title: Mental Health Mobile Apps: From Infusion to Diffusion in the Mental Health Social System

abstract: The roles of mental health educators and professionals in the diffusion of mental health mobile apps are addressed in this viewpoint article. Mental health mobile apps are emerging technologies that fit under the broad heading of mobile health (mHealth). mHealth, encompassed within electronic health (eHealth), reflects the use of mobile devices for the practice of public health. Well-designed mental health mobile apps that present content in interactive, engaging, and stimulating ways can promote cognitive learning, personal growth, and mental health enhancement. As key influencers in the mental health social system, counselor educators and professional associations may either help or hinder diffusion of beneficial mHealth technologies. ::: As mental health mobile apps move towards ubiquity, research will continue to be conducted. The studies published thus far, combined with the potential of mental health mobile apps for learning and personal growth, offer enough evidence to compel mental health professionals to infuse these technologies into education and practice. Counselor educators and professional associations must use their influential leadership roles to train students and practitioners in how to research, evaluate, and integrate mental health mobile apps into practice. ::: ::: The objectives of this article are to (1) increase awareness of mHealth and mental health mobile apps, (2) demonstrate the potential for continued growth in mental health mobile apps based on technology use and acceptance theory, mHealth organizational initiatives, and evidence about how humans learn, (3) discuss evidence-based benefits of mental health mobile apps, (4) examine the current state of mHealth diffusion in the mental health profession, and (5) offer solutions for impelling innovation diffusion by infusing mental health mobile apps into education, training, and clinical settings. This discussion has implications for counselor educators, mental health practitioners, associations, continuing education providers, and app developers. [JMIR Ment Health 2015;2(1):e10]


title: “He Killed My Mommy!” Murder or Attempted Murder of a Child's Mother

abstract: The murder or attempted murder of a child's mother in the context of domestic violence is a significant event in a child's life and carries with it multiple additional possible stressors. Children experiencing these situations have not, to date, been well studied. This paper discusses what is currently known in the literature about these children and provides some initial descriptive sociodemographic and contextual data regarding 237 children who experienced the murder (N = 146) or attempted murder (N = 91) of their mother by a current or estranged intimate partner. These data are part of a 10-city study to identify the risk factors in intimate partner homicide. This descriptive study suggests the alarming prevalence of children exposed to their mothers' murders or attempted murders, the paucity of systematic interventions provided to these children, the likelihood of their exposure to prior marital violence and/or child abuse, the multiple stressors they encounter after the incident, and some of the demographic variables associated with femicide and attempted femicide. Affected children are most likely to be under the age of 10 at the time of the homicide or attempted homicide with mothers who are between the ages of 30 and 39, working but earning less than $25,000 per year. Poor African American women and their children are especially vulnerable. Many of the children actually witnessed the homicide or attempted homicide (35% of homicide cases; 62% of the attempteds) or found their mother after the attack (37% of homicides cases; 28% of attempteds), but significant numbers of these children received no or little intervention. The critical needs for community-based prevention and intervention programs are discussed.


title: A 0.4-mW/Gb/s Near-Ground Receiver Front-End With Replica Transconductance Termination Calibration for a 16-Gb/s Source-Series Terminated Transceiver

abstract: This paper describes a low-power receiver front-end in a bidirectional near-ground source-series terminated (SST) interface implemented in a 40-nm CMOS process, which supports low-common mode differential NRZ signaling up to 16-Gb/s data rates. The high-speed operation is enabled by utilizing a common-gate amplifier stage with replica transconductance impedance calibration that accurately terminates the channel in the presence of receiver input loading. The near-ground low-impedance receiver also incorporates common-mode gain cancellation and in-situ equalization calibration to achieve reliable data reception at 16 Gb/s with better than 0.4 mW/Gb/s power efficiency over a memory link with more than 15 dB loss at the Nyquist frequency.


title: SpliceVec: Distributed feature representations for splice junction prediction

abstract: Identification of intron boundaries, called splice junctions, is an important part of delineating gene structure and functions. This also provides valuable insights into the role of alternative splicing in increasing functional diversity of genes. Identification of splice junctions through RNA-seq is by mapping short reads to the reference genome which is prone to errors due to random sequence matches. This encourages identification of splicing junctions through computational methods based on machine learning. Existing models are dependent on feature extraction and selection for capturing splicing signals lying in the vicinity of splice junctions. But such manually extracted features are not exhaustive. We introduce distributed feature representation, SpliceVec, to avoid explicit and biased feature extraction generally adopted for such tasks. SpliceVec is based on two widely used distributed representation models in natural language processing. Learned feature representation in form of SpliceVec is fed to multilayer perceptron for splice junction classification task. An intrinsic evaluation of SpliceVec indicates that it is able to group true and false sites distinctly. Our study on optimal context to be considered for feature extraction indicates inclusion of entire intronic sequence to be better than flanking upstream and downstream region around splice junctions. Further, SpliceVec is invariant to canonical and non-canonical splice junction detection. The proposed model is consistent in its performance even with reduced dataset and class-imbalanced dataset. SpliceVec is computationally efficient and can be trained with user-defined data as well.


title: Specifying IT Security Awareness

abstract: IT users are faced with various threats on a daily basis. Unfortunately, not all possible dangers are known to them, such that the users fall an easy victim to attacks. For this reason, IT specialists demand for higher IT security awareness. Although researchers and practitioners exercise ongoing efforts in this area, their work often lacks a concise definition of the term "security awareness". Since there is no agreement on the term, different (and sometimes not compatible) ways of raising and measuring security awareness exist. This paper is an effort to give an overview of this phenomenon and to show that awareness in IT security is not standardized yet.


title: A Secure Cluster-Based Multipath Routing Protocol for WMSNs

abstract: The new characteristics of Wireless Multimedia Sensor Network (WMSN) and its design issues brought by handling different traffic classes of multimedia content (video streams, audio, and still images) as well as scalar data over the network, make the proposed routing protocols for typical WSNs not directly applicable for WMSNs. Handling real-time multimedia data requires both energy efficiency and QoS assurance in order to ensure efficient utility of different capabilities of sensor resources and correct delivery of collected information. In this paper, we propose a Secure Cluster-based Multipath Routing protocol for WMSNs, SCMR, to satisfy the requirements of delivering different data types and support high data rate multimedia traffic. SCMR exploits the hierarchical structure of powerful cluster heads and the optimized multiple paths to support timeliness and reliable high data rate multimedia communication with minimum energy dissipation. Also, we present a light-weight distributed security mechanism of key management in order to secure the communication between sensor nodes and protect the network against different types of attacks. Performance evaluation from simulation results demonstrates a significant performance improvement comparing with existing protocols (which do not even provide any kind of security feature) in terms of average end-to-end delay, network throughput, packet delivery ratio, and energy consumption.


title: Static disassembly of obfuscated binaries

abstract: Disassembly is the process of recovering a symbolic representation of a program's machine code instructions from its binary representation. Recently, a number of techniques have been proposed that attempt to foil the disassembly process. These techniques are very effective against state-of-the-art disassemblers, preventing a substantial fraction of a binary program from being disassembled correctly. This could allow an attacker to hide malicious code from static analysis tools that depend on correct disassembler output (such as virus scanners). ::: ::: The paper presents novel binary analysis techniques that substantially improve the success of the disassembly process when confronted with obfuscated binaries. Based on control flow graph information and statistical methods, a large fraction of the program's instructions can be correctly identified. An evaluation of the accuracy and the performance of our tool is provided, along with a comparison to several state-of-the-art disassemblers.


Learning Synchronous Grammar Patterns for Assisted Writing for Second Language Learners


title: Blocking-resistant communication through domain fronting

abstract: We describe "domain fronting," a versatile censorship circumvention technique that hides the re- mote endpoint of a communication. Domain fronting works at the application layer, using HTTPS, to com- municate with a forbidden host while appearing to com- municate with some other host, permitted by the cen- sor. The key idea is the use of dierent domain names at dierent layers of communication. One domain appears on the "outside" of an HTTPS request—in the DNS re- quest and TLS Server Name Indication—while another domain appears on the "inside"—in the HTTP Host header, invisible to the censor under HTTPS encryp- tion. A censor, unable to distinguish fronted and non- fronted trac to a domain, must choose between allow- ing circumvention trac and blocking the domain en- tirely, which results in expensive collateral damage. Do- main fronting is easy to deploy and use and does not re- quire special cooperation by network intermediaries. We identify a number of hard-to-block web services, such as content delivery networks, that support domain-fronted connections and are useful for censorship circumvention. Domain fronting, in various forms, is now a circumven- tion workhorse. We describe several months of deploy- ment experience in the Tor, Lantern, and Psiphon cir- cumvention systems, whose domain-fronting transports now connect thousands of users daily and transfer many terabytes per month.


title: Combining Semantic and Geometric Features for Object Class Segmentation of Indoor Scenes

abstract: Scene understanding is a necessary prerequisite for robots acting autonomously in complex environments. Low-cost RGB-D cameras such as Microsoft Kinect enabled new methods for analyzing indoor scenes and are now ubiquitously used in indoor robotics. We investigate strategies for efficient pixelwise object class labeling of indoor scenes that combine both pretrained semantic features transferred from a large color image dataset and geometric features, computed relative to the room structures, including a novel distance-from-wall feature, which encodes the proximity of scene points to a detected major wall of the room. We evaluate our approach on the popular NYU v2 dataset. Several deep learning models are tested, which are designed to exploit different characteristics of the data. This includes feature learning with two different pooling sizes. Our results indicate that combining semantic and geometric features yields significantly improved results for the task of object class segmentation.


title: Automated mitosis detection based on eXclusive Independent Component Analysis

abstract: In this paper, we propose an approach for automated mitosis detection, which provides critical information during performing breast cancer prognosis. Essentially, the problem of mitotic detection involves irregular shape object classification. It is a very challenging task. In this paper, a novel algorithm, named eXclusive Independent Component Analysis (XICA) is proposed. The XICA is an extension of a generic ICA, but focusing the components of differences (called exclusive basis set) between two classes of training patterns rather than the major (independent) components. Based on the residuals obtained from the relative computing involving the exclusive basis set of the relative training patterns, the automated mitosis detection is performed. By computing the residual of the relative exclusive basis set, we are able to classify the given testing patterns. The proposed approach has been tested on a mitosis image set provided by a ICPR2012 contest. It contains 226 mitosis in 35 color images. It achieved accurate rate 100% in training patterns and 83.513% in testing patterns.


title: Biometric recognition of newborns: Identification using footprints

abstract: Infant abduction, swapping, missing, illegal adoptions and kidnapping are increasing issues in the hospitals, birthing centers, health centers and in other places where multiple births occur simultaneously. A biometric system using the footprint of the newborn is proposed to overcome such issues. An online system with specialized preprocessing steps is developed for the images with a normal and a special background setup as collected in the database. This system demonstrates the versatility of the proposed method.


title: Duplicate Reduction in Graph Mining: Approaches, Analysis, and Evaluation

abstract: At the core of graph mining lies independent expansion of substructures where a substructure (also referred to as a subgraph) independently grows into a number of larger substructures in each iteration. Such an independent expansion, invariably, leads to the generation of duplicates. In the presence of graph partitions, duplicates are generated both within and across partitions. Eliminating these duplicates (for correctness) not only incurs generation and storage cost but also additional computation for its elimination. Our primary aim is to design techniques to reduce generating duplicate substructures as we show that they cannot be eliminated. This paper introduces three constraint-based optimization techniques, each significantly improving the overall mining cost by reducing the number of duplicates generated. These alternatives provide flexibility to choose the right technique based on graph properties. We establish theoretical correctness of each technique as well as its analysis with respect to graph characteristics such as degree, number of unique labels, and label distribution. We also investigate the applicability of their combination for improvements in duplicate reduction. Finally, we discuss the effects of the constraints with respect to the partitioning schemes used in graph mining. Our experiments demonstrate significant benefits of these constraints in terms of storage, computation, and communication cost (specific to partitioned approaches) across graphs with varied characteristics.


title: Part-of-Speech Tagging from 97 % to 100%: Is It Time for Some Linguistics?

abstract: I examine what would be necessary to move part-of-speech tagging performance from its current level of about 97.3% token accuracy (56% sentence accuracy) to close to 100% accuracy. I suggest that it must still be possible to greatly increase tagging performance and examine some useful improvements that have recently been made to the Stanford Part-of-Speech Tagger. However, an error analysis of some of the remaining errors suggests that there is limited further mileage to be had either from better machine learning or better features in a discriminative sequence classifier. The prospects for further gains from semisupervised learning also seem quite limited. Rather, I suggest and begin to demonstrate that the largest opportunity for further progress comes from improving the taxonomic basis of the linguistic resources from which taggers are trained. That is, from improved descriptive linguistics. However, I conclude by suggesting that there are also limits to this process. The status of some words may not be able to be adequately captured by assigning them to one of a small number of categories. While conventions can be used in such cases to improve tagging consistency, they lack a strong linguistic basis.


title: Managing the Next Wave of Enterprise Systems: Leveraging Lessons from ERP

abstract: Highly complex enterprise software packages have become a standard way of competing in many industries. By the late 1990s, enterprise resource planning (ERP) systems had enabled Fortune 500 firms to present one face to their customers via integrated cross-functional business processes, centralized databases, and point-and-click access to real-time operational data across the business. Today, enterprise systems for supply chain management (SCM) and customer relationship management (CRM) are following in the steps of ERP, and, in many cases, providing a foundation for e-business. Supplier relationship management systems are on the horizon. Based on our ERP research, we have identified five factors for successful implementation: (1) top management is engaged in the project, not just involved; (2) project leaders are veterans, and team members are decision makers; (3) third parties fill gaps in expertise and transfer their knowledge; (4) change management goes hand-in-hand with project planning; (5) A satisficing mindset prevails. Furthermore, we found that a projects position on the maturity curve (early adopter, early majority, or late majority) can influence the implementation route. These five success factors and three maturity curve positions are illustrated in three anonymous cases. The result is lessons for managing the complexities of the next wave of enterprise systems.


title: Effect of Virtual Reality on Adolescent Pain During Burn Wound Care

abstract: Abstract The objective of this study was to compare the effect of virtual reality to passive distraction and standard care on burn treatment pain in adolescents.This single-blinded, randomized controlled study enrolled 30 adolescents who were 10 to 17 years of age from the burn clinic of a large children's hospital. After providing informed consent/assent, these participants were randomly assigned to one of three groups during wound care: standard care, passive distraction watching a movie, or virtual reality (VR) using a tripod-arm device rather than an immersive helmet. Before wound care, participants completed the Spielberger's State-Trait Anxiety Inventory for Children and Pre-Procedure Questionnaire while blinded to group assignment. A total of 28 participants completed the study and rated treatment pain after wound care by using the Adolescent Pediatric Pain Tool and completed a Post-Procedure Questionnaire. The VR group reported less pain during wound care than either the passive distraction or standard care group as determined by multivariable linear regression adjusted for age, sex, preprocedure pain, state anxiety, opiate use, and treatment length. The VR group was the only group to have an estimated decrease in pain perception from baseline preprocedure pain to procedural pain reported. Adolescents pretreated with opiate analgesics and female adolescents reported more pain during wound care.This between-subjects clinical study provides further support for VR, even without requiring wearing of an immersive helmet, in lessening burn wound care pain in adolescents. Passive distraction by watching a movie may be less effective in reducing treatment pain. Additional between-subjects randomized controlled trials with larger samples of children and during other healthcare treatments may further support VR's effectiveness in pediatric procedural pain management.


title: Knowledge management in multinational companies: the repatriates’ role in the competitive advantage in subsidiaries

abstract: This paper addresses the topic of knowledge management in multinational companies (MNCs). Its purpose is to examine the role of expatriates in knowledge acquisition and transfer within MNCs. Specifically it focuses on knowledge acquisition and transfer from one MNC head office located in Germany to two Portuguese subsidiaries as a basis for competitive advantage in their Portuguese subsidiaries. A qualitative research methodology is used, specifically through an exploratory case study approach, which examines how international assignments are important for the role of expatriates in knowledge acquisition and transfer between foreign head offices and their Portuguese subsidiaries. The data were collected through semi structured interviews to 10 Portuguese repatriates from two Portuguese subsidiaries of one foreign MNC. The findings suggest that the reasons that lead to expatriating employees from Portuguese subsidiaries to foreign head offices are connected to (1) knowledge management strategies to development the subsidiary’s performance; (2) new skills and knowledge acquisition by future team leaders and business/product managers in Portuguese subsidiaries; (3) procuring knowledge, from agents in head office, to be disseminated amongst co workers in Portuguese subsidiaries; (4) acquiring global management skills, impossible to acquire locally and; (5) developing global projects within MNC. Also our results show that knowledge acquisition and transfer from foreign head office, through subsidiaries’ expatriates, contributes directly to the Portuguese subsidiaries’ innovation, improved performance, competitive advantage and growth in the economic sectors in which they operate. Moreover, evidence reveals that expatriation is seen as a strategy to fulfil some of the main organisational objectives through their expatriates (e.g., create new products and business markets, develop and incorporate new organisational techniques and processes, integrate global teams within multinational corporation with a responsibility on the definition of global objectives). The results obtained suggest that expatriates have a central role in acquiring and transferring strategic knowledge from MNC head office to their subsidiaries located in Portugal. Based on the findings, the paper discusses in detail the main theoretical and managerial implications. Suggestions for further research are also presented. The study’s main limitation is the small size of the sample, but its findings and methodology are quite original and significant.


title: Combining haptic human-machine interaction with predictive path planning for lane-keeping and collision avoidance systems

abstract: This paper presents a first approach for a haptic human-machine interface combined with a novel lane-keeping and collision-avoidance assistance system approach, as well as the results of a first exploration study with human test drivers. The assistance system approach is based on a potential field predictive path planning algorithm that incorporates the drivers wishes commanded by the steering wheel angle, the brake pedal or throttle, and the intended maneuver. For the design of the haptic human-machine interface the assistance torque characteristic at the handwheel is shaped and the path planning parameters are held constant. In the exploration, both driving data as well as questionnaires are evaluated. The results show good acceptance for the lane-keeping assistance while the collision avoidance assistance needs to be improved.


title: UbiFlow: Mobility management in urban-scale software defined IoT

abstract: The growing of Internet of Things (IoT) devices has resulted in a number of urban-scale deployments of IoT multinetworks, where heterogeneous wireless communication solutions coexist. Managing the multinetworks for mobile IoT access is a key challenge. Software-defined networking (SDN) is emerging as a promising paradigm for quick configuration of network devices, but its application in multinetworks with frequent IoT access is not well studied. In this paper we present UbiFlow, the first software-defined IoT system for ubiquitous flow control and mobility management in multinetworks. UbiFlow adopts distributed controllers to divide urban-scale SDN into different geographic partitions. A distributed hashing based overlay structure is proposed to maintain network scalability and consistency. Based on this UbiFlow overlay structure, relevant issues pertaining to mobility management such as scalable control, fault tolerance, and load balancing have been carefully examined and studied. The UbiFlow controller differentiates flow scheduling based on the per-device requirement and whole-partition capability. Therefore, it can present a network status view and optimized selection of access points in multinetworks to satisfy IoT flow requests, while guaranteeing network performance in each partition. Simulation and realistic testbed experiments confirm that UbiFlow can successfully achieve scalable mobility management and robust flow scheduling in IoT multinetworks.


title: On Profiling Bots in Social Media

abstract: The popularity of social media platforms such as Twitter has led to the proliferation of automated bots, creating both opportunities and challenges in information dissemination, user engagements, and quality of services. Past works on profiling bots had been focused largely on malicious bots, with the assumption that these bots should be removed. In this work, however, we find many bots that are benign, and propose a new, broader categorization of bots based on their behaviors. This includes broadcast, consumption, and spam bots. To facilitate comprehensive analyses of bots and how they compare to human accounts, we develop a systematic profiling framework that includes a rich set of features and classifier bank. We conduct extensive experiments to evaluate the performances of different classifiers under varying time windows, identify the key features of bots, and infer about bots in a larger Twitter population. Our analysis encompasses more than 159K bot and human (non-bot) accounts in Twitter. The results provide interesting insights on the behavioral traits of both benign and malicious bots.


title: MAISE: An Interconnect Simulation Engine for Timing and Noise Analysis

abstract: This paper describes MAISE, an embedded linear circuit simulator for use mainly within timing and noise analysis tools. MAISE achieves the fastest possible analysis performance over a wide range of circuit sizes and topologies by an adaptive architecture that allows applying the most efficient combination of model reduction algorithms and linear solvers for each class of circuits. The main pillar of adaptability in MAISE is a novel nodal-analysis formulation (PNA) which permits the use of symmetric, positive-definite Cholesky solvers for all circuit topologies. Moreover, frequently occurring special cases, e.g., inductor-resistor tree structures result in particular types of matrices that are solved by an even faster linear time algorithm. Model order reduction algorithms employed in MAISE exploit symmetry and positive-definiteness whenever available and use symmetric-Lanczos iteration and nonstandard inner-products for generating the Krylov subspace basis. The efficiency of the new simulator is supported by a wide range of industrial examples.


title: Expression of oxytocin, progesterone, and estrogen receptors in the reproductive tract of bitches with pyometra.

abstract: Canine pyometra is considered a serious and life-threatening condition. Due to the relationship among sex steroid hormones, oxytocin receptor (OTR) expression, and canine pyometra pathogenesis, this study aimed to investigate the expression of oxytocin, progesterone, and estrogen receptors in the reproductive tissues of canines with pyometra by real-time PCR and immunohistochemistry. A total of 27 pyometra bitches were classified into open- and closed-cervix pyometra groups based on the presence of vaginal discharge. Moreover, 15 normal bitches in the luteal phase served as a control group. The results showed that OTR gene expression in the ovary of pyometra bitches was higher than that of normal bitches, whereas the level of OTR gene expression in the cervix of pyometra bitches was less than that of normal bitches (P < 0.05). Conversely, a lower OTR H-score in ovarian follicles was observed in pyometra bitches compared with normal bitches, whereas a higher percentage of OTR-positive immunostaining in uteri and cervices were found in pyometra bitches compared with normal bitches (P < 0.05). Moreover, the H-scores of estrogen receptor alpha in uteri and cervices of pyometra bitches were less than that of normal bitches (P < 0.05). However, the localization of the OTR and sex steroid receptors between groups of pyometra bitches was not different. Our findings suggest that pyometra pathogenesis is associated with a change in expression of OTR and sex steroid receptors in the canine reproductive tract. However, cervical dilation in bitches with pyometra was not influenced by the expression of OTR and sex steroid receptors.


title: Model to Predict the Behavior of Customers Churn at the Industry

abstract: In order to continue life-sustaining competitive advantage, many organizations focus on maximizing the marketing relationship with their customer lifetime value and customer churn management. In fact, more organizations are realizing that their most valuable resource is their current customer base. In the present study are to go through a database collected from 300 customers, including an insurance company in Iran has been used. In order to check the model presented with a desire to review a decision tree classification methods (C5.0, CART, CHAID, and Quest), Bayesian networks and neural networks will be paid with respect to sample. Survey results can help managers, marketers in this arena is in various industries. Reduction strategies appropriate to offer in this field. The entire paper must be in A4 size and “Moderate” margin


title: Autonomous Agricultural Robot and its Row Guidance

abstract: A vision-based row guidance method is presented to guide a robot platform which is designed independently to drive through the row crops in a field according to the design concept of open architecture. Then, the offset and heading angle of the robot platform are detected in real time to guide the platform on the basis of recognition of a crop row using machine vision. And the control scheme of the platform is proposed to carry out row guidance. Finally, the preliminary experiments of row guidance were implemented in a vegetable field. Experimental results show that algorithms of row identification and row guidance are effective according to the parameters measured and analyzed such as the heading angle and the offset for row guidance and the difference between the motion trajectory of the robot and the expected trajectory. And the accuracy of row guidance is up to ± 35mm, which means that the robot can move with a sufficiently high accuracy.


title: The vulvar dermatoses.

abstract: INTRODUCTION ::: Dermatologic diseases of the vulva may cause dyspareunia. These disorders may be overlooked by gynecologists and urologists because of lack of residency training experience. Dermatologists who are most familiar with these diseases are infrequently trained in vulvovaginal examination. As such, these disorders are often improperly diagnosed and treated. ::: ::: ::: AIM ::: To describe the presentation and management of the major vulvar dermatoses including irritant and allergic contact dermatitis, lichen sclerosus, lichen simplex chronicus, and lichen planus. ::: ::: ::: MAIN OUTCOME MEASURE ::: Data from a peer review literature search on the topic of vulvar dermatoses. ::: ::: ::: METHODS ::: The literature for this review article was obtained through a Medline search. Appropriate dermatology textbooks were utilized for additional information. ::: ::: ::: RESULTS ::: A comprehensive survey of the vulvar dermatoses. ::: ::: ::: CONCLUSION ::: Vulvar dermatoses must be considered a part of the differential diagnosis of any woman with a sexual pain disorder. As such, healthcare providers who evaluate and treat women with dyspareunia must become familiar with the most common dermatologic disorders of the vulva.


title: CONTEXTUAL EVENT INFORMATION EXTRACTOR FOR EMAILS

abstract: A powerful tool for planning and announcement of Events is Email. Automatic detection of the Occurrence (Title) and its contextual information (Location, Temporal information, Participants) associated with the email is surely desirable to help the users manage and plan important Events. A lot of work has been done in the area of Event detection but it has various limitations from different perspectives. Firstly, the existing work mainly targets text streams like news stories, scientific documents, articles etc that are somewhat structured documents with sufficient event description as compare to the Emails that have structured, semi-structured and unstructured short descriptions with a plenty of description styles. Secondly the objective in most of the research is to detect new or hot events. Thirdly, much of the existing work aims on reporting events and our objective is to support Event Planning and Management. Another lacking thing is the use of publication time as the temporal information instead of actual temporal information contained within text that is indeed required for Event planning and management task. We have used Finite State Automata (FSA) to extract phrases revealing the Places, temporal information and the actual occurrence. The results are evaluated using different measures. Experiments show that the proposed approach performed well on the Email data Corpus.


title: Efficient key-dependent message authentication in reconfigurable hardware

abstract: Cryptographic message authentication is a growing need for FPGA-based embedded systems. In this paper a customized FPGA implementation of a GHASH function that is used in AES-GCM, a widely-used message authentication protocol, is described. The implementation limits GHASH logic utilization by specializing the hardware implementation on a per-key basis. The implemented module can generate a 128bit message authentication code in both pipelined and unpipelined versions. The pipelined GHASH version achieves an authentication throughput of more than 14 Gbit/s on a Spartan-3 FPGA and 292 Gbit/s on a Virtex-6 device. To promote adoption in the field, the complete source code for this work has been made publically-available.


title: Conceptual design of a lower limb exoskeleton actuated by shape memory alloys for assisting elderly people in stair climbing

abstract: Since interest in exoskeletons and active orthoses has been growing in recent years, optimal design and weight reduction of such systems was one of the main concerns of researchers. This study's purpose is to develop a lower limb exoskeleton for assisting elderly people in stair climbing. To this end, using shape memory alloy (SMA) actuators was under focus. As the first step of the research, in this paper, a conceptual design of such a light weight wearable robot was studied. Through the analysis implemented in Matlab and verified through ADAMS, benefits in addition to weaknesses of system are elaborated. It is shown that using such system would be helpful for special people.


title: Performance Measures For Information Extraction

abstract: While precision and recall have served the information extraction community well as two separate measures of system performance, we show that the F -measure, the weighted harmonic mean of precision and recall, exhibits certain undesirable behaviors. To overcome these limitations, we define an error measure, the slot error rate, which combines the different types of error directly, without having to resort to precision and recall as preliminary measures. The slot error rate is analogous to the word error rate that is used for measuring speech recognition performance; it is intended to be a measure of the cost to the user for the system to make the different types of errors.


title: Fast Image Classification for Monument Recognition

abstract: Content-based image classification is a wide research field that addresses the landmark recognition problem. Among the many classification techniques proposed, the k-nearest neighbor (kNN) is one of the most simple and widely used methods. In this article, we use kNN classification and landmark recognition techniques to address the problem of monument recognition in images. We propose two novel approaches that exploit kNN classification technique in conjunction with local visual descriptors. The first approach is based on a relaxed definition of the local feature based image to image similarity and allows standard kNN classification to be efficiently executed with the support of access methods for similarity search. The second approach uses kNN classification to classify local features rather than images. An image is classified evaluating the consensus among the classification of its local features. In this case, access methods for similarity search can be used to make the classification approach efficient. The proposed strategies were extensively tested and compared against other state-of-the-art alternatives in a monument and cultural heritage landmark recognition setting. The results proved the superiority of our approaches. An additional relevant contribution of this work is the exhaustive comparison of various types of local features and image matching solutions for recognition of monuments and cultural heritage related landmarks.


title: Brand Image and Fan Loyalty in Professional Team Sport: A Refined Model and Empirical Assessment

abstract: This study highlights the importance of brand image for fan loyalty in team sport. A parsimonious 4-factor, 20-indicator structure effectively represents brand image. In contrast to Keller’s proposed model, relationships between the brand image’s components were discovered. Thus, in line with means-end theory, a brand-image model should incorporate causalities among brand attributes, benefits, and attitudes. Fan loyalty is positively influenced by a fan’s brand attitude. Relationships among the brand-image dimensions and loyalty are confirmed via structural equation modeling. The non-product-related brand attributes (i.e., logo or tradition) have a particularly large impact on attitudes and behavior. They represent promising starting points for a successful and differentiating team brand strategy. In marketing practice, an increasing interest in customer relationships has


title: Brain overgrowth in autism during a critical time in development: implications for frontal pyramidal neuron and interneuron development and connectivity

abstract: While abnormalities in head circumference in autism have been observed for decades, it is only recently that scientists have begun to focus in on the developmental origins of such a phenomenon. In this article we review past and present literature on abnormalities in head circumference, as well as recent developmental MRI studies of brain growth in this disorder. We hypothesize that brain growth abnormalities are greatest in frontal lobes, particularly affecting large neurons such as pyramidal cells, and speculate how this abnormality might affect neurofunctional circuitry in autism. The relationship to clinical characteristics and other disorders of macrencephaly are discussed.


title: Multilayer neural networks for hierarchical patterns

abstract: We propose a new class of neural network models with multilayer architecture for storing hierarchically correlated patterns. These models are solved in the replica-symmetric approximation and it is found that their storing capacity, still linear in the number of neurons, is much larger than that of the traditional Hopfield-type models.


title: Speech Emotion Recognition Using Fourier Parameters

abstract: Recently, studies have been performed on harmony features for speech emotion recognition. It is found in our study that the first- and second-order differences of harmony features also play an important role in speech emotion recognition. Therefore, we propose a new Fourier parameter model using the perceptual content of voice quality and the first- and second-order differences for speaker-independent speech emotion recognition. Experimental results show that the proposed Fourier parameter (FP) features are effective in identifying various emotional states in speech signals. They improve the recognition rates over the methods using Mel frequency cepstral coefficient (MFCC) features by 16.2, 6.8 and 16.6 points on the German database (EMODB), Chinese language database (CASIA) and Chinese elderly emotion database (EESDB). In particular, when combining FP with MFCC, the recognition rates can be further improved on the aforementioned databases by 17.5, 10 and 10.5 points, respectively.


title: Comparison of the efficacy and safety of rosuvastatin versus atorvastatin, simvastatin, and pravastatin across doses (STELLAR* Trial).

abstract: The primary objective of this 6-week, parallel-group, open-label, randomized, multicenter trial was to compare rosuvastatin with atorvastatin, pravastatin, and simvastatin across dose ranges for reduction of low-density lipoprotein (LDL) cholesterol. Secondary objectives included comparing rosuvastatin with comparators for other lipid modifications and achievement of National Cholesterol Education Program Adult Treatment Panel III and Joint European Task Force LDL cholesterol goals. After a dietary lead-in period, 2,431 adults with hypercholesterolemia (LDL cholesterol > or =160 and <250 mg/dl; triglycerides <400 mg/dl) were randomized to treatment with rosuvastatin 10, 20, 40, or 80 mg; atorvastatin 10, 20, 40, or 80 mg; simvastatin 10, 20, 40, or 80 mg; or pravastatin 10, 20, or 40 mg. At 6 weeks, across-dose analyses showed that rosuvastatin 10 to 80 mg reduced LDL cholesterol by a mean of 8.2% more than atorvastatin 10 to 80 mg, 26% more than pravastatin 10 to 40 mg, and 12% to 18% more than simvastatin 10 to 80 mg (all p <0.001). Mean percent changes in high-density lipoprotein cholesterol in the rosuvastatin groups were +7.7% to +9.6% compared with +2.1% to +6.8% in all other groups. Across dose ranges, rosuvastatin reduced total cholesterol significantly more (p <0.001) than all comparators and triglycerides significantly more (p <0.001) than simvastatin and pravastatin. Adult Treatment Panel III LDL cholesterol goals were achieved by 82% to 89% of patients treated with rosuvastatin 10 to 40 mg compared with 69% to 85% of patients treated with atorvastatin 10 to 80 mg; the European LDL cholesterol goal of <3.0 mmol/L was achieved by 79% to 92% in rosuvastatin groups compared with 52% to 81% in atorvastatin groups. Drug tolerability was similar across treatments.


Effective Crowd Annotation for Relation Extraction


title: Using Univariate Bezier Distributions to Model Simulation Input Processes

abstract: In this paper we describe a graphical, interactive technique for modeling univariate simulation input processes with a distribution family based on Bezier curves. This family has an open-ended parameterization and is capable of accurately representing an unlimited variety of distributional shapes. Our input-modeling technique is implemented in a self-contained, Windows-based software system called PRIME-PRobabilistic Input Modeling Environment. Several examples illustrate the application of this technique.


title: Using social media for work: Losing your time or improving your work?

abstract: Social Media have been gaining in popularity worldwide over the last years at an increasingly growing rate. The introduction of social media in companies enables a new method of communication among colleagues and with customers. Although social media are in the top of the agenda for many companies to date, there seems to be very limited understanding of the usage of social media for work purposes. In this study, we investigate whether employees make use of social media for work purposes, what values increase this usage, and if that usage is related with their performance. Responses from 1799 employees in the insurance industry were used to examine the impact of social media on work. Results confirmed that in the case of social media for work, employees make extended use of them no matter their age. We found also that both utilitarian and hedonic values influence employees to use more social media for their work, at least in the insurance sector. Last but not least, this study confirms that there is an important relation between the use of social media and the work performance.


title: The Past, Present, and Future of Silicon Photonics

abstract: The pace of the development of silicon photonics has quickened since 2004 due to investment by industry and government. Commercial state-of-the-art CMOS silicon-on-insulator (SOI) foundries are now being utilized in a crucial test of 1.55-mum monolithic optoelectronic (OE) integration, a test sponsored by the Defense Advanced Research Projects Agency (DARPA). The preliminary results indicate that the silicon photonics are truly CMOS compatible. RD however, lasing has not yet been attained. The new paradigm for the Si-based photonic and optoelectric integrated circuits is that these chip-scale networks, when suitably designed, will operate at a wavelength anywhere within the broad spectral range of 1.2-100 mum, with cryocooling needed in some cases


title: Equilibrium Constants Determination of the Species Formation in the Al(III)-H 2 O System by Integration of 27 Al-NMR Signals and Fitting with Species Fractions

abstract: A quantitative methodology to determine formation con- stants that takes into consideration the 27 Al-NMR signals of Al(III)- H2O systems has been analyzed, where area fractions beneath the peaks recorded were associated with the corresponding species frac- tions. The values of the formation constants were compared with the model obtained by potentiometric studies. In agreement with previ- ous reports, the -log*β values obtained by NMR and potentiometry, at 0.020 M ≤ (Al(III))total ≤ 0.500 M are 12.96 ± 0.02 for Al(OH)3, 13.50±0.02 for Al3(OH)4 5+ , and 109.70±0.09 for Al13(OH)32 7+ , by assuming that Al(OH) 2+ has a value of 5.29. Additionally the equi- librium constant values obtained allowed fitting of the conductimetry titrations in the 6.00(10- 3 ) M ≤ (Al(III))total ≤ 0.120 M. The algorithm presented in this work gives a support to establish that the area frac- tions of NMR signals are proportional to the species concentration fractions.


title: Epidermal Impedance Sensing Sheets for Precision Hydration Assessment and Spatial Mapping

abstract: This paper presents a class of hydration monitor that uses ultrathin, stretchable sheets with arrays of embedded impedance sensors for precise measurement and spatially multiplexed mapping. The devices contain miniaturized capacitive electrodes arranged in a matrix format, capable of integration with skin in a conformal, intimate manner due to the overall skin-like physical properties. These “epidermal” systems noninvasively quantify regional variations in skin hydration, at uniform or variable skin depths. Experimental results demonstrate that the devices possess excellent uniformity, with favorable precision and accuracy. Theoretical models capture the underlying physics of the measurement and enable quantitative interpretation of the experimental results. These devices are appealing for applications ranging from skin care and dermatology, to cosmetology and health/wellness monitoring, with the additional potential for combined use with other classes of sensors for comprehensive, quantitative physiological assessment via the skin.


title: Modeling collinear WATs for parametric yield enhancement in semiconductor manufacturing

abstract: Yield enhancement is one of the major objectives for semiconductor manufacturing. In addition to the usual functional yield that needs to achieve a much higher ratio of good dies in a wafer, parametric yield requires other important factors that can improve the quality and capability of the wafer. Owing to the much more complexity for the processes of wafers in today's semiconductor technology, it is difficult to find the root causes that may affect the parameter yield. Based on the selected important factors that may affect the parametric yield, this study aims to provide an efficient analytic framework for modeling the factors with high correlations. A modified PLS approach (mPLS) is applied in this study for model building that can simultaneously handle collinear problems and provide reasonable explanations with physical meaning.


title: Hyaluronidase injection for the treatment of eyelid edema: a retrospective analysis of 20 patients

abstract: Hyaluronidase (Hylase Dessau®) is a hyaluronic acid-metabolizing enzyme, which has been shown to loosen the extracellular matrix, thereby improving the diffusion of local anesthetics. Lower eyelid edema is a common post-interventional complication of cosmetic procedures performed in the lid region, such as the injection of hyaluronic acid fillers for tear-trough augmentation. The purpose of this study was to validate the efficacy of hyaluronidase in the management of lower eyelid edema. We performed a retrospective analysis with 20 patients with lower eyelid edema. Most patients (n = 14) presented with edema following hyaluronic acid injection (tear-trough augmentation), whereas the minority (n = 6) were treated due to idiopathic edema (malar edema or malar mounds). Patients were treated by local infiltration of approximately 0.2 ml to 0.5 ml of hyaluronidase (Hylase Dessau® 20 IU to 75 IU) per eyelid. Photographs were taken prior to and seven days after infiltration. Hyaluronidase was found to reduce effectively and rapidly or resolve eyelid edema after a single injection. No relevant adverse effects were observed. However, it must be noted that a hyaluronidase injection may also dissolve injected hyaluronic acid fillers and may therefore negatively affect tear-trough augmentations. While the effects of a treatment for edema due to tear-trough augmentation were permanent, malar edema and malar mounds reoccurred within two to three weeks. The infiltration of hyaluronidase is rapid, safe and currently the only effective option for the management of eyelid edema. No relevant adverse effects were observed.


title: A high frequency high voltage power supply

abstract: Novel compact high frequency high voltage (HV) power supply is introduced for small volume, lightweight, ultra fast high voltage output application. The two key factors high frequency HV transformer and voltage multiplier diodes are evaluated in this paper. The HV resonant tank size is only 1L for 400kHz∼500kHz 35kV 2kW output, the rise time for output HV is around 100us by lab prototype experiment results.


title: Learning to Generate with Memory

abstract: Memory units have been widely used to enrich the capabilities of deep networks on capturing long-term dependencies in reasoning and prediction tasks, but little investigation exists on deep generative models (DGMs) which are good at inferring high-level invariant representations from unlabeled data. This paper presents a deep generative model with a possibly large external memory and an attention mechanism to capture the local detail information that is often lost in the bottom-up abstraction process in representation learning. By adopting a smooth attention model, the whole network is trained end-to-end by optimizing a variational bound of data likelihood via auto-encoding variational Bayesian methods, where an asymmetric recognition network is learnt jointly to infer high-level invariant representations. The asymmetric architecture can reduce the competition between bottom-up invariant feature extraction and top-down generation of instance details. Our experiments on several datasets demonstrate that memory can significantly boost the performance of DGMs on various tasks, including density estimation, image generation, and missing value imputation, and DGMs with memory can achieve state-of-the-art quantitative results.


title: Spatial autocorrelation and the selection of simultaneous autoregressive models

abstract: Aim Spatial autocorrelation is a frequent phenomenon in ecological data and can affect estimates of model coefficients and inference from statistical models. Here, we test the performance of three different simultaneous autoregressive (SAR) model types (spatial error = SAR err , lagged = SAR lag and mixed = SAR mix ) and common ordinary least squares (OLS) regression when accounting for spatial autocorrelation in species distribution data using four artificial data sets with known (but different) spatial autocorrelation structures. Methods We evaluate the performance of SAR models by examining spatial patterns in model residuals (with correlograms and residual maps), by comparing model parameter estimates with true values, and by assessing their type I error control with calibration curves. We calculate a total of 3240 SAR models and illustrate how the best models [in terms of minimum residual spatial autocorrelation (minRSA), maximum model fit ( R 2 ), or Akaike information criterion (AIC)] can be identified using model selection procedures. Results Our study shows that the performance of SAR models depends on model specification (i.e. model type, neighbourhood distance, coding styles of spatial weights matrices) and on the kind of spatial autocorrelation present. SAR model parameter estimates might not be more precise than those from OLS regressions in all cases. SAR err models were the most reliable SAR models and performed well in all cases (independent of the kind of spatial autocorrelation induced and whether models were selected by minRSA, R 2 or AIC), whereas OLS, SAR lag and SAR mix models showed weak type I error control and/or unpredictable biases in parameter estimates. Main conclusions SAR err models are recommended for use when dealing with spatially autocorrelated species distribution data. SAR lag and SAR mix might not always give better estimates of model coefficients than OLS, and can thus generate bias. Other spatial modelling techniques should be assessed comprehensively to test their predictive performance and accuracy for biogeographical and macroecological research.


title: Composition and decomposition of GANs

abstract: In this work, we propose a composition/decomposition framework for adversarially training generative models on composed data - data where each sample can be thought of as being constructed from a fixed number of components. In our framework, samples are generated by sampling components from component generators and feeding these components to a composition function which combines them into a"composed sample". This compositional training approach improves the modularity, extensibility and interpretability of Generative Adversarial Networks (GANs) - providing a principled way to incrementally construct complex models out of simpler component models, and allowing for explicit"division of responsibility"between these components. Using this framework, we define a family of learning tasks and evaluate their feasibility on two datasets in two different data modalities (image and text). Lastly, we derive sufficient conditions such that these compositional generative models are identifiable. Our work provides a principled approach to building on pre-trained generative models or for exploiting the compositional nature of data distributions to train extensible and interpretable models.


title: Local Higher-Order Graph Clustering

abstract: Local graph clustering methods aim to find a cluster of nodes by exploring a small region of the graph. These methods are attractive because they enable targeted clustering around a given seed node and are faster than traditional global graph clustering methods because their runtime does not depend on the size of the input graph. However, current local graph partitioning methods are not designed to account for the higher-order structures crucial to the network, nor can they effectively handle directed networks. Here we introduce a new class of local graph clustering methods that address these issues by incorporating higher-order network information captured by small subgraphs, also called network motifs. We develop the Motif-based Approximate Personalized PageRank (MAPPR) algorithm that finds clusters containing a seed node with minimal \emph{motif conductance}, a generalization of the conductance metric for network motifs. We generalize existing theory to prove the fast running time (independent of the size of the graph) and obtain theoretical guarantees on the cluster quality (in terms of motif conductance). We also develop a theory of node neighborhoods for finding sets that have small motif conductance, and apply these results to the case of finding good seed nodes to use as input to the MAPPR algorithm. Experimental validation on community detection tasks in both synthetic and real-world networks, shows that our new framework MAPPR outperforms the current edge-based personalized PageRank methodology.


title: PHD-GIFs: Personalized Highlight Detection for Automatic GIF Creation

abstract: Highlight detection models are typically trained to identify cues that make visual content appealing or interesting for the general public, with the objective of reducing a video to such moments. However, this "interestingness" of a video segment or image is subjective. Thus, such highlight models provide results of limited relevance for the individual user. On the other hand, training one model per user is inefficient and requires large amounts of personal information which is typically not available. To overcome these limitations, we present a global ranking model which can condition on a particular user's interests. Rather than training one model per user, our model is personalized via its inputs, which allows it to effectively adapt its predictions, given only a few user-specific examples. To train this model, we create a large-scale dataset of users and the GIFs they created, giving us an accurate indication of their interests. Our experiments show that using the user history substantially improves the prediction accuracy. On a test set of 850 videos, our model improves the recall by 8% with respect to generic highlight detectors. Furthermore, our method proves more precise than the user-agnostic baselines even with only one single person-specific example.


Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural Networks


title: Changes as First-Class Citizens: A Research Perspective on Modern Software Tooling

abstract: Software must evolve to keep up with an ever-changing context, the real world. We discuss an emergent trend in software evolution research revolving around the central notion that drives evolution: Change. By reifying change, and by modelling it as a first-class entity, researchers can now analyse the complex phenomenon known as software evolution with an unprecedented degree of accuracy. We present a Systematic Mapping Study of 86 articles to give an overview on the state of the art in this area of research and present a roadmap with open issues and future directions.


title: On Experiments in Design Science Research and Theory Development: A Literature Review

abstract: Experimental research is an established method in Information Systems Research and well suited for Design Science Research (DSR). Our review of 71 published research articles aims at contributing to the DSR body of knowledge by systematically collecting the dependent and independent variables experimentally tested. Here, we identify a lack of cumulative research in DSR as only very few theory constructs are (re-)used. The paper provides several implications for theory and an outlook on potentially fruitful avenues for future research.


title: Three Implementations of SquishQL, a Simple RDF Query Language

abstract: RDF provides a basic way to represent data for the Semantic Web. We have been experimenting with the query paradigm for working with RDF data in semantic web applications. Query of RDF data provides a declarative access mechanism that is suitable for application usage and remote access. We describe work on a conceptual model for querying RDF data that refines ideas first presented in at the W3C workshop on Query Languages [14] and the design of one possible syntax, derived from [7], that is suitable for application programmers. Further, we present experience gained in three implementations of the query language.


title: Declarative interaction design for data visualization

abstract: Declarative visualization grammars can accelerate development, facilitate retargeting across platforms, and allow language-level optimizations. However, existing declarative visualization languages are primarily concerned with visual encoding, and rely on imperative event handlers for interactive behaviors. In response, we introduce a model of declarative interaction design for data visualizations. Adopting methods from reactive programming, we model low-level events as composable data streams from which we form higher-level semantic signals. Signals feed predicates and scale inversions, which allow us to generalize interactive selections at the level of item geometry (pixels) into interactive queries over the data domain. Production rules then use these queries to manipulate the visualization's appearance. To facilitate reuse and sharing, these constructs can be encapsulated as named interactors: standalone, purely declarative specifications of interaction techniques. We assess our model's feasibility and expressivity by instantiating it with extensions to the Vega visualization grammar. Through a diverse range of examples, we demonstrate coverage over an established taxonomy of visualization interaction techniques.


title: Single-channel speaker diarization based on spatial features

abstract: Speaker diarization has gained much importance over the past five years in helping overcome key challenges faced by automatic meeting transcription systems. Current state-of-the-art algorithms can only utilize spatial information when multi-microphone recordings are available. In this paper, we propose the novel use of reverberation as a source of spatial information obtained from single-channel recordings to perform speaker diarization. The proposed system is shown to reduce speaker classification errors by 34% when compared with current MFCC based single-channel systems.


title: On Network Throughput Variability in Microsoft Azure Cloud

abstract: The dependence of the industry on cloud-based infrastructures has grown much faster than our understanding of the performance limits and dynamics of these environments. An aspect only marginally analyzed in the past is related to the performance of the intra-cloud network connecting the virtual machines (VMs) deployed in the same data center. The few available works either do not exhaustively describe the adopted methodology or employed different approaches causing the analyses to be hard to replicate, and the results to be hard to compare. In addition, cloud customers can today highly customize their cloud environments while previous works considered only a few of the scenarios in which a customer may operate. In this paper, we provide an intra-cloud network performance characterization of Microsoft (MS) Azure, a leading provider only preliminary investigated from this angle. We first propose and thoroughly detail a methodology to carry out similar analyses, thus encouraging its replication also in other contexts; then we apply this methodology to characterize the intra-cloud network performance in terms of maximum network throughput. More specifically, we investigate whether and how the achievable throughput between two VMs varies (i) over time; (ii) when the customer operates different decisions on VM size, network configuration, geographic region, and transport protocol; and (iii) when the customer operates the same decisions on these factors. Our analysis aims at addressing the gap existing in the literature by providing the most exhaustive and detailed results about the intra-cloud network performance for MS Azure today available.


title: Strategic Directions in Artificial Intelligence

abstract: This article was reprinted with permission from "ACM Computing Surveys: 28(4): December 1996, 653-670. original is available from ACM.


title: ANTECEDENTS AND MATERNAL HEALTH OUTCOMES OF UNINTENDED PREGNANCY: A SYSTEMATIC REVIEW

abstract: Background: While concerns about adverse health outcomes of unintended pregnancies for the mother have been expressed, there has only been limited research on the outcomes of unintended pregnancies. This review provides an overview of antecedents and maternal health outcomes of unintended pregnancies (UIPs) carried to term live birth. Methods: The authors extracted published and unpublished data on current evidence of UIP using the search engines MEDLINE, PubMed and EMBASE and presented the findings in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results: The systematic review included 64 studies with a mean quality score of 4.94 (SD ± 0.72). It involved 671,046 reproductive age women who had given birth to a single child. Socio-demographic and reproductive characteristics were antecedents for and/or consequences of UIPs. Unintended pregnancies were associated with reduced use of prenatal healthcare services, illicit drug use, intimate partner violence, and adverse maternal mental and physical health. Conclusions: Antecedents of an UIP may include socio-demographic and reproductive characteristics of women, as well as family and socio-cultural characteristics of a society. Findings on adverse outcomes of UIPs were mixed, but are generally suggestive of negative outcomes. Longitudinal multi-wave studies with appropriate comparison groups are needed to fill the gaps and identify research and policy alternatives. Key words: antecedents, maternal health outcomes, unintended pregnancy, systematic review


title: A Compact 28GHz Bandpass Filter using Quartz Folded Waveguide

abstract: For massive MIMO antenna units in 5G, a compact quartz folded waveguide bandpass filter in 28 GHz band with wide passband has been developed. To design the filter, some analyses using EM simulation were carried out and a unique construction has been proposed. An air gap coupling-window has been found to be insensitive against gap distance variations in fabrication. An asymmetrical Y-shaped terminal for TEM-TE mode transition has shown lower external Q less than 10. The fabricated folded waveguide filter has a size of $\mathbf{8}.\mathbf{9}\times \mathbf{14}.\mathbf{85}\times \mathbf{1}.\mathbf{6}\ \mathbf{mm}^{3}$ and realizes a compact I/O terminal layout. The measured insertion loss is very low such as 0.9 dB which is better than conventional straight type by 0.3 dB, keeping with 50 dB min. attenuations.


title: Video-to-Video Synthesis

abstract: We study the problem of video-to-video synthesis, whose goal is to learn a mapping function from an input source video (e.g., a sequence of semantic segmentation masks) to an output photorealistic video that precisely depicts the content of the source video. While its image counterpart, the image-to-image synthesis problem, is a popular topic, the video-to-video synthesis problem is less explored in the literature. Without understanding temporal dynamics, directly applying existing image synthesis approaches to an input video often results in temporally incoherent videos of low visual quality. In this paper, we propose a novel video-to-video synthesis approach under the generative adversarial learning framework. Through carefully-designed generator and discriminator architectures, coupled with a spatio-temporal adversarial objective, we achieve high-resolution, photorealistic, temporally coherent video results on a diverse set of input formats including segmentation masks, sketches, and poses. Experiments on multiple benchmarks show the advantage of our method compared to strong baselines. In particular, our model is capable of synthesizing 2K resolution videos of street scenes up to 30 seconds long, which significantly advances the state-of-the-art of video synthesis. Finally, we apply our approach to future video prediction, outperforming several state-of-the-art competing systems.


title: A systematic literature review on Health Recommender Systems

abstract: Health Information Systems are becoming an important platform for healthcare services. In this context, Health Recommender Systems (HRS) are presented as complementary tools in decision making processes in healthcare services. Health Recommender Systems increase usability of technologies and reduce information overload in processes. In this paper, a literature review was conducted by following a review procedure. Major approaches in HRS were outlined and findings were discussed. The paper presented current developments in the market, challenges and opportunities regarding to HRS and emerging approaches. It is believed that this study is an illuminating start-up point for HRS literature review.


title: Coauthorship and citation networks for statisticians: Comment

abstract: This is a comment on the paper arXiv:1410.2840 by Ji and Jin, to appear in the AOAS.


title: The effects of perceived risk and technology type on users' acceptance of technologies

abstract: Previous studies on technology adoption disagree regarding the relative magnitude of the effects of perceived usefulness and perceived ease of use. However these studies did not consider moderating variables. We investigated four potential moderating variables - perceived risk, technology type, user experience, and gender - in users' technology adoption. Their moderating effects were tested in an empirical study of 161 subjects. Results showed that perceived risk, technology type, and gender were significant moderating variables. However the effects of user experience were marginal after the variance of errors was removed.


title: Development of Novel Non-Contact Electrodes for Mobile Electrocardiogram Monitoring System

abstract: Real-time monitoring of cardiac health is helpful for patients with cardiovascular disease. Many telemedicine systems based on ubiquitous computing and communication techniques have been proposed for monitoring the user's electrocardiogram (ECG) anywhere and anytime. Usually, wet electrodes are used in these telemedicine systems. However, wet electrodes require conduction gels and skin preparation that can be inconvenient and uncomfortable for users. In order to overcome this issue, a new non-contact electrode circuit was proposed and applied in developing a mobile electrocardiogram monitoring system. The proposed non-contact electrode can measure bio-potentials across thin clothing, allowing it to be embedded in a user's normal clothing to monitor ECG in daily life. We attempted to simplify the design of these non-contact electrodes to reduce power consumption while continuing to provide good signal quality. The electrical specifications and the performance of monitoring arrhythmia in clinical settings were also validated to investigate the reliability of the proposed design. Experimental results show that the proposed non-contact electrode provides good signal quality for measuring ECG across thin clothes.


title: The problem of identifying misogynist language on Twitter (and other online social spaces)

abstract: Misogynist abuse has now become serious enough to attract attention from scholars of Law [7]. Social network platform providers have been forced to address this issue, such that Twitter is now very clear about what constitutes abusive behaviour, and has responded by updating their trust and safety rules [16].


The School Bus : A Neglected Children ’ s Environment


title: Constraint-based motion optimization using a statistical dynamic model

abstract: In this paper, we present a technique for generating animation from a variety of user-defined constraints. We pose constraint-based motion synthesis as a maximum a posterior (MAP) problem and develop an optimization framework that generates natural motion satisfying user constraints. The system automatically learns a statistical dynamic model from motion capture data and then enforces it as a motion prior. This motion prior, together with user-defined constraints, comprises a trajectory optimization problem. Solving this problem in the low-dimensional space yields optimal natural motion that achieves the goals specified by the user. We demonstrate the effectiveness of this approach by generating whole-body and facial motion from a variety of spatial-temporal constraints.


title: Best terms: an efficient feature-selection algorithm for text categorization

abstract: In this paper, we propose a new feature-selection algorithm for text classification, called best terms (BT). The complexity of BT is linear in respect to the number of the training-set documents and is independent from both the vocabulary size and the number of categories. We evaluate BT on two benchmark document collections, Reuters-21578 and 20-Newsgroups, using two classification algorithms, naive Bayes (NB) and support vector machines (SVM). Our experimental results, comparing BT with an extensive and representative list of feature-selection algorithms, show that (1) BT is faster than the existing feature-selection algorithms; (2) BT leads to a considerable increase in the classification accuracy of NB and SVM as measured by the F1 measure; (3) BT leads to a considerable improvement in the speed of NB and SVM; in most cases, the training time of SVM has dropped by an order of magnitude; (4) in most cases, the combination of BT with the simple, but very fast, NB algorithm leads to classification accuracy comparable with SVM while sometimes it is even more accurate.


title: Understanding the factors driving m‐learning adoption: a literature review

abstract: Purpose – By surveying current literature, the purposes of this paper are twofold: to identify current situation of mobile learning (m‐learning) adoption and specify the challenges and to identify the factors driving m‐learning adoption.Design/methodology/approach – The paper reviews literature related to: m‐learning applications and challenging issues and adoption researches on m‐learning and related topics. A reflection on the unique nature of m‐learning adoption building upon the literature reviewed contributes to a new conceptual model.Findings – Even if m‐learning is fast evolving, the review of literature reveals a challenge as to how to promote m‐learning adoption. In this light, the paper extends the scope of literature reviewed to the theories and factors relating to different roles m‐learning users have into consideration, namely, technology user, consumer and learner, in an attempt to offer a more complete understanding of m‐learning adoption. Insights are drawn from the proposed model.Practica...


Ternary and quaternary logic to binary bit conversion CMOS integrated circuit design using multiple input floating gate MOSFETs


title: Ten quick tips for machine learning in computational biology

abstract: Machine learning has become a pivotal tool for many projects in computational biology, bioinformatics, and health informatics. Nevertheless, beginners and biomedical researchers often do not have enough experience to run a data mining project effectively, and therefore can follow incorrect practices, that may lead to common mistakes or over-optimistic results. With this review, we present ten quick tips to take advantage of machine learning in any computational biology context, by avoiding some common errors that we observed hundreds of times in multiple bioinformatics projects. We believe our ten suggestions can strongly help any machine learning practitioner to carry on a successful project in computational biology and related sciences.


title: Reaction RuleML 1.0: standardized semantic reaction rules

abstract: RuleML is a family of XML languages whose modular system of schemas permits high-precision (Web) rule interchange. The family's top-level distinction is deliberation rules vs. reaction rules. In this paper we address the Reaction RuleML subfamily of RuleML and survey related work. Reaction RuleML is a standardized rule markup/serialization language and semantic interchange format for reaction rules and rule-based event processing. Reaction rules include distributed Complex Event Processing (CEP), Knowledge Representation (KR) calculi, as well as Event-Condition-Action (ECA) rules, Production (CA) rules, and Trigger (EA) rules. Reaction RuleML 1.0 incorporates this reactive spectrum of rules into RuleML employing a system of step-wise extensions of the Deliberation RuleML 1.0 foundation.


title: Hybrid Dialog State Tracker with ASR Features

abstract: This paper presents a hybrid dialog state tracker enhanced by trainable Spoken Language Understanding (SLU) for slot-filling dialog systems. Our architecture is inspired by previously proposed neural-network-based belief-tracking systems. In addition, we extended some parts of our modular architecture with differentiable rules to allow end-to-end training. We hypothesize that these rules allow our tracker to generalize better than pure machine-learning based systems. For evaluation, we used the Dialog State Tracking Challenge (DSTC) 2 dataset - a popular belief tracking testbed with dialogs from restaurant information system. To our knowledge, our hybrid tracker sets a new state-of-the-art result in three out of four categories within the DSTC2.


title: Genetic approaches for graph partitioning: a survey

abstract: The graph partitioning problem occurs in numerous applications such as circuit placement, matrix factorization, load balancing, and community detection. For this problem, genetic algorithm is a representative approach with competitive performance with many related papers being published. Although there are a number of surveys on graph partitioning, none of them deals with genetic algorithms in much detail. In this survey, a number of problem-specific issues in applying genetic algorithms to the graph partitioning problem are discussed; the issues include encoding, crossover, normalization, and balancing.


title: A New Digital Control Algorithm to Achieve Optimal Dynamic Performance in DC-to-DC Converters

abstract: In this paper, a new control algorithm is proposed to achieve optimal dynamic performance for dc-to-dc converters under a load current change and for a given set of circuit parameters, such as the output inductor, output capacitor, switching frequency, input voltage, and output voltage. Using the concept of capacitor charge balance, the proposed algorithm predicts the optimal transient response for a dc-to-dc converter during a large signal load current change. During steady state operation, conventional current mode proportional-integral-derivative (PID) is used. During large signal transient conditions, the new control algorithm takes over. The equations needed to calculate the transient time and the required duty cycle series are presented. By using the proposed algorithm, the optimal transient performances, including the smallest output voltage overshoot/undershoot and the shortest recovery time, is achieved. In addition, since the large signal dynamic response of the power converter is successfully predicted, the large signal stability is guaranteed. Experimental results show that the proposed method produces superior dynamic performance over a conventional current mode PID controller.


title: Novel approaches to human activity recognition based on accelerometer data

abstract: An increasing number of works have investigated the use of convolutional neural network (ConvNets) approaches to perform human activity recognition (HAR) based on wearable sensor data. These approaches present state-of-the-art results in HAR, outperforming traditional approaches, such as handcrafted methods and 1D convolutions. Motivated by this, in this work we propose a set of methods to enhance ConvNets for HAR. First, we propose a data augmentation which enables the ConvNets to learn more adequately the patterns of the signal. Second, we exploit the attitude estimation of the accelerometer data to devise a set of novel feature descriptors which allow the ConvNets to better discriminate the activities. Finally, we propose a novel ConvNet architecture to explore the patterns among the accelerometer axes throughout the layers that compose the network. We demonstrate that this is a simpler way of improving the activity recognition instead of proposing more complex architectures, serving as direction to future works with the purpose of building ConvNets architectures. The experimental results show that our proposed methods achieve notable improvements and outperform existing state-of-the-art methods.


title: Generating Liquid Simulations with Deformation-aware Neural Networks

abstract: We propose a novel approach for deformation-aware neural networks that learn the weighting and synthesis of dense volumetric deformation fields. Our method specifically targets the space-time representation of physical surfaces from liquid simulations. Liquids exhibit highly complex, non-linear behavior under changing simulation conditions such as different initial conditions. Our algorithm captures these complex phenomena in two stages: a first neural network computes a weighting function for a set of pre-computed deformations, while a second network directly generates a deformation field for refining the surface. Key for successful training runs in this setting is a suitable loss function that encodes the effect of the deformations, and a robust calculation of the corresponding gradients. To demonstrate the effectiveness of our approach, we showcase our method with several complex examples of flowing liquids with topology changes. Our representation makes it possible to rapidly generate the desired implicit surfaces. We have implemented a mobile application to demonstrate that real-time interactions with complex liquid effects are possible with our approach.


title: Leveraging Track Relationships for Web Service Recommendation

abstract: Existing Web services recommendation approaches are based on usage statistics or QoS properties, leaving aside the evolution of the services' ecosystem. These approaches do not always capture new or more recent users' preferences resulting in recommendations with possibly obsolete or less relevant services. In this paper, we describe a novel Web services recommendation approach where the services' ecosystem is represented as a heterogeneous multi-graph, and edges may have different semantics. The recommendation process relies on data mining techniques to suggest services "of interest" to a user.


title: The Dog as a Model for Understanding Human Social Behavior

abstract: Abstract The traditional approach for studying the evolutionary emergence of human social cognition is based on comparisons with apes and monkeys as model species with a homologous relationship to other primates and humans. Recently, however, research interest has focused on other species offering analogous models for the evolution of human social cognitive abilities. Here we suggest that convergent social evolution in dogs can be used to model the early state of human social evolution, suggesting that functionally analogous forms of many traits of the human behavioral complex are present in dogs. We argue that the dog as a model species is unique among domesticated species because (a) many aspects of dog behavior are functionally analogous to the corresponding human traits, (b) socialization to humans is a natural process in dogs, (c) comparison with the ancestor is important for convergent modeling, and (d) the dog represents a natural experimental model for studying human behavior.


title: A Bayesian Network Scoring Metric That Is Based On Globally Uniform Parameter Priors

abstract: We introduce a new Bayesian network (BN) scoring metric called the Global Uniform (GU) metric. This metric is based on a particular type of default parameter prior. Such priors may be useful when a BN developer is not willing or able to specify domain-specific parameter priors. The GU parameter prior specifies that every prior joint probability distribution P consistent with a BN structure S is considered to be equally likely. Distribution P is consistent with S if P includes just the set of independence relations defined by S. ::: ::: We show that the GU metric addresses some undesirable behavior of the BDeu and K2 Bayesian network scoring metrics, which also use particular forms of default parameter priors. A closed form formula for computing GU for special classes of BNs is derived. Efficiently computing GU for an arbitrary BN remains an open problem.


title: CityPulse: Large Scale Data Analytics Framework for Smart Cities

abstract: Our world and our lives are changing in many ways. Communication, networking, and computing technologies are among the most influential enablers that shape our lives today. Digital data and connected worlds of physical objects, people, and devices are rapidly changing the way we work, travel, socialize, and interact with our surroundings, and they have a profound impact on different domains, such as healthcare, environmental monitoring, urban systems, and control and management applications, among several other areas. Cities currently face an increasing demand for providing services that can have an impact on people’s everyday lives. The CityPulse framework supports smart city service creation by means of a distributed system for semantic discovery, data analytics, and interpretation of large-scale (near-)real-time Internet of Things data and social media data streams. To goal is to break away from silo applications and enable cross-domain data integration. The CityPulse framework integrates multimodal, mixed quality, uncertain and incomplete data to create reliable, dependable information and continuously adapts data processing techniques to meet the quality of information requirements from end users. Different than existing solutions that mainly offer unified views of the data, the CityPulse framework is also equipped with powerful data analytics modules that perform intelligent data aggregation, event detection, quality assessment, contextual filtering, and decision support. This paper presents the framework, describes its components, and demonstrates how they interact to support easy development of custom-made applications for citizens. The benefits and the effectiveness of the framework are demonstrated in a use-case scenario implementation presented in this paper.


Meta-Learning for Few-shot Camera-Adaptive Color Constancy.


title: Human hand modeling from surface anatomy

abstract: The human hand is an important interface with complex shape and movement. In virtual reality and gaming applications the use of an individualized rather than generic hand representation can increase the sense of immersion and in some cases may lead to more effortless and accurate interaction with the virtual world. We present a method for constructing a person-specific model from a single canonically posed palm image of the hand without human guidance. Tensor voting is employed to extract the principal creases on the palmar surface. Joint locations are estimated using extracted features and analysis of surface anatomy. The skin geometry of a generic 3D hand model is deformed using radial basis functions guided by correspondences to the extracted surface anatomy and hand contours. The result is a 3D model of an individual's hand, with similar joint locations, contours, and skin texture.


title: A column-driver IC with improved DVO for TFT-LCDs

abstract: This paper presents an 8-bit column-driver IC with improved deviation of voltage output (DVO) for thin-film-transistor (TFT) liquid crystal displays (LCDs). The various DVO results contributed by the output buffer of a column driver are predicted by using Monte Carlo simulation under different variation conditions. Relying on this prediction, a better compromise can be achieved between DVO and chip size. This work was implemented using 0.35-μm CMOS technology and the measured maximum DVO is only 6.2 mV.


title: meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting

abstract: We propose a simple yet effective technique for neural network learning. The forward propagation is computed as usual. In back propagation, only a small subset of the full gradient is computed to update the model parameters. The gradient vectors are sparsified in such a way that only the top-$k$ elements (in terms of magnitude) are kept. As a result, only $k$ rows or columns (depending on the layout) of the weight matrix are modified, leading to a linear reduction ($k$ divided by the vector dimension) in the computational cost. Surprisingly, experimental results demonstrate that we can update only 1--4\% of the weights at each back propagation pass. This does not result in a larger number of training iterations. More interestingly, the accuracy of the resulting models is actually improved rather than degraded, and a detailed analysis is given.


title: Generative Adversarial Networks for MR-CT Deformable Image Registration

abstract: Deformable Image Registration (DIR) of MR and CT images is one of the most challenging registration task, due to the inherent structural differences of the modalities and the missing dense ground truth. Recently cycle Generative Adversarial Networks (cycle-GANs) have been used to learn the intensity relationship between these 2 modalities for unpaired brain data. Yet its usefulness for DIR was not assessed. ::: In this study we evaluate the DIR performance for thoracic and abdominal organs after synthesis by cycle-GAN. We show that geometric changes, which differentiate the two populations (e.g. inhale vs. exhale), are readily synthesized as well. This causes substantial problems for any application which relies on spatial correspondences being preserved between the real and the synthesized image (e.g. plan, segmentation, landmark propagation). To alleviate this problem, we investigated reducing the spatial information provided to the discriminator by decreasing the size of its receptive fields. ::: Image synthesis was learned from 17 unpaired subjects per modality. Registration performance was evaluated with respect to manual segmentations of 11 structures for 3 subjects from the VISERAL challenge. State-of-the-art DIR methods based on Normalized Mutual Information (NMI), Modality Independent Neighborhood Descriptor (MIND) and their novel combination achieved a mean segmentation overlap ratio of 76.7, 67.7, 76.9%, respectively. This dropped to 69.1% or less when registering images synthesized by cycle-GAN based on local correlation, due to the poor performance on the thoracic region, where large lung volume changes were synthesized. Performance for the abdominal region was similar to that of CT-MRI NMI registration (77.4 vs. 78.8%) when using 3D synthesizing MRIs (12 slices) and medium sized receptive fields for the discriminator.


title: Abstraction and reformulation in artificial intelligence

abstract: This paper contributes in two ways to the aims of this special issue on abstraction. The first is to show that there are compelling reasons motivating the use of abstraction in the purely computational realm of artificial intelligence. The second is to contribute to the overall discussion of the nature of abstraction by providing examples of the abstraction processes currently used in artificial intelligence. Although each type of abstraction is specific to a somewhat narrow context, it is hoped that collectively they illustrate the richness and variety of abstraction in its fullest sense.


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title: Social networking: Communication revolution or evolution?

abstract: Social networks and the need to communicate are universal human conditions. A general assumption is that communication technologies help to increase and strengthen social ties. The Internet provides many social networking opportunities. But how do social networking sites affect individual relationships? Do people use social networking sites to expand their personal networks, to find people who have had similar experiences, to discuss a common hobby, for the potential of offline dating? Or, do people spend time on networking sites to deepen their existing personal networks and stay connected to old friends or distant family? What is the nature of the communications that transpire on social networking sites? Is it personal, emotional, private, and important; or trivial, informal, and public? We examined the literature on social networking sites and conducted our own studies of how students on American college campuses engage in social networking.


title: Phosphonomethylated polyethylenimine resin for recovery of uranium from seawater

abstract: Branched polyethylenimine (BPEI) was crosslinked by a bis-epoxide to give a resin (CL-BPEI), which was further functionalized by phosphorous acid / formaldehyde leading to phosphonomethylated CL-BPEI (PhosCL-BPEI). The latter resin has been found to recover uranium from natural seawater very efficiently but not the former resin; with column method PhosCL-BPEI recovered 85-64 % of uranium in the original seawater at SV=170–680 hr−1. The highest recovery rate obtained so far was 46.8 μg-U/g-resin/6 hr at SV=680 hr−1. PhosCL-BPEI could be used repeatedly without appreciable decrease of the efficiency. The molar ratio of phosphonomethyl group to amino groups in the resin affects the adsorption ability very much, the adequate P/N ratio being ∼0.25. Both PhosCL-BPEI and CL-BPEI showed a large adsorption capacity from an aqueous solution of uranyl ions. It is stressed, however, that the phosphonomethylation of CL-BPEI is very important for the uranium recovery from seawater.


title: Gesture's Role in Speaking, Learning, and Creating Language

abstract: When speakers talk, they gesture. The goal of this review is to investigate the contribution that these gestures make to how we communicate and think. Gesture can play a role in communication and thought at many timespans. We explore, in turn, gesture’s contribution to how language is produced and understood in the moment; its contribution to how we learn language and other cognitive skills; and its contribution to how language is created over generations, over childhood, and on the spot. We find that the gestures speakers produce when they talk are integral to communication and can be harnessed in a number of ways. (a) Gesture reflects speakers’ thoughts, often their unspoken thoughts, and thus can serve as a window onto cognition. Encouraging speakers to gesture can thus provide another route for teachers, clinicians, interviewers, etc., to better understand their communication partners. (b) Gesture can change speakers’ thoughts. Encouraging gesture thus has the potential to change how students, patients, witnesses, etc., think about a problem and, as a result, alter the course of learning, therapy, or an interchange. (c) Gesture provides building blocks that can be used to construct a language. By watching how children and adults who do not already have a language put those blocks together, we can observe the process of language creation. Our hands are with us at all times and thus provide researchers and learners with an ever-present tool for understanding how we talk and think.


title: An efficient image matching method using Speed Up Robust Features

abstract: The kernel of stereo vision system is stereo matching. In this paper proposed, an efficient image matching algorithm based on Speed Up Robust Features (SURF). Compared with traditional methods, this approach has enormous advantages of less computations and short time-consuming. The approach uses SURF algorithm to extract feature points. The feature descriptor of the approach is determined by Haar wavelet. The RANdom SAmple Consensus (RANSAC) algorithm is used to eliminate the false matches and wrong match point. The experimental results show that the approach is robust and has fast matching speed. That makes an important application field of 3D reconstruction.


title: Altered Tbx1 gene expression is associated with abnormal oesophageal development in the adriamycin mouse model of oesophageal atresia/tracheo-oesophageal fistula

abstract: Introduction ::: Oesophageal atresia/tracheo-oesophageal atresia (OA/TOF) frequently arises with associated anomalies and has been clinically linked with 22q11 deletion syndromes, a group of conditions due to Tbx1 gene mutation which include Di George syndrome. Tbx1 and Tbx2 genes modulate pharyngeal and cardiac development, but are also expressed in the developing foregut and are known to interact with key signalling pathways described in oesophageal formation including bone morphogenic proteins. The adriamycin mouse model (AMM) reliably displays OA/TOF-like foregut malformations providing a powerful system for investigating the disturbances in gene regulation and morphology involved in tracheo-oesophageal malformations. We hypothesised that foregut abnormalities observed in the AMM are associated with altered Tbx1 and Tbx2 gene expression.