diagnostic category in the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5). The purpose of this review is to describe and synthesize the relevant literature from language and autism spectrum disorder (ASD) research relating to pragmatic language impairment and other previously used terms that relate to SCD. The long-standing debate regarding how social communication/pragmatic impairments overlap and/or differ from language impairments, ASD, and other neurodevelopmental disorders is examined. The possible impact of the addition of SCD diagnostic category and directions for future research are also discussed.


title: CRAB – EXPLORATION ROVER WITH ADVANCED OBSTACLE NEGOTIATION CAPABILITIES

abstract: Many exploration rovers have been proposed in the past, but none was compared to other systems. Therefore, this paper not only introduces the locomotion concept CRAB, but also compares it to other rovers regarding obstacle negotiation capabilities. Instead of relying only on simulation, a comparison was also performed with real breadboards. In reality the CRAB is compared to RCL-E (Concept E by RCL); the simulations include the MER (Mars Exploration Rover by NASA) as well. The simulations predict significantly superior performance of CRAB compared to RCL-E, which is confirmed by the tests on hardware. The performance of CRAB and MER is similar; however, the CRAB has the same behavior in forward and backward motion because it is a symmetrical structure, while the MER has significantly inferior performance in backward motion.


title: Design and implementation of high-speed communication modem using ultrasonic sensors for underwater sensor networks

abstract: Underwater sensor networks (USN) for ocean development and disaster prevention have been emerged as one of interesting research topics recently. Since low-power, high-speed and inexpensive communication modem is a prerequisite for deployment of USN, in this paper we design and implement an underwater modem by utilizing general-purpose waterproof ultrasonic sensors. Also, we make some experiments in a water tank containing two point-to-point modems. According to the experiments, nearly error-free communication is possible at a distance of 1 meter with a data rate of 1 kbps and communication quality is rapidly deteriorated by the increase of data rate.


title: Learning Deep Boltzmann Machines using Adaptive MCMC

abstract: When modeling high-dimensional richly structured data, it is often the case that the distribution defined by the Deep Boltzmann Machine (DBM) has a rough energy landscape with many local minima separated by high energy barriers. The commonly used Gibbs sampler tends to get trapped in one local mode, which often results in unstable learning dynamics and leads to poor parameter estimates. In this paper, we concentrate on learning DBM's using adaptive MCMC algorithms. We first show a close connection between Fast PCD and adaptive MCMC. We then develop a Coupled Adaptive Simulated Tempering algorithm that can be used to better explore a highly multimodal energy landscape. Finally, we demonstrate that the proposed algorithm considerably improves parameter estimates, particularly when learning large-scale DBM's.


Concepts and semantic relations in information science


title: The Molecular and Systems Biology of Memory

abstract: Learning and memory are two of the most magical capabilities of our mind. Learning is the biological process of acquiring new knowledge about the world, and memory is the process of retaining and reconstructing that knowledge over time. Most of our knowledge of the world and most of our skills are not innate but learned. Thus, we are who we are in large part because of what we have learned and what we remember and forget. In this Review, we examine the molecular, cellular, and circuit mechanisms that underlie how memories are made, stored, retrieved, and lost.


title: Extended Gaussian images

abstract: This is a primer on extended Gaussian images. Extended Gaussian images are useful for representing the shapes of surfaces. They can be computed easily from: 1. needle maps obtained using photometric stereo; or 2. depth maps generated by ranging devices or binocular stereo. Importantly, they can also be determined simply from geometric models of the objects. Extended Gaussian images can be of use in at least two of the tasks facing a machine vision system: 1. recognition, and 2. determining the attitude in space of an object. Here, the extended Gaussian image is defined and some of its properties discussed. An elaboration for nonconvex objects is presented and several examples are shown.


title: Customized course advising: investigating engineering student success with incoming profiles and patterns of concurrent course enrollment

abstract: Every college student registers for courses from a catalog of numerous offerings each term. Selecting the courses in which to enroll, and in what combinations, can dramatically impact each student's chances for academic success. Taking inspiration from the STEM Academy, we wanted to identify the characteristics of engineering students who graduate with 3.0 or above grade point average. The overall goal of the Customized Course Advising project is to determine the optimal term-by-term course selections for all engineering students based on their incoming characteristics and previous course history and performance, paying particular attention to concurrent enrollment. We found that ACT Math, SAT Math, and Advanced Placement exam can be effective measures to measure the students' academic preparation level. Also, we found that some concurrent course-enrollment patterns are highly predictive of first-term and overall academic success.


title: The Tapenade automatic differentiation tool: Principles, model, and specification

abstract: Tapenade is an Automatic Differentiation (AD) tool which, given a Fortran or C code that computes a function, creates a new code that computes its tangent or adjoint derivatives. Tapenade puts particular emphasis on adjoint differentiation, which computes gradients at a remarkably low cost. This article describes the principles of Tapenade, a subset of the general principles of AD. We motivate and illustrate with examples the AD model of Tapenade, that is, the structure of differentiated codes and the strategies used to make them more efficient. Along with this informal description, we formally specify this model by means of data-flow equations and rules of Operational Semantics, making this the reference specification of the tangent and adjoint modes of Tapenade. One benefit we expect from this formal specification is the capacity to formally study the AD model itself, especially for the adjoint mode and its sophisticated strategies. This article also describes the architectural choices of the implementation of Tapenade. We describe the current performance of Tapenade on a set of codes that include industrial-size applications. We present the extensions of the tool that are planned in a foreseeable future, deriving from our ongoing research on AD.


title: The Stellar tree: a Compact Representation for Simplicial Complexes and Beyond

abstract: The efficient representation and management of simplicial and cell complexes is an active research topic in several fields, including geometric modeling, computer graphics, scientific visualization, and geographic data processing. In this paper, we propose the Stellar tree, a topological data structure for performing efficient topological queries on simplicial and non-simplicial complexes. We prove that a Stellar tree provides a scalable, compact and flexible data structure to represent these complexes, using a fraction of the memory required by a corresponding topological data structure on the global complex.


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abstract: This paper examines the characteristics and value of designed instruction grounded in the constructivist theory. It also attempts to connect the theory to the prevailing technology paradigms to establish an alignment between pedagogical and technological considerations in support of the assumptions arising from constructivism. Distance learning provides a unique context in which to infuse constructivist principles where learners are expected to function as self-motivated, self-directed, interactive, collaborative participants in their learning experiences by virtue of their physical location. Hence, the aim of this paper is to provide a clear link between the theoretical principles of constructivism, the construction of technologysupported learning environments, and the practice of distance education. The questions driving the argument in this paper include: What do constructivist perspectives offer instructional design and practice? What do computing technologies offer? And what do the two afford in combination? In particular, how do the two combine to transform distance learning from a highly industrialized mass production model to one that emphasizes subjective construction of knowledge and meaning derived from individual experiences.


OFDM Simulation Using Matlab


title: Differentiating Objects by Motion: Joint Detection and Tracking of Small Flying Objects

abstract: While generic object detection has achieved large improvements with rich feature hierarchies from deep nets, detecting small objects with poor visual cues remains challenging. Motion cues from multiple frames may be more informative for detecting such hard-to-distinguish objects in each frame. However, how to encode discriminative motion patterns, such as deformations and pose changes that characterize objects, has remained an open question. To learn them and thereby realize small object detection, we present a neural model called the Recurrent Correlational Network, where detection and tracking are jointly performed over a multi-frame representation learned through a single, trainable, and end-to-end network. A convolutional long short-term memory network is utilized for learning informative appearance change for detection, while learned representation is shared in tracking for enhancing its performance. In experiments with datasets containing images of scenes with small flying objects, such as birds and unmanned aerial vehicles, the proposed method yielded consistent improvements in detection performance over deep single-frame detectors and existing motion-based detectors. Furthermore, our network performs as well as state-of-the-art generic object trackers when it was evaluated as a tracker on the bird dataset.


title: Music, Movies and Meaning: Communication in Film-Makers' Search for Pre-Existing Music, and the Implications for Music Information Retrieval

abstract: While the use of music to accompany moving images is widespread, the information behaviour, communicative practice and decision making by creative professionals within this area of the music industry is an under-researched area. This investigation discusses the use of music in films and advertising focusing on communication and meaning of the music and introduces a reflexive communication model. The model is discussed in relation to interviews with a sample of music professionals who search for and use music for their work. Key factors in this process include stakeholders, briefs, product knowledge and relevance. Searching by both content and context is important, although the final decision when matching music to picture is partly intuitive and determined by a range of stakeholders.


title: Performing OLAP over Graph Data: Query Language, Implementation, and a Case Study

abstract: In current Big Data scenarios, traditional data warehousing and Online Analytical Processing (OLAP) operations on cubes are clearly not sufficient to address the current data analysis requirements. Nevertheless, OLAP operations and models can expand the possibilities of graph analysis beyond the traditional graph-based computation. In spite of this, there is not much work on the problem of taking OLAP analysis to the graph data model. In previous work we proposed a multidimensional (MD) data model for graph analysis, that considers not only the basic graph data, but background information in the form of dimension hierarchies as well. The graphs in our model are node- and edge-labelled directed multi-hypergraphs, called graphoids, defined at several different levels of granularity. In this paper we show how we implemented this proposal over the widely used Neo4J graph database, discuss implementation issues, and present a detailed case study to show how OLAP operations can be used on graphs.


title: A Top-Down Design Methodology for Ultrahigh-Performance Hashing Cores

abstract: Many cryptographic primitives that are used in cryptographic schemes and security protocols such as SET, PKI, IPSec, and VPNs utilize hash functions, which form a special family of cryptographic algorithms. Applications that use these security schemes are becoming very popular as time goes by and this means that some of these applications call for higher throughput either due to their rapid acceptance by the market or due to their nature. In this work, a new methodology is presented for achieving high operating frequency and throughput for the implementations of all widely used-and those expected to be used in the near future-hash functions such as MD-5, SHA-1, RIPEMD (all versions), SHA-256, SHA-384, SHA-512, and so forth. In the proposed methodology, five different techniques have been developed and combined with the finest way so as to achieve the maximum performance. Compared to conventional pipelined implementations of hash functions (in FPGAs), the proposed methodology can lead even to a 160 percent throughput increase.


title: A Wideband and Compact GaN MMIC Doherty Amplifier for Microwave Link Applications

abstract: This paper addresses the limitations and difficulties, in terms of DC-current density restrictions, and process limitations, associated with implementing impedance inverters with high characteristic impedance for monolithic microwave integrated circuit (MMIC) Doherty power amplifiers (DPAs). It is theoretically shown that impedance inverters with high characteristic impedance can be realized by utilizing the output capacitance of the active devices, together with a compact Tee-network of transmission lines with feasible linewidths. The utility of the impedance inverter is proven by design and fabrication of a GaN MMIC-DPA for microwave link applications. Continuous wave (CW) measurements demonstrate a maximum output power of 35±0.5dBm over a frequency range of 6.6-8.5 GHz. The power added efficiency (PAE) in 9 dB output power back-off (OPBO) is better than 30% in a frequency range of 6.7-7.8 GHz. Moreover, linearized modulated measurements, employing a 10 MHz 256-QAM signal with 7.8 dB peak to average power ratio (PAPR), demonstrate higher than 35% average PAE, with 27.5 ± 0.2 dBm average output power, and an adjacent channel power ratio (ACPR) less than -45 dBc, across a 6.8-8.5 GHz frequency range. The fabricated chip-size measures 2.1 mm × 1.5mm.


title: Extracting Academic Activity Transaction in Chinese Documents

abstract: As academic relationship networks are implied in various academic activities which relate to study and work experiences of scholars, extracting the transaction information of academic activities is important for mining academic relations networks. For the characteristics of long-distance dependencies in Chinese sentences of academic activities, this paper proposes to extract the transaction information from Chinese documents by using sequence forecast based on Conditional Random Fields (CRF). Often, in research project applications, the resumes of applicant and team members include many complex Chinese sentences about academic activities. We design novel methods to analyze special sentence patterns in those resumes. More specifically, we focus on the design of feature templates according to the sentences characteristics of academic activities, and employ the regular matching method to deal with inaccurate words segmentation, especially for academic-specific words. Through evaluating tests, we choose the optimum feature templates and input to CRF++ model to label trunk words of the sentences. The transaction information extraction of academic activities is implemented. Experimental results show the effectiveness of the proposed approach.


title: Water-walking devices

abstract: We report recent efforts in the design and construction of water-walking machines inspired by insects and spiders. The fundamental physical constraints on the size, proportion and dynamics of natural water-walkers are enumerated and used as design criteria for analogous mechanical devices. We report devices capable of rowing along the surface, leaping off the surface and climbing menisci by deforming the free surface. The most critical design constraint is that the devices be lightweight and non-wetting. Microscale manufacturing techniques and new man-made materials such as hydrophobic coatings and thermally actuated wires are implemented. Using high-speed cinematography and flow visualization, we compare the functionality and dynamics of our devices with those of their natural counterparts.


title: Clothing cosegmentation for recognizing people

abstract: Researches have verified that clothing provides information about the identity of the individual. To extract features from the clothing, the clothing region first must be localized or segmented in the image. At the same time, given multiple images of the same person wearing the same clothing, we expect to improve the effectiveness of clothing segmentation. Therefore, the identity recognition and clothing segmentation problems are inter-twined; a good solution for one aides in the solution for the other. We build on this idea by analyzing the mutual information between pixel locations near the face and the identity of the person to learn a global clothing mask. We segment the clothing region in each image using graph cuts based on a clothing model learned from one or multiple images believed to be the same person wearing the same clothing. We use facial features and clothing features to recognize individuals in other images. The results show that clothing segmentation provides a significant improvement in recognition accuracy for large image collections, and useful clothing masks are simultaneously produced. A further significant contribution is that we introduce a publicly available consumer image collection where each individual is identified. We hope this dataset allows the vision community to more easily compare results for tasks related to recognizing people in consumer image collections.


title: Deep learning in business analytics and operations research: Models, applications and managerial implications

abstract: Abstract Business analytics refers to methods and practices that create value through data for individuals, firms, and organizations. This field is currently experiencing a radical shift due to the advent of deep learning: deep neural networks promise improvements in prediction performance as compared to models from traditional machine learning. However, our research into the existing body of literature reveals a scarcity of research works utilizing deep learning in our discipline. Accordingly, the objectives of this overview article are as follows: (1) we review research on deep learning for business analytics from an operational point of view. (2) We motivate why researchers and practitioners from business analytics should utilize deep neural networks and review potential use cases, necessary requirements, and benefits. (3) We investigate the added value to operations research in different case studies with real data from entrepreneurial undertakings. All such cases demonstrate improvements in operational performance over traditional machine learning and thus direct value gains. (4) We provide guidelines and implications for researchers, managers and practitioners in operations research who want to advance their capabilities for business analytics with regard to deep learning. (5) Our computational experiments find that default, out-of-the-box architectures are often suboptimal and thus highlight the value of customized architectures by proposing a novel deep-embedded network.


title: Deep learning for dehazing: Comparison and analysis

abstract: We compare a recent dehazing method based on deep learning, Dehazenet, with traditional state-of-the-art approaches , on benchmark data with reference. Dehazenet estimates the depth map from transmission factor on a single color image, which is used to inverse the Koschmieder model of imaging in the presence of haze. In this sense, the solution is still attached to the Koschmieder model. We demonstrate that the transmission is very well estimated by the network, but also that this method exhibits the same limitation than others due to the use of the same imaging model.