title: Multisource Remote Sensing Data Classification Based on Convolutional Neural Network

abstract: As a list of remotely sensed data sources is available, how to efficiently exploit useful information from multisource data for better Earth observation becomes an interesting but challenging problem. In this paper, the classification fusion of hyperspectral imagery (HSI) and data from other multiple sensors, such as light detection and ranging (LiDAR) data, is investigated with the state-of-the-art deep learning, named the two-branch convolution neural network (CNN). More specific, a two-tunnel CNN framework is first developed to extract spectral-spatial features from HSI; besides, the CNN with cascade block is designed for feature extraction from LiDAR or high-resolution visual image. In the feature fusion stage, the spatial and spectral features of HSI are first integrated in a dual-tunnel branch, and then combined with other data features extracted from a cascade network. Experimental results based on several multisource data demonstrate the proposed two-branch CNN that can achieve more excellent classification performance than some existing methods.


title: Ensemble Sales Forecasting Study in Semiconductor Industry

abstract: Sales forecasting plays a prominent role in business planning and business strategy. The value and importance of advance information is a cornerstone of planning activity, and a well-set forecast goal can guide sale-force more efficiently. In this paper CPU sales forecasting of Intel Corporation, a multinational semiconductor industry, was considered. Past sale, future booking, exchange rates, Gross domestic product (GDP) forecasting, seasonality and other indicators were innovatively incorporated into the quantitative modeling. Benefit from the recent advances in computation power and software development, millions of models built upon multiple regressions, time series analysis, random forest and boosting tree were executed in parallel. The models with smaller validation errors were selected to form the ensemble model. To better capture the distinct characteristics, forecasting models were implemented at lead time and lines of business level. The moving windows validation process automatically selected the models which closely represent current market condition. The weekly cadence forecasting schema allowed the model to response effectively to market fluctuation. Generic variable importance analysis was also developed to increase the model interpretability. Rather than assuming fixed distribution, this non-parametric permutation variable importance analysis provided a general framework across methods to evaluate the variable importance. This variable importance framework can further extend to classification problem by modifying the mean absolute percentage error(MAPE) into misclassify error. Please find the demo code at : this https URL


title: Summarizing Microblogs Automatically

abstract: In this paper, we focus on a recent Web trend called microblogging, and in particular a site called Twitter. The content of such a site is an extraordinarily large number of small textual messages, posted by millions of users, at random or in response to perceived events or situations. We have developed an algorithm that takes a trending phrase or any phrase specified by a user, collects a large number of posts containing the phrase, and provides an automatically created summary of the posts related to the term. We present examples of summaries we produce along with initial evaluation.


title: Conception and Manufacturing of a Projectile-Drone Hybrid System

abstract: The French-German Research Institute of Saint Louis proposed an innovative concept of a microair vehicle (MAV). The gun-launched MAV (GLMAV) is a new and original type of discreet and robust miniature hybrid system of about 1 kg for a very quick observation mission by air. The GLMAV in a projectile configuration is launched by a portable dedicated tube weighing less than 10 kg. After a ballistic flight, the GLMAV is transformed into a miniature drone with automatic deployment of coaxial counter-rotating rotors. The GLMAV in drone configuration is slowed down to be able to execute hovering or/and maneuvering flights. In few seconds, aerial observation down and forward can begin in order to capture information, because it is equipped with an embarked vision system with real-time image transmission. This paper is first focused on the conception of the launcher and of the platform in order to incorporate all the features coming from multiple disciplines. This hybrid system conception is linked to an iterative process between all fields of research such as aerodynamics, interior and exterior ballistics, mechanics, electronics, optronics and signal transmission, guidance, navigation, and control. All theoretical studies presented in this paper have then converged toward the manufacturing of the dedicated ballistic launcher and of the GLMAV platform. The latter is now ready for the autopilot implementation in the near future.


title: Partial Transfer Entropy on Rank Vectors

abstract: For the evaluation of information flow in bivariate time series, information measures have been employed, such as the transfer entropy (TE), the symbolic transfer entropy (STE), defined similarly to TE but on the ranks of the components of the reconstructed vectors, and the transfer entropy on rank vectors (TERV), similar to STE but forming the ranks for the future samples of the response system with regard to the current reconstructed vector. Here we extend TERV for multivariate time series, and account for the presence of confounding variables, called partial transfer entropy on ranks (PTERV). We investigate the asymptotic properties of PTERV, and also partial STE (PSTE), construct parametric significance tests under approximations with Gaussian and gamma null distributions, and show that the parametric tests cannot achieve the power of the randomization test using time-shifted surrogates. Using simulations on known coupled dynamical systems and applying parametric and randomization significance tests, we show that PTERV performs better than PSTE but worse than the partial transfer entropy (PTE). However, PTERV, unlike PTE, is robust to the presence of drifts in the time series and it is also not affected by the level of detrending.


title: Deep Learning in Spiking Neural Networks

abstract: Abstract In recent years, deep learning has revolutionized the field of machine learning, for computer vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is trained, most often in a supervised manner using backpropagation. Vast amounts of labeled training examples are required, but the resulting classification accuracy is truly impressive, sometimes outperforming humans. Neurons in an ANN are characterized by a single, static, continuous-valued activation. Yet biological neurons use discrete spikes to compute and transmit information, and the spike times, in addition to the spike rates, matter. Spiking neural networks (SNNs) are thus more biologically realistic than ANNs, and are arguably the only viable option if one wants to understand how the brain computes at the neuronal description level. The spikes of biological neurons are sparse in time and space, and event-driven. Combined with bio-plausible local learning rules, this makes it easier to build low-power, neuromorphic hardware for SNNs. However, training deep SNNs remains a challenge. Spiking neurons’ transfer function is usually non-differentiable, which prevents using backpropagation. Here we review recent supervised and unsupervised methods to train deep SNNs, and compare them in terms of accuracy and computational cost. The emerging picture is that SNNs still lag behind ANNs in terms of accuracy, but the gap is decreasing, and can even vanish on some tasks, while SNNs typically require many fewer operations and are the better candidates to process spatio-temporal data.


title: Hybrid dermoscopy image classification framework based on deep convolutional neural network and Fisher vector

abstract: Dermoscopy image is usually used in early diagnosis of malignant melanoma. The diagnosis accuracy by visual inspection is highly relied on the dermatologist's clinical experience. Due to the inaccuracy, subjectivity, and poor reproducibility of human judgement, an automatic recognition algorithm of dermoscopy image is highly desired. In this work, we present a hybrid classification framework for dermoscopy image assessment by combining deep convolutional neural network (CNN), Fisher vector (FV) and support vector machine (SVM). Specifically, the deep representations of subimages at various locations of a rescaled dermoscopy image are first extracted via a natural image dataset pre-trained on CNN. Then we adopt an orderless visual statistics based FV encoding methods to aggregate these features to build more invariant representations. Finally, the FV encoded representations are classified for diagnosis using a linear SVM. Compared with traditional low-level visual features based recognition approaches, our scheme is simpler and requires no complex preprocessing. Furthermore, the orderless representations are less sensitive to geometric deformation. We evaluate our proposed method on the ISBI 2016 Skin lesion challenge dataset and promising results are obtained. Also, we achieve consistent improvement in accuracy even without fine-tuning the CNN.


title: Three-Port Triple-Half-Bridge Bidirectional Converter With Zero-Voltage Switching

abstract: A three-port triple-half-bridge bidirectional dc-dc converter topology is proposed in this paper. The topology comprises a high-frequency three-winding transformer and three half-bridges, one of which is a boost half-bridge interfacing a power port with a wide operating voltage. The three half-bridges are coupled by the transformer, thereby providing galvanic isolation for all the power ports. The converter is controlled by phase shift, which achieves the primary power flow control, in combination with pulsewidth modulation (PWM). Because of the particular structure of the boost half-bridge, voltage variations at the port can be compensated for by operating the boost half-bridge, together with the other two half-bridges, at an appropriate duty cycle to keep a constant voltage across the half-bridge. The resulting waveforms applied to the transformer windings are asymmetrical due to the automatic volt-seconds balancing of the half-bridges. With the PWM control it is possible to reduce the rms loss and to extend the zero-voltage switching operating range to the entire phase shift region. A fuel cell and supercapacitor generation system is presented as an embodiment of the proposed multiport topology. The theoretical considerations are verified by simulation and with experimental results from a 1 kW prototype.


title: A Hybrid Gripper With Soft Material and Rigid Structures

abstract: Various robotic grippers have been developed over the past several decades for robotic manipulators. Especially, the soft grippers based on the soft pneumatic actuator (SPA) have been studied actively, since it offers more pliable bending motion, inherent compliance, and a simple morphological structure. However, few studies have focused on simultaneously improving the fingertip force and actuation speed within the specified design parameters. In this study, we developed a hybrid gripper that incorporates both soft and rigid components to improve the fingertip force and actuation speed simultaneously based on three design principles: first, the degree of bending is proportional to the ratio of the rigid structure; second, a concave chamber design is preferred for large longitudinal strain; and third, a round shape between soft and rigid materials increases the fingertip force. The suggested principles were verified using the finite element methods. The improved performance of the hybrid gripper was verified experimentally and compared with the performance of a conventional SPAs. The ability of the hybrid gripper to grasp different objects was evaluated and was applied in a teleoperated system.


title: Low power 4-Bit Arithmetic Logic Unit using Full-Swing GDI technique

abstract: Power dissipation and area of the circuit are the main issues in the electronics industry, this paper provides a design of 4-Bit Arithmetic Logic Unit (ALU) using Full-Swing GDI Technique, which considered an effective method for low power digital design while reducing the area of the circuit compared to other logic styles. The proposed ALU design consists of 2×1 Multiplexer, 4×1 Multiplexer and low power Full Adder cell to realize the arithmetic and logic operations. The simulation carried out using Cadence Virtuoso using 65nm TSMC process. The results show that the proposed design consume less power using less number of transistors, while achieving full swing operation compared to previous work.


title: Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index

abstract: In this study, we attempt to model and predict the direction of return on market index of the Taiwan Stock Exchange, one of the fastest growing financial exchanges in developing Asian countries. Our motivation is based on the notion that trading strategies guided by forecasts of the direction of price movement may be more effective and lead to higher profits. The probabilistic neural network (PNN) is used to forecast the direction of index return after it is trained by historical data. Statistical performance of the PNN forecasts are measured and compared with that of the generalized methods of moments (GMM) with Kalman filter. Moreover, the forecasts are applied to various index trading strategies, of which the performances are compared with those generated by the buy-and-hold strategy as well as the investment strategies guided by forecasts estimated by the random walk model and the parametric GMM models. Empirical results show that the PNN-based investment strategies obtain higher returns than other investment strategies examined in this study. Influences of length of investment horizon and commission rate are also considered.


title: "Recommended For You": A First Look at Content Recommendation Networks

abstract: One advertising format that has grown significantly in recent years are known as Content Recommendation Networks (CRNs). CRNs are responsible for the widgets full of links that appear under headlines like "Recommended For You" and "Things You Might Like". Although CRNs have become quite popular with publishers, users complain about the low-quality of content promoted by CRNs, while regulators in the US and Europe have faulted CRNs for failing to label sponsored links as advertisements. In this study, we present a first look at five of the largest CRNs, including their footprint on the web, how their recommendations are labeled, and who their advertisers are. Our findings reveal that CRNs still fail to prominently disclose the paid nature of their sponsored content. This suggests that additional intervention is necessary to promote accepted best-practices in the nascent CRN marketplace, and ultimately protect online users.


Trust as a Commodity


title: Accurate electrical battery model capable of predicting runtime and I-V performance

abstract: Low power dissipation and maximum battery runtime are crucial in portable electronics. With accurate and efficient circuit and battery models in hand, circuit designers can predict and optimize battery runtime and circuit performance. In this paper, an accurate, intuitive, and comprehensive electrical battery model is proposed and implemented in a Cadence environment. This model accounts for all dynamic characteristics of the battery, from nonlinear open-circuit voltage, current-, temperature-, cycle number-, and storage time-dependent capacity to transient response. A simplified model neglecting the effects of self-discharge, cycle number, and temperature, which are nonconsequential in low-power Li-ion-supplied applications, is validated with experimental data on NiMH and polymer Li-ion batteries. Less than 0.4% runtime error and 30-mV maximum error voltage show that the proposed model predicts both the battery runtime and I-V performance accurately. The model can also be easily extended to other battery and power sourcing technologies.


title: Spectrum sensing measurement using GNU radio & USRP software radio platform

abstract: Spectrum utilization can be significantly improved by adopting cognitive radio (CR) technology. Such radios are able to sense the spectral environment and use this information to opportunistically provide wireless links that meet the user communications re quirements optimally. To achieve the goal of cognitive radio, it is a fundamental requirement that the cognitive user (CU) performs spectrum sensing to detect the presence of the primary user (PU) signal before a spectrum is accessed as to avoid harmful in terference. Therefore, two probabilities are of interest; the probability of detection, P d and the probability of false alarm, P fa . In this paper, we investigate sensing performance implemented on real -time testbed of GNU Radio and USRP Software Defined Radio (SDR) communi cation platform operating at 2.48 GHz with a bandwidth of 4 MHz. Energy detector utilizing 1024 FFT bin is the sensing mechanism used in the experimental set -up. The acquired experimental results of P d and P fa are duly analyzed and verifie d to be comparable to the curve of the theoretical framework for line -of-sight indoor environment. It is observed that at a target P fa of 5%, the optimal decision threshold for PU detection is -39 dB. The plot of measured number of samples needed for a des ired P d for various received signal levels, representing various signal -to-noise (SNR) conditions, is also included. At SNR of 0 dB and a target Quality of Service (QoS) set at P d of 90%, it is found out that the required sensing time for our GNU Radio USR P based CR system is equal to 31.59ms.


title: Indian Movie Face Database: A benchmark for face recognition under wide variations

abstract: Recognizing human faces in the wild is emerging as a critically important, and technically challenging computer vision problem. With a few notable exceptions, most previous works in the last several decades have focused on recognizing faces captured in a laboratory setting. However, with the introduction of databases such as LFW and Pubfigs, face recognition community is gradually shifting its focus on much more challenging unconstrained settings. Since its introduction, LFW verification benchmark is getting a lot of attention with various researchers contributing towards state-of-the-results. To further boost the unconstrained face recognition research, we introduce a more challenging Indian Movie Face Database (IMFDB) that has much more variability compared to LFW and Pubfigs. The database consists of 34512 faces of 100 known actors collected from approximately 103 Indian movies. Unlike LFW and Pubfigs which used face detectors to automatically detect the faces from the web collection, faces in IMFDB are detected manually from all the movies. Manual selection of faces from movies resulted in high degree of variability (in scale, pose, expression, illumination, age, occlusion, makeup) which one could ever see in natural world. IMFDB is the first face database that provides a detailed annotation in terms of age, pose, gender, expression, amount of occlusion, for each face which may help other face related applications.


title: Branch flow model: Relaxations and convexification

abstract: We propose a branch flow model for the analysis and optimization of mesh as well as radial networks. The model leads to a new approach to solving optimal power flow (OPF) problems that consists of two relaxation steps. The first step eliminates the voltage and current angles and the second step approximates the resulting problem by a conic program that can be solved efficiently. For radial networks, we prove that both relaxation steps are always exact, provided there are no upper bounds on loads. For mesh networks, the conic relaxation is always exact and we characterize when the angle relaxation may fail. We propose a simple method to convexify a mesh network using phase shifters so that both relaxation steps are always exact and OPF for the convexified network can always be solved efficiently for a globally optimal solution. We prove that convexification requires phase shifters only outside a spanning tree of the network graph and their placement depends only on network topology, not on power flows, generation, loads, or operating constraints. Since power networks are sparse, the number of required phase shifters may be relatively small.


Smart Grid — Safe , Secure , Self-Healing Challenges and Opportunities in Power System Security , Resiliency , and Privacy


title: MahNMF: Manhattan Non-negative Matrix Factorization

abstract: Non-negative matrix factorization (NMF) approximates a non-negative matrix $X$ by a product of two non-negative low-rank factor matrices $W$ and $H$. NMF and its extensions minimize either the Kullback-Leibler divergence or the Euclidean distance between $X$ and $W^T H$ to model the Poisson noise or the Gaussian noise. In practice, when the noise distribution is heavy tailed, they cannot perform well. This paper presents Manhattan NMF (MahNMF) which minimizes the Manhattan distance between $X$ and $W^T H$ for modeling the heavy tailed Laplacian noise. Similar to sparse and low-rank matrix decompositions, MahNMF robustly estimates the low-rank part and the sparse part of a non-negative matrix and thus performs effectively when data are contaminated by outliers. We extend MahNMF for various practical applications by developing box-constrained MahNMF, manifold regularized MahNMF, group sparse MahNMF, elastic net inducing MahNMF, and symmetric MahNMF. The major contribution of this paper lies in two fast optimization algorithms for MahNMF and its extensions: the rank-one residual iteration (RRI) method and Nesterov's smoothing method. In particular, by approximating the residual matrix by the outer product of one row of W and one row of $H$ in MahNMF, we develop an RRI method to iteratively update each variable of $W$ and $H$ in a closed form solution. Although RRI is efficient for small scale MahNMF and some of its extensions, it is neither scalable to large scale matrices nor flexible enough to optimize all MahNMF extensions. Since the objective functions of MahNMF and its extensions are neither convex nor smooth, we apply Nesterov's smoothing method to recursively optimize one factor matrix with another matrix fixed. By setting the smoothing parameter inversely proportional to the iteration number, we improve the approximation accuracy iteratively for both MahNMF and its extensions.


title: Digital Image Enhancement and Noise Filtering by Use of Local Statistics

abstract: Computational techniques involving contrast enhancement and noise filtering on two-dimensional image arrays are developed based on their local mean and variance. These algorithms are nonrecursive and do not require the use of any kind of transform. They share the same characteristics in that each pixel is processed independently. Consequently, this approach has an obvious advantage when used in real-time digital image processing applications and where a parallel processor can be used. For both the additive and multiplicative cases, the a priori mean and variance of each pixel is derived from its local mean and variance. Then, the minimum mean-square error estimator in its simplest form is applied to obtain the noise filtering algorithms. For multiplicative noise a statistical optimal linear approximation is made. Experimental results show that such an assumption yields a very effective filtering algorithm. Examples on images containing 256 × 256 pixels are given. Results show that in most cases the techniques developed in this paper are readily adaptable to real-time image processing.


title: On the Feasibility of Creating Double-Identity Fingerprints

abstract: A double-identity fingerprint is a fake fingerprint created by combining features from two different fingers, so that it has a high chance to be falsely matched with fingerprints from both fingers. This paper studies the feasibility of creating double-identity fingerprints by proposing two possible techniques and evaluating to what extent they may be used to fool the state-of-the-art fingerprint recognition systems. The results of systematic experiments suggest that existing algorithms are highly vulnerable to this specific attack (about 90% chance of success at FAR = 0.1%) and that the fingerprint patterns generated might be realistic enough to fool human examiners.


title: R.: Indoor segmentation and support inference from RGBD images. In: ECCV

abstract: We present an approach to interpret the major surfaces, objects, and support relations of an indoor scene from an RGBD image. Most existing work ignores physical interactions or is applied only to tidy rooms and hallways. Our goal is to parse typical, often messy, indoor scenes into floor, walls, supporting surfaces, and object regions, and to recover support relationships. One of our main interests is to better understand how 3D cues can best inform a structured 3D interpretation. We also contribute a novel integer programming formulation to infer physical support relations. We offer a new dataset of 1449 RGBD images, capturing 464 diverse indoor scenes, with detailed annotations. Our experiments demonstrate our ability to infer support relations in complex scenes and verify that our 3D scene cues and inferred support lead to better object segmentation.


Biopharmaceutics Classification System: A Regulatory Approach


title: Modified Basis Pursuit Denoising(modified-BPDN) for noisy compressive sensing with partially known support

abstract: In this work, we study the problem of reconstructing a sparse signal from a limited number of linear ‘incoherent’ noisy measurements, when a part of its support is known. The known part of the support may be available from prior knowledge or from the previous time instant (in applications requiring recursive reconstruction of a time sequence of sparse signals, e.g. dynamic MRI). We study a modification of Basis Pursuit Denoising (BPDN) and bound its reconstruction error. A key feature of our work is that the bounds that we obtain are computable. Hence, we are able to use Monte Carlo to study their average behavior as the size of the unknown support increases. We also demonstrate that when the unknown support size is small, modified-BPDN bounds are much tighter than those for BPDN, and hold under much weaker sufficient conditions (require fewer measurements).


title: Bidirectional LSTM Recurrent Neural Network for Keyphrase Extraction

abstract: To achieve state-of-the-art performance, keyphrase extraction systems rely on domain-specific knowledge and sophisticated features. In this paper, we propose a neural network architecture based on a Bidirectional Long Short-Term Memory Recurrent Neural Network that is able to detect the main topics on the input documents without the need of defining new hand-crafted features. A preliminary experimental evaluation on the well-known INSPEC dataset confirms the effectiveness of the proposed solution.


title: Link Prediction in Social Networks Using Markov Random Field

abstract: Link prediction is an important task for social networks analysis, which also has applications in other domains such as information retrieval, recommender systems and e-commerce. The task is related to predicting the probable connection between two nodes in the netwok. These links are subjected to loss because of the improper creation or the lack of reflection of links in the networks; so it`s possible to develop or complete these networks and recycle the lost items and information through link prediction. In order to discover and predict these links we need the information of the nodes in the network. The information are usually extracted from the network`s graph and utilized as factors for recognition. There exist a variety of techniques for link prediction, amongst them, the most practical and current one is supervised learning based approach. In this approach, the link prediction is considered as binary classifier that each pair of nodes can be 0 or 1. The value of 0 indicates no connection between nodes and 1 means that there is a connection between them. In this research, while studying probabilistic graphical models, we use Markov random field (MRF) for link prediction problem in social networks. Experimentl results on Flicker dataset showed the proposed method was better than previous methods in precision and recall.


Weakly supervised semantic segmentation using superpixel pooling network


Distributed Representations of Expertise.


ChemTok: A New Rule Based Tokenizer for Chemical Named Entity Recognition


Impact of a 6-week non-energy-restricted ketogenic diet on physical fitness, body composition and biochemical parameters in healthy adults


title: Semantic Class Learning from the Web with Hyponym Pattern Linkage Graphs

abstract: We present a novel approach to weakly supervised semantic class learning from the web, using a single powerful hyponym pattern combined with graph structures, which capture two properties associated with pattern-based extractions: popularity and productivity. Intuitively, a candidate is popular if it was discovered many times by other instances in the hyponym pattern. A candidate is productive if it frequently leads to the discovery of other instances. Together, these two measures capture not only frequency of occurrence, but also cross-checking that the candidate occurs both near the class name and near other class members. We developed two algorithms that begin with just a class name and one seed instance and then automatically generate a ranked list of new class instances. We conducted experiments on four semantic classes and consistently achieved high accuracies.


title: Underactuated Finger Mechanism Using Contractible Slider-Cranks and Stackable Four-Bar Linkages

abstract: This paper presents an underactuated finger mechanism that is able to conduct both self-adaptive grasping and natural motions, such as flexion and extension. The robotic finger is realized with a three degrees-of-freedom mechanism composed of stackable four-bar linkages and contractible slider-cranks having a linear spring in each mechanism layer. Kinematics and static force analysis are performed to reveal the operation principle of the proposed finger mechanism. The procedure for synthesizing the linkages is also suggested using Freudenstein's equation. Finally, both simulations and experiments are conducted to confirm the design feasibility.


title: Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn

abstract: Hyperopt-sklearn is a new software project that provides automatic algorithm configuration of the Scikit-learn machine learning library. Following Auto-Weka, we take the view that the choice of classifier and even the choice of preprocessing module can be taken together to represent a single large hyper- parameter optimization problem. We use Hyperopt to define a search space that encompasses many standard components (e.g. SVM, RF, KNN, PCA, TFIDF) and common patterns of composing them together. We demonstrate, using search algorithms in Hyperopt and standard benchmarking data sets (MNIST, 20-Newsgroups, Convex Shapes), that searching this space is practical and effective. In particular, we improve on best-known scores for the model space for both MNIST and Convex Shapes.


title: Customer Simulation for Direct Marketing Experiments

abstract: Optimization of control policies for corporate customer relationship management (CRM) systems can boost customer satisfaction, reduce attrition, and increase expected lifetime value of the customer base. However, evaluation of these policies is often complicated. Policies can be evaluated with real-life marketing interactions, but such evaluation can be prohibitively expensive and time consuming. Customer simulators learned from data are an inexpensive alternative suitable for rapid campaign tests. We summarize the literature on the evaluation of direct marketing policies through simulation and propose a decomposition of the problem into distinct tasks: (a) generation of the initial client database snapshot and (b) propagation of clients through time in response to company actions. We present open-source simulators trained and validated on two direct marketing data sets of varying size and complexity.


title: Factorization of Latent Variables in Distributional Semantic Models

abstract: This paper discusses the use of factorization techniques in distributional semantic models. We focus on a method for redistributing the weight of latent variables, which has previously been shown to improve the performance of distributional semantic models. However, this result has not been replicated and remains poorly understood. We refine the method, and provide additional theoretical justification, as well as empirical results that demonstrate the viability of the proposed approach.


title: a universal need for positive self-regard

abstract: It is assumed that people seek positive self-regard; that is, they are motivated to possess, enhance, and maintain positive self-views. The cross-cultural generalizability of such motivations was addressed by examining Japanese culture. Anthropological, sociological, and psychological analyses revealed that many elements of Japanese culture are incongruent with such motivations. Moreover, the empirical literature provides scant evidence for a need for positive self-regard among Japanese and indicates that a self-critical focus is more characteristic of Japanese. It is argued that the need for self-regard must be culturally variant because the constructions of self and regard themselves differ across cultures. The need for positive self-regard, as it is currently conceptualized, is not a universal, but rather is rooted in significant aspects of North American culture. Conventional interpretations of positive self-regard are too narrow to encompass the Japanese experience.


title: Prenatal Diagnosis of the Hemodynamics of Fetal Renal Disease by Color Doppler Ultrasound

abstract: Objective: Hemodynamic analysis of the fetal renal artery elucidated the function of the renal glomerulus and renal tubule in normal growth fetus and was weighed against fetal renal


title: Subnational Debt of China: The Politics-Finance Nexus

abstract: Using unique proprietary loan-level data, we provide the first comprehensive study on China’s local government debt, which was off-balance sheet. Policy and commercial banks, both of which are state owned, are the main financiers of local governments. We find that notwithstanding their prevalent nonprofit goal of raising social welfare, policy loans perform remarkably better than commercial loans. Distressed local governments would choose to default on commercial bank loans but avoid default on policy bank loans, which are strategically more important for local politicians’ promotions. Our findings suggest that career concerns of politicians can serve as a discipline on government borrowers.


title: Transition From Islanded to Grid-Connected Mode of Microgrids With Voltage-Based Droop Control

abstract: Microgrids are able to provide a coordinated integration of the increasing share of distributed generation (DG) units in the network. The primary control of the DG units is generally performed by droop-based control algorithms that avoid communication. The voltage-based droop (VBD) control is developed for islanded low-voltage microgrids with a high share of renewable energy sources. With VBD control, both dispatchable and less-dispatchable units will contribute in the power sharing and balancing. The priority for power changes is automatically set dependent on the terminal voltages. In this way, the renewables change their output power in more extreme voltage conditions compared to the dispatchable units, hence, only when necessary for the reliability of the network. This facilitates the integration of renewable units and improves the reliability of the network. This paper focusses on modifying the VBD control strategy to enable a smooth transition between the islanded and the grid-connected mode of the microgrid. The VBD control can operate in both modes. Therefore, for islanding, no specific measures are required. To reconnect the microgrid to the utility network, the modified VBD control synchronizes the voltage of a specified DG unit with the utility voltage. It is shown that this synchronization procedure significantly limits the switching transient and enables a smooth mode transfer.


title: Descending stairs locomotion and somatosensory control for an erect wheel-legged service robot

abstract: In this paper, an erect stair climbing robot, which has been able to ascend stairs, is further developed for descending stairs automatically. The somatosensory control functions are also implemented for a human rider to drive the robot on flat ground as a personal vehicle instinctively. In order to face front whether climbing up or down stairs, the robot is designed with proper mechanical structure and mass distribution. Moreover, the perception and control methods of descending stairs are modified from the methodologies of ascending stairs in our previous works so that the robot can handle both ascending and descending stairs effectively under similar principles. As for the somatosensory control part, the functions of controlling the robot to move back and forth using the rider's body motion is realized, which has more advantages than Segway PT since it can stand still when the rider wants the platform to stop moving, and the robot motion under the control of the human rider is stable enough for indoor applications. In the experiments, the stair-climbing abilities and somatosensory control functions are both successfully demonstrated, which proves that the design and implementation of the robot system are feasible and efficient. We are looking forward for more functions and applications of the robot, including our ultimate goal of carrying a human rider to climb stairs in an erect way with somatosensory control simultaneously.


title: Fault tolerant methods for reliability in FPGAs

abstract: Reliability and process variability are serious issues for FPGAs in the future. Fortunately FPGAs have the ability to reconfigure in the field and at runtime, thus providing opportunities to overcome some of these issues. This paper provides the first comprehensive survey of fault detection methods and fault tolerance schemes specifically for FPGAs, with the goal of laying a strong foundation for future research in this field. All methods and schemes are qualitatively compared and some particularly promising approaches highlighted.


title: A methodology for evaluating robustness of face recognition algorithms with respect to variations in pose angle and illumination angle

abstract: In this paper, we present a methodology for precisely comparing the robustness of face recognition algorithms with respect to changes in pose angle and illumination angle. For this study, we have chosen four widely-used algorithms: two subspace analysis methods (principle component analysis (PCA) and linear discriminant analysis (LDA)) and two probabilistic learning methods (hidden Markov models (HMM) and Bayesian intra-personal classifier (BIC)). We compare the recognition robustness of these algorithms using a novel database (FacePix) that captures face images with a wide range of pose angles and illumination angles. We propose a method for deriving a robustness measure for each of these algorithms, with respect to pose and illumination angle changes. The results of this comparison indicate that the subspace methods perform more robustly than the probabilistic learning methods in the presence of pose and illumination angle changes.


title: A Data-Driven Framework for Residential Electric Vehicle Charging Load Profile Generation

abstract: Residential electric vehicle charging load profile is indispensable to achieve reliable control strategies for mitigating negative effects on power distribution system due to emerging electrified transportation. This paper introduces a data-driven framework of charging load profile generation for residential plug-in electric vehicles. Real world historical residential charging behavior data is utilized to construct empirical charging decision making model by using machine learning algorithm. A multiple channels method with kernel density estimation is proposed to construct probability density functions for estimating charging duration based on parking duration. A generation algorithm considering parking time and travel demand dependency is introduced to generate residential charging behaviors. This framework is extensible to generate various charging load profiles and simulate varied residential charging scenarios under different number of households and charging rates. This will be crucial for designing and validating residential charging control strategies.


title: A taxonomy of event time representations

abstract: Terms such as "simulated time", "simulation time", "virtual time", "logical time", and "real time" appear throughout the modeling and simulation literature as a means of describing the timing, ordering, and/or processing of events. Unfortunately, this vocabulary can become a source of confusion due to subtle inconsistencies in how the terms are interpreted. Here we categorize mathematical representations of event times, review their formal properties such as causal consistency and causal characterization, identify formal relationships among the representations, and present a taxonomy to clarify the proper meanings of the most common notions of time in a simulation context. A thorough look at the various event time representations suggests new research opportunities, such as the repurposing of distributed computing techniques for debugging both parallel and sequential discrete event simulations.


Labor Market Equilibration : Evidence from Uber


title: A Ka-band YIG-based local oscillator for astronomical heterodyne receiver

abstract: A Ka-band local oscillator generator based on YIG-tuned oscillator is designed, fabricated and measured for astronomical telescope applications. The optimized bandwidth analysis can overcome high phase noise problems caused by poor microwave photonic source noise and to improve the outband phase noise of LO by at least 10.5 dB. The experimental results show that the 33.8 GHz LO phase noise of -107 dBc/Hz is achieved at offset frequency of 700 kHz and exhibits a reference spurs of -71 dBc.


title: High Performance Chip-Partitioned Millimeter Wave Passive Devices on Smooth and Fine Pitch InFO RDL

abstract: High performance millimeter wave passive devices are realized on smooth, fine pitch InFO redistribution layer (RDL). These passive devices are balun, power combiner, coupler, and microstrip line and the electrical performances are measured from 0.1GHz to 67 GHz through VNA. The measurement results show that the transmission loss of on-InFO balun (4.3 dB), the power divider (4.3 dB), and the coupler (4.9 dB) outperforms on-chip one by 2.1 dB, 1 dB, and 0.2 dB, respectively. While the transmission loss of microstrip line (0.34 dB/mm) is better than on-chip one by 0.17 dB/mm at 60 GHz. Furthermore, the parasitic of InFO chip-package interconnection has been investigated and compared to other technologies with and without solder bumps. The parasitic resistance, inductance, and capacitance for InFO interconnection are 75 %, 76 %, and 14 % lower than those for chip-last, face-down technology. Parasitic resistance for InFO RDL is 10 % lower than that for chip-first face-down technology with uneven RDL.


Tolman – Oppenheimer – Volkoff ( TOV ) Stars


title: A Swallowable Smart Pill for Local Drug Delivery

abstract: In this paper, a smart pill for local drug delivery is presented. The pill is designed to achieve local therapy on gastrointestinal tissue thanks to a novel drug delivery system (DDS). The DDS consists of a miniature electrolytic pump. This microfabricated pump is ideal for medical applications because of its low power consumption, compatibility with small size batteries, integrability on miniaturized systems, and biocompatibility. The actuation principle of the micropump relies on the electrolysis of a water-based solution, which is separated from a drug reservoir by an elastic membrane. The electrolytically produced gases pressurize the electrolytic solution reservoir, causing the deflection of the elastic membrane. Such deflection, in turn, forces the drug out of its reservoir through a nozzle. In order to measure and monitor the membrane displacement, and therefore the volume of drug ejected, a strain gauge sensor has been prepared using a conductive thermoplastic nanocomposite elastomer (CTPE). The sensor is fixed on the deformable membrane. The CTPE shows high sensitivity and allows to customize the resistance of the device to obtain low power consumption. Thus, by measuring the current though the electrolytic cell and monitoring membrane deformation, the volume of the ejected drug can be controlled. [2015-0200]


title: Probabilistic spatial context models for scene content understanding

abstract: Scene content understanding facilitates a large number of applications, ranging from content-based image retrieval to other multimedia applications. Material detection refers to the problem of identifying key semantic material types (such as sky, grass, foliage, water, and snow in images). In this paper, we present a holistic approach to determining scene content, based on a set of individual material detection algorithms, as well as probabilistic spatial context models. A major limitation of individual material detectors is the significant number of misclassifications that occur because of the similarities in color and texture characteristics of various material types. We have developed a spatial context-aware material detection system that reduces misclassification by constraining the beliefs to conform to the probabilistic spatial context models. Experimental results show that the accuracy of materials detection is improved by 13% using the spatial context models over the individual material detectors themselves.


title: Autonomous vehicle planning system design under perception limitation in pedestrian environment

abstract: Autonomous driving within the pedestrian environment is always challenging, as the perception ability is limited by the crowdedness and the planning process is constrained by the complicated human behaviors. In this paper, we present a vehicle planning system for self-driving with limited perception in the pedestrian environment. Acknowledging the difficulty of obstacle detection and tracking within the crowded pedestrian environment, only the raw LIDAR sensing data is employed for the purpose of traversability analysis and vehicle planning. The designed vehicle planning system has been experimentally validated to be robust and safe within the populated pedestrian environment.


title: Getting Started with ADOL-C

abstract: The C++ package ADOL-C described in this paper facilitates the evaluation of ::: first and higher derivatives of vector functions that are defined ::: by computer programs written in C or C++. ::: The numerical values of derivative vectors are obtained free ::: of truncation errors at mostly a small multiple of the run time and ::: a fix small multiple random access memory required by the given ::: function evaluation program. ::: ::: Derivative matrices are obtained by columns, by rows or in sparse ::: format. This tutorial describes the source code modification required ::: for the application of ADOL-C, the most frequently used drivers to ::: evaluate derivatives and some recent developments.


title: CELSR1 is a positive regulator of endothelial cell migration and angiogenesis

abstract: Cadherin is an epidermal growth factor and laminin-G seven-pass G-type receptor 1 (CELSR1) is a key component of the noncanonical Wnt/planar cell polarity (PCP) pathway that critically regulates endothelial cell proliferation and angiogenesis. In this study, we examined the biological significance of CELSR1 in endothelial cell migration and angiogenesis. For this, we applied both gain-of-function and loss-of-function approaches. To increase the endogenous expression of CELSR1, we used the transcription activator-like effector (TALE) technology and constructed an artificial TALE-VP64 activator. To knock down the expression of CELSR1, we generated lentivirus containing short hairpin RNA sequences targeting different regions of CELSR1 mRNA. Following up- or down-regulation of CELSR1 in human aortic endothelial cells (HAEC), we assessed in vitro cell proliferation by MTT assay, migration by scratch and transwell migration assays, and angiogenesis by tube formation analysis. We found that CELSR1 was endogenously expressed in human umbilical vein endothelial cells (HUVEC) and HAEC. When focusing on HAEC, we found that upregulating CELSR1 expression significantly promoted cell growth, while knocking down CELSR1 inhibited the growth (p < 0.05). Using both scratch and transwell migration assays, we observed a positive correlation between CELSR1 expression and cell migratory capability. In addition, CELSR1 upregulation led to higher levels of tube formation in HAEC, while downregulating CELSR1 expression decreased tube formation (p < 0.05). Mechanistically, CELSR1-regulated migration and tube formation was mediated through disheveled segment polarity protein 3 (Dvl3). In conclusion, CELSR1 plays an important role in regulating multiple phenotypes of endothelial cells, including proliferation, migration, and formation of capillary-like structures.


Recognition of 2 D Barcode Images Using Edge Detection and Morphological Operation


title: Reference ranges for uterine artery mean pulsatility index at 11-41 weeks of gestation

abstract: OBJECTIVES ::: To construct gestational age (GA)-based reference ranges for the uterine artery (UtA) mean pulsatility index (PI) at 11-41 weeks of pregnancy. ::: ::: ::: METHODS ::: A prospective cross-sectional observational study was carried out of 20 consecutive singleton pregnancies for each completed gestational week at 11-41 weeks. UtAs were examined by color and pulsed Doppler imaging, and the mean PI, as well as the presence or absence of a bilateral protodiastolic notch, were recorded. Polynomials were fitted by means of least-square regression to estimate the relationship between the mean UtA-PI and GA. ::: ::: ::: RESULTS ::: A total of 620 women were included. A second-degree polynomial (Log(e) mean UtA-PI = 1.39 - 0.012 x GA + GA(2) x 0.0000198, with GA measured in days), after a natural logarithmic transformation, was selected to model our data. There was a significant decrease in the mean UtA-PI between 11 weeks (mean PI, 1.79; 95(th) centile, 2.70) and 34 weeks (mean PI, 0.70; 95(th) centile, 0.99). It then became more stable up until 41 weeks (mean PI, 0.65; 95(th) centile, 0.89). ::: ::: ::: CONCLUSIONS ::: The mean UtA-PI shows a progressive decrease until the late stages of pregnancy. Reference ranges for mean UtA-PI may have clinical value in screening for placenta-associated diseases in the early stages of pregnancy, and in evaluating patients with pregnancy-induced hypertension and/or small-for-gestational age fetuses during the third trimester.


title: A robust camera-projector calibration method to be used in vein contrast enhancement systems

abstract: In many applications in image processing, we need to use a camera and projector together. The first step in these applications is calibrating the projector and the camera. In our application (to make a system to visualize body veins), we need to calibrate them on a nonplanar surface. Furthermore, it is possible that bothersome factors, noise, low contrast and nonuniform illumination to be present in camera-taken images. This makes corner extraction a non trivial task. In this paper a camera-projector calibration method is presented that is robust to the above factors. Then the corresponding corners between the projector image and camera image are used to find the transformation function between the two images. Since the surface of interest (the body skin) is not planar, nonplanar or local transformations are needed in this application. Finally some quantitative measurement is applied to evaluate the performance of the transformation function.


title: Gamification of Education: A Review of Literature

abstract: We synthesized the literature on gamification of education by conducting a review of the literature on gamification in the educational and learning context. Based on our review, we identified several game design elements that are used in education. These game design elements include points, levels/stages, badges, leaderboards, prizes, progress bars, storyline, and feedback. We provided examples from the literature to illustrate the application of gamification in the educational context.


title: Real-Time CPU-Based Large-Scale Three-Dimensional Mesh Reconstruction

abstract: In Robotics, especially in this era of autonomous driving, mapping is one key ability of a robot to be able to navigate through an environment, localize on it and analyze its traversability. To allow for real-time execution on constrained hardware, the map usually estimated by feature-based or semi-dense SLAM algorithms is a sparse point cloud; a richer and more complete representation of the environment is desirable. Existing dense mapping algorithms require extensive use of GPU computing and they hardly scale to large environments; incremental algorithms from sparse points still represent an effective solution when light computational effort is needed and big sequences have to be processed in real-time. In this paper we improved and extended the state of the art incremental manifold mesh algorithm proposed in [1] and extended in [2]. While these algorithms do not achieve real-time and they embed points from SLAM or Structure from Motion only when their position is fixed, in this paper we propose the first incremental algorithm able to reconstruct a manifold mesh in real-time through single core CPU processing which is also able to modify the mesh according to 3D points updates from the underlying SLAM algorithm. We tested our algorithm against two state of the art incremental mesh mapping systems on the KITTI dataset, and we showed that, while accuracy is comparable, our approach is able to reach real-time performances thanks to an order of magnitude speed-up.


title: Influence of transformer oil acidity on insulating paper degradation

abstract: The transformer oil on Hydro-Quebec's grid is a major investment. Therefore, it is essential to preserve the oil's quality thus the value of the asset is maintained and, more important, a high level of reliability on the equipment is ensured. In order to preserve this non-renewable resource, Hydro-Quebec decided in the 1990's to reclaim the oil of its power transformers. The quality of the oil has an impact on its own properties (dielectric, physico-chemical), and eventually on the solid insulation and its degradation rate. In order to optimize the reclamation activities and establish criteria to initiate the reclaiming operation, this manuscript presents the preliminary results on the influence of the initial oil acidity on the paper degradation rate. The results show that the degradation rates for Kraft paper and thermally upgraded paper at 110°C increase when using oil with a relative low acidity. Moreover, the degradation rate of the thermally upgraded paper seems to be relatively more affected by oil acidity than for Kraft paper.


title: Effects of Peer Education and Orientation Tour on Anxiety in Patient Candidates for Coronary Angiography

abstract: Background: Coronary angiography is a stressful procedure for most patients. The physiological responses caused by anxiety during coronary angiography increase the likelihood of dysrhythmia, coronary artery spasm, and rupture. ::: ::: Objectives: This study compared the effects of peer education and an orientation tour on anxiety in patients who were candidates for coronary artery angiography. ::: ::: Patients and Methods: This single blind quasi-experimental study was conducted in 2014. A total of 177 patients who were candidates for coronary artery angiography were divided into three groups: a peer education group, an orientation tour group, and a control group. The patients in the peer education group were trained by a peer educator, and the patients in in the orientation tour group were trained by the researcher, who worked in the angiography unit. The DASS-21 questionnaire was used to measure the patients’ anxiety levels before the intervention and two hours before undergoing the coronary angiography. The data were analyzed using a Chi-square test, analysis of variance, the Kruskal-Wallis, Wilcoxon, Mann-Whitney U tests, and an interquartile range. ::: ::: Results: The three groups did not significantly differ regarding the mean anxiety scores before the intervention. However, a significant difference was observed between the mean anxiety scores of the three groups after the intervention (P = 0.0001). In the peer education group, the mean anxiety score was 5.34 ± 2.52 and decreased to 3.69 ± 2.87 after the intervention (P = 0.0001). In the orientation tour group, the mean anxiety was 5.53 ± 3.49, which and changed to 3.10 ± 2.22 (P = 0.0001). However, the mean anxiety score significantly increased in the control group (5.66 ± 2.94 vs. 6.53 ± 3.43, P = 0.017). ::: ::: Conclusions: Both methods of peer education and orientation tour decreased the anxiety levels in patients undergoing coronary artery angiography. Therefore, these approaches should be carried out according to the hospital condition and facilities.


title: Mapping brain circuits of reward and motivation: In the footsteps of Ann Kelley

abstract: a b s t r a c t Ann Kelley was a scientific pioneer in reward neuroscience. Her many notable discoveries included demonstrations of accumbens/striatal circuitry roles in eating behavior and in food reward, explorations of limbic interactions with hypothalamic regulatory circuits, and additional interactions of motivation circuits with learning functions. Ann Kelley’s accomplishments inspired other researchers to follow in her footsteps, including our own laboratory group. Here we describe results from several lines of our research that sprang in part from earlier findings by Kelley and colleagues. We describe hedonic hotspots for generating intense pleasure ‘liking’, separate identities of ‘wanting’ versus ‘liking’ systems, a novel role for dorsal neostriatum in generating motivation to eat, a limbic keyboard mechanism in nucleus accumbens for generating intense desire versus intense dread, and dynamic limbic transformations of learned memories into motivation. We describe how origins for each of these themes can be traced to fundamental contributions by Ann Kelley.


title: It Takes Two to Tango: Towards Theory of AI's Mind

abstract: Theory of Mind is the ability to attribute mental states (beliefs, intents, knowledge, perspectives, etc.) to others and recognize that these mental states may differ from one's own. Theory of Mind is critical to effective communication and to teams demonstrating higher collective performance. To effectively leverage the progress in Artificial Intelligence (AI) to make our lives more productive, it is important for humans and AI to work well together in a team. Traditionally, there has been much emphasis on research to make AI more accurate, and (to a lesser extent) on having it better understand human intentions, tendencies, beliefs, and contexts. The latter involves making AI more human-like and having it develop a theory of our minds. In this work, we argue that for human-AI teams to be effective, humans must also develop a theory of AI's mind (ToAIM) - get to know its strengths, weaknesses, beliefs, and quirks. We instantiate these ideas within the domain of Visual Question Answering (VQA). We find that using just a few examples (50), lay people can be trained to better predict responses and oncoming failures of a complex VQA model. We further evaluate the role existing explanation (or interpretability) modalities play in helping humans build ToAIM. Explainable AI has received considerable scientific and popular attention in recent times. Surprisingly, we find that having access to the model's internal states - its confidence in its top-k predictions, explicit or implicit attention maps which highlight regions in the image (and words in the question) the model is looking at (and listening to) while answering a question about an image - do not help people better predict its behavior.


title: Poverty’s Most Insidious Damage

abstract: Because the brain is the organ from which all cognition and emotion originates, healthy human brain development represents the foundation of our civilization. Accordingly, there is perhaps nothing more important thata societymustdo than foster and protect the brain development of our children. Building on a well-established body of behavioral data and a smaller but expanding body of neuroimaging data, Hair et al1 provide even more powerful evidence of the tangible detrimental effects of growing up in poverty on brain development and related academic outcomes in childhood. Using data from the National Institutes of Health Magnetic Resonance Imaging Study of Normal Brain Development, the investigators demonstrated that children living 1.5 times below the federal poverty level had smaller volumes of several brain regions critical for cognitive and academic performance (gray matter, frontal and temporal lobes, and the hippocampus). While smaller brain volumes in children reared in poverty have been previously demonstrated in several investigations2,3 and poor academic and cognitive outcomes of children living in poverty have been well known for several decades,4 Hair et al1 went further to elucidate the mechanism of this relationship. The findings of the Hair et al study1 showed that poor cognitive and academic performance among children living in poverty was mediated by a smaller hippocampus and frontal and temporal lobes and that the decrease in volume of the latter 2 structures explained as much as 15% to 20% of the achievement deficits found. Given the nature of the study sample investigated, where children facing numerous other risk factors for poor brain development were screened out, it is likely that the effects reported represent an underestimate of the magnitude of risk in the general population. These study findings should be understood in the contextof thewell-establishedanimal literatureandemergingdata inhumans showing that environment, bothphysical andpsychosocial,hasamaterialeffectonthedevelopmentof thebrain. This early childhood experience–dependent plasticity underscores both the relative high vulnerability and adaptability of the developing human brain. Further, data from experimental studies of children who are reared in institutional settings, experiencing relative deprivation, and randomized to therapeutic foster homeshave shown that these environmental effects onbraindevelopment aremorepowerful during the highlyneuroplastic periodof early childhood.5,6 This convergingdevelopmental evidence givesnewmeaning to the importance of protecting and enhancing the nurturing environment for developing young children during this window of opportunity in the early years of life. In keeping with this body of evidence and adding to the findings of Hair et al,1 prior research from our own group has also shown that the deleterious effects of poverty on the development of the hippocampus were partially mediated by maternal support experienced during the preschool period of development.7 Taken together, these findings now elucidate a clear and coherent risk trajectory in which caregiving nurturance mediates the development of key brain regions in the context of poverty and that the development of these brain structures mediates academic outcomes. This line of evidence demonstrates that enhancing parental support during early childhood is a critical and specific intervention target for more effective prevention of poor cognitive and academic outcomes for children living in extreme poverty. The importance of such neuroscience-informed interventions has been emphasized by investigators in this field.8 In keeping with this conclusion but based on an economic analysis, Heckman and colleagues9 demonstrated that psychosocial interventions in the form of enhanced stimulation, support, and nurturance applied during the preschool period of development have a very powerful positive effect on adult outcomes. The Heckman et al analysis9 also clearly shows that environmental enhancements applied during the preschool period have a far more robust effect when compared with similar interventions applied in later childhood, adolescence, and early adulthood. After decades of research, there is now a powerful converging literature on the efficacy of early childhood interventions to support caregiver nurturance for child outcomes.10 These findings provide very strong evidence that a public health focus on enhancing the early nurturing environment for the developing child at risk is not only a critical but cost-effective approach. Given that an alarming 22% of US children are estimated to be living in poverty,11 early childhood interventions to support the nurturing environment for these children must now become our top public health priority for the good of all. In developmental science and medicine, it is not often that aspects of a public health problem’s etiology and solution become clearly elucidated. It is even less common that feasible and cost-effective solutions to such problems are discovered and within reach. Based on this, scientific literature on the damaging effects of poverty on child brain develRelated article Opinion


title: Exploring Temporal Preservation Networks for Precise Temporal Action Localization

abstract: Temporal action localization is an important task of computer vision. Though a variety of methods have been proposed, it still remains an open question how to predict the temporal boundaries of action segments precisely. Most works use segment-level classifiers to select video segments pre-determined by action proposal or dense sliding windows. However, in order to achieve more precise action boundaries, a temporal localization system should make dense predictions at a fine granularity. A newly proposed work exploits Convolutional-Deconvolutional-Convolutional (CDC) filters to upsample the predictions of 3D ConvNets, making it possible to perform per-frame action predictions and achieving promising performance in terms of temporal action localization. However, CDC network loses temporal information partially due to the temporal downsampling operation. In this paper, we propose an elegant and powerful Temporal Preservation Convolutional (TPC) Network that equips 3D ConvNets with TPC filters. TPC network can fully preserve temporal resolution and downsample the spatial resolution simultaneously, enabling frame-level granularity action localization. TPC network can be trained in an end-to-end manner. Experiment results on public datasets show that TPC network achieves significant improvement on per-frame action prediction and competing results on segment-level temporal action localization.


title: General theory of skin reinforcement

abstract: Macroscopic mechanical properties of human skin in vivo cannot be considered independent of adjacent subcutaneous white adipose tissue (sWAT). The layered system skin/sWAT appears as the hierarchical structural composite in which single layers behave as fiber-reinforced structures. Effective macroscopic mechanical properties of such composites are mainly determined either by the properties of the skin or by those of the sWAT, dependent on the conditions of mechanical loading. Mechanical interactions between the skin and the adjacent sWAT associated with a mismatch in the mechanical moduli of these two layers can lead to production of the skin wrinkles. Reinforcement of the composite skin/sWAT can take place in different ways. It can be provided through reorientation of collagen fibers under applied loading, through production of new bonds between existing collagen fibers and through induction of additional collagen structures. Effectiveness of this type of reinforcement is strongly dependent on the type of mechanical loading. Different physical interventions induce the reinforcement of at least one of these two layers, thus increasing the effective macroscopic stiffness of the total composite. At the same time, the standalone reinforcement of the skin appears to be less effective to achieve a delay or a reduction of the apparent signs of skin aging relative to the reinforcement of the sWAT.


title: A 67% PAE, 100 W GaN power amplifier with on-chip harmonic tuning circuits for C-band space applications

abstract: This paper describes a high efficiency and high output power GaN power amplifier for C-band space applications. The amplifier uses on-chip harmonic tuned FETs to improve dc-to-rf conversion efficiency. A 2nd harmonic input tuning circuit is incorporated into each unit on-chip FET cell and realizes high precise control of 2nd harmonic input impedance. In addition, 2nd and 3rd harmonic output impedances are optimized with external output matching circuits. A 100 W power amplifier with 4-chips achieves a 67.0% PAE (72.4% drain efficiency) at 3.7 GHz under CW operating conditions. To the best of our knowledge, this is the highest efficiency of C-band power amplifiers ever reported with over 100 W output power.


title: Segmenting iris images in the visible spectrum with applications in mobile biometrics

abstract: An overview of mobile device based biometric recognition.An outline of various steps involved in iris recognition.A review of some popular iris segmentation algorithms applicable for images acquired in the visible (VIS) spectrum. The widespread use of mobile devices with Internet connectivity has resulted in the storage and transmission of sensitive data. This has heightened the need to perform reliable user authentication on mobile devices in order to prevent an adversary from accessing such data. Biometrics, the science of recognizing individuals based on their biological and behavioral traits, has the potential to be leveraged for this purpose. In this work, we briefly discuss the suitability of using the iris texture for biometric recognition in mobile devices. One of the critical components of an iris recognition system is the segmentation module which separates the iris from other ocular attributes. Since current mobile devices acquire color images of an object, we conduct a literature review for performing automated iris segmentation in the visible spectrum. The goal is to convey the possibility of successfully incorporating iris recognition in mobile devices.


title: Critical Success Factors for ERP Implementation: The Case of Jordan

abstract: ERP systems are complex systems that face high probability of failure. Implementing such systems need careful planning and guarding against factors for failure. This study tried to explore the major key success factors (KSFs) that will turn the implementation process to a success. The study utilized 60 responses from managers and executives of local Jordanian companies in a pursuit for the highest KSFs in the Jordanian environment. Results are reported with conclusions and future work at the end of this paper.


title: Continued Decline of Malaria in The Gambia with Implications for Elimination

abstract: BACKGROUND ::: A substantial decline in malaria was reported to have occurred over several years until 2007 in the western part of The Gambia, encouraging consideration of future elimination in this previously highly endemic region. Scale up of interventions has since increased with support from the Global Fund and other donors. ::: ::: ::: METHODOLOGY/PRINCIPAL FINDINGS ::: We continued to examine laboratory records at four health facilities previously studied and investigated six additional facilities for a 7 year period, adding data from 243,707 slide examinations, to determine trends throughout the country until the end of 2009. We actively detected infections in a community cohort of 800 children living in rural villages throughout the 2008 malaria season, and assayed serological changes in another rural population between 2006 and 2009. Proportions of malaria positive slides declined significantly at all of the 10 health facilities between 2003 (annual mean across all sites, 38.7%) and 2009 (annual mean, 7.9%). Statistical modelling of trends confirmed significant seasonality and decline over time at each facility. Slide positivity was lowest in 2009 at all sites, except two where lowest levels were observed in 2006. Mapping households of cases presenting at the latter sites in 2007-2009 indicated that these were not restricted to a few residual foci. Only 2.8% (22/800) of a rural cohort of children had a malaria episode in the 2008 season, and there was substantial serological decline between 2006 and 2009 in a separate rural area. ::: ::: ::: CONCLUSIONS ::: Malaria has continued to decline in The Gambia, as indicated by a downward trend in slide positivity at health facilities, and unprecedented low incidence and seroprevalence in community surveys. We recommend intensification of control interventions for several years to further reduce incidence, prior to considering an elimination programme.


title: cBAD: ICDAR2017 Competition on Baseline Detection

abstract: The cBAD competition aims at benchmarking state-of-the-art baseline detection algorithms. It is in line with previous competitions such as the ICDAR 2013 Handwriting Segmentation Contest. A new, challenging, dataset was created to test the behavior of state-of-the-art systems on real world data. Since traditional evaluation schemes are not applicable to the size and modality of this dataset, we present a new one that introduces baselines to measure performance. We received submissions from five different teams.


title: Genetic Algorithms and the Optimal Allocation of Trials

abstract: This study gives a formal setting to the difficult optimization problems characterized by the conjunction of (1) substantial complexity and initial uncertainty, (2) the necessity of acquiring new i...


title: Learning-Assisted Automated Reasoning with Flyspeck

abstract: The considerable mathematical knowledge encoded by the Flyspeck project is combined with external automated theorem provers (ATPs) and machine-learning premise selection methods trained on the Flyspeck proofs, producing an AI system capable of proving a wide range of mathematical conjectures automatically. The performance of this architecture is evaluated in a bootstrapping scenario emulating the development of Flyspeck from axioms to the last theorem, each time using only the previous theorems and proofs. It is shown that 39 % of the 14185 theorems could be proved in a push-button mode (without any high-level advice and user interaction) in 30 seconds of real time on a fourteen-CPU workstation. The necessary work involves: (i) an implementation of sound translations of the HOL Light logic to ATP formalisms: untyped first-order, polymorphic typed first-order, and typed higher-order, (ii) export of the dependency information from HOL Light and ATP proofs for the machine learners, and (iii) choice of suitable representations and methods for learning from previous proofs, and their integration as advisors with HOL Light. This work is described and discussed here, and an initial analysis of the body of proofs that were found fully automatically is provided.


title: A multicriteria decision analysis model for faculty evaluation

abstract: In the context of increasing demands for social and financial accountability of universities, the required implementation of transparent faculty evaluation systems constitutes a challenge and an opportunity for universities strategically aligning the activity of academic staff with the university goals. However, despite growing interest in the performance appraisal of faculty, only a few reported studies propose models that cover the full range of academic activities and the models in use are typically based on ad hoc scoring systems that lack theoretical soundness. This article approaches faculty evaluation from an innovative comprehensive perspective. Based on the concepts and methods of multiple criteria value measurement, it proposes a new faculty evaluation model that addresses the whole range of academic activities and can be applied within and across distinct scientific areas, while respecting their specificities. Constructed through a socio-technical process, the model was designed for and adopted by the Instituto Superior Tecnico, the engineering school of the Technical University of Lisbon. The model has a two-level hierarchical additive structure, with top-level evaluation areas specified by second-level evaluation criteria. A bottom non-additive third level accounts for the quantitative and qualitative dimensions of academic activity related to each evaluation criterion. The model allows (a) the comparison of the performance of academic staff with performance targets reflecting the strategic policy concerns of university management; (b) the definition of the multicriteria value profile of each faculty member at the top level of the evaluation areas; (c) the computation of an overall value score for each faculty member, through an optimisation procedure that makes use of a flexible system of weights and (d) the assignment of faculty members to rating categories.


title: Fast, FPGA-based Rainbow Table creation for attacking encrypted mobile communications

abstract: Encryption algorithms utilized in mobile communication systems have been under attack since their introduction, and many of these attacks have been successful in practical settings. One such example, A5/1 used in GSM, was attacked using “Rainbow Tables”, i.e. pre-computed tables that trade long offline computation and large storage for runtime efficiency when cracking the code. Traditionally, Rainbow Tables were used to reverse password hashes. Their application against A5/1 opened up a new domain of exploitation. In this paper, we present an FPGA-based architecture for the efficient creation of Rainbow Tables for the A5/3 block cipher that is used in 2nd and 3rd generation mobile communication systems. The overall goal is to extract the encryption key, provided we have a ciphertext block under a known plaintext attack. The presented architecture exploits the parallelism in the Rainbow Table creation process, and using a Virtext5 LX330T achieves speedups around 9x and 550x for one and 64 compute engines respectively. We show that due to the limited available memory in our experimental setup, our approach achieves high success rates for a key space reduced to 242. We then demonstrate how we can seamlessly extend the proposed architecture to efficiently create much larger Rainbow Tables for the full key-space.


title: A deep scattering spectrum — Deep Siamese network pipeline for unsupervised acoustic modeling

abstract: Recent work has explored deep architectures for learning acoustic features in an unsupervised or weakly-supervised way for phone recognition. Here we investigate the role of the input features, and in particular we test whether standard mel-scaled filterbanks could be replaced by inherently richer representations, such as derived from an analytic scattering spectrum. We use a Siamese network using lexical side information similar to a well-performing architecture used in the Zero Resource Speech Challenge (2015), and show a substantial improvement when the filterbanks are replaced by scattering features, even though these features yield similar performance when tested without training. This shows that unsupervised and weakly-supervised architectures can benefit from richer features than the traditional ones.


title: Light Field Neural Network

abstract: We introduce an optical neural network system made by off-the-shelf components. In order to test the evaluate the physical property of the proposed system, we are making a prototype. After further discussions with our cooperators, we are agreed that the prototype implementation may take longer time than we expected earlier. Therefore we reach a consensus on withdrawing the paper until the physical data is available.


title: A design for two-wheeled self-balancing robot based on Kalman filter and LQR

abstract: This paper introduces a method to control a two-wheeled self-balancing robot based on Kalman filter and LQR algorithm. It is incorporated with hardware design, signal processing and control algorithm design. In this system the angle signals from a gyroscope and an accelerometer, disposed by Kalman filter, are combined with LQR controller to accomplish the controlling of the robot. The purpose of simplifying Kalman filter is to make the signal processing using fewer system resources and reduce the burden of the system. A mathematical model was established according to the physical model. Based on the mathematical model, the LQR controller was designed for the attitude and position controlling of the robot. The experiment and simulation based on the prototype robot make it clear that the system could achieve a state of equilibrium and position tracking with a initial disturbance in a short time.


title: The quest for location-privacy in opportunistic mobile social networks

abstract: Location-tracking is a major privacy problem, especially with high penetration of smart-phones and GPS-enabled devices around the world. In this paper, we propose a Hybrid and Social-aware Location-Privacy in Opportunistic mobile social networks (HSLPO), a collaborative and distributed obfuscation protocol that offers location-privacy k-anonymity. HSLPO discovers the users' own social network and use it to obfuscate requests and hide the original sender's location from the Location based service (LBS) while minimizing the overhead needed to perform obfuscation. We performed extensive simulations over a map-based pseudo realistic environment to compare HSLPO to exiting protocols. Results show that HSLPO can reach higher location-privacy levels and quality of service, in term of success ratio, than other protocols while maintaining low overheads.


title: Loss Analysis of Quasi Z-Source Inverter Using Superjunction-MOSFET

abstract: This paper proposes to improve quasi Z-source inverter (QZSI) performance by using Superjunction-MOSFET. 2 kW class laboratory prototypes were fabricated to confirm this proposal and to compare the performance with the conventional inverter system using Si-IGBT. From loss breakdown of the both methods in various boost ratios and load conditions, it was confirmed that the proposed method can be operated with lower total loss than the conventional method in lower boost ratio operations by its reduced inverter conduction loss. On the other hand, in higher boost ratio operation, the proposed method had higher conversion loss than the conventional method due to increased switching and inductor losses.


Bidirectional LSTM-CNNs-CRF Models for POS Tagging


title: Understanding experience in interactive systems

abstract: Understanding experience is a critical issue for a variety of professions, especially design. To understand experience and the user experience that results from interacting with products, designers conduct situated research activities focused on the interactions between people and products, and the experience that results. This paper attempts to clarify experience in interactive systems. We characterize current approaches to experience from a number of disciplines, and present a framework for designing experience for interactive system. We show how the framework can be applied by members of a multidisciplinary team to understand and generate the kinds of interactions and experiences new product and system designs might offer.


title: AN INDUCTIVE POSITION SENSOR ASIC

abstract: A principle and realization of an ASIC for a fast, precise, and high resolution Contact-less Inductive Position Sensor (CIPOS), is described. The inductive contact-less sensor is suitable for angular or linear position sensing. Advantage of used measurement principle is, that it is strictly ratio metric independent of absolute values. The mechanical robustness, insensitivity to temperature variation, electrical or magnetic fields as well as its mechanical tolerances make the sensor ideal for harsh automotive environment. The sensor fulfils all the requirements for safety relevant applications. The presented ASIC includes sensor excitation, precise input signal analog and digital processing as well as the output signal transmitter. The signal path and main blocks are described.


title: A Study of K-Nearest Neighbour as an Imputation Method

abstract: Data quality is a major concern in Machine Learning and other correlated areas such as Knowledge Discovery from Databases (KDD). As most Machine Learning algorithms induce knowledge strictly from data, the quality of the knowledge extracted is largely determined by the quality of the underlying data. One relevant problem in data quality is the presence of missing data. Despite the frequent occurrence of missing data, many Machine Learning algorithms handle missing data in a rather naive way. Missing data treatment should be carefully thought, otherwise bias might be introduced into the knowledge induced. In this work, we analyse the use of the k-nearest neighbour as an imputation method. Imputation is a term that denotes a procedure that replaces the missing values in a data set by some plausible values. Our analysis indicates that missing data imputation based on the k-nearest neighbour algorithm can outperform the internal methods used by C4.5 and CN2 to treat missing data.


title: Heterogeneous integration technology for MEMS-LSI multi-chip module

abstract: We developed novel interconnection technology for heterogeneous integration of MEMS and LSI multi-chip module, in which MEMS and LSI chips would be horizontally integrated on substrate and vertically stacked each others. The cavity chip composed of deep Cu TSV-beam lead wires was developed for interconnecting MEMS chips with high step height of more than 100um. Fundamental characteristics were successfully obtained from pressure sensing MEMS chip with 360µm thickness, which was connected to the substrate by the cavity chip. MEMS and LSI chips were vertically integrated by using the cavity chip without changes of chip design and extra processes. This interconnection technology can give strong solution for heterogeneous integration of MEMS and LSI chips multi-chip module.


title: Truss Decomposition of Probabilistic Graphs: Semantics and Algorithms

abstract: A key operation in network analysis is the discovery of cohesive subgraphs. The notion of $k$-truss has gained considerable popularity in this regard, based on its rich structure and efficient computability. However, many complex networks such as social, biological and communication networks feature uncertainty, best modeled using probabilities. Unfortunately the problem of discovering k-trusses in probabilistic graphs has received little attention to date. In this paper, given a probabilistic graph G, number k and parameter γ --(0,1], we define a (k,γ)-truss as a maximal connected subgraph H ⊆ G, in which for each edge, the probability that it is contained in at least (k-2) triangles is at least γ. We develop an efficient dynamic programming algorithm for decomposing a probabilistic graph into such maximal (k,γ)-trusses. The above definition of a (k,γ)-truss is local in that the "witness" graphs that has the (k-2) triangles containing an edge in H may be quite different for distinct edges. Hence, we also propose: a global (k,γ)-truss, which in addition to being a local (k,γ)-truss, has to satisfy the condition that the probability that H contains a k-truss is at least γ. We show that unlike local (k,γ)-trusses, the global (k,γ)-truss decomposition on a probabilistic graph is intractable. We propose a novel sampling technique which enables approximate discovery of global (k,γ)-trusses with high probability. Our extensive experiments on real datasets demonstrate the efficacy of our proposed approach and the usefulness of local and global (k,γ)-truss.


title: Machine Theory of Mind

abstract: Theory of mind (ToM; Premack & Woodruff, 1978) broadly refers to humans' ability to represent the mental states of others, including their desires, beliefs, and intentions. We propose to train a machine to build such models too. We design a Theory of Mind neural network -- a ToMnet -- which uses meta-learning to build models of the agents it encounters, from observations of their behaviour alone. Through this process, it acquires a strong prior model for agents' behaviour, as well as the ability to bootstrap to richer predictions about agents' characteristics and mental states using only a small number of behavioural observations. We apply the ToMnet to agents behaving in simple gridworld environments, showing that it learns to model random, algorithmic, and deep reinforcement learning agents from varied populations, and that it passes classic ToM tasks such as the "Sally-Anne" test (Wimmer & Perner, 1983; Baron-Cohen et al., 1985) of recognising that others can hold false beliefs about the world. We argue that this system -- which autonomously learns how to model other agents in its world -- is an important step forward for developing multi-agent AI systems, for building intermediating technology for machine-human interaction, and for advancing the progress on interpretable AI.


title: Comparative Evaluation of Background Subtraction Algorithms in Remote Scene Videos Captured by MWIR Sensors

abstract: Background subtraction (BS) is one of the most commonly encountered tasks in video analysis and tracking systems. It distinguishes the foreground (moving objects) from the video sequences captured by static imaging sensors. Background subtraction in remote scene infrared (IR) video is important and common to lots of fields. This paper provides a Remote Scene IR Dataset captured by our designed medium-wave infrared (MWIR) sensor. Each video sequence in this dataset is identified with specific BS challenges and the pixel-wise ground truth of foreground (FG) for each frame is also provided. A series of experiments were conducted to evaluate BS algorithms on this proposed dataset. The overall performance of BS algorithms and the processor/memory requirements were compared. Proper evaluation metrics or criteria were employed to evaluate the capability of each BS algorithm to handle different kinds of BS challenges represented in this dataset. The results and conclusions in this paper provide valid references to develop new BS algorithm for remote scene IR video sequence, and some of them are not only limited to remote scene or IR video sequence but also generic for background subtraction. The Remote Scene IR dataset and the foreground masks detected by each evaluated BS algorithm are available online: https://github.com/JerryYaoGl/BSEvaluationRemoteSceneIR.


title: Consumer perceptions on web advertisements and motivation factors to purchase in the online shopping

abstract: The principal objective of this study was to investigate the mediating and moderating effects of product involvement and trust toward websites in relation to the effects of the attributes of web advertisements on customers' purchase intentions. We collected data consisting of a total of 264 responses from individuals with previous experience with purchasing products from online shopping mall sites, and utilized the PLS (partial least squares) method to analyze the collected data. Our findings demonstrated that consumers with greater product involvement tended to shop in shopping malls, where they could obtain more information on products, and that consumers with low product involvement tended to be more willing to consider entertainment. Additionally, consumers with higher levels of trust toward websites tended to be influenced by perceived entertainment and thus stay longer at shopping sites; conversely, consumers with low levels of trust toward websites tended to be more willing to consider perceived informativeness as minimizing environmental uncertainty. On the basis of our results, online shopping malls must stress the intrinsic attributes of products to attract highly-involved consumers and must present a benevolent corporate image for consumers concerned with better service. Our results also demonstrated that, for less-involved consumers who wish to search for products to present, it may be appropriate to mix entertainment components with product information, and also to implement creative web interface designs for more effective browsing.


title: Vocational Interests and Performance: A Quantitative Summary of Over 60 Years of Research

abstract: Despite early claims that vocational interests could be used to distinguish successful workers and superior students from their peers, interest measures are generally ignored in the employee selection literature. Nevertheless, theoretical descriptions of vocational interests from vocational and educational psychology have proposed that interest constructs should be related to performance and persistence in work and academic settings. Moreover, on the basis of Holland’s (1959, 1997) theoretical predictions, congruence indices, which quantify the degree of similarity or person–environment fit between individuals and their occupations, should be more strongly related to performance than interest scores alone. Using a comprehensive review of the interest literature that spans more than 60 years of research, a meta-analysis was conducted to examine the veracity of these claims. A literature search identified 60 studies and approximately 568 correlations that addressed the relationship between interests and per...


title: Learning rotation invariant convolutional filters for texture classification

abstract: We present a method for learning discriminative filters using a shallow Convolutional Neural Network (CNN). We encode rotation invariance directly in the model by tying the weights of groups of filters to several rotated versions of the canonical filter in the group. These filters can be used to extract rotation invariant features well-suited for image classification. We test this learning procedure on a texture classification benchmark, where the orientations of the training images differ from those of the test images. We obtain results comparable to the state-of-the-art. Compared to standard shallow CNNs, the proposed method obtains higher classification performance while reducing by an order of magnitude the number of parameters to be learned.


title: A 266nW multi-chopper amplifier with 1.38 noise efficiency factor for neural signal recording

abstract: A low power high efficiency neural signal recording amplifier with a novel multi-chopper technique is proposed and implemented in 180nm CMOS. The input referred rms noise is 1.54μV (1-500Hz) with 266nA tail current. The result corresponds to a 1.38 noise efficiency factor, which is the best reported among current state-of-the-art amplifiers.


title: How Gamification Is Being Implemented in MOOCs? A Systematic Literature Review

abstract: Although MOOCs are being established as a very popular technology to support learning, they are often criticized for their lack of support to active pedagogies and the high drop-out rates. One approach to face this problem is gamification, due to the promising benefits already shown at small-scale environments. Attending to the current and growing use of game elements in MOOCs, this paper presents a systematic literature review of the usage of gamification in MOOCs, aimed at analyzing how gamification is being implemented in MOOCs, and to identify unexplored research opportunities in this field. The results show that gamification is still at an early stage in MOOCs, and it is being implemented in similar ways to those at small scale contexts.


title: Serious Games in Cultural Heritage

abstract: Although the widespread use of gaming for leisure purposes has been well documented, the use of games to support cultural heritage purposes, such as historical teaching and learning, or for enhancing museum visits, has been less well considered. The state-of-the-art in serious game technology is identical to that of the state-of-the-art in entertainment games technology. As a result the field of serious heritage games concerns itself with recent advances in computer games, real-time computer graphics, virtual and augmented reality and artificial intelligence. On the other hand, the main strengths of serious gaming applications may be generalised as being in the areas of communication, visual expression of information, collaboration mechanisms, interactivity and entertainment. In this report, we will focus on the state-of-the-art with respect to the theories, methods and technologies used in serious heritage games. We provide an overview of existing literature of relevance to the domain, discuss the strengths and weaknesses of the described methods and point out unsolved problems and challenges. In addition, several case studies illustrating the application of methods and technologies used in cultural heritage are presented.


title: Lateral prefrontal cortex and self-control in intertemporal choice

abstract: Disruption of function of left, but not right, lateral prefrontal cortex (LPFC) with low-frequency repetitive transcranial magnetic stimulation (rTMS) increased choices of immediate rewards over larger delayed rewards. rTMS did not change choices involving only delayed rewards or valuation judgments of immediate and delayed rewards, providing causal evidence for a neural lateral-prefrontal cortex–based self-control mechanism in intertemporal choice.