title: Highly accurate fast lung CT registration

abstract: Lung registration in thoracic CT scans has received much attention in the medical imaging community. Possible applications range from follow-up analysis, motion correction for radiation therapy, monitoring of air flow and pulmonary function to lung elasticity analysis. In a clinical environment, runtime is always a critical issue, ruling out quite a few excellent registration approaches. In this paper, a highly efficient variational lung registration method based on minimizing the normalized gradient fields distance measure with curvature regularization is presented. The method ensures diffeomorphic deformations by an additional volume regularization. Supplemental user knowledge, like a segmentation of the lungs, may be incorporated as well. The accuracy of our method was evaluated on 40 test cases from clinical routine. In the EMPIRE10 lung registration challenge, our scheme ranks third, with respect to various validation criteria, out of 28 algorithms with an average landmark distance of 0.72 mm. The average runtime is about 1:50 min on a standard PC, making it by far the fastest approach of the top-ranking algorithms. Additionally, the ten publicly available DIR-Lab inhale-exhale scan pairs were registered to subvoxel accuracy at computation times of only 20 seconds. Our method thus combines very attractive runtimes with state-of-the-art accuracy in a unique way.


Addiction and the Brain: Development, Not Disease


title: Drug Recommendation toward Safe Polypharmacy

abstract: Adverse drug reactions (ADRs) induced from high-order drug-drug interactions (DDIs) due to polypharmacy - simultaneous use of multiple drugs - represent a significant public health problem. Unfortunately, computational effects to facilitate future polypharmacy, particularly to assist safe multi-drug prescription, are still in their infancy. We formally formulate the to-avoid and safe drug recommendation problems when multiple drugs have been taken simultaneously. We develop a joint model with a recommendation component and an ADR label prediction component to recommend for a prescription a set of to-avoid/safe drugs that will induce/will not induce ADRs if taken together with the prescription. We also develop real drug-drug interaction datasets and corresponding evaluation protocols.


Report on a general problem-solving program.


title: Anhedonia After a Selective Bilateral Lesion of the Globus Pallidus

abstract: Mr. A, a 34-year-old man, came to our outpatient clinic for treatment of a major depressive episode. His history was also notable for polysubstance abuse and dependence in sustained remission, with prior abuse of alcohol, LSD, and other hallucinogens and prior dependence on marijuana, cocaine, opiates, and Ecstasy (MDMA [3,4-methylenedioxymethamphetamine]). He began using alcohol at age 9, marijuana at age 12, cocaine at age 13, opiates at age 20, and Ecstasy at age 21. He had a history of depressive symptoms 6 years earlier in the context of active substance dependence and chronic back pain but had no other prior psychiatric diagnosis or treatment. One year before presentation to our clinic, he had been smoking cocaine daily, using Ecstasy several days a week, and consuming two to 10 alcoholic drinks daily but reported no depressive symptoms. After consuming cocaine, Ecstasy, oxycodone, and methadone at a party, he became aggressive and was brought to an emergency room. There, he ingested all of his remaining methadone to prevent it from being discovered. He reported no suicidal intentions surrounding this ingestion. He became unresponsive, hypoxic, and hypotensive. Mr. A was resuscitated and then stabilized in an intensive care unit over 4 days. After this overdose, Mr. A became acutely depressed. He endorsed a depressed mood, anhedonia, low energy, difficulties concentrating and remembering, feelings of hopelessness and guilt, poor self-esteem, social isolation, increased sleep, and a 20-Ib weight gain over the ensuing year. He reported the disappearance of drug cravings and remained abstinent from all recreational drugs other than an occasional glass of wine with dinner. He reported that he no longer experienced pleasure from drinking alcohol. Four serial urine toxicology screens were negative over 6 months. Mr. A developed a resting tremor of his left hand, slight rigidity of his right arm and left leg, and slowing of rapid alternating movements of his left hand, all of which were still present 16 months after the overdose, as assessed by a movement disorders specialist. He had no prior history of neurological problems. A magnetic resonance imaging (MRI) scan of his brain revealed a selective bilateral lesion of the globus pallidus (Figure 1), with clear involvement of the internal globus pallidus on the right and both internal and external globus pallidus on the left. The bilateral lesion was small, selective, and restricted to the globus pallidus. There were no other lesions or findings on the MRI. Comprehensive neuropsychological tests revealed intact cognitive functions, including attention, working memory, and executive function, and an indicated absence of a frontal-subcortical syndrome (Table 1). Basic blood tests, including a CBC, a basic metabolic panel, liver function tests, and thyroid function tests, were all within normal limits at the time Mr. A came to our clinic. There was no known family history of affective disorders or substance abuse.


title: Black-box data-efficient policy search for robotics

abstract: The most data-efficient algorithms for reinforcement learning (RL) in robotics are based on uncertain dynamical models: after each episode, they first learn a dynamical model of the robot, then they use an optimization algorithm to find a policy that maximizes the expected return given the model and its uncertainties. It is often believed that this optimization can be tractable only if analytical, gradient-based algorithms are used; however, these algorithms require using specific families of reward functions and policies, which greatly limits the flexibility of the overall approach. In this paper, we introduce a novel model-based RL algorithm, called Black-DROPS (Black-box Data-efficient RObot Policy Search) that: (1) does not impose any constraint on the reward function or the policy (they are treated as black-boxes), (2) is as data-efficient as the state-of-the-art algorithm for data-efficient RL in robotics, and (3) is as fast (or faster) than analytical approaches when several cores are available. The key idea is to replace the gradient-based optimization algorithm with a parallel, black-box algorithm that takes into account the model uncertainties. We demonstrate the performance of our new algorithm on two standard control benchmark problems (in simulation) and a low-cost robotic manipulator (with a real robot).


title: Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning

abstract: Developing a safe and efficient collision avoidance policy for multiple robots is challenging in the decentralized scenarios where each robot generates its paths without observing other robots' states and intents. While other distributed multi-robot collision avoidance systems exist, they often require extracting agent-level features to plan a local collision-free action, which can be computationally prohibitive and not robust. More importantly, in practice the performance of these methods are much lower than their centralized counterparts. We present a decentralized sensor-level collision avoidance policy for multi-robot systems, which directly maps raw sensor measurements to an agent's steering commands in terms of movement velocity. As a first step toward reducing the performance gap between decentralized and centralized methods, we present a multi-scenario multi-stage training framework to learn an optimal policy. The policy is trained over a large number of robots on rich, complex environments simultaneously using a policy gradient based reinforcement learning algorithm. We validate the learned sensor-level collision avoidance policy in a variety of simulated scenarios with thorough performance evaluations and show that the final learned policy is able to find time efficient, collision-free paths for a large-scale robot system. We also demonstrate that the learned policy can be well generalized to new scenarios that do not appear in the entire training period, including navigating a heterogeneous group of robots and a large-scale scenario with 100 robots. Videos are available at https://sites.google.com/view/drlmaca.


title: Cheetah-cub-S: Steering of a quadruped robot using trunk motion

abstract: A reliable concept for steering was realized by integrating a spine-like structure with one DoF into a quadruped robot — Cheetah-cub-S. We increased maneuverability dramatically compared to a robot with rigid spine, while keeping controlling effort low. The robot weights 1160 g at 0.1 m (height), 0.105 m (width) and 0.205 m (length). Based on qualitative tests, it is able to run 0.36 m/s and turn within a radius of 0.51 m (equivalent to 2.48 body lengths) at 0.31 m/s (equivalent to 34°/s). We could steer the quadruped around three objects in a 7.13 m (23 s @ 0.31 m/s) slalom within our test area. Due to its modularity, Cheetah-cub-S is a versatile mobile platform and components such as the spine segment, legs, motors or steering mechanism can be exchanged, leading to easier development of even higher performing robot-versions.


title: High-Dimensional Variable Selection for Survival Data

abstract: The minimal depth of a maximal subtree is a dimensionless order statistic measuring the predictiveness of a variable in a survival tree. We derive the distribution of the minimal depth and use it for high-dimensional variable selection using random survival forests. In big p and small n problems (where p is the dimension and n is the sample size), the distribution of the minimal depth reveals a “ceiling effect” in which a tree simply cannot be grown deep enough to properly identify predictive variables. Motivated by this limitation, we develop a new regularized algorithm, termed RSF-Variable Hunting. This algorithm exploits maximal subtrees for effective variable selection under such scenarios. Several applications are presented demonstrating the methodology, including the problem of gene selection using microarray data. In this work we focus only on survival settings, although our methodology also applies to other random forests applications, including regression and classification settings. All examples...


title: Transaction attributes and software outsourcing success: an empirical investigation of transaction cost theory

abstract: In recent years, an increasing amount of attention has been paid to information systems (IS) outsourcing by practitioners as well as academics. However, our understanding of the factors that affect outsourcing success is hardly complete. By adopting transaction cost theory (TCT) as the theoretical foundation, this study analyses the implications of transaction attributes on the consequences of customized software outsourcing practice. The research model includes three exogenous variables (contractor reputation, asset specificity and uncertainty) and two endogenous variables (post-contractual opportunism and outsourcing success). The moderating effects of asset specificity on the relationships between uncertainty and the endogenous variables are also examined. Based on data col- lected in Taiwan, a research model is constructed to test the hypotheses derived from the theory. The empirical evidence is generally supportive of the theory but with some important exceptions. In particular, contractor reputation and uncer- tainty have the predicted effects on the contractor's post-contractual opportunism perceived by the client and outsourcing success, but asset specificity shows a negative effect on post-contractual opportunism and a positive effect on out- sourcing success, which are opposite to the typical predictions of TCT. Thus, in addition to the supportive findings of the theory, this study also raises an impor- tant research question regarding the effects of asset specificity on outsourcing for future research to explore.


What's in an Explanation? Characterizing Knowledge and Inference Requirements for Elementary Science Exams


title: Incorporating Scaffolding and Tutor Context into Bayesian Knowledge Tracing to Predict Inquiry Skill Acquisition

abstract: In this paper, we incorporate scaffolding and change of tutor context within the Bayesian Knowledge Tracing (BKT) framework to track students’ developing inquiry skills. These skills are demonstrated as students experiment within interactive simulations for two science topics. Our aim is twofold. First, we desire to improve the models’ predictive performance by adding these factors. Second, we aim to interpret these extended models to reveal if our scaffolding approach is effective, and if inquiry skills transfer across the topics. We found that incorporating scaffolding yielded better predictions of individual students’ performance over the classic BKT model. By interpreting our models, we found that scaffolding appears to be effective at helping students acquire these skills, and that the skills transfer across topics.


Segmentation of Brain MRI Images using Fuzzy C-Means and DWT


title: The long noncoding RNA Six3OS acts in trans to regulate retinal development by modulating Six3 activity

abstract: BackgroundThousands of different long non-coding RNAs are expressed during embryonic development, but the function of these molecules remains largely unexplored.ResultsHere we characterize the expression and function of Six3OS, a long non-coding RNA that is transcribed from the distal promoter region of the gene encoding the homeodomain transcription factor Six3. Overexpression and knockdown analysis of Six3OS reveals that it plays an essential role in regulating retinal cell specification. We further observe that Six3OS regulates Six3 activity in developing retina, but does not do so by modulating Six3 expression. Finally, we show that Six3OS binds directly to Ezh2 and Eya family members, indicating that Six3OS can act as a molecular scaffold to recruit histone modification enzymes to Six3 target genes.ConclusionsOur findings demonstrate a novel mechanism by which promoter-associated long non-coding RNAs can modulate the activity of their associated protein coding genes, and highlight the importance of this diverse class of molecules in the control of neural development.


Vector Space Models for Scientific Document Summarization


title: Relation between lineal energy distribution and relative biological effectiveness for photon beams according to the microdosimetric kinetic model.

abstract: Our cell survival data showed the obvious dependence of RBE on photon energy: The RBE value for 200 kV X-rays was approximately 10% greater than those for mega-voltage photon beams. In radiation therapy using mega-voltage photon beams, the photon energy distribution outside the field is different with that in the radiation field because of a large number of low energy scattering photons. Hence, the RBE values outside the field become greater. To evaluate the increase in RBE, the method of deriving the RBE using the Microdosimetric Kinetic model (MK model) was proposed in this study. The MK model has two kinds of the parameters, tissue-specific parameters and the dose-mean lineal energy derived from the lineal energy distributions measured with a Tissue-Equivalent Proportional Counter (TEPC). The lineal energy distributions with the same geometries of the cell irradiations for 200 kV X-rays, (60)Co γ-rays, and 6 MV X-rays were obtained with the TEPC and Monte Carlo code GEANT4. The measured lineal energy distribution for 200 kV X-rays was quite different from those for mega-voltage photon beams. The dose-mean lineal energy of 200 kV X-rays showed the greatest value, 4.51 keV/µm, comparing with 2.34 and 2.36 keV/µm for (60)Co γ-rays and 6 MV X-rays, respectively. By using the results of the TEPC and cell irradiations, the tissue-specific parameters in the MK model were determined. As a result, the RBE of the photon beams (y(D): 2~5 keV/µm) in arbitrary conditions can be derived by the measurements only or the calculations only of the dose-mean lineal energy.


Template-Free Construction of Poems with Thematic Cohesion and Enjambment


title: Optimal Photovoltaic Array Reconfiguration to Reduce Partial Shading Losses

abstract: Partial shading of a photovoltaic array is the condition under which different modules in the array experience different irradiance levels due to shading. This difference causes mismatch between the modules, leading to undesirable effects such as reduction in generated power and hot spots. The severity of these effects can be considerably reduced by photovoltaic array reconfiguration. This paper proposes a novel mathematical formulation for the optimal reconfiguration of photovoltaic arrays to minimize partial shading losses. The paper formulates the reconfiguration problem as a mixed integer quadratic programming problem and finds the optimal solution using a branch and bound algorithm. The proposed formulation can be used for an equal or nonequal number of modules per row. Moreover, it can be used for fully reconfigurable or partially reconfigurable arrays. The improvement resulting from the reconfiguration with respect to the existing photovoltaic interconnections is demonstrated by extensive simulation results.


title: Causes of Growth Depensation in Red Drum, Sciaenops Ocellatus, Larvae

abstract: Individual growth rates, and thus sizes at a given age, vary within cohorts of fishes reared in the laboratory, field enclosures, and the wild, but the causes of this variability are poorly understood. We examined effects of three variables (food availability, a diet shift, and fish density) on growth depensation in red drum, Sciaenops ocellatus, larvae in laboratory experiments. Absolute variance in length of larvae was greater with higher food availability, and relative variance (CV) for a cohort at 14 d posthatching was positively correlated with its CV at 7 d posthatching, over which time CV doubled. Supplementation of a rotifer diet with Artemia nauplii (diet shift) increased growth rate and length variance, but not CV. Individually-reared and group-reared larvae had similar growth rates, but group-reared larvae exhibited higher CVs than individually-reared larvae. The observation that food availability and diet shifts affected absolute size variance, while fish density affected relative size variance of red drum larvae, indicates that both absolute and relative size variance are important to understand how environmental factors affect growth depensation.


title: Assessing the Need for Master Naturalist Programs

abstract: We present a focus group-based needs assessment for a Master Naturalist program that would increase environmental education capacity in our state using trained volunteers. This assessment explored the potential benefits, challenges, and structure of the program. We conclude that the program would fill an existing need by providing research-based information on environmental issues; we should collaborate broadly with other environmental education programs; the program must become financially self-sufficient after initial supported development; and we need to work with environmental education professionals in the state to ensure the program does not infringe on their job security.


title: Secure, Decentralized Energy Resource Management Using the Ethereum Blockchain

abstract: While blockchain services hold great promise to improve many different industries, there are significant cybersecurity concerns which must be addressed. In this paper, we investigate security considerations for an Ethereum blockchain hosting a distributed energy management application. We have simulated a microgrid with ten buildings in the northeast U.S., and results of the transaction distribution and electricity utilization are presented. We also present the effects on energy distribution when one or two smart meters have their identities corrupted. We then propose a new approach to digital identity management that would require smart meters to authenticate with the blockchain ledger and mitigate identity-spoofing attacks. Applications of this approach to defense against port scans and DDoS, attacks are also discussed.


title: Early Fault Detection of Machine Tools Based on Deep Learning and Dynamic Identification

abstract: In modern digital manufacturing, nearly 79.6% of the downtime of a machine tool is caused by its mechanical failures. Predictive maintenance (PdM) is a useful way to minimize the machine downtime and the associated costs. One of the challenges with PdM is early fault detection under time-varying operational conditions, which means mining sensitive fault features from condition signals in long-term running. However, fault features are often weakened and disturbed by the time-varying harmonics and noise during a machining process. Existing analysis methods of these complex and diverse data are inefficient and time-consuming. This paper proposes a novel method for early fault detection under time-varying conditions. In this study, a deep learning model is constructed to automatically select the impulse responses from the vibration signals in long-term running of 288 days. Then, dynamic properties are identified from the selected impulse responses to detect the early mechanical fault under time-varying conditions. Compared to traditional methods, the experimental results in this paper have proved that our method was not affected by time-varying conditions and showed considerable potential for early fault detection in manufacturing.


title: Precision Landing System on H-Octocopter Drone Using Complementary Filter

abstract: An automatic landing system is required on a long-range drone because the position of the vehicle cannot be reached visually by the pilot. The autopilot system must be able to correct the drone movement dynamically in accordance with its flying altitude. The current article describes autopilot system on an H-Octocopter drone using image processing and complementary filter. This paper proposes a new approach to reduce oscillations during the landing phase on a big drone. The drone flies above 10 meters to a provided coordinate using GPS data, to check for the existence of the landing area. This process is done visually using the camera. PID controller is used to correct the movement by calculate error distance detected by camera. The controller also includes altitude parameters on its calculations through a complementary filter. The controller output is the PWM signals which control the movement and altitude of the vehicle. The signal then transferred to Flight Controller through serial communication, so that, the drone able to correct its movement. From the experiments, the accuracy is around 0.56 meters and it can be done in 18 seconds.


title: Polyphase filters - A model for teaching the art of discovery in DSP

abstract: By its very nature DSP is a mathematically heavy topic and to fully understand it students need to understand the mathematical developments underlying DSP topics. However, relying solely on mathematical developments often clouds the true nature of the foundation of a result. It is likely that students who master the mathematics may still not truly grasp the key ideas of a topic. Furthermore, teaching DSP topics by merely “going through the mathematics” deprives students of learning the art of discovery that will make them good researchers. This paper uses the topic of polyphase decimation and interpolation to illustrate how it is possible to maintain rigor yet teach using less mathematical approaches that show students how researchers think when developing new ideas.


title: An OAI-Based Filtering Service for CITIDEL from NDLTD

abstract: One goal of the Computing and Information Technology Interactive Digital Educational Library (CITIDEL) is to maximize the number of computing-related resources available to computer science scholars and practitioners through it. In this paper, we describe a set of experiments designed to help this goal by adding to CITIDEL a sub-collection of computing related electronic theses and dissertations (ETDs) automatically extracted from the Networked Digital Library of Theses and Dissertations (NDLTD) OAI Union Catalog. We analyze the metadata quality of the NDLTD OAI Union Catalog and describe three different experiments that combine different sources of evidence to improve the accuracy in filtering out the computing related entries.


title: Confidence Measures for Neural Network Classifiers

abstract: A polysaccharide or polyvinyl alcohol containing a mixture of nitrite ester groups with sulfate or nitrate ester groups with the mixture of ester groups being substantially uniformly distributed among the polymer units of the polysaccharide or polyvinyl alcohol.


Evolution Strategies for Deep Neural Network Models Design


Comparative analytical study of the approach stage to perform the spike skill of the volleyball game and its relationship to accuracy


title: Exploring Asymmetric Encoder-Decoder Structure for Context-based Sentence Representation Learning

abstract: Context information plays an important role in human language understanding, and it is also useful for machines to learn vector representations of language. In this paper, we explore an asymmetric encoder-decoder structure for unsupervised context-based sentence representation learning. As a result, we build an encoder-decoder architecture with an RNN encoder and a CNN decoder, and we show that neither an autoregressive decoder nor an RNN decoder is required. We further combine a suite of effective designs to significantly improve model efficiency while also achieving better performance. Our model is trained on two different large unlabeled corpora, and in both cases transferability is evaluated on a set of downstream language understanding tasks. We empirically show that our model is simple and fast while producing rich sentence representations that excel in downstream tasks.


title: Contextual Region-Based Convolutional Neural Network with Multilayer Fusion for SAR Ship Detection

abstract: Synthetic aperture radar (SAR) ship detection has been playing an increasingly essential role in marine monitoring in recent years. The lack of detailed information about ships in wide swath SAR imagery poses difficulty for traditional methods in exploring effective features for ship discrimination. Being capable of feature representation, deep neural networks have achieved dramatic progress in object detection recently. However, most of them suffer from the missing detection of small-sized targets, which means that few of them are able to be employed directly in SAR ship detection tasks. This paper discloses an elaborately designed deep hierarchical network, namely a contextual region-based convolutional neural network with multilayer fusion, for SAR ship detection, which is composed of a region proposal network (RPN) with high network resolution and an object detection network with contextual features. Instead of using low-resolution feature maps from a single layer for proposal generation in a RPN, the proposed method employs an intermediate layer combined with a downscaled shallow layer and an up-sampled deep layer to produce region proposals. In the object detection network, the region proposals are projected onto multiple layers with region of interest (ROI) pooling to extract the corresponding ROI features and contextual features around the ROI. After normalization and rescaling, they are subsequently concatenated into an integrated feature vector for final outputs. The proposed framework fuses the deep semantic and shallow high-resolution features, improving the detection performance for small-sized ships. The additional contextual features provide complementary information for classification and help to rule out false alarms. Experiments based on the Sentinel-1 dataset, which contains twenty-seven SAR images with 7986 labeled ships, verify that the proposed method achieves an excellent performance in SAR ship detection.


title: Fine-grained evaluation on face detection in the wild

abstract: Current evaluation datasets for face detection, which is of great value in real-world applications, are still somewhat out-of-date. We propose a new face detection dataset MALF (short for Multi-Attribute Labelled Faces), which contains 5,250 images collected from the Internet and ∼12,000 labelled faces. The MALF dataset highlights in two main features: 1) It is the largest dataset for evaluation of face detection in the wild, and the annotation of multiple facial attributes makes it possible for fine-grained performance analysis. 2) To reveal the ‘true’ performances of algorithms in practice, MALF adopts an evaluation metric that puts stress on the recall rate at a relatively low false alarm rate. Besides providing a large dataset for face detection evaluation, this paper also collects more than 20 state-of-the-art algorithms, both from academia and industry, and conducts a fine-grained comparative evaluation of these algorithms, which can be considered as a summary of past advances made in face detection. The dataset and up-to-date results of the evaluation can be found at http: //www.cbsr.ia.ac.cn/faceevaluation/.


title: A Privacy-Reserved Approach for Message Forwarding in Opportunistic Networks

abstract: Opportunistic Network (OppNet) is an emerging communication paradigm, by which nodes inside forward messages through personal contact opportunities. Recently, numerous studies have focused on predicting nodes meeting to promote routing efficiency and reduce transmission delay. However, individual privacy would likely be revealed to strangers or attackers during the execution of prediction. In this paper, we construct a privacy-reserved network framework for message forwarding to guarantee both efficient communication and individual privacy in OppNets, including a security-based prediction method and an attribute-based cryptosystem. Simulation results demonstrate that, our algorithm outperforms TRSS on average delivery ratio, generally by 15% for dropping probability and data tempered probability.


title: A Theory of Destructive Entrepreneurship

abstract: Policy interest since the early 1980s has focused in different ways on the creation of a large, productive, taxable economy - in which entrepreneurship plays a role for employment, income growth and innovation. The current understanding of various forms of entrepreneurship remains incomplete, focusing largely on productive and unproductive entrepreneurship. However, destructive entrepreneurship plays an important role in many, if not most, economies. This paper addresses the conceptual gap in the allocation of entrepreneurship by proposing a theory of destructive entrepreneurship.


title: UAVs Applied to the Counting and Monitoring of Animals

abstract: The advantages of intelligent approaches such as the conjunction of artificial vision and the use of Unmanned Aerial Vehicles (UAVs) have been recently emerging. This paper presents a focused on obtaining scans of large areas of livestock system. Counting and monitoring of animal species can be performed with video recordings taken from UAVs. Moreover the system keeps track of the number of animals detected by analyzing the images taken with the UAVs cameras. Several tests have been performed to evaluate this system and preliminary results and the conclusions are presented in this paper.


Unsupervised Anomaly Detection in Sequences Using Long Short Term Memory Recurrent Neural Networks


title: Crosswalks, metadata harvesting, federated searching, metasearching: using metadata to connect users and information

abstract: Since the turn of the millennium, instantaneous access to a wide variety of content via the Web has ceased to be considered “bleeding-edge technology” and instead has become expected. In fact, from 2000 to the time of this writing, there has been continued exponential growth in the number of digital projects providing online access to a range of information resources: Web pages, full-text articles and books, cultural heritage resources (including images of works of art, architecture, and material culture), and other intellectual content, including born digital objects. Users increasingly expect that the Web will serve as a portal to the entire universe of knowledge. Recently, Google Scholar, Yahoo! and OCLC’s WorldCat (a union catalog of the holdings of national and international libraries) have joined forces to direct users to the closest library that owns the book they are seeking, whether it is available in print or online or both.1 Global access to the universe of traditional print materials and digital resources has become more than ever the goal of many institutions that create and/or manage digital resources. Unfortunately, there are still no magic programming scripts that can create seamless access to the right information in the right context so that it can be efficiently retrieved and understood. At this point, most institutions (including governments, libraries, archives, museums, and commercial enterprises) have moved from in-house manual systems to automated systems in order to provide the most efficient means to control


title: Redefining “Learning” in Statistical Learning: What Does an Online Measure Reveal About the Assimilation of Visual Regularities?

abstract: From a theoretical perspective, most discussions of statistical learning (SL) have focused on the possible "statistical" properties that are the object of learning. Much less attention has been given to defining what "learning" is in the context of "statistical learning." One major difficulty is that SL research has been monitoring participants' performance in laboratory settings with a strikingly narrow set of tasks, where learning is typically assessed offline, through a set of two-alternative-forced-choice questions, which follow a brief visual or auditory familiarization stream. Is that all there is to characterizing SL abilities? Here we adopt a novel perspective for investigating the processing of regularities in the visual modality. By tracking online performance in a self-paced SL paradigm, we focus on the trajectory of learning. In a set of three experiments we show that this paradigm provides a reliable and valid signature of SL performance, and it offers important insights for understanding how statistical regularities are perceived and assimilated in the visual modality. This demonstrates the promise of integrating different operational measures to our theory of SL.


title: Horizon 2020 and Beyond: On the 5G Operating System for a True Digital Society

abstract: The advanced fifth-generation (5G) infrastructure will not only be a sheer evolution of the current network generations but, more significantly, a revolution in the information and communication technology (ICT) field. The 5G technology will efficiently enable new secure, dependable, ultrareliable, and delay-critical services to everyone and everything, such as cognitive objects and cyberphysical systems (CPSs). A fully immersive experience and anything as a service are the primary drivers for a global adoption and market uptake of new technology components, beyond today's client-server model, where the network has been reduced to a mere pipe of bits. The network will become the nervous system of the true digital society and economy. This article gives fundamental insight into how the 5G mobile communications system is being designed to be powerful and, especially, flexible enough, thus meeting the foreseen and unknown traffic scenarios and services requirements. We also present how a massive adoption and exploitation of mobile-edge computing (MEC), software-defined networking (SDN), network functions virtualization (NFV), and services virtualization will make the 5G operating system (OS) feasible and business viable.


title: A cohort study relating urban green space with mortality in Ontario, Canada☆

abstract: Abstract Parks and green space areas are important to human health for psychological and physiological reasons. There have been few evaluations of access to green space on mortality. This paper describes a cohort study of approximately 575,000 adults, 35 years of age and older, who resided in 10 urban areas in Ontario, Canada, between 1982 and 1986. Individuals were identified from income tax filings, and vital status was determined up to December 31, 2004 through record linkage to the Canadian Mortality Data Base. Place of residence was defined by postal code data that were extracted from income tax filings. Urban green space was defined by Landsat satellite retrievals with the Normalized Difference Vegetation Index and this was assigned to individuals' place of residence at inception into the cohort using both a 30 m grid cell and a 500 m buffer. The proportional hazards model was used to estimate rate ratios (RRs) and their corresponding 95% confidence intervals (CI) for selected underlying causes of death. The rate ratios were adjusted for income, marital status, ambient air pollution, and contextual neighborhood characteristics. About 187,000 subjects died during follow-up. An increase in the interquartile range of green space, using a 500 m buffer, was associated with reduced non-accidental mortality (RR=0.95, 95% CI=0.94–0.96). Reductions in mortality with increased residential green space were observed for each underlying cause of death; the strongest association was found for respiratory disease mortality (RR=0.91, 95% CI=0.89–0.93). Risk estimates were essentially unchanged after adjusting for ambient air pollution. Our study suggests that green space in urban environments was associated with long-term reduction in mortality although this finding should be interpreted cautiously as this association may be influenced by residual confounding of sociodemographic and lifestyle factors. Further research is needed to: confirm these findings, better understand the relationships between access to green space and behavioral risk factors for mortality, and identify what green space characteristics may confer the greatest health benefit.


title: Self-supervised learning model for skin cancer diagnosis

abstract: Automated diagnosis of skin cancer is an active area of research with different classification methods proposed so far. However, classification models based on insufficient labeled training data can badly influence the diagnosis process if there is no self-advising and semi supervising capability in the model. This paper presents a semi supervised, self-advised learning model for automated recognition of melanoma using dermoscopic images. Deep belief architecture is constructed using labeled data together with unlabeled data, and fine tuning done by an exponential loss function in order to maximize separation of labeled data. In parallel a self-advised SVM algorithm is used to enhance classification results by counteracting the effect of misclassified data. To increase generalization capability and redundancy of the model, polynomial and radial basis function based SA-SVMs and Deep network are trained using training samples randomly chosen via a bootstrap technique. Then the results are aggregated using least square estimation weighting. The proposed model is tested on a collection of 100 dermoscopic images. The variation in classification error is analyzed with respect to the ratio of labeled and unlabeled data used in the training phase. The classification performance is compared with some popular classification methods and the proposed model using the deep neural processing outperforms most of the popular techniques including KNN, ANN, SVM and semi supervised algorithms like Expectation maximization and transductive SVM.


title: Enigma: Decentralized Computation Platform with Guaranteed Privacy

abstract: A peer-to-peer network, enabling different parties to jointly store and run computations on data while keeping the data completely private. Enigma's computational model is based on a highly optimized version of secure multi-party computation, guaranteed by a verifiable secret-sharing scheme. For storage, we use a modified distributed hashtable for holding secret-shared data. An external blockchain is utilized as the controller of the network, manages access control, identities and serves as a tamper-proof log of events. Security deposits and fees incentivize operation, correctness and fairness of the system. Similar to Bitcoin, Enigma removes the need for a trusted third party, enabling autonomous control of personal data. For the first time, users are able to share their data with cryptographic guarantees regarding their privacy.


title: Social network user influence sense-making and dynamics prediction

abstract: Identifying influential users and predicting their ‘‘network impact’’ on social networks have attracted tremendous interest from both academia and industry. Various definitions of ‘‘influence’’ and many methods for calculating influence scores have been provided for different empirical purposes and they often lack the in-depth analysis of the ‘‘characteristics’’ of the output influence. In addition, most of the developed algorithms and tools are mainly dependent on the static network structure instead of the dynamic diffusion process over the network, and are thus essentially based on descriptive models instead of predictive models. Consequently, very few existing works consider the dynamic propagation of influence in continuous time due to infinite steps for simulation. In this paper, we provide an evaluation framework to systematically measure the ‘‘characteristics’’ of the influence from the following three dimensions: (i) Monomorphism vs. Polymorphism; (ii) High Latency vs. Low Latency; and (iii) Information Inventor vs. Information Spreader. We propose a dynamic information propagation model based on Continuous-Time Markov Process to predict the influence dynamics of social network users, where the nodes in the propagation sequences are the users, and the edges connect users who refer to the same topic contiguously on time. Finally we present a comprehensive empirical study on a large-scale twitter dataset to compare the influence metrics within our proposed evaluation framework. Experimental results validate our ideas and demonstrate the prediction performance of our proposed algorithms.


title: Functional specialization for semantic and phonological processing in the left inferior prefrontal cortex

abstract: Abstract Neuroimaging and neuropsychological studies have implicated left inferior prefrontal cortex (LIPC) in both semantic and phonological processing. In this study, functional magnetic resonance imaging was used to examine whether separate LIPC regions participate in each of these types of processing. Performance of a semantic decision task resulted in extensive LIPC activation compared to a perceptual control task. Phonological processing of words and pseudowords in a syllable-counting task resulted in activation of the dorsal aspect of the left inferior frontal gyrus near the inferior frontal sulcus (BA 44/45) compared to a perceptual control task, with greater activation for nonwords compared to words. In a direct comparison of semantic and phonological tasks, semantic processing preferentially activated the ventral aspect of the left inferior frontal gyrus (BA 47/45). A review of the literature demonstrated a similar distinction between left prefrontal regions involved in semantic processing and phonological/lexical processing. The results suggest that a distinct region in the left inferior frontal cortex is involved in semantic processing, whereas other regions may subserve phonological processes engaged during both semantic and phonological tasks.


Reddit: A Gold Mine for Personality Prediction


Computer Games as a Part of Children ' s Culture


title: Modelling structure and predicting dynamics of discussion threads in online boards

abstract: Internet boards are platforms for online discussion about a variety of topics. On these boards, individuals may start a new thread on a specific matter or leave comments in an existing discussion. The resulting collective process leads to the formation of ‘discussion trees’, where nodes represent a post and comments, and an edge represents a ‘reply-to’ relation. The structure of discussion trees has been analysed in previous works, but mainly from a static perspective. In this article, we focus on the structural and dynamical properties of discussion trees by modelling their formation as a self-exciting Hawkes process with a branching structure. We first study a Reddit data set of all posts and comments submitted between 2008 and 2014 to show that the structure of the trees resemble those produced by a Galton–Watson process with a special root offspring distribution. The dynamical aspect of the model is then used to predict future commenting activity and the final size of a discussion tree. We compare the efficiency of our approach with previous works and show its superiority for the prediction of discussion dynamics. In particular, we find that the Reddit discussion trees, being fundamentally broader than for example the Twitter cascades and developing with a qualitatively different dynamics, require appropriate models. The tree structure and the dynamics are influenced mainly by the age of the discussion, rather than by the number of acquired comments. The post and the comments are further commented by different processes, which is a key ingredient in modelling of discussions.


title: Skin Color Region Detection in MPEG Video Sequences

abstract: Human faces provide a useful cue in indexing video content. We present a highly efficient system that can rapidly detect human face regions in MPEG video sequences. The underlying algorithm takes the inverse quantized discrete cosine transform (DCT) coefficients of MPEG video as the input, and outputs the locations of the detected face regions. The algorithm consists of three stages, where chrominance, shape, and frequency information are used, respectively. By detecting faces directly in the compressed domain, there is no need to carry out the inverse DCT transform, so that the algorithm can run faster than the real time. In our experiments, the algorithm detected 85-92% of the faces in three test sets, including both intraframe and interframe coded image frames from news video. The average run time ranges from 13-33 ms per frame. The algorithm can be applied to JPEG unconstrained images or motion JPEG video as well.


3 D Object Detection with a Deformable 3 D Cuboid Model


title: Discrete Gabor transform

abstract: A feasible algorithm for implementing the Gabor expansion, the coefficients of which are computed by the discrete Gabor transform (DGT), is presented. For a given synthesis window and sampling pattern, computing the auxiliary biorthogonal function of the DGT is nothing more than solving a linear system. The DGT presented applies for both finite as well as infinite sequences. By exploiting the nonuniqueness of the auxiliary biorthogonal function at oversampling an orthogonal like DGT is obtained. As the discrete Fourier transform (DFT) is a discrete realization of the continuous-time Fourier transform, similarly, the DGT introduced provides a feasible vehicle to implement the useful Gabor expansion. >


title: Adolescence , pornography and harm

abstract: This paper examines the many factors that underpin pornography exposure, and stresses how the risk factors for exposure and problematic sexual behaviours intersect to contribute to harm. An understanding of the complex interplay of factors such as gender, age, attitude, personal characteristics and social context of use is important in the development of strategies that will assist young people to avoid any potential adverse outcomes. The available evidence remains highly incomplete, and its interpretation is highly contested, so the paper highlights the need for longitudinal studies of use and of actual behaviour, and for studies that focus on cultural contexts and emerging media.


title: Enhanced Sentiment Learning Using Twitter Hashtags and Smileys

abstract: Automated identification of diverse sentiment types can be beneficial for many NLP systems such as review summarization and public media analysis. In some of these systems there is an option of assigning a sentiment value to a single sentence or a very short text. ::: ::: In this paper we propose a supervised sentiment classification framework which is based on data from Twitter, a popular microblogging service. By utilizing 50 Twitter tags and 15 smileys as sentiment labels, this framework avoids the need for labor intensive manual annotation, allowing identification and classification of diverse sentiment types of short texts. We evaluate the contribution of different feature types for sentiment classification and show that our framework successfully identifies sentiment types of untagged sentences. The quality of the sentiment identification was also confirmed by human judges. We also explore dependencies and overlap between different sentiment types represented by smileys and Twitter hashtags.


title: Sensing-Throughput Tradeoff for Cognitive Radio Networks

abstract: In cognitive radio networks, the secondary network (users) are allowed to utilize the frequency bands of primary network (users) when they are not currently being used. To support this function, the secondary users are required to sense the radio frequency environment, and once the primary user is found to be active, the secondary users have to vacate the channel within certain amount of time. There are two parameters related to channel sensing: probability of detection and probability of false alarm. The higher the detection probability, the better the primary users can be protected. However, from the secondary users' perspective, the lower the false alarm probability, the more chances the channel can be reused, thus the higher the achievable throughput for the secondary users. In this paper, we study the fundamental tradeoff between sensing capability and achievable throughput of the secondary users. Particularly, we study the design of sensing slot duration to maximize the achievable throughput for the secondary users under the constraint that the primary users are sufficiently protected. Using energy detection scheme, we prove that there indeed exists an optimal sensing time which provides the best tradeoff. Cooperative sensing is also studied based on the methodology of the proposed sensing-throughput tradeoff. Computer simulations are presented to evaluate the proposed tradeoff methodology.


title: An evaluation of multi-resolution storage for sensor networks

abstract: Wireless sensor networks enable dense sensing of the environment, offering unprecedented opportunities for observing the physical world. Centralized data collection and analysis adversely impact sensor node lifetime. Previous sensor network research has, therefore, focused on in network aggregation and query processing, but has done so for applications where the features of interest are known a priori. When features are not known a priori, as is the case with many scientific applications in dense sensor arrays, efficient support for multi-resolution storage and iterative, drill-down queries is essential.Our system demonstrates the use of in-network wavelet-based summarization and progressive aging of summaries in support of long-term querying in storage and communication-constrained networks. We evaluate the performance of our linux implementation and show that it achieves: (a) low communication overhead for multi-resolution summarization, (b) highly efficient drill-down search over such summaries, and (c) efficient use of network storage capacity through load-balancing and progressive aging of summaries.


title: Dermoscopy of cutaneous lymphangioma circumscriptum

abstract: Lymphangiomas are congenital lymphatic malformations. They are clinically characterized by clusters of translucent vesicles, and on dermoscopy, yellow lacunae surrounded by pale septa as well as reddish to bluish lacunae have been described. A young male presented with a seven-year history of a vesicular lesion. Dermoscopy revealed multiple white-yellowish well-circumscribed roundish areas (lacunae) surrounded by pale septa. A few lacunae contained blood, which was characteristically accumulated in the lowest part of the lacuna, resulting in an appearance similar to the so-called "hypopyon" of the eye. We suggest a new "dermatologic" metaphoric term to desrcibe this peculiar feature (half-and-half lacuna).


title: Robot Control Through Brain Computer Interface For Patterns Generation

abstract: A Brain Computer Interface (BCI) system processes and translates neuronal signals, that mainly comes from EEG instruments, into commands for controlling electronic devices. This system can allow people with motor disabilities to control external devices through the real‐time modulation of their brain waves. In this context an EEG‐based BCI system that allows creative luminous artistic representations is here presented. The system that has been designed and realized in our laboratory interfaces the BCI2000 platform performing real‐time analysis of EEG signals with a couple of moving luminescent twin robots. Experiments are also presented.


title: Automated Categorization in the International Patent Classification C. J. Fall *

abstract: A new reference collection of patent documents for training and testing automated categorization systems is established and described in detail. This collection is tailored for automating the attribution of international patent classification codes to patent applications and is made publicly available for future research work. We report the results of applying a variety of machine learning algorithms to the automated categorization of English-language patent documents. This procedure involves a complex hierarchical taxonomy, within which we classify documents into 114 classes and 451 subclasses. Several measures of categorization success are described and evaluated. We investigate how best to resolve the training problems related to the attribution of multiple classification codes to each patent document.


title: Analysis and Design of the Classical CMOS Schmitt Trigger in Subthreshold Operation

abstract: In this paper, the classical CMOS Schmitt trigger (ST) operating in the subthreshold regime is analyzed. The complete DC voltage transfer characteristic of the CMOS ST is determined. The metastable segment of the characteristic is explained in terms of the negative resistance of the NMOS and PMOS subcircuits of the ST. Small-signal analysis is carried out to determine the minimum supply voltage at which the hysteresis appears and to obtain a rough estimation of the hysteresis width. It is shown that the theoretical minimum supply voltage required to obtain hysteresis is 2ln( $2+\surd 5$ )kT/q = 75 mV at room temperature. A test chip with CMOS Schmitt triggers was designed and fabricated in a 180 nm technology in order to study their operation at supply voltages between 50 mV and 1000 mV.


title: Participants' perspective on maintaining behaviour change: a qualitative study within the European Diabetes Prevention Study

abstract: BackgroundThe European Diabetes Prevention Study (EDIPS) is an RCT of diet and exercise interventions in people with impaired glucose tolerance. We undertook a qualitative study, nested within the EDIPS in Newcastle-upon-Tyne, UK, aiming to understand the experience of participants who maintained behaviour change, in order to inform future interventions.MethodsParticipants were purposively sampled, according to success criteria for diet and physical activity change maintenance, and invited to attend individual semi-structured interviews. Fifteen participants completed an interview and reflected on their experience over three to five years. We used the Framework method to analyse the transcribed data.ResultsMain themes were identified as factors that help (props) and those that hinder (burdens) behaviour change maintenance at different organisational levels: individual (both physical and psychological), social and environmental. Pre-existing physical conditions (such as arthritis) and social demands (such as caring for an ageing relative) hindered, whereas the benefits of becoming fitter and of having social and professional support helped, participants in maintaining behaviour change. Participants' long term experiences highlighted the salience of the continuous change in their physical, social and environmental conditions over time.ConclusionThe construct of props and burdens facilitates a holistic view of participants' behaviour. Efforts to encourage behaviour change maintenance should take account of context and the way this changes over time, and should include strategies to address these issues. The experience of participants who maintain behaviour change highlights the challenges for the wider implementation of diabetes prevention strategies.Trial Registration(ISRCTN 15670600)


title: Compact Hybrid-Integrated 100-Gb/s TOSA Using EADFB Laser Array and AWG Multiplexer

abstract: We have developed a compact hybrid-integrated four-lane $\times 25.8$ Gb/s transmitter optical sub-assembly (TOSA) for a 100 Gb/s transceiver for 40-km transmission. The TOSA has a simple configuration in which a four-channel electroabsorption modulator integrated distributed feedback (EADFB) laser array is directly attached to the input waveguide end-faces of a silica-based arrayed waveguide grating (AWG) multiplexer. We integrated a lateral tapered spot-size converter for the EADFB laser to achieve a high optical butt coupling efficiency between the EADFB lasers array and the AWG multiplexer. We confirmed that the TOSA, which is $19.9~\text {mm} \,\times \, 6~\text {mm} \,\times \, 5.8$ mm, achieved error-free operation for a 40-km transmission with a high average optical output power of over 0.3 dBm at an operating temperature of 55 °C.


title: Rudiments of rough sets

abstract: Abstract Worldwide, there has been a rapid growth in interest in rough set theory and its applications in recent years. Evidence of this can be found in the increasing number of high-quality articles on rough sets and related topics that have been published in a variety of international journals, symposia, workshops, and international conferences in recent years. In addition, many international workshops and conferences have included special sessions on the theory and applications of rough sets in their programs. Rough set theory has led to many interesting applications and extensions. It seems that the rough set approach is fundamentally important in artificial intelligence and cognitive sciences, especially in research areas such as machine learning, intelligent systems, inductive reasoning, pattern recognition, mereology, knowledge discovery, decision analysis, and expert systems. In the article, we present the basic concepts of rough set theory and point out some rough set-based research directions and applications.


title: Semantically Smooth Knowledge Graph Embedding

abstract: This paper considers the problem of embedding Knowledge Graphs (KGs) consisting of entities and relations into lowdimensional vector spaces. Most of the existing methods perform this task based solely on observed facts. The only requirement is that the learned embeddings should be compatible within each individual fact. In this paper, aiming at further discovering the intrinsic geometric structure of the embedding space, we propose Semantically Smooth Embedding (SSE). The key idea of SSE is to take full advantage of additional semantic information and enforce the embedding space to be semantically smooth, i.e., entities belonging to the same semantic category will lie close to each other in the embedding space. Two manifold learning algorithms Laplacian Eigenmaps and Locally Linear Embedding are used to model the smoothness assumption. Both are formulated as geometrically based regularization terms to constrain the embedding task. We empirically evaluate SSE in two benchmark tasks of link prediction and triple classification, and achieve significant and consistent improvements over state-of-the-art methods. Furthermore, SSE is a general framework. The smoothness assumption can be imposed to a wide variety of embedding models, and it can also be constructed using other information besides entities’ semantic categories.


title: Low cost 60 GHz millimeter-wave microstrip patch antenna array using low-loss planar feeding scheme

abstract: A novel low cost feeding mechanism based upon new synthesized planar waveguide substrate integrated waveguide (SIW), is presented for microstrip antenna arrays (MPAs) at millimeter-wave (mmW) band to avoid feeding loss, specially the conduction loss. As an example, SIW-fed two dimensional (2D) 2×2 MPA array @ 60 GHz is introduced. The presented antenna is simulated by HFSS to validate its performance for mmW applications. The simulated results show a broadside gain of 11.71 dB within an operating impedance bandwidth of 500 MHz around a center frequency of 59.95 GHz. Also, the simulated radiation efficiency of the developed antenna is better than 85%, which is higher than that of conventional mmW microstrip antenna array.


title: DocuBurst: Visualizing Document Content using Language Structure

abstract: Textual data is at the forefront of information management problems today. One response has been the development of visualizations of text data. These visualizations, commonly based on simple attributes such as relative word frequency, have become increasingly popular tools. We extend this direction, presenting the first visualization of document content which combines word frequency with the human-created structure in lexical databases to create a visualization that also reflects semantic content. DocuBurst is a radial, space-filling layout of hyponymy (the IS-A relation), overlaid with occurrence counts of words in a document of interest to provide visual summaries at varying levels of granularity. Interactive document analysis is supported with geometric and semantic zoom, selectable focus on individual words, and linked access to source text.


title: Semi-supervised Learning on Graphs with Generative Adversarial Nets

abstract: We investigate how generative adversarial nets (GANs) can help semi-supervised learning on graphs. We first provide insights on working principles of adversarial learning over graphs and then present GraphSGAN, a novel approach to semi-supervised learning on graphs. In GraphSGAN, generator and classifier networks play a novel competitive game. At equilibrium, generator generates fake samples in low-density areas between subgraphs. In order to discriminate fake samples from the real, classifier implicitly takes the density property of subgraph into consideration. An efficient adversarial learning algorithm has been developed to improve traditional normalized graph Laplacian regularization with a theoretical guarantee. Experimental results on several different genres of datasets show that the proposed GraphSGAN significantly outperforms several state-of-the-art methods. GraphSGAN can be also trained using mini-batch, thus enjoys the scalability advantage.


title: Temporally Coherent Video Harmonization Using Adversarial Networks

abstract: Compositing is one of the most important editing operations for images and videos. The process of improving the realism of composite results is often called harmonization. Previous approaches for harmonization mainly focus on images. In this paper, we take one step further to attack the problem of video harmonization. Specifically, we train a convolutional neural network in an adversarial way, exploiting a pixel-wise disharmony discriminator to achieve more realistic harmonized results and introducing a temporal loss to increase temporal consistency between consecutive harmonized frames. Thanks to the pixel-wise disharmony discriminator, we are also able to relieve the need of input foreground masks. Since existing video datasets which have ground-truth foreground masks and optical flows are not sufficiently large, we propose a simple yet efficient method to build up a synthetic dataset supporting supervised training of the proposed adversarial network. The experiments show that training on our synthetic dataset generalizes well to the real-world composite dataset. In addition, our method successfully incorporates temporal consistency during training and achieves more harmonious visual results than previous methods.


title: Deep learning in biomedicine

abstract: Deep learning is beginning to impact biological research and biomedical applications as a result of its ability to integrate vast datasets, learn arbitrarily complex relationships and incorporate existing knowledge. Already, deep learning models can predict, with varying degrees of success, how genetic variation alters cellular processes involved in pathogenesis, which small molecules will modulate the activity of therapeutically relevant proteins, and whether radiographic images are indicative of disease. However, the flexibility of deep learning creates new challenges in guaranteeing the performance of deployed systems and in establishing trust with stakeholders, clinicians and regulators, who require a rationale for decision making. We argue that these challenges will be overcome using the same flexibility that created them; for example, by training deep models so that they can output a rationale for their predictions. Significant research in this direction will be needed to realize the full potential of deep learning in biomedicine.


title: Improving Agility and Elasticity in Bare-metal Clouds

abstract: Bare-metal clouds are an emerging infrastructure-as-a-service (IaaS) that leases physical machines (bare-metal instances) rather than virtual machines, allowing resource-intensive applications to have exclusive access to physical hardware. Unfortunately, bare-metal instances require time-consuming or OS-specific tasks for deployment due to the lack of virtualization layers, thereby sacrificing several beneficial features of traditional IaaS clouds such as agility, elasticity, and OS transparency. We present BMcast, an OS deployment system with a special-purpose de-virtualizable virtual machine monitor (VMM) that supports quick and OS-transparent startup of bare-metal instances. BMcast performs streaming OS deployment while allowing direct access to physical hardware from the guest OS, and then disappears after completing the deployment. Quick startup of instances improves agility and elasticity significantly, and OS transparency greatly simplifies management tasks for cloud customers. Experimental results have confirmed that BMcast initiated a bare-metal instance 8.6 times faster than image copying, and database performance on BMcast during streaming OS deployment was comparable to that on a state-of-the-art VMM without performing deployment. BMcast incurred zero overhead after de-virtualization.


title: Reasoning about Meaning in Natural Language with Compact Closed Categories and Frobenius Algebras

abstract: Compact closed categories have found applications in modeling quantum information protocols by Abramsky-Coecke. They also provide semantics for Lambek's pregroup algebras, applied to formalizing the grammatical structure of natural language, and are implicit in a distributional model of word meaning based on vector spaces. Specifically, in previous work Coecke-Clark-Sadrzadeh used the product category of pregroups with vector spaces and provided a distributional model of meaning for sentences. We recast this theory in terms of strongly monoidal functors and advance it via Frobenius algebras over vector spaces. The former are used to formalize topological quantum field theories by Atiyah and Baez-Dolan, and the latter are used to model classical data in quantum protocols by Coecke-Pavlovic-Vicary. The Frobenius algebras enable us to work in a single space in which meanings of words, phrases, and sentences of any structure live. Hence we can compare meanings of different language constructs and enhance the applicability of the theory. We report on experimental results on a number of language tasks and verify the theoretical predictions.


title: A robotic and kinetic design for interactive architecture

abstract: Buildings with interactive systems using less energy, offering more occupant comfort, and featuring better space efficiency, flexibility and smartness has become a compelling challenge in our changing world. This paper introduces a robotic and kinetic design for interactive architecture relating to the design of smart and sustainable living environments. The kernel of interactive architecture is the integration of mechanics, electronics, and computers in a robotic building module that can sense and transform itself in response to change. A prototype of interactive architecture called Eco-Spirit is presented. Key functionalities of the Eco-Spirit system are identified with respect to five components: a mechanical vehicle, a motor system, an environmental awareness system, a photovoltaic solar module, and a control circuit system. Methods and techniques for developing such capabilities are described. Applications for using the Eco-Spirit system in support of interactive architecture are reviewed. The paper closes with a discussion of research issues, challenges, and future research directions.


title: Smokey: Ubiquitous smoking detection with commercial WiFi infrastructures

abstract: Even though indoor smoking ban is being put into practice in civilized countries, existing vision or sensor-based smoking detection methods cannot provide ubiquitous smoking detection. In this paper, we take the first attempt to build a ubiquitous passive smoking detection system, which leverages the patterns smoking leaves on WiFi signals to identify the smoking activity even in the non-line-of-sight and through-wall environments. We study the behaviors of smokers and leverage the common features to recognize the series of motions during smoking, avoiding the target-dependent training set to achieve the high accuracy. We design a foreground detection based motion acquisition method to extract the meaningful information from multiple noisy subcarriers even influenced by posture changes. Without requirements of target's compliance, we leverage the rhythmical patterns of smoking to reduce the detection false positives. We prototype Smokey with the commodity WiFi infrastructure and evaluate its performance in real environments. Experimental results show Smokey is accurate and robust in various scenarios.


title: An Approach of a Decision Support and Home Monitoring System for Patients with Neurological Disorders using Internet of Things Concepts

abstract: The Internet of Things and information and CommunicationTechnologies applied in development of health care systems have reached an evolutionary process. This paper presentsthe development of an integrated intelligent system for Parkinson's disease Screening.The Decision Support and Home Monitoring System aredesigned to assist and support physicians in diagnosis, home monitoring, medical treatment, medical prescriptions, rehabilitation and progress of his patients with Parkinson's disease. The system will be extended in future research for other Neurological Disorders.This paper has an interdisciplinary character and includes areas such as e-Health, Internet of Things, Information and Communication Technologyand Artificial Intelligence with their application in medical domain.


title: No (Privacy) News is Good News: An Analysis of New York Times and Guardian Privacy News from 2010–2016

abstract: Privacy news influences end-user attitudes and behaviors as well as product and policy development, and so is an important data source for understanding privacy perceptions. We provide a largescale text mining of privacy news, focusing on patterns in sentiment and keywords. This is a challenging task given the lack of a privacy news repository and a ground truth for sentiment. Using high-precision data sets from two popular news sources in the U. S. and U. K., the New York Times and the Guardian, we find negative privacy news is far more common than positive. In addition, in the NYT, privacy news is more prominently reported than many world events involving significant human suffering. Our analysis provides a rich snapshot of this driver of privacy perceptions and demonstrates that news facilitates the systematization of privacy knowledge.


title: Galaxy: a web-based genome analysis tool for experimentalists.

abstract: High-throughput data production has revolutionized molecular biology. However, massive increases in data generation capacity require analysis approaches that are more sophisticated, and often very computationally intensive. Thus, making sense of high-throughput data requires informatics support. Galaxy (http://galaxyproject.org) is a software system that provides this support through a framework that gives experimentalists simple interfaces to powerful tools, while automatically managing the computational details. Galaxy is distributed both as a publicly available Web service, which provides tools for the analysis of genomic, comparative genomic, and functional genomic data, or a downloadable package that can be deployed in individual laboratories. Either way, it allows experimentalists without informatics or programming expertise to perform complex large-scale analysis with just a Web browser.


title: Design and Optimization of Area-Constrained Wirelessly Powered CMOS UWB SoC for Localization Applications

abstract: This paper discusses the design of a batteryless wirelessly powered ultra-wideband (UWB) system-on-a-chip (SoC) tag for area-and-volume-constrained localization applications such as insect tracking. Key challenges for wirelessly powered operation at 10-m range include the design of high-sensitivity rectifiers and low-voltage high-efficiency UWB transmitters (TX). An antenna-rectifier co-design methodology is presented for sensitivity optimization under area constraints. A 300-nA power management unit (PMU) and low-voltage (0.8-V) UWB TX increases tag operating range by ensuring high rectifier sensitivity under loaded conditions and reducing required rectifier output voltage. The rectifier, PMU, and UWB TX are integrated in 65-nm CMOS, and the rectifier demonstrates state-of-the-art $-\hbox{30.7–dBm}$ sensitivity for 1-V output voltage with only 1.3 $\hbox{cm}^{2}$ antenna area, representing a 2.3 $\,\times $ improvement in sensitivity over previously published work, at 2.6 $\,\times $ higher frequency with 9 $\,\times $ smaller antenna area, translating into a 50% longer range at the same frequency. The 0.8-V UWB TX consumes 64 pJ/pulse at 28-MHz pulse repetition rate and achieves 2.4 GHz $-\hbox{10-dB}$ bandwidth. Wireless measurements demonstrate sub-10-cm range resolution at ranges exceeding 10 $~$ m. Tag measurements in typical office environments demonstrate 20-m-range RF-energy harvesting with 36-dBm effective-isotropic radiated power in the 2.4-GHz ISM band.


title: Design of a High-Efficiency and High-Power Inverted Doherty Amplifier

abstract: In this paper, we present a design method for a compact inverted Doherty power amplifier (IDPA), which has high-efficiency and high-power characteristics. An optimum load matching network and an additional offset line, after the matching network of the carrier amplifier, dynamically modulate the load impedance according to the input power drive, while the conventional Doherty power amplifier uses a quarter-wave line to do it. The operational principles and design guide are provided. For experimental verification, a 50-W Doherty amplifier was designed for the pi/4 differential quadrature phase-shift keying application at the 860-MHz band. The measured performance of the IDPA was compared with that of the balanced class-AB amplifier with the same output matching network. At an output power of 50 W, the IDPA performs with 3.16 dB better adjacent channel power ratio (-28 versus -24.84 dBc) and 6.15% higher power-added efficiency (59.02 versus 52.87%) than the class-AB amplifier does.


title: Coupled segmentation and similarity detection for architectural models

abstract: Recent shape retrieval and interactive modeling algorithms enable the re-use of existing models in many applications. However, most of those techniques require a pre-labeled model with some semantic information. We introduce a fully automatic approach to simultaneously segment and detect similarities within an existing 3D architectural model. Our framework approaches the segmentation problem as a weighted minimum set cover over an input triangle soup, and maximizes the repetition of similar segments to find a best set of unique component types and instances. The solution for this set-cover formulation starts with a search space reduction to eliminate unlikely combinations of triangles, and continues with a combinatorial optimization within each disjoint subspace that outputs the components and their types. We show the discovered components of a variety of architectural models obtained from public databases. We demonstrate experiments testing the robustness of our algorithm, in terms of threshold sensitivity, vertex displacement, and triangulation variations of the original model. In addition, we compare our components with those of competing approaches and evaluate our results against user-based segmentations. We have processed a database of 50 buildings, with various structures and over 200K polygons per building, with a segmentation time averaging up to 4 minutes.


title: NOMAD: Non-locking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion

abstract: We develop an efficient parallel distributed algorithm for matrix completion, named NOMAD (Non-locking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion). NOMAD is a decentralized algorithm with non-blocking communication between processors. One of the key features of NOMAD is that the ownership of a variable is asynchronously transferred between processors in a decentralized fashion. As a consequence it is a lock-free parallel algorithm. In spite of being asynchronous, the variable updates of NOMAD are serializable, that is, there is an equivalent update ordering in a serial implementation. NOMAD outperforms synchronous algorithms which require explicit bulk synchronization after every iteration: our extensive empirical evaluation shows that not only does our algorithm perform well in distributed setting on commodity hardware, but also outperforms state-of-the-art algorithms on a HPC cluster both in multi-core and distributed memory settings.


title: Focused, Aroused, but so Distractible: Temporal Perspectives on Multitasking and Communications

abstract: A common assumption in studies of interruptions is that one is focused in an activity and then distracted by other stimuli. We take the reverse perspective and examine whether one might first be in an attentional state that makes one susceptible to communications typically associated with distraction. We explore the confluence of multitasking and workplace communications from three temporal perspectives -- prior to an interaction, when tasks and communications are interleaved, and at the end of the day. Using logging techniques and experience sampling, we observed 32 employees in situ for five days. We found that certain attentional states lead people to be more susceptible to particular types of interaction. Rote work is followed by more Facebook or face-to-face interaction. Focused and aroused states are followed by more email. The more time in email and face-fo-face interaction, and the more total screen switches, the less productive people feel at the day's end. We present the notion of emotional homeostasis along with new directions for multitasking research.


title: The acoustic response of reacting sprays in cross flow models to forced excitations

abstract: Thermo-acoustic instabilities can occur in combustion systems due to unsteady heat release. The derivation of transfer functions is a method to describe the systems dynamics, which is applied to analyse and to control these instabilities. In this work, validated models for spray atomization, evaporation and combustion in OpenFOAM are used to investigate their response to acoustic forcing. Mono-frequency excitations are employed, with a frequency range from 100Hz to 1000Hz in 100 Hz intervals. The fuel considered in this study is hexadecane, for which a reduced kinetic mechanism is available. A primary atomization model, together with a secondary atomization model, is employed to simulate a spray in cross flow. The evaporation model is a zero-dimensional model developed for multicomponent droplets at high ambient pressures and temperatures. The combustion model comprehends a blend of the FGM (Flamelet Generated Manifold) and the SCRS (Simple Chemical Reaction System) models. The acoustic response of each model, in terms of its transfer function, is determined by applying forcing at the inlet boundary. The penetration and global SMD are monitored at 80 mm downstream from the injection point and forcing frequencies are clearly found in the response of both quantities. The rate at which the droplet diameter shrinks is tracked in the droplet vaporization, and a non-linear response is encountered due to its dependence on the relative velocity. Similarly, the heat release is used for 1-D premixed flames, showing forcing frequencies in the model response too.


title: VLSI Architecture Design of FM0/Manchester Codec With 100% Hardware Utilization Rate for DSRC-Based Sensor Nodes in ITS Applications

abstract: Dedicated short-range communication (DSRC) plays an important role in sensor networking for intelligent transportation system applications. How to achieve a higher hardware efficiency becomes an attractive issue to design each critical building bock in sensor node. DSRC standards usually adopt either FM0 code or Manchester code as a coding technique to enhance signal reliability. In this paper, a fully reused VLSI architecture of FM0/Manchester codec with a hardware utilization rate (HUR) of 100% is proposed for DSRC-based sensor node. It is based on the half-cycle processing model (HCPM). The HCPM includes three core techniques: 1) half-cycle logic partition; 2) reused-based retiming; and 3) Boolean function reshaping. The HCPM can improve the HUR of FM0/Manchester codec from 27.33% to 100% with the reduction of the transistor count from 86 to 66. A 100% HUR means every transistor is activated; therefore, a more power is consumed. With a design tradeoff between HUR and power consumption, this paper still presents a higher energy efficiency. This paper is realized in TSMC 0.18- $\mu \text{m}$ 1P6M CMOS technology. The silicon area of core circuit is $33\,\,\times \,\,120~\mu \text{m}^{2}$ . The experiment results demonstrate that this paper presents a competitive performance with 100% HUR compared with the existing works. With this paper, DSRC-based sensor nodes can present a 100% HUR FM0/Manchester codec, fully supporting DSRC standards of Europe, USA, and Japan.


title: Semiotic Engineering Methods for Scientific Research in HCI

abstract: Semiotic engineering was originally proposed as a semiotic approach to designing user interface languages. Over the years, with research done at the Department of Informatics of the Pontifical Catholic University of Rio de Janeiro, it evolved into a semiotic theory of human-computer interaction (HCI). It views HCI as computer-mediated communication between designers and users at interaction time. The system speaks for its designers in various types of conversations specified at design time. These conversations communicate the designers' understanding of who the users are, what they know the users want or need to do, in which preferred ways, and why. The designers' message to users includes even the interactive language in which users will have to communicate back with the system in order to achieve their specific goals. Hence, the process is, in fact, one of communication about communication, or metacommunication. Semiotic engineering has two methods to evaluate the quality of metacommunication in HCI: the semiotic inspection method (SIM) and the communicability evaluation method (CEM). Up to now, they have been mainly used and discussed in technical contexts, focusing on how to detect problems and how to improve the metacommunication of specific systems. In this book, Clarisse de Souza and Carla Leito discuss how SIM and CEM, which are both qualitative methods, can also be used in scientific contexts to generate new knowledge about HCI. The discussion goes into deep considerations about scientific methodology, calling the reader's attention to the essence of qualitative methods in research and the kinds of results they can produce. To illustrate their points, the authors present an extensive case study with a free open-source digital audio editor called Audacity. They show how the results obtained with a triangulation of SIM and CEM point at new research avenues not only for semiotic engineering and HCI but also for other areas of computer science such as software engineering and programming. Table of Contents: Introduction / Essence of Semiotic Engineering / Semiotic Engineering Methods / Case Study with Audacity / Lessons Learned with Semiotic Engineering Methods / The Near Future of Semiotic Engineering


title: A Framework for the Automated Analysis of Subcellular Patterns in Human Protein Atlas Images

abstract: The systematic study of subcellular location patterns is required to fully characterize the human proteome, as subcellular location provides critical context necessary for understanding a protein's function. The analysis of tens of thousands of expressed proteins for the many cell types and cellular conditions under which they may be found creates a need for automated subcellular pattern analysis. We therefore describe the application of automated methods, previously developed and validated by our laboratory on fluorescence micrographs of cultured cell lines, to analyze subcellular patterns in tissue images from the Human Protein Atlas. The Atlas currently contains images of over 3000 protein patterns in various human tissues obtained using immunohistochemistry. We chose a 16 protein subset from the Atlas that reflects the major classes of subcellular location. We then separated DNA and protein staining in the images, extracted various features from each image, and trained a support vector machine classifier to recognize the protein patterns. Our results show that our system can distinguish the patterns with 83% accuracy in 45 different tissues, and when only the most confident classifications are considered, this rises to 97%. These results are encouraging given that the tissues contain many different cell types organized in different manners, and that the Atlas images are of moderate resolution. The approach described is an important starting point for automatically assigning subcellular locations on a proteome-wide basis for collections of tissue images such as the Atlas.


title: Reference ranges for Doppler assessment of uterine artery resistance and pulsatility indices in dichorionic twin pregnancies

abstract: OBJECTIVES ::: To construct reference ranges of uterine artery Doppler parameters against gestation in twin pregnancies and to present charts of the pulsatility index (PI) and resistance index (RI) for clinical use. ::: ::: ::: METHODS ::: This was an observational study of the uterine circulation in 557 women with dichorionic twin pregnancies at 17-38 weeks' gestation. Uterine artery measurements were performed by color and pulsed Doppler imaging. The uterine artery PI and RI were calculated as a mean of both sides. Reference ranges were constructed by regression of each index on gestational age. For this purpose, a regression model with fractional polynomials was fitted to the data. Furthermore, the presence of uterine artery notching was recorded. ::: ::: ::: RESULTS ::: New reference curves and tables of percentiles of the uterine artery PI and RI are presented. The calculated indices both showed a significant decrease with gestational age. Compared with singleton values, mean twin PI values were lower during the whole course of pregnancy. The prevalence of bilateral notching was 4.6% at 17-19 weeks' gestation and 3.1% at 20-24 weeks. ::: ::: ::: CONCLUSIONS ::: Uterine artery Doppler indices in twin pregnancies are lower than in singleton pregnancies. Therefore, the use of uterine artery reference ranges adapted to twin gestations seems more appropriate for identifying low- and high-risk groups.


title: Heterogeneous Vehicular Networking: A Survey on Architecture, Challenges, and Solutions

abstract: With the rapid development of the Intelligent Transportation System (ITS), vehicular communication networks have been widely studied in recent years. Dedicated Short Range Communication (DSRC) can provide efficient real-time information exchange among vehicles without the need of pervasive roadside communication infrastructure. Although mobile cellular networks are capable of providing wide coverage for vehicular users, the requirements of services that require stringent real-time safety cannot always be guaranteed by cellular networks. Therefore, the Heterogeneous Vehicular NETwork (HetVNET), which integrates cellular networks with DSRC, is a potential solution for meeting the communication requirements of the ITS. Although there are a plethora of reported studies on either DSRC or cellular networks, joint research of these two areas is still at its infancy. This paper provides a comprehensive survey on recent wireless networks techniques applied to HetVNETs. Firstly, the requirements and use cases of safety and non-safety services are summarized and compared. Consequently, a HetVNET framework that utilizes a variety of wireless networking techniques is presented, followed by the descriptions of various applications for some typical scenarios. Building such HetVNETs requires a deep understanding of heterogeneity and its associated challenges. Thus, major challenges and solutions that are related to both the Medium Access Control (MAC) and network layers in HetVNETs are studied and discussed in detail. Finally, we outline open issues that help to identify new research directions in HetVNETs.


title: Study of IC aging on ring oscillator physical unclonable functions

abstract: Silicon Physical Unclonable Functions (PUF) are novel circuits that exploit the random variations that exist in CMOS manufacturing process to generate chip-unique random bits. As their applications are security based, it is highly desired to have PUF responses that are very stable against the reliability issues that exist in Integrated Circuits (IC). One of the major sources of unreliability in the technology nodes 90nm and below is device aging. Aging is primarily due to phenomena like Bias Temperature Instability (BTI) and Hot Carrier Injection (HCI). In this work, we study the effect of device aging on the stability of Ring Oscillator PUFs for different PUF circuit-level choices and operating conditions. We observe that most of the PUF's aging instability happens early in its lifetime. Due to the typical differential nature of PUF structures, stability does not change significantly with age. Further, a high correlation has been observed between instability that is caused due to aging and instability that is caused due to temperature. In various RO-PUF setups and operating conditions, we observe that around 4% of the PUF bits are prone to instability due to aging.


title: Developing Web Applications

abstract: One approach to developing service-oriented Web applications is to transform high-level business models to a composition language that implements business processes with Web services. Object-oriented analysis and design and UML-based diagrams are typically used in the software development process. In this paper, we propose using flow as a fundamental notion underlying understanding of activities in Web Services. We discuss the development of business processes through introduction of a conceptual model as a framework for design. We scrutinize current modeling used in transformation methodologies, and then introduce a flow-based conceptualization of services through case studies with a high-level business description.


title: Gathering evidence: use of visual security cues in web browsers

abstract: Web browsers support secure online transactions, and provide visual feedback mechanisms to inform the user about security. These mechanisms have had little evaluation to determine how easily they are noticed and how effectively they are used. This paper describes a preliminary study conducted to determine which elements are noted, which are ignored, and how easily they are found. We collected eyetracker data to study user's attention to browser security, and gathered additional subjective data through questionnaires. Our results demonstrated that while the lock icon is commonly viewed, its interactive capability is essentially ignored. We also found that certificate information is rarely used, and that people stop looking for security information after they have signed into a site. These initial results provide insights into how browser security cues might be improved.


title: A cephalometric study of the Class II correction effects of the Eureka Spring.

abstract: The effect of the Eureka Spring (ES) appliance was investigated on 37 consecutively treated, noncompliant patients with bilateral Class II malocclusions. Lateral cephalographs were taken at the start of orthodontic treatment (T1), at insertion of the ES (T2), and at removal of the ES (T3). The average treatment interval between T2 and T3 was four months. The Class II correction occurred almost entirely by dentoalveolar movement and was almost equally distributed between the maxillary and mandibular dentitions. The rate of molar correction was 0.7 mm/mo. There was no change in anterior face height, mandibular plane angle, palatal plane angle, or gonial angle with treatment. There was a 2 degrees change in the occlusal plane resulting from intrusion of the maxillary molar and the mandibular incisor. Based on the results in this sample, the ES appliance was very effective in correcting Class II malocclusions in noncompliant patients without increasing the vertical dimension.


title: A permanent magnet motor drive without a shaft sensor

abstract: A simple control for a permanent motor drive is described which provides a wide speed range without the use of a shaft sensor. Two line-to-line voltages and two stator currents are sensed and processed in analog form to produce the stator flux linkage space vector. The angle of this vector is then used in a microcontroller to produce the appropriate stator current command signals for the hysteresis current controller of the inverter so that near unity power factor can be achieved over a wide range of torque and speed. A speed signal is derived from the rate of change of angle of the flux linkage. A drift compensation program is proposed to avoid calculation errors in the determination of angle position and speed. The control system has been implemented on a 5 kW motor using Nd-Fe-B magnets. The closed loop speed control has been shown to be effective down to a frequency of less than 1 Hz, thus providing a wide range of speed control. An open loop starting program is used to accelerate the motor up to this limit frequency with minimum speed oscillation. >


title: Transactional and Transformational Leadership Impacts on Organizational Learning

abstract: This study empirically relates two important areas of management research: the full-range theory of leadership and the organizational learning process. Specifically, this contribution addresses three issues: (1) the impact of transformational leadership and (2) of transactional leadership on the organizational learning process and (3) whether the influence of transformational leadership is stronger than of a transactional type of leadership. The results show that transformational leadership has a strong impact on all four constructs of organizational learning. A direct impact is evident only regarding information acquisition and behavioural and cognitive changes.


title: Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations

abstract: While there is currently a lot of enthusiasm about"big data", useful data is usually"small"and expensive to acquire. In this paper, we present a new paradigm of learning partial differential equations from {\em small} data. In particular, we introduce \emph{hidden physics models}, which are essentially data-efficient learning machines capable of leveraging the underlying laws of physics, expressed by time dependent and nonlinear partial differential equations, to extract patterns from high-dimensional data generated from experiments. The proposed methodology may be applied to the problem of learning, system identification, or data-driven discovery of partial differential equations. Our framework relies on Gaussian processes, a powerful tool for probabilistic inference over functions, that enables us to strike a balance between model complexity and data fitting. The effectiveness of the proposed approach is demonstrated through a variety of canonical problems, spanning a number of scientific domains, including the Navier-Stokes, Schr\"odinger, Kuramoto-Sivashinsky, and time dependent linear fractional equations. The methodology provides a promising new direction for harnessing the long-standing developments of classical methods in applied mathematics and mathematical physics to design learning machines with the ability to operate in complex domains without requiring large quantities of data.