title: Idealized media images and adolescent body image: "comparing" boys and girls

abstract: Abstract Sociocultural theories of body image suggest that body dissatisfaction results from unrealistic societal beauty ideals, and one way of transmitting these ideals is through the mass media. The present research aimed to examine the effect of exposure to images of idealized beauty in the media on adolescent girls’ and boys’ body image. The participants (595 adolescents) viewed television commercials containing either images of the thin ideal for women, images of the muscular ideal for men, or non-appearance television commercials. Body dissatisfaction was measured before and after commercial viewing. It was found that exposure to idealized commercials led to increased body dissatisfaction for girls but not for boys. Idealized commercials led to increased negative mood and appearance comparison for girls and boys, although the effect on appearance comparison was stronger for girls. Further, participants high on appearance investment reported greater appearance comparison after viewing idealized commercials than those less strongly invested in their appearance. The results suggest the immediate impact of the media on body image is both stronger and more normative for girls than for boys, but that some boys may also be affected.


title: The physical classroom environment : roles, conceptions, and preferences

abstract: The problem addressed in this thesis is that a considerable proportion of students around the world attend school in inadequate facilities, which is detrimental for the students’ learning outcome. ...


title: Antimicrobial Properties of Plant Essential Oils against Human Pathogens and Their Mode of Action: An Updated Review

abstract: A wide range of medicinal and aromatic plants (MAPs) have been explored for their essential oils in the past few decades. Essential oils are complex volatile compounds, synthesized naturally in different plant parts during the process of secondary metabolism. Essential oils have great potential in the field of biomedicine as they effectively destroy several bacterial, fungal, and viral pathogens. The presence of different types of aldehydes, phenolics, terpenes, and other antimicrobial compounds means that the essential oils are effective against a diverse range of pathogens. The reactivity of essential oil depends upon the nature, composition, and orientation of its functional groups. The aim of this article is to review the antimicrobial potential of essential oils secreted from MAPs and their possible mechanisms of action against human pathogens. This comprehensive review will benefit researchers who wish to explore the potential of essential oils in the development of novel broad-spectrum key molecules against a broad range of drug-resistant pathogenic microbes.


title: Smart Suit: Soft power suit with semi-active assist mechanism - prototype for supporting waist and knee joint -

abstract: We have been developing “mart Suit”as a soft and light-weight wearable power assist system. A prototype for preventing low-back injury in agricultural works and its semi-active assist mechanism have been developed in the previous study. The previous prototype succeeded to reduce about 14% of average muscle fatigues of body trunk in waist extension/flexion motion. In this paper, we describe a prototype of smart suit for supporting waist and knee joint, and its control method for preventing the displacement of the adjustable assist force mechanism in order to keep the assist efficiency.


title: Progressive smooth transition for four-switch buck-boost converter in photovoltaic applications

abstract: The objective of this paper is to propose a progressive smooth transition (PST) control strategy for the four-switch buck-boost converter (FSBBC) that is used for photovoltaic (PV) applications. The proposed PST control strategy can maintain the maximum power point tracking (MPPT) feature during the mode-change transition of the FSBBC and increase the output power of the PV system. Computer simulations and experimental results are presented to verify the performance of the proposed PST strategy.


title: The TerraSAR-X Staring Spotlight Mode Concept

abstract: The paper investigates the possibility to enhance the TerraSAR-X (TSX) azimuth resolution by means of staring spotlight imaging in combination with an extended azimuth pattern steering. The current TSX spotlight modes are briefly reviewed, and the azimuth steering limitations are discussed. Based on a realistic TSX azimuth pattern simulation and performance estimation using a Kepler orbit and the WGS84 reference ellipsoid, the key performance parameters are estimated for an increasing azimuth pattern steering angle span. The azimuth ambiguity performance is identified to be the driving performance parameter. Experimental TSX staring spotlight high-resolution images are presented. They demonstrate and verify the envisaged performance estimation. The paper concludes with a concept for a high-resolution TSX staring spotlight mode that serves as baseline for the upcoming operational implementation.


title: Circularly Polarized Bandwidth-Enhanced Crossed Dipole Antenna With a Simple Single Parasitic Element

abstract: A method of using a simple single parasitic element to significantly enhance a 3-dB axial-ratio (AR) bandwidth of a crossed dipole antenna is presented. This approach is verified by introducing one bowtie-shaped parasitic element between two arms of crossed bowtie dipoles to generate additional resonance and thereby significantly broadening AR bandwidth. The final design, with an overall size of 0.79 λo × 0.79 λo × 0.27 λo ( λo is the free-space wavelength of circular polarized center frequency), resulted in very large measured –10-dB impedance and 3-dB AR bandwidths ranging from 1.9 to 3.9 GHz (∼68.9%) and from 2.05 to 3.75 GHz (∼58.6%), respectively. In addition, the antenna also yielded a right-hand circular polarization with stable far-field radiation patterns and an average broadside gain of approximately 8.2 dBic.


title: Uncalibrated Near-Light Photometric Stereo

abstract: In this work we solve the uncalibrated photometric stereo problem with lights placed near the scene. We investigate different image formation models and find the one that best fits our observations. Although the devised model is more complex than its far-light counterpart, we show that under a global linear ambiguity the reconstruction is possible up to a rotation and scaling, which can be easily fixed. We also propose a solution for reconstructing the normal map, the albedo, the light positions and the light intensities of a scene given only a sequence of near-light images. This is done in an alternating minimization framework which first estimates both the normals and the albedo, and then the light positions and intensities. We validate our method on real world experiments and show that a near-light model leads to a significant improvement in the surface reconstruction compared to the classic distant illumination case.


title: Axon growth inhibition: signals from the p75 neurotrophin receptor

abstract: Axon growth after CNS injury is restricted in part by several inhibitors found in myelin. The inhibition requires interaction of the Nogo receptor with the p75 neurotrophin receptor. A new paper now shows that the Rho inhibitor Rho-GDIα mediates this signaling.


title: Spectral voice conversion for text-to-speech synthesis

abstract: A new voice conversion algorithm that modifies a source speaker's speech to sound as if produced by a target speaker is presented. It is applied to a residual-excited LPC text-to-speech diphone synthesizer. Spectral parameters are mapped using a locally linear transformation based on Gaussian mixture models whose parameters are trained by joint density estimation. The LPC residuals are adjusted to match the target speakers average pitch. To study effects of the amount of training on performance, data sets of varying sizes are created by automatically selecting subsets of all available diphones by a vector quantization method. In an objective evaluation, the proposed method is found to perform more reliably for small training sets than a previous approach. In perceptual tests, it was shown that nearly optimal spectral conversion performance was achieved, even with a small amount of training data. However, speech quality improved with increases in the training set size.


title: Community Detection in Large-Scale Bipartite Networks

abstract: Community detection in networks receives much attention recently. Most of the previous works are for unipartite networks composed of only one type of nodes. In real world situations, however, there are many bipartite networks composed of two types of nodes. In this paper, we propose a fast algorithm called LP&BRIM for community detection in large-scale bipartite networks. It is based on a joint strategy of two developed algorithms -- label propagation (LP), a very fast community detection algorithm, and BRIM, an algorithm for generating better community structure by recursively inducing divisions between the two types of nodes in bipartite networks. Through experiments, we demonstrate that this new algorithm successfully finds meaningful community structures in large-scale bipartite networks in reasonable time limit.


title: RSI-CB: A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data

abstract: Remote sensing image classification is a fundamental task in remote sensing image processing. Remote sensing field still lacks of such a large-scale benchmark compared to ImageNet, Place2. We propose a remote sensing image classification benchmark (RSI-CB) based on crowd-source data which is massive, scalable, and diversity. Using crowdsource data, we can efficiently annotate ground objects in remotes sensing image by point of interests, vectors data from OSM or other crowd-source data. Based on this method, we construct a worldwide large-scale benchmark for remote sensing image classification. In this benchmark, there are two sub datasets with 256 * 256 and 128 * 128 size respectively since different convolution neural networks requirement different image size. The former sub dataset contains 6 categories with 35 subclasses with total of more than 24,000 images; the later one contains 6 categories with 45 subclasses with total of more than 36,000 images. The six categories are agricultural land, construction land and facilities, transportation and facilities, water and water conservancy facilities, woodland and other land, and each category has several subclasses. This classification system is defined according to the national standard of land use classification in China, and is inspired by the hierarchy mechanism of ImageNet. Finally, we have done a large number of experiments to compare RSI-CB with SAT-4, UC-Merced datasets on handcrafted features, such as such as SIFT, and classical CNN models, such as AlexNet, VGG, GoogleNet, and ResNet. We also show CNN models trained by RSI-CB have good performance when transfer to other dataset, i.e. UC-Merced, and good generalization ability. The experiments show that RSI-CB is more suitable as a benchmark for remote sensing image classification task than other ones in big data era, and can be potentially used in practical applications.


title: Face Recognition-Based Mobile Automatic Classroom Attendance Management System

abstract: Classroom attendance check is a contributing factor to student participation and the final success in the courses. Taking attendance by calling out names or passing around an attendance sheet are both time-consuming, and especially the latter is open to easy fraud. As an alternative, RFID, wireless, fingerprint, and iris and face recognition-based methods have been tested and developed for this purpose. Although these methods have some pros, high system installation costs are the main disadvantage. The present paper aims to propose a face recognition-based mobile automatic classroom attendance management system needing no extra equipment. To this end, a filtering system based on Euclidean distances calculated by three face recognition techniques, namely Eigenfaces, Fisherfaces and Local Binary Pattern, has been developed for face recognition. The proposed system includes three different mobile applications for teachers, students, and parents to be installed on their smart phones to manage and perform the real-time attendance-taking process. The proposed system was tested among students at Ankara University, and the results obtained were very satisfactory.


title: A Wireless Sensor Network Using GNSS Receivers for a Short-Term Assessment of the Modal Properties of the Neckartal Bridge

abstract: In this article, we present a novel structural health monitoring system based on a wireless sensor network for GNSS (global navigation satellite system) receivers. The GNSS network presented here consists of three GNSS rover stations and one base station that are deployed at the Neckartal bridge on the Autobahn A81 in southwest Germany. The newly-developed GNSS sensor nodes support satellite data logging up to a sampling rate of 20 Hz. Due to the ultra-low-power consumption achieved by the wake-up receiver during inactive periods, the nodes offer a lifetime from 20 to almost 200 days, without energy harvesting and depending on the satellite data logging period. By performing differential post-processing, precise positioning information in the millimeter range could be achieved. Using the GNSS sensors, we determined resonant frequencies at 0.33 Hz and 1.31 Hz, mainly in the lateral direction of the bridge. To verify the GNSS results, we placed an accelerometer on the bridge. The frequencies detected by the acceleration sensor correspond well to the frequencies found by the GNSS sensors, although the accelerometer measured further higher frequencies as it is probably more sensitive to small amplitudes.


title: Conductivity tensor mapping of the human brain using diffusion tensor MRI

abstract: Knowledge of the electrical conductivity properties of excitable tissues is essential for relating the electromagnetic fields generated by the tissue to the underlying electrophysiological currents. Efforts to characterize these endogenous currents from measurements of the associated electromagnetic fields would significantly benefit from the ability to measure the electrical conductivity properties of the tissue noninvasively. Here, using an effective medium approach, we show how the electrical conductivity tensor of tissue can be quantitatively inferred from the water selfdiffusion tensor as measured by diffusion tensor magnetic resonance imaging. The effective medium model indicates a strong linear relationship between the conductivity and diffusion tensor eigenvalues (respectively, s and d) in agreement with theoretical bounds and experimental measurements presented here (syd ’ 0.844 6 0.0545 Szsymm3, r2 5 0.945). The extension to other biological transport phenomena is also discussed.


title: Analytical inverse kinematic solution with self-motion constraint for the 7-DOF restore robot arm

abstract: This paper presents an analytical inverse kinematic (IK) solution for the seven degree-of-freedom (7-DOF) “Restore” robot arm with non-zero joint offsets. We provide two closed-form solutions - the shoulder-elbow-wrist (SEW) IK and the θ ::: 1 ::: -based solution. Both allow self-motion and together they solve the problem of algorithmic singularities within the workspace. Non-zero link offsets require careful selection of shoulder, elbow, and wrist locations. The solution is simple, fast and exact, providing full solution space (i.e. all eight solutions) per pose.


title: Experience in System Design for Human-Robot Teaming in Urban Search and Rescue.

abstract: The paper describes experience with applying a user-centric design methodology in developing systems for human-robot teaming in Urban Search & Rescue. A human-robot team consists of several robots (rovers/UGVs, microcopter/UAVs), several humans at an off-site command post (mission commander, UGV operators) and one on-site human (UAV operator). This system has been developed in close cooperation with several rescue organizations, and has been deployed in a real-life tunnel accident use case. The human-robot team jointly explores an accident site, communicating using a multi-modal team interface, and spoken dialogue. The paper describes the development of this complex socio-technical system per se, as well as recent experience in evaluating the performance of this system.


title: Compact dual band three way bagley polygon power divider using composite right/left handed (CRLH) transmission lines

abstract: A compact dual band three way power divider based on the Bagley Polygon has been implemented using composite right/left handed (CRLH) transmission lines consisting of microstrip lines and lumped elements for the GSM frequencies of 860MHz and 1.92GHz. Also, a dual band power divider consisting of conventional quarter wavelength (λ/4) transmission lines with shunt connections of open and short stubs has been implemented for comparison. An advantage of using the Bagley Polygon for three way power divider is that it allows an arbitrary phase selection at the third port by using single or dual band, CRLH or conventional transmission lines. The CRLH based power divider shows an area of 7.95 cm2 and a bandwidth of approximately 8% at both bands while the comparison structure shows an area of 51.98 cm2, a bandwidth of 5.8% at 860MHz, and 2.6% at 1.92GHz. Less than 5.5dB insertion loss has been achieved in both cases. Also, full wave structure simulation has been performed and the simulation results agree well with the measurement results.


title: Correlation of educational status and clinicopathological characteristics of breast cancer: A single center experience.

abstract: PURPOSE ::: Educational status may be an important parameter in assessing breast cancer risk and prognosis. The purpose of this study was to investigate the correlation between the level of education and clinicopathological characteristics of breast cancer, including tumor grade, HER-2 and estrogen receptor (ER) status, tumor size, axillary lymph node involvement and metastasis. ::: ::: ::: METHODS ::: The study included 1800 women who were diagnosed with invasive breast cancer during 2005-2013 at Hacettepe University Cancer Institute. Patients were divided into three groups according to their educational status at the time of diagnosis as follows: low (illiterate and elementary school, 5 years or less of education), medium (secondary school and upper secondary school, 6-12 years of education) and high (university level, more than 12 years of education). The associations between educational status and clinicopathologic features of breast cancer at the time of diagnosis were evaluated. ::: ::: ::: RESULTS ::: In all patient, a significant relationship was found between educational status and T stages (p<0.0001). Patients with higher educational levels were reported to have smaller tumor size regardless to their age and were less likely to have axillary lymph node involvement (p=0.001) or metastasis (p=0.001). A significant correlation was found between educational status and ER positivity in patients over 50 years of age (p=0.03). When the patients of all ages were evaluated, no statistically significant correlation was shown (p=0.27) between educational status and ER positivity. A significant relationship was found between educational status and HER-2 status (p=0.003), regardless of the patients' age. HER-2 positivity increased in patients with low educational status, however this significance was lost in patients over the age of 50 (p=0.1). ::: ::: ::: CONCLUSION ::: The relationship between educational status and biological factors in breast cancer are not conclusive as yet, but this particular study revealed that educational status played a major influence in each of the five breast cancer prognostic factors: ER status, HER-2 status, tumor size, lymph node status and metastasis.


title: Design of a highly integrated underactuated finger towards prosthetic hand

abstract: This paper presents a highly integrated underactuated anthropomorphic finger with three joints and two DOFs using a novel coupled-adaptive linkage mechanism. The finger has two main motion modes: coupled motion in free space and self-adaptive motion when contacting with the object. Compared with conventional prosthetic finger, the combination of these two motion modes improves the anthropomorphic characteristics and grasping capacity remarkably. Besides, kinds of sensors embedded, force, position and tactile sensor in the finger not only intensify proactive perceptive ability, but also ameliorate the control ability. Moreover, according to a series of grasp experiments, the anthropomorphic finger can accomplish coupled motion and self-adaptive motion stably.


title: Unequal Teeth Widths for Torque Ripple Reduction in Permanent Magnet Synchronous Machines With Fractional-Slot Non-Overlapping Windings

abstract: Permanent magnet synchronous machines (PMSMs) with fractional-slot non-overlapping windings, also known as tooth-coil winding PMSMs (TCW PMSM), have been under intensive research during the latest decade. There are many optimization routines explained and implemented in the literature to improve the characteristics of this machine type. This paper introduces a new technique for torque ripple minimization in TCW PMSM. The source of torque harmonics is also described. The low-order torque harmonics can be harmful for a variety of applications, such as direct drive wind generators, direct drive light vehicle electrical motors, and for some high-precision servo applications. The reduction of the torque ripple harmonics with the lowest orders (6th and 12th) is realized by machine geometry optimization technique using finite element analysis. The presented optimization technique includes the stator geometry adjustment in TCW PMSMs with rotor surface permanent magnets and with rotor embedded permanent magnets. Influence of the permanent magnet skewing on the torque ripple reduction and cogging torque elimination was also investigated. It was implemented separately and together with the stator optimization technique. As a result, the reduction of some torque ripple harmonics was attained.


title: Prescient Human Fetuses Thrive

abstract: Fetal detection of adversity is a conserved trait that allows many species to adapt their early developmental trajectories to ensure survival. According to the fetal-programming model, exposure to stressful or hostile conditions in utero is associated with compromised development and a lifelong risk of adverse health outcomes. In a longitudinal study, we examined the consequences of prenatal and postnatal exposure to adversity for infant development. We found increased motor and mental development during the 1st year of life among infants whose mothers experienced congruent levels of depressive symptoms during and after pregnancy, even when the levels of symptoms were relatively high and the prenatal and postnatal environments were unfavorable. Congruence between prenatal and postnatal environments prepares the fetus for postnatal life and confers an adaptive advantage for critical survival functions during early development.


Automatic Design of Decision-Tree Induction Algorithms


title: Credibility ranking of tweets during high impact events

abstract: Twitter has evolved from being a conversation or opinion sharing medium among friends into a platform to share and disseminate information about current events. Events in the real world create a corresponding spur of posts (tweets) on Twitter. Not all content posted on Twitter is trustworthy or useful in providing information about the event. In this paper, we analyzed the credibility of information in tweets corresponding to fourteen high impact news events of 2011 around the globe. From the data we analyzed, on average 30% of total tweets posted about an event contained situational information about the event while 14% was spam. Only 17% of the total tweets posted about the event contained situational awareness information that was credible. Using regression analysis, we identified the important content and sourced based features, which can predict the credibility of information in a tweet. Prominent content based features were number of unique characters, swear words, pronouns, and emoticons in a tweet, and user based features like the number of followers and length of username. We adopted a supervised machine learning and relevance feedback approach using the above features, to rank tweets according to their credibility score. The performance of our ranking algorithm significantly enhanced when we applied re-ranking strategy. Results show that extraction of credible information from Twitter can be automated with high confidence.


title: A novel broadband dual-polarization antenna with high isolation

abstract: A novel broadband dual-polarization antenna composed of two orthogonal dipoles with high isolation is presented in this paper. Due to properly utilizing the strong regional mutual coupling between the two dipoles, a very good impedance matching and a sufficiently high isolation can be achieved simultaneously, the reasons for which will be explained at the conference. The measured results show that it has a 53.7% impedance bandwidth of 1.65GHz-2.86GHz with VSWR 32dB. As an important advantage, the input impedance of the antenna can be well controlled within certain limits by adjusting the configuration, thus the antenna can be fed directly by coaxial cables without using impedance transformers or other feeding circuits. Besides, it has a simple and compact configuration.


Application of Technological Pedagogical Content Knowledge Framework to Elementary Mathematics Teaching: A Scale Adaptation Study


title: A Computational Framework for Complementary Situational Awareness (CSA) in Surgical Assistant Robots

abstract: Robotic surgical systems have contributed greatly to the advancement of minimally invasive surgery (MIS). More specifically, telesurgical robots have provided enhanced dexterity to surgeons performing MIS procedures. However, current robotic teleoperated systems have only limited situational awareness of the patient anatomy and surgical environment that would typically be available to a surgeon in an open surgery. Although the endoscopic view enhances the visualization of the anatomy, perceptual understanding of the environment and anatomy is still lacking due to the absence of sensory feedback. To address these limitations, we present an algorithmic software framework to provide Complementary Situational Awareness (CSA) in a surgical assistant. This framework aims at improving the human-robot relationship by providing elaborate guidance and sensory feedback capabilities for the surgeon in complex MIS procedures. Unlike traditional teleoperation, this framework enables the user to telemanipulate the situational model in a virtual environment and uses that information to command the slave robot with appropriate admittance gains and environmental constraints. Simultaneously, the situational model is updated based on interaction of the slave robot with the task space environment. We provide various high-level and mid-level components to provide CSA and illustrate the necessary capabilities required for any robotic platform to readily incorporate CSA. We also demonstrate the use of our framework for constrained model-mediated teleoperation using the open-source da Vinci Research Kit (dVRK) hardware.


title: Design of a compliant joint actuator for lower-limb exoskeleton robot

abstract: For the rigid actuators of conventional exoskeletons are prone to produce motion impact and jitter, so this paper presents a novel design of compliant joint actuator for solve this situation. For the patient with lower limb disability lose the force perception will lead to them can't perceive the movement impact of the exoskeleton, which can reduce the rehabilitation effect or may cause secondary damage to the wearer. The new areas of robotic actuation have been established that requires more versatile structure. The design of new joint actuator adds compliant mechanism in motion transmission part which has a buffer effect to inhibit the external impacts and shocks, and can measure the output torque of the actuator. And in this paper, the detailed design of the exoskeleton structure is proposed based on the concept of compliant actuator. The content of this paper focuses on the mechanism design of the device and makes finite element simulation in ANSYS. Finally, simulation results of the compliant mechanism can describe the design for new actuator which aims at providing a more friendly interaction than existing exoskeleton actuator.


title: Predation on bats by Great Kiskadees

abstract: Great Kiskadees (Pitangus sulphuratus) are found in a variety of habitats from Argentina north to the United States and are the most generalist tyrant flycatcher in both foraging behavior and food habits. These kiskadees are known to occasionally prey on small vertebrates, but, to our knowledge, bats have never been reported as a prey item. We observed a breeding pair of Great Kiskadees preying on bats (Myotis spp.) at the field station Base de Estudos do Pantanal (BEP) in the southern Pantanal, Brazil. At BEP, there are fissures under the building's floor slabs that allow two species of bats, black myotis (Myotis nigricans) and silver-tipped myotis (M. albescens), to access internal galleries and use them as day roosts. We found that bats, insects, and fruits were the most common food items fed to nestlings by adult kiskadees. Bats (N = 10) were captured when kiskadees landed on ground below a building, looked up through a fissure, and then reached through the fissure and captured a bat in their bill. On one occasion, a kiskadee flew from a perch and captured a bat in flight. Our observations provide further evidence of the opportunistic feeding behavior of Great Kiskadees.


title: Attributed Signed Network Embedding

abstract: The major task of network embedding is to learn low-dimensional vector representations of social-network nodes. It facilitates many analytical tasks such as link prediction and node clustering and thus has attracted increasing attention. The majority of existing embedding algorithms are designed for unsigned social networks. However, many social media networks have both positive and negative links, for which unsigned algorithms have little utility. Recent findings in signed network analysis suggest that negative links have distinct properties and added value over positive links. This brings about both challenges and opportunities for signed network embedding. In addition, user attributes, which encode properties and interests of users, provide complementary information to network structures and have the potential to improve signed network embedding. Therefore, in this paper, we study the novel problem of signed social network embedding with attributes. We propose a novel framework SNEA, which exploits the network structure and user attributes simultaneously for network representation learning. Experimental results on link prediction and node clustering with real-world datasets demonstrate the effectiveness of SNEA.


EMBASSI: Electronic Multimedia and Service Assistance


title: Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function

abstract: Person re-identification across cameras remains a very challenging problem, especially when there are no overlapping fields of view between cameras. In this paper, we present a novel multi-channel parts-based convolutional neural network (CNN) model under the triplet framework for person re-identification. Specifically, the proposed CNN model consists of multiple channels to jointly learn both the global full-body and local body-parts features of the input persons. The CNN model is trained by an improved triplet loss function that serves to pull the instances of the same person closer, and at the same time push the instances belonging to different persons farther from each other in the learned feature space. Extensive comparative evaluations demonstrate that our proposed method significantly outperforms many state-of-the-art approaches, including both traditional and deep network-based ones, on the challenging i-LIDS, VIPeR, PRID2011 and CUHK01 datasets.


title: Gaze typing compared with input by head and hand

abstract: This paper investigates the usability of gaze-typing systems for disabled people in a broad perspective that takes into account the usage scenarios and the particular users that these systems benefit. Design goals for a gaze-typing system are identified: productivity above 25 words per minute, robust tracking, high availability, and support of multimodal input. A detailed investigation of the efficiency and user satisfaction with a Danish and a Japanese gaze-typing system compares it to head- and mouse (hand) - typing. We found gaze typing to be more erroneous than the other two modalities. Gaze typing was just as fast as head typing, and both were slower than mouse (hand-) typing. Possibilities for design improvements are discussed.


title: A Survey of Ontology Learning Approaches

abstract: The problem that ontology learning deals with is the knowledge acquisition bottleneck, that is to say the difficulty to actually model the knowledge relevant to the domain of interest. Ontologies are the vehicle by which we can model and share the knowledge among various applications in a specific domain. So many research developed several ontology learning approaches and systems. In this paper, we present a survey for the different approaches in ontology learning from semi-structured and unstructured date


title: CDAS: A Crowdsourcing Data Analytics System

abstract: Some complex problems, such as image tagging and natural language processing, are very challenging for computers, where even state-of-the-art technology is yet able to provide satisfactory accuracy. Therefore, rather than relying solely on developing new and better algorithms to handle such tasks, we look to the crowdsourcing solution -- employing human participation -- to make good the shortfall in current technology. Crowdsourcing is a good supplement to many computer tasks. A complex job may be divided into computer-oriented tasks and human-oriented tasks, which are then assigned to machines and humans respectively. ::: ::: To leverage the power of crowdsourcing, we design and implement a Crowdsourcing Data Analytics System, CDAS. CDAS is a framework designed to support the deployment of various crowdsourcing applications. The core part of CDAS is a quality-sensitive answering model, which guides the crowdsourcing engine to process and monitor the human tasks. In this paper, we introduce the principles of our quality-sensitive model. To satisfy user required accuracy, the model guides the crowdsourcing query engine for the design and processing of the corresponding crowdsourcing jobs. It provides an estimated accuracy for each generated result based on the human workers' historical performances. When verifying the quality of the result, the model employs an online strategy to reduce waiting time. To show the effectiveness of the model, we implement and deploy two analytics jobs on CDAS, a twitter sentiment analytics job and an image tagging job. We use real Twitter and Flickr data as our queries respectively. We compare our approaches with state-of-the-art classification and image annotation techniques. The results show that the human-assisted methods can indeed achieve a much higher accuracy. By embedding the quality-sensitive model into crowdsourcing query engine, we effectively reduce the processing cost while maintaining the required query answer quality.


title: Two-Stream 3D Convolutional Neural Network for Skeleton-Based Action Recognition

abstract: It remains a challenge to efficiently extract spatialtemporal information from skeleton sequences for 3D human action recognition. Although most recent action recognition methods are based on Recurrent Neural Networks which present outstanding performance, one of the shortcomings of these methods is the tendency to overemphasize the temporal information. Since 3D convolutional neural network(3D CNN) is a powerful tool to simultaneously learn features from both spatial and temporal dimensions through capturing the correlations between three dimensional signals, this paper proposes a novel two-stream model using 3D CNN. To our best knowledge, this is the first application of 3D CNN in skeleton-based action recognition. Our method consists of three stages. First, skeleton joints are mapped into a 3D coordinate space and then encoding the spatial and temporal information, respectively. Second, 3D CNN models are seperately adopted to extract deep features from two streams. Third, to enhance the ability of deep features to capture global relationships, we extend every stream into multitemporal version. Extensive experiments on the SmartHome dataset and the large-scale NTU RGB-D dataset demonstrate that our method outperforms most of RNN-based methods, which verify the complementary property between spatial and temporal information and the robustness to noise.


title: Epigenetics in Alternative Pre-mRNA Splicing

abstract: Alternative splicing plays critical roles in differentiation, development, and disease and is a major source for protein diversity in higher eukaryotes. Analysis of alternative splicing regulation has traditionally focused on RNA sequence elements and their associated splicing factors, but recent provocative studies point to a key function of chromatin structure and histone modifications in alternative splicing regulation. These insights suggest that epigenetic regulation determines not only what parts of the genome are expressed but also how they are spliced.


title: A Study on False Channel Condition Reporting Attacks in Wireless Networks

abstract: Wireless networking protocols are increasingly being designed to exploit a user's measured channel condition; we call such protocols channel-aware . Each user reports the measured channel condition to a manager of wireless resources and a channel-aware protocol uses these reports to determine how resources are allocated to users. In a channel-aware protocol, each user's reported channel condition affects the performance of every other user. The deployment of channel-aware protocols increases the risks posed by false channel-condition feedback. In this paper, we study what happens in the presence of an attacker that falsely reports its channel condition. We perform case studies on channel-aware network protocols to understand how an attack can use false feedback and how much the attack can affect network performance. The results of the case studies show that we need a secure channel condition estimation algorithm to fundamentally defend against the channel-condition misreporting attack. We design such an algorithm and evaluate our algorithm through analysis and simulation. Our evaluation quantifies the effect of our algorithm on system performance as well as the security and the performance of our algorithm.


title: Millimeter wave phased array antenna for modern wireless communication systems

abstract: This paper reports on the development of a switched-beam phased array antenna which is the first 35 GHz Butler Matrix based phased array on a single layer substrate. The structure consists of four linearly placed rectangular patch antennas, four hybrids, two crossovers, two 45° phase shifters and a corporate feed network. In the array, in order to steer the beam, a 4×4 Butler Matrix has been successfully designed and simulated. The results show that the beam can be nearly steered −45°/ −15°/+15°/+45°. The scanned gain of the phased array antenna is nearly 8 dBi. This switched beam forming system can be used in modern wireless communication systems.


title: Extensive Tests of Autonomous Driving Technologies

abstract: This paper presents the vision of the Artificial Vision and Intelligent Systems Laboratory (VisLab) on future automated vehicles, ranging from sensor selection up to their extensive testing. VisLab's design choices are explained using the BRAiVE autonomous vehicle prototype as an example. BRAiVE, which is specifically designed to develop, test, and demonstrate advanced safety applications with different automation levels, features a high integration level and a low-cost sensor suite, which are mainly based on vision, as opposed to many other autonomous vehicle implementations based on expensive and invasive sensors. The importance of performing extensive tests to validate the design choices is considered to be a hard requirement, and different tests have been organized, including an intercontinental trip from Italy to China. This paper also presents the test, the main challenges, and the vehicles that have been specifically developed for this test, which was performed by four autonomous vehicles based on BRAiVE's architecture. This paper also includes final remarks on VisLab's perspective on future vehicles' sensor suite.


title: CONTROLLED AND ADAPTIVE MESH ZIPPERING

abstract: Merging meshes is a recurrent need in geometry modeling and it is a critical step in the 3D acquisition pipeline, where it is used for building a single mesh from several range scans. A pioneering simple and effective solution to merging is represented by the Zippering algorithm (Turk and Levoy, 1994), which consists of simply stitching the meshes together along their borders. In this paper we propose a new extended version of the zippering algorithm that enables the user to control the resulting mesh by introducing quality criteria in the selection of redundant data, and allows to zip together meshes with different granularity by an ad hoc refinement algorithm.


The Gilbert-Johnson-Keerthi Distance Algorithm


title: Critical thinking and critical appraisal: the chicken and the egg?

abstract: Having attended the first National Workshop on Teaching Evidence-Based Medicine (Oxford, 1995), one of us actually has the T-shirt declaring his critical appraisal credentials. But although trained in clinical epidemiology, both before and since the workshop, the same person continues daily to doubt his critical thinking skills. The profusion of mini-guides and short courses in Critical Appraisal and Evidence-Based Practice has enriched our curricula for over a decade,1 but perhaps basic skills of critical thought should take precedence in developing our day-to-day practice, which is rather less informed by the published evidence than we would care to admit. ::: ::: Although the term c ritical thinking was used in the General Medical Council's 1993 edition of Tomorrow's Doctors, it has now been dropped in favour of phrases such as: an ability ‘to gain, assess, apply and integrate new knowledge … ‘; or an ability ‘to integrate and critically evaluate evidence … ’ .2 While a shift towards problem-based learning has occurred in some UK medical schools, the proof that such curricula can enhance critical thinking may be elusive.3 However, many of the contributions to the teaching of critical thinking have not come from medical schools, but rather from philosophy and psychology departments, and there are now regular International Conferences on Critical Thinking.4 Perhaps it is time to learn some lessons from the ‘professionals’. Below we describe a few local initiatives to promote critical thinking at an undergraduate and post-graduate level. We give a brief overview of some material covered in a one semester course (for third-year medical students), and briefly describe a few take-home messages that postgraduates have also found helpful. Space permits description of only a small selection of this material. ::: ::: To be human is to be fallible. Further, most of us prefer to expend the least …


title: Online Batch Selection for Faster Training of Neural Networks

abstract: Deep neural networks are commonly trained using stochastic non-convex optimization procedures, which are driven by gradient information estimated on fractions (batches) of the dataset. While it is commonly accepted that batch size is an important parameter for offline tuning, the benefits of online selection of batches remain poorly understood. We investigate online batch selection strategies for two state-of-the-art methods of stochastic gradient-based optimization, AdaDelta and Adam. As the loss function to be minimized for the whole dataset is an aggregation of loss functions of individual datapoints, intuitively, datapoints with the greatest loss should be considered (selected in a batch) more frequently. However, the limitations of this intuition and the proper control of the selection pressure over time are open questions. We propose a simple strategy where all datapoints are ranked w.r.t. their latest known loss value and the probability to be selected decays exponentially as a function of rank. Our experimental results on the MNIST dataset suggest that selecting batches speeds up both AdaDelta and Adam by a factor of about 5.


title: When generating a prediction boosts learning: The element of surprise

abstract: Abstract Using both behavioral and eye-tracking methodology, we tested whether and how asking students to generate predictions is an efficient technique to improve learning. In particular, we designed two tasks to test whether the surprise induced by outcomes that violate expectations enhances learning. Data from the first task revealed that asking participants to generate predictions, as compared to making post hoc evaluations, facilitated acquisition of geography knowledge. Pupillometry measurements revealed that expectancy-violating outcomes led to a surprise response only when a prediction was made beforehand, and that the strength of this response was positively related to the amount of learning. Data from the second task demonstrated that making predictions about the outcomes of soccer matches specifically improved memory for expectancy-violating events. These results suggest that a specific benefit of making predictions in learning contexts is that it creates the opportunity for the learner to be surprised. Implications for theory and educational practice are discussed.


title: Leveraging web resources for keyword assignment to short text documents

abstract: Assigning relevant keywords to documents is very important for efficient retrieval, clustering and management of the documents. Especially with the web corpus deluged with digital documents, automation of this task is of prime importance. Keyword assignment is a broad topic of research which refers to tagging of document with keywords, key-phrases or topics. For text documents, the keyword assignment techniques have been developed under two sub-topics: automatic keyword extraction (AKE) and automatic key-phrase abstraction. However, the approaches developed in the literature for full text documents cannot be used to assign keywords to low text content documents like twitter feeds, news clips, product reviews or even short scholarly text. In this work, we point out several practical challenges encountered in tagging such low text content documents. As a solution to these challenges, we show that the proposed approaches which leverage knowledge from several open source web resources enhance the quality of the tags (keywords) assigned to the low text content documents. The performance of the proposed approach is tested on real world corpus consisting of scholarly documents with text content ranging from only the text in the title of the document (5-10 words) to the summary text/abstract (100- 150 words). We find that the proposed approach not just improves the accuracy of keyword assignment but offer a computationally efficient solution which can be used in real world applications.


title: An improved shuffled frog leaping algorithm for robot path planning

abstract: Path planning is one of the most significant and challenging subjects in robot control field. In this paper, a path planning method based on an improved shuffled frog leaping algorithm is proposed. In the proposed approach, a novel updating mechanism based on the median strategy is used to avoid local optimal solution problem in the general shuffled frog leaping algorithm. Furthermore, the fitness function is modified to make the path generated by the shuffled frog leaping algorithm smoother. In each iteration, the globally best frog is obtained and its position is used to lead the movement of the robot. Finally, some simulation experiments are carried out. The experimental results show the feasibility and effectiveness of the proposed algorithm in path planning for mobile robots.


title: RETRACTED ARTICLE: A method of multi-criteria set recognition based on deep feature representation

abstract: The large variations in the angle of different camera views and illumination can change the appearance of a lot of people, which makes human re identification is still a challenging problem. Therefore, the development of robust feature descriptors and the design of discriminative distance metrics to measure similarity between pedestrian images are two key aspects of human re identification. In this paper, we propose a method to improve the performance of the re identification using depth learning and multiple metric ensembles. First, we use a variety of data sets to train the general convolutional neural network (CNN), which is used to extract the features of the training and test set after deep level. Deep architecture makes it possible for people to learn more abstract and internal features that are robust to changes in viewpoint and illumination. Then, we utilize the deep features of the training set to learn a specific distance metric and combine it with the cosine distance metric. Multi metric sets can be used to measure the similarity between different images. Finally, a large number of experiments show that our method can effectively improve the recognition performance compared to the state-of-the-art methods. Index Terms—Person Re-identification, Convolutional Neural Network, Multiple Metric Ensembles.


title: An improved illumination model for shaded display

abstract: To accurately render a two-dimensional image of a three-dimensional scene, global illumination information that affects the intensity of each pixel of the image must be known at the time the intensity is calculated. In a simplified form, this information is stored in a tree of “rays” extending from the viewer to the first surface encountered and from there to other surfaces and to the light sources. A visible surface algorithm creates this tree for each pixel of the display and passes it to the shader. The shader then traverses the tree to determine the intensity of the light received by the viewer. Consideration of all of these factors allows the shader to accurately simulate true reflection, shadows, and refraction, as well as the effects simulated by conventional shaders. Anti-aliasing is included as an integral part of the visibility calculations. Surfaces displayed include curved as well as polygonal surfaces.


title: Learning unfair trading: A market manipulation analysis from the reinforcement learning perspective

abstract: Market manipulation is a strategy used by traders to alter the price of financial securities. One type of manipulation is based on the process of buying or selling assets by using several trading strategies, among them spoofing is a popular strategy and is considered illegal by market regulators. Some promising tools have been developed to detect manipulation, but cases can still be found in the markets. In this paper we model spoofing and pinging trading, two strategies that differ in the legal background but share the same elemental concept of market manipulation. We use a reinforcement learning framework within the full and partial observability of Markov decision processes and analyse the underlying behaviour of the manipulators by finding the causes of what encourages the traders to perform fraudulent activities. This reveals procedures to counter the problem that may be helpful to market regulators as our model predicts the activity of spoofers.


title: Model-Based Reinforcement Learning in Contextual Decision Processes

abstract: We study the sample complexity of model-based reinforcement learning in general contextual decision processes. We design new algorithms for RL with an abstract model class and analyze their statistical properties. Our algorithms have sample complexity governed by a new structural parameter called the witness rank, which we show to be small in several settings of interest, including Factored MDPs and reactive POMDPs. We also show that the witness rank of a problem is never larger than the recently proposed Bellman rank parameter governing the sample complexity of the model-free algorithm OLIVE (Jiang et al., 2017), the only other provably sample efficient algorithm at this level of generality. Focusing on the special case of Factored MDPs, we prove an exponential lower bound for all model-free approaches, including OLIVE, which when combined with our algorithmic results demonstrates exponential separation between model-based and model-free RL in some rich-observation settings.


title: A 92 mW, 20 dB gain, broadband lumped SiGe amplifier with bandwidth exceeding 67 GHz

abstract: A compact, power-efficient broadband amplifier is demonstrated in a 0.13-micron SiGe BiCMOS process. The amplifier uses a lumped design topology with a shunt-feedback Darlington input stage and an emitter-follower buffered cascode post-amplifier stage. The overall amplifier consumes 92 mW DC power, and exhibits 20-dB gain. The reported 67-GHz bandwidth is limited by available test equipment, and the post-extraction simulated 3-dB bandwidth is 82 GHz, which implies a gain-bandwidth (GBW) of 820 GHz. The amplifier features a low group delay variation to enable high data rate. The post-extraction simulated group delay is 13+/-2 ps from 1 GHz to 100 GHz. The chip occupies an area of 0.28 mm2 including pads, and the core area is only 0.04 mm2, which includes all active devices and peaking inductors. This amplifier shows a figure-of-merit GBW/Pdc of 7.3 and 8.9 GHz/mW, assuming bandwidth of 67 GHz and 82 GHz respectively, which are among the best results to date.


title: Entity disambiguation with hierarchical topic models

abstract: Disambiguating entity references by annotating them with unique ids from a catalog is a critical step in the enrichment of unstructured content. In this paper, we show that topic models, such as Latent Dirichlet Allocation (LDA) and its hierarchical variants, form a natural class of models for learning accurate entity disambiguation models from crowd-sourced knowledge bases such as Wikipedia. Our main contribution is a semi-supervised hierarchical model called Wikipedia-based Pachinko Allocation Model} (WPAM) that exploits: (1) All words in the Wikipedia corpus to learn word-entity associations (unlike existing approaches that only use words in a small fixed window around annotated entity references in Wikipedia pages), (2) Wikipedia annotations to appropriately bias the assignment of entity labels to annotated (and co-occurring unannotated) words during model learning, and (3) Wikipedia's category hierarchy to capture co-occurrence patterns among entities. We also propose a scheme for pruning spurious nodes from Wikipedia's crowd-sourced category hierarchy. In our experiments with multiple real-life datasets, we show that WPAM outperforms state-of-the-art baselines by as much as 16% in terms of disambiguation accuracy.


title: GPC Temperature Control of A Simulation Model Infant-Incubator and Practice with Arduino Board

abstract: The thermal environment surrounding preterm neonates in closed incubators is regulated via air temperature control mode. At present, these control modes do not take account of all the thermal parameters involved in a pattern of incubator such as the thermal parameters of preterm neonates (birth weight < 1000grams). The objective of this work is to design and validate a generalized predictive control (GPC) that takes into account the closed incubator model as well as the newborn premature model. Then, we implemented this control law on a DRAGER neonatal incubator with and without newborn using microcontroller card. Methods: The design of the predictive control law is based on a prediction model. The developed model allows us to take into account all the thermal exchanges (radioactive, conductive, convective and evaporative) and the various interactions between the environment of the incubator and the premature newborn. Results: The predictive control law and the simulation model developed in Matlab/Simulink environment make it possible to evaluate the quality of the mode of control of the air temperature to which newborn must be raised. The results of the simulation and implementation of the air temperature inside the incubator (with newborn and without newborn) prove the feasibility and effectiveness of the proposed GPC controller compared with a proportional–integral–derivative controller (PID controller).


title: Generalization of TORCS car racing controllers with artificial neural networks and linear regression analysis

abstract: Designing controllers for simulated cars is a challenging task because there are numerous sensor inputs and a lot of actuators to be controlled. Although it is possible to use domain-expert knowledge on the car racing, it is not trivial to represent the knowledge into controllers and tune the parameters for them. Therefore, it is natural to adopt machine learning approach into the knowledge-oriented controllers to enhance their performance and minimize tedious parameter tunings. In this paper, we try to enhance our own heuristic controllers using machine learning models which decide the appropriate parameters of the heuristic controllers given the current sensory inputs. At first, we predict the desired speed only using the equations derived by the linear regression analysis for the both curved and straight track segments. Because the decision on reducing speed before the corner is more complex than the one in the straight line and the corners, it is necessary to use a non-linear model such as artificial neural networks. Secondly, the linear regression and artificial neural networks are specialized to predict desired speed in different situations. Experimental results on TORCS-based car racing simulations show that the combination of the two machine learning algorithms with the heuristic outperforms other alternatives (heuristic only, heuristic+linear regression, and heuristic+artificial neural networks).


title: Real-time people detection and tracking for indoor surveillance using multiple top-view depth cameras

abstract: This paper proposes a real-time indoor surveillance system which installs multiple depth cameras from vertical top-view to track humans. This system leads to a novel framework to solve the traditional challenge of surveillance through tracking of multiple persons, such as severe occlusion, similar appearance, illumination changes, and outline deformation. To cover the entire space of indoor surveillance scene, the image stitching based on the cameras' spatial relation is also utilized. The background subtraction of the stitched top-view image can then be performed to extract the foreground objects in the cluttered environment. The detection scheme including the graph-based segmentation, the head hemiellipsoid model, and the geodesic distance map are cascaded to detect humans. Moreover, the shape feature based on diffusion distance is designed to verify the human tracking hypotheses within particle filter. The experimental results demonstrate the real-time performance and robustness in comparison with several state-of-the-art detection and tracking algorithms.


title: A Method for Temporal Fault Tree Analysis Using Intuitionistic Fuzzy Set and Expert Elicitation

abstract: Temporal fault trees (TFTs), an extension of classical Boolean fault trees, can model time-dependent failure behaviour of dynamic systems. The methodologies used for quantitative analysis of TFTs include algebraic solutions, Petri nets (PN), and Bayesian networks (BN). In these approaches, precise failure data of components are usually used to calculate the probability of the top event of a TFT. However, it can be problematic to obtain these precise data due to the imprecise and incomplete information about the components of a system. In this paper, we propose a framework that combines intuitionistic fuzzy set theory and expert elicitation to enable quantitative analysis of TFTs of dynamic systems with uncertain data. Experts’ opinions are taken into account to compute the failure probability of the basic events of the TFT as intuitionistic fuzzy numbers. Subsequently, for the algebraic approach, the intuitionistic fuzzy operators for the logic gates of TFT are defined to quantify the TFT. On the other hand, for the quantification of TFTs via PN and BN-based approaches, the intuitionistic fuzzy numbers are defuzzified to be used in these approaches. As a result, the framework can be used with all the currently available TFT analysis approaches. The effectiveness of the proposed framework is illustrated via application to a practical system and through a comparison of the results of each approach.


title: Comedo-like openings in melanoma*

abstract: We describe a case of melanoma with the presence of comedo-like openings at dermoscopy. These structures, typical of seborrheic keratosis, represent an uncommon finding in melanoma. We emphasize the importance of searching for specific dermoscopic criteria for melanocytic lesions during the examination of a pigmented lesion, despite possible observations of characteristic structures of non-melanocytic lesions, in order to increase the accuracy in the diagnosis of melanoma.


title: A software-defined architecture and prototype for disaggregated memory rack scale systems

abstract: Disaggregation and rack-scale systems have the potential of drastically increasing TCO and utilization of cloud datacenters, while maintaining performance. In this paper, we present a novel rack-scale system architecture featuring software-defined remote memory disaggregation. Our hardware design and operating system extensions enable unmodified applications to dynamically attach to memory segments residing on physically remote memory pools and use such remote segments in a byte-addressable manner, as if they were local to the application. Our system features also a control plane that automates software-defined dynamic matching of compute to memory resources, as driven by datacenter workload needs. We prototyped our system on the commercially available Zynq Ultrascale+ MPSoC platform. To our knowledge, this is the first time a software-defined disaggregated system has been prototyped on commercial hardware and evaluated through industry standard software benchmarks. Our initial results — using benchmarks that are artificially highly adversarial in terms of memory bandwidth — show that disaggregated memory access exhibits a round-trip latency of only 134 clock cycles; and a throughput penalty of as low as 55%, relative to locally-attached memory. We also discuss estimations as to how our findings may translate to applications with pragmatically milder memory aggressiveness levels, as well as innovation avenues across the stack opened up by our work.


title: Detecting Phishing Emails the Natural Language Way

abstract: Phishing causes billions of dollars in damage every year and poses a serious threat to the Internet economy. Email is still the most commonly used medium to launch phishing attacks [1]. In this paper, we present a comprehensive natural language based scheme to detect phishing emails using features that are invariant and fundamentally characterize phishing. Our scheme utilizes all the information present in an email, namely, the header, the links and the text in the body. Although it is obvious that a phishing email is designed to elicit an action from the intended victim, none of the existing detection schemes use this fact to identify phishing emails. Our detection protocol is designed specifically to distinguish between “actionable” and “informational” emails. To this end, we incorporate natural language techniques in phishing detection. We also utilize contextual information, when available, to detect phishing: we study the problem of phishing detection within the contextual confines of the user’s email box and demonstrate that context plays an important role in detection. To the best of our knowledge, this is the first scheme that utilizes natural language techniques and contextual information to detect phishing. We show that our scheme outperforms existing phishing detection schemes. Finally, our protocol detects phishing at the email level rather than detecting masqueraded websites. This is crucial to prevent the victim from clicking any harmful links in the email. Our implementation called PhishNet-NLP, operates between a user’s mail transfer agent (MTA) and mail user agent (MUA) and processes each arriving email for phishing attacks even before reaching the inbox.


title: The Diagnosis of Asynchronous Sequential Switching Systems

abstract: This paper considers the problem of automatically testing a sequential switching circuit. It is assumed that the sequential circuit is nonclocked in order that the same automatic tester may be used for a wide class of circuits. The program for the tester is to be generated by an IBM 7090 computer from the logical description of the circuit to be tested. The specific problem considered here is to write a program for the 7090 in order to accomplish this purpose. A method of solution and a brief description of the program are given and a worked example is supplied.


title: A controlled study of virtual reality exposure therapy for the fear of flying.

abstract: Fear of flying (FOF) affects an estimated 10-25% of the population. Patients with FOF (N = 49) were randomly assigned to virtual reality exposure (VRE) therapy, standard exposure (SE) therapy, or a wait-list (WL) control. Treatment consisted of 8 sessions over 6 weeks, with 4 sessions of anxiety management training followed by either exposure to a virtual airplane (VRE) or exposure to an actual airplane at the airport (SE). A posttreatment flight on a commercial airline measured participants' willingness to fly and anxiety during flight immediately after treatment. The results indicated that VRE and SE were both superior to WL, with no differences between VRE and SE. The gains observed in treatment were maintained at a 6-month follow up. By 6 months posttreatment, 93% of VRE participants and 93% of SE participants had flown. VRE therapy and SE therapy for treatment of FOF were unequivocally supported in this controlled study.


title: The AIP Model of EMDR Therapy and Pathogenic Memories

abstract: Eye Movement Desensitization and Reprocessing therapy has been widely recognized as an efficacious treatment for post-traumatic stress disorder (PTSD). In the last years more insight has been gained regarding the efficacy of EMDR therapy in a broad field of mental disorders beyond PTSD. The cornerstone of EMDR therapy is its unique model of pathogenesis and change: the adaptive information processing model (AIP). The AIP model developed by F. Shapiro has found support and differentiation in recent studies on the importance of memories in the pathogenesis of a range of mental disorders beside PTSD. However theoretical publica-tions or research on the application of the AIP model are still rare. The increasing acceptance of ideas that relate the origin of many mental disorders to the formation and consolidation of implicit dysfunctional memory, lead to formation of the theory of pathogenic memories. Within the theory of pathogenic memories these implicit dysfunctional memories are consid-ered to form basis of a variety of mental disorders. The theory of pathogenic memories seems compatible to the AIP model of EMDR therapy, which offers strategies to effectively acess and transmute these memories leading to amelioration or resolution of symptoms. Merging the AIP model with the theory of pathogenic memories may initiate research. In consequence patients suffering from such ‚memory based’ disorders may be earlier diagnosed and treated more effectively.


title: Current practice of liquid chromatography-mass spectrometry in metabolomics and metabonomics.

abstract: Based on publication and citation numbers liquid chromatography (LC-MS) has become the major analytical technology in the field of global metabolite profiling. This dominance reflects significant investments from both the research community and instrument manufacturers. Here an overview of the approaches taken for LC-MS-based metabolomics research is given, describing critical steps in the realisation of such studies: study design and its needs, specific technological problems to be addressed and major obstacles in data treatment and biomarker identification. The current state of the art for LC-MS-based analysis in metabonomics/metabolomics is described including recent developments in liquid chromatography, mass spectrometry and data treatment as these are applied in metabolomics underlining the challenges, limitations and prospects for metabolomics research. Examples of the application of metabolite profiling in the life sciences focusing on disease biomarker discovery are highlighted. In addition, new developments and future prospects are described.


title: A Compact Broadband Riblet-Type Three-Way Power Divider in Rectangular Waveguide

abstract: A compact broadband three-way power divider based on a Riblet type coupler is proposed and designed in W-band. Coupling occurs among three adjacent waveguides through narrow walls. Two cylindrical posts located in the coupling region, help to achieve efficient coupling within shorter length and also to reduce reflections. The tapering of the input central waveguide improves the return loss of the power divider. The proposed power divider is validated with experimental measurements and measured power division ratio is −5.2 dB ± 0.25 dB. The return loss and isolation between the output ports is better than 20 dB over 88.75-97.5 GHz frequency range. The amplitude imbalance between the output ports is less than ± 0.4 dB. The fractional bandwidth for the power divider is 9.47% and the coupling length is $1.7\lambda _{\mathrm {\mathbf {g}}}$ . The simulated and measured results are in close agreement.


title: Interactive Attention Transfer Network for Cross-Domain Sentiment Classification

abstract: Cross-domain sentiment classification refers to utilizing useful knowledge in the source domain to help sentiment classification in the target domain which has few or no labeled data. Most existing methods mainly concentrate on extracting common features between domains. Unfortunately, they cannot fully consider the effects of the aspect (e.g., the battery life in reviewing an electronic product) information of the sentences. In order to better solve this problem, we propose an Interactive Attention Transfer Network (IATN) for crossdomain sentiment classification. IATN provides an interactive attention transfer mechanism, which can better transfer sentiment across domains by incorporating information of both sentences and aspects. Specifically, IATN comprises two attention networks, one of them is to identify the common features between domains through domain classification, and the other aims to extract information from the aspects by using the common features as a bridge. Then, we conduct interactive attention learning for those two networks so that both the sentences and the aspects can influence the final sentiment representation. Extensive experiments on the Amazon reviews dataset and crowdfunding reviews dataset not only demonstrate the effectiveness and universality of our method, but also give an interpretable way to track the attention information for sentiment.


title: Isolated Three-Phase High Power Factor Rectifier Based on the SEPIC Converter Operating in Discontinuous Conduction Mode

abstract: This paper presents the analysis and design of a three-phase high power factor rectifier, based on the dc-dc single-ended primary-inductance converter (SEPIC) operating in discontinuous conduction mode, with output voltage regulation and high frequency isolation. The input high power factor is naturally attained through the operational mode without the use of current sensors and a current control loop. To validate the theoretical analysis, a design example and experimental results for a 4-kW, 380-V line-to-line input voltage, 400-V output voltage, 0.998 power factor, 40-kHz switching frequency, and 4% input current total harmonic distortion laboratory prototype are presented, considering two distinct modulators. In addition, experimental results for the output voltage closed-loop control are presented.


title: Predictors of early growth in academic achievement: the head-toes-knees-shoulders task

abstract: Children’s behavioral self-regulation and executive function (EF; including attentional or cognitive flexibility, working memory, and inhibitory control) are strong predictors of academic achievement. The present study examined the psychometric properties of a measure of behavioral self-regulation called the Head-Toes-Knees-Shoulders (HTKS) by assessing construct validity, including relations to EF measures, and predictive validity to academic achievement growth between prekindergarten and kindergarten. In the fall and spring of prekindergarten and kindergarten, 208 children (51% enrolled in Head Start) were assessed on the HTKS, measures of cognitive flexibility, working memory (WM), and inhibitory control, and measures of emergent literacy, mathematics, and vocabulary. For construct validity, the HTKS was significantly related to cognitive flexibility, working memory, and inhibitory control in prekindergarten and kindergarten. For predictive validity in prekindergarten, a random effects model indicated that the HTKS significantly predicted growth in mathematics, whereas a cognitive flexibility task significantly predicted growth in mathematics and vocabulary. In kindergarten, the HTKS was the only measure to significantly predict growth in all academic outcomes. An alternative conservative analytical approach, a fixed effects analysis (FEA) model, also indicated that growth in both the HTKS and measures of EF significantly predicted growth in mathematics over four time points between prekindergarten and kindergarten. Results demonstrate that the HTKS involves cognitive flexibility, working memory, and inhibitory control, and is substantively implicated in early achievement, with the strongest relations found for growth in achievement during kindergarten and associations with emergent mathematics.


title: Multimodal Dialogs (MMD): A large-scale dataset for studying multimodal domain-aware conversations

abstract: Owing to the success of deep learning techniques for tasks such as Q/A and text-based dialog, there is an increasing demand for AI agents in several domains such as retail, travel, entertainment, etc. that can carry on multimodal conversations with humans employing both text and images within a dialog seamlessly. However, deep learning research is this area has been limited primarily due to the lack of availability of large-scale, open conversation datasets. To overcome this bottleneck, in this paper we introduce the task of multi-modal, domain-aware conversations, and propose the MMD benchmark dataset to- wards this task. This dataset was gathered by working in close coordination with large number of domain experts in the retail domain and consists of over 150K conversation sessions between shoppers and sales agents. With this dataset, we propose 5 new sub-tasks for multimodal conversations along with their evaluation methodology. We also propose two novel multi-modal deep learning models in the encode- attend-decode paradigm and demonstrate their performance on two of the sub-tasks, namely text response generation and best image response selection. These experiments serve to establish baseline performance numbers and open new directions of research for each of these sub-tasks.


title: A parallel and reconfigurable architecture for efficient OMP compressive sensing reconstruction

abstract: Compressive Sensing (CS) is a novel scheme, in which a signal that is sparse in a known transform domain can be reconstructed using fewer samples. However, the signal reconstruction techniques are computationally intensive and power consuming, which make them impractical for embedded applications. This work presents a parallel and reconfigurable architecture for Orthogonal Matching Pursuit (OMP) algorithm, one of the most popular CS reconstruction algorithms. In this paper, we are proposing the first reconfigurable OMP CS reconstruction architecture which can take different image sizes with sparsity up to 32. The aim is to minimize the hardware complexity, area and power consumption, and improve the reconstruction latency while meeting the reconstruction accuracy. First, the accuracy of reconstructed images is analyzed for different sparsity values and fixed point word length reduction. Next, efficient parallelization techniques are applied to reconstruct signals with variant signal lengths of N. The OMP algorithm is mainly divided into three kernels, where each kernel is parallelized to reduce execution time, and efficient reuse of the matrix operators allows us to reduce area. The proposed architecture can reconstruct images of different sizes and measurements and is implemented on a Xilinx Virtex 7 FPGA. The results indicate that, for a 128x128 image reconstruction, the proposed reconfigurable architecture is 2.67x to 1.8x faster than the previous non-reconfigurable work which is less complex and uses much smaller sparsity.


title: Crawling Online Social Graphs

abstract: Extensive research has been conducted on top of online social networks (OSNs), while little attention has been paid to the data collection process. Due to the large scale of OSNs and their privacy control policies, a partial data set is often used for analysis. The data set analyzed is decided by many factors including the choice of seeds, node selection algorithms, and the sample size. These factors may introduce biases and further contaminate or even skew the results. To evaluate the impact of different factors, this paper examines the OSN graph crawling problem, where the nodes are OSN users and the edges are the links (or relationship) among these users. More specifically, by looking at various factors in the crawling process, the following problems are addressed in this paper:* Efficiency: How fast different crawlers discover nodes/links;* Sensitivity: How different OSNs and the number of protected users affect crawlers;* Bias: How major graph properties are skewed.To the best of our knowledge, our simulations on four real world online social graphs provide the first in-depth empirical answers to these questions.


title: Handling mobility in IoT applications using the MQTT protocol

abstract: Connectivity clearly plays an important role in Internet of Things (IoT) solutions, and the efficient handling of mobility is crucial for the overall performance of IoT applications. Currently, the most widely adopted protocols for IoT and Machine to Machine (M2M) environments, namely MQTT, CoAP or LWM2M, are directly dependent on the TCP/IP protocol suite. This suite is highly reliable when using wired networks, but it is not the best solution in the presence of intermittent connections. In this work we provide a solution to improve MQTT with an emphasis on mobile scenarios. The advantage of the solution we propose is making the system more immune to changes in the point of attachment of mobile devices. This way we avoid IoT service developers having to explicitly consider this issue. Moreover, our solution does not need extra support from the network through protocols like MobileIP or LISP. The obtained results show that our proposal, based on intermediate buffering, guarantees that there is no information loss during hand-off periods due to node mobility; furthermore, based on discrete event simulation results, we determine the maximum number of sources and the required amount of buffers for a mobile node.


title: What Do Mobile App Users Complain About?

abstract: Mobile-app quality is becoming an increasingly important issue. These apps are generally delivered through app stores that let users post reviews. These reviews provide a rich data source you can leverage to understand user-reported issues. Researchers qualitatively studied 6,390 low-rated user reviews for 20 free-to-download iOS apps. They uncovered 12 types of user complaints. The most frequent complaints were functional errors, feature requests, and app crashes. Complaints about privacy and ethical issues and hidden app costs most negatively affected ratings. In 11 percent of the reviews, users attributed their complaints to a recent app update. This study provides insight into the user-reported issues of iOS apps, along with their frequency and impact, which can help developers better prioritize their limited quality assurance resources.


title: An agile v-model for medical device software development to overcome the challenges with plan-driven software development lifecycles

abstract: Through the use of semi structured interviews with medical device software organizations it emerged that medical device software organizations are experiencing difficulties when following plan driven Software Development Life Cycles (SDLC), particularly in the area of requirements management. To attempt to resolve these issues an examination of the non-regulated industry was performed to determine if lessons learned there could be applied to the development of medical device software. This examination revealed that agile methods are being widely adopted in the non-regulated software industry. To learn if agile methods could be adopted when developing medical device software a mapping study was performed which looked for instances of where agile methods have been used in regulated industries and where they have been adopted, to what success. This mapping study revealed that incorporating agile practices with the existing plan driven SDLC is the most favourable choice for medical device software organizations. This research aims to develop a SDLC which has a foundation of a plan driven SDLC which incorporates agile practices which can be followed when developing regulatory compliant software.


title: Towards a General Framework for Evaluating Software Development Methodologies

abstract: It has become essential to scrutinize and evaluate software development methodologies, mainly because of their increasing number and variety. Evaluation is required to gain a better understanding of the features, strengths, and weaknesses of the methodologies. The results of such evaluations can be leveraged to identify the methodology most appropriate for a specific context. Moreover, methodology improvement and evolution can be accelerated using these results. However, despite extensive research, there is still a need for a feature/criterion set that is general enough to allow methodologies to be evaluated regardless of their types. We propose a general evaluation framework which addresses this requirement. In order to improve the applicability of the proposed framework, all the features – general and specific – are arranged in a hierarchy along with their corresponding criteria. Providing different levels of abstraction enables users to choose the suitable criteria based on the context. Major evaluation frameworks for object-oriented, agent-oriented, and aspect-oriented methodologies have been studied and assessed against the proposed framework to demonstrate its reliability and validity.


title: A dynamic predictive maintenance policy for complex multi-component systems

abstract: The use of prognostic methods in maintenance in order to predict remaining useful life is receiving more attention over the past years. The use of these techniques in maintenance decision making and optimization in multi-component systems is however a still underexplored area. The objective of this paper is to optimally plan maintenance for a multi-component system based on prognostic/predictive information while considering different component dependencies (i.e. economic, structural and stochastic dependence). Consequently, this paper presents a dynamic predictive maintenance policy for multi-component systems that minimizes the long-term mean maintenance cost per unit time. The proposed maintenance policy is a dynamic method as the maintenance schedule is updated when new information on the degradation and remaining useful life of components becomes available. The performance, regarding the objective of minimal long-term mean cost per unit time, of the developed dynamic predictive maintenance policy is compared to five other conventional maintenance policies, these are: block-based maintenance, age-based maintenance, age-based maintenance with grouping, inspection condition-based maintenance and continuous condition-based maintenance. The ability of the predictive maintenance policy to react to changing component deterioration and dependencies within a multi-component system is quantified and the results show significant cost savings.


title: Soft pneumatic grippers embedded with stretchable electroadhesion

abstract: Current soft pneumatic grippers cannot robustly grasp flat materials and flexible objects on curved surfaces without distorting them. Current electroadhesive grippers, on the other hand, are difficult to actively deform to complex shapes to pick up free-form surfaces or objects. An easy-to-implement PneuEA gripper is proposed by the integration of an electroadhesive gripper and a two-fingered soft pneumatic gripper. The electroadhesive gripper was fabricated by segmenting a soft conductive silicon sheet into a two-part electrode design and embedding it in a soft dielectric elastomer. The two-fingered soft pneumatic gripper was manufactured using a standard soft lithography approach. This novel integration has combined the benefits of both the electroadhesive and soft pneumatic grippers. As a result, the proposed PneuEA gripper was not only able to pick-and-place flat and flexible materials such as a porous cloth but also delicate objects such as a light bulb. By combining two soft touch sensors with the electroadhesive, an intelligent and shape-adaptive PneuEA material handling system has been developed. This work is expected to widen the applications of both soft gripper and electroadhesion technologies.


title: Towards Interactive Curation & Automatic Tuning of ML Pipelines

abstract: Democratizing Data Science requires a fundamental rethinking of the way data analytics and model discovery is done. Available tools for analyzing massive data sets and curating machine learning models are limited in a number of fundamental ways. First, existing tools require well-trained data scientists to select the appropriate techniques to build models and to evaluate their outcomes. Second, existing tools require heavy data preparation steps and are often too slow to give interactive feedback to domain experts in the model building process, severely limiting the possible interactions. Third, current tools do not provide adequate analysis of statistical risk factors in the model development. In this work, we present the first iteration of QuIC-M (pronounced quick-m), an interactive human-in-the-loop data exploration and model building suite. The goal is to enable domain experts to build the machine learning pipelines an order of magnitude faster than machine learning experts while having model qualities comparable to expert solutions.


title: Meta-path-based link prediction in schema-rich heterogeneous information network

abstract: Recent years have witnessed the boom of heterogeneous information network (HIN), which contains different types of nodes and relations. Complex structure and rich semantics are unique features of HIN. Meta-path, the sequence of object types and relations connecting them, has been widely used to mine this semantic information in HIN. Link prediction is an important data mining task to predict the potential links among nodes, which are required in many applications, e.g., filling missing links. The contemporary link prediction is usually based on simple HIN whose schema is bipartite or star schema. In these works, the meta-paths should be predefined or enumerated. However, in many real networked data, it is hard to describe their network structures with simple schema. For example, the RDF-formatted Knowledge Graph which includes tens of thousands types of objects and links is a kind of schema-rich HIN. In this kind of schema-rich HIN, it is impossible to enumerate meta-paths so that the contemporary work is invalid. In this paper, we study link prediction in schema-rich HIN and propose a novel method named Link Prediction with automatic meta Path (LiPaP). The LiPaP designs an algorithm called automatic meta-path generation to automatically extract meta-paths from schema-rich HIN in the approximate order of relevance and adopt a supervised method with likelihood function to learn the weights of extracted meta-paths. Extensive experiments on real knowledge database, Yago, demonstrate that LiPaP is an effective, steady and efficient approach.


title: Developing Maturity Models for IT Management

abstract: Maturity models are valuable instruments for IT managers because they allow the assessment of the current situation of a company as well as the identification of reasonable improvement measures. Over the last few years, more than a hundred maturity models have been developed to support IT management. They address a broad range of different application areas, comprising holistic assessments of IT management as well as appraisals of specific subareas (e. g. Business Process Management, Business Intelligence).


title: Enhancing Light Blob Detection for Intelligent Headlight Control Using Lane Detection

abstract: In this paper, we propose an enhanced method for detecting light blobs (LBs) for intelligent headlight control (IHC). The main function of the IHC system is to automatically convert high-beam headlights to low beam when vehicles are found in the vicinity. Thus, to implement the IHC, it is necessary to detect preceding or oncoming vehicles. Generally, this process of detecting vehicles is done by detecting LBs in the images. Previous works regarding LB detection can largely be categorized into two approaches by the image type they use: low-exposure (LE) images or autoexposure (AE) images. While they each have their own strengths and weaknesses, the proposed method combines them by integrating the use of the partial region of the AE image confined by the lane detection information and the LE image. Consequently, the proposed method detects headlights at various distances and taillights at close distances using LE images while handling taillights at distant locations by exploiting the confined AE images. This approach enhances the performance of detecting the distant LBs while maintaining low false detections.


title: Understanding resilience: New approaches for preventing and treating PTSD

abstract: All individuals experience stressful life events, and up to 84% of the general population will experience at least one potentially traumatic event. In some cases, acute or chronic stressors lead to the development of posttraumatic stress disorder (PTSD) or other psychopathology; however, the majority of people are resilient to such effects. Resilience is the ability to adapt successfully in the face of stress and adversity. A wealth of research has begun to identify the genetic, epigenetic, neural, and environmental underpinnings of resilience, and has indicated that resilience is mediated by adaptive changes encompassing several environmental factors, neural circuits, numerous neurotransmitters, and molecular pathways. The first part of this review focuses on recent findings regarding the genetic, epigenetic, developmental, psychosocial, and neurochemical factors as well as neural circuits and molecular pathways that underlie the development of resilience. Emerging and exciting areas of research and novel methodological approaches, including genome-wide gene expression studies, immune, endocannabinoid, oxytocin, and glutamatergic systems, are explored to help delineate innovative mechanisms that may contribute to resilience. The second part reviews several interventions and preventative approaches designed to enhance resilience in both developmental and adult populations. Specifically, the review will delineate approaches aimed to bolster resilience in individuals with PTSD. Furthermore, we discuss novel pharmacologic approaches, including the N-methyl-d-aspartate (NMDA) receptor ketamine and neuropeptide Y (NPY), as exciting new prospects for not only the treatment of PTSD but as new targets to enhance resilience. Our growing understanding of resilience and interventions will hopefully lead to the development of new strategies for not just treating PTSD but also screening and early identification of at-risk youth and adults. Taken together, efforts aimed at dissemination and implementation of novel interventions to enhance resilience will have to keep pace with the growth of new preventive and treatment strategies.


title: #bias: Measuring the Tweeting Behavior of Propagandists

abstract: Twitter is an efficient conduit of information for millions of usersaround the world. Its ability to quickly spread information to a largenumber of people makes it an efficient way to shape information and,hence, shape public opinion. We study the tweeting behavior of Twitter propagandists, users who consistently expressthe same opinion or ideology, focusing on two online communities: the2010 Nevada senate race and the 2011 debt-ceiling debate. We identify several extreme tweeting patterns thatcould characterize users who spread propaganda: (1) sending high volumesof tweets over short periods of time, (2) retweeting whilepublishing little original content, (3) quickly retweeting, and (4) colluding with other, seeminglyunrelated, users to send duplicate or near-duplicate messages on thesame topic simultaneously. These four features appear to distinguishtweeters who spread propaganda from other more neutral users and could serve asstarting point for developing behavioral-based propaganda detection techniquesfor Twitter.


title: Transmission Lines Loaded With Bisymmetric Resonators and Their Application to Angular Displacement and Velocity Sensors

abstract: This paper is focused on the analysis of coplanar waveguides (CPWs) loaded with circularly shaped electric-LC (ELC) resonators, the latter consisting of two coplanar loops connected in parallel through a common gap. Specifically, the resonator axis is aligned with the CPW axis, and a dynamic loading with ELC rotation is considered. Since the ELC resonator is bisymmetric, i.e., it exhibits two orthogonal symmetry planes, the angular orientation range is limited to 90°. It is shown that the transmission and reflection coefficients of the structure depend on the angular orientation of the ELC. In particular, the loaded CPW behaves as a transmission line-type (i.e., all-pass) structure for a certain ELC orientation (0°) since the resonator is not excited. However, by rotating the ELC, magnetic coupling to the line arises, and a notch in the transmission coefficient (with orientation dependent depth and bandwidth) appears. This feature is exploited to implement angular displacement sensors by measuring the notch depth in the transmission coefficient. To gain more insight on sensor design, the lumped element equivalent-circuit model for ELC-loaded CPWs with arbitrary ELC orientation is proposed and validated. Based on this approach, a prototype displacement sensor is designed and characterized. It is shown that by introducing additional elements (a circulator and an envelope detector), novel and high precision angular velocity sensors can also be implemented. An angular velocity sensor is thus proposed, characterized, and satisfactorily validated. The proposed solution for angular sensing is robust against environmental variations since it is based on the geometrical alignment/misalignment between the symmetry planes of the coupled elements.


title: Multi-task multi-dimensional hawkes processes for modeling event sequences

abstract: We propose a Multi-task Multi-dimensional Hawkes Process (MMHP) for modeling event sequences where there exist multiple triggering patterns within sequences and structures across sequences. MMHP is able to model the dynamics of multiple sequences jointly by imposing structural constraints and thus systematically uncover clustering structure among sequences. We propose an effective and robust optimization algorithm to learn MMHP models, which takes advantage of alternating direction method of multipliers (ADMM), majorization minimization and Euler-Lagrange equations. Our experimental results demonstrate that MMHP performs well on both synthetic and real data.


title: The Corticostriatal and Corticosubthalamic Pathways: Two Entries, One Target. So What?

abstract: The basal ganglia receive cortical inputs through two main stations – the striatum and the subthalamic nucleus (STN). The information flowing along the corticostriatal system is transmitted to the basal ganglia circuitry via the “direct and indirect” striatofugal pathways, while information that flows through the subthalamic nucleus is transmitted along the so-called “hyperdirect” pathway. The functional significance of this dual entry system is not clear. Although the corticostriatal system has been thoroughly characterized anatomically and electrophysiologically, such is not the case for the corticosubthalamic system. In order to provide further insights into the intricacy of this complex anatomical organization, this review examines and compares the anatomical and functional organization of the corticostriatal and corticosubthalamic systems, and highlight some key issues that must be addressed to better understand the mechanisms by which these two neural systems may interact to regulate basal ganglia functions and dysfunctions.