title: A 34Gbaud Linear Transimpedance Amplifier with Automatic Gain Control for 200Gb/s DP-16QAM Optical Coherent Receivers

abstract: A 34Gbaud TIA achieves 1.5% THD at 1mAppd input, 20pA/sqrt(Hz) input referred noise density, output amplitude of up to 1Vppd and maintains 27GHz bandwidth over 43dB of transimpedance gain dynamic range, enabling a 200Gb/s dual-polarization 16QAM coherent receiver operation.


title: segDeepM: Exploiting segmentation and context in deep neural networks for object detection

abstract: In this paper, we propose an approach that exploits object segmentation in order to improve the accuracy of object detection. We frame the problem as inference in a Markov Random Field, in which each detection hypothesis scores object appearance as well as contextual information using Convolutional Neural Networks, and allows the hypothesis to choose and score a segment out of a large pool of accurate object segmentation proposals. This enables the detector to incorporate additional evidence when it is available and thus results in more accurate detections. Our experiments show an improvement of 4.1% in mAP over the R-CNN baseline on PASCAL VOC 2010, and 3.4% over the current state-of-the-art, demonstrating the power of our approach.


title: Generalized Nash Equilibria for the Service Provisioning Problem in Cloud Systems

abstract: In recent years, the evolution and the widespread adoption of virtualization, service-oriented architectures, autonomic, and utility computing have converged letting a new paradigm to emerge: cloud computing. Clouds allow the on-demand delivering of software, hardware, and data as services. Currently, the cloud offer is becoming wider day by day because all the major IT companies and service providers, like Microsoft, Google, Amazon, HP, IBM, and VMWare, have started providing solutions involving this new technological paradigm. As cloud-based services are more numerous and dynamic, the development of efficient service provisioning policies becomes increasingly challenging. In this paper, we take the perspective of Software as a Service (SaaS) providers that host their applications at an Infrastructure as a Service (IaaS) provider. Each SaaS needs to comply with quality-of-service requirements, specified in service-level agreement (SLA) contracts with the end users, which determine the revenues and penalties on the basis of the achieved performance level. SaaS providers want to maximize their revenues from SLAs, while minimizing the cost of use of resources supplied by the IaaS provider. Moreover, SaaS providers compete and bid for the use of infrastructural resources. On the other hand, the IaaS wants to maximize the revenues obtained providing virtualized resources. In this paper, we model the service provisioning problem as a generalized Nash game and we show the existence of equilibria for such game. Moreover, we propose two solution methods based on the best-reply dynamics, and we prove their convergence in a finite number of iterations to a generalized Nash equilibrium. In particular, we develop an efficient distributed algorithm for the runtime allocation of IaaS resources among competing SaaS providers. We demonstrate the effectiveness of our approach by simulation and performing tests on a real prototype environment deployed on Amazon EC2. Results show that, compared to other state-of-the-art solutions, our model can improve the efficiency of the cloud system evaluated in terms of Price of Anarchy by 50-70 percent.


title: Flocks, herds and schools: A distributed behavioral model

abstract: The aggregate motion of a flock of birds, a herd of land animals, or a school of fish is a beautiful and familiar part of the natural world. But this type of complex motion is rarely seen in computer animation. This paper explores an approach based on simulation as an alternative to scripting the paths of each bird individually. The simulated flock is an elaboration of a particle systems, with the simulated birds being the particles. The aggregate motion of the simulated flock is created by a distributed behavioral model much like that at work in a natural flock; the birds choose their own course. Each simulated bird is implemented as an independent actor that navigates according to its local perception of the dynamic environment, the laws of simulated physics that rule its motion, and a set of behaviors programmed into it by the "animator." The aggregate motion of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual simulated birds.


title: Embedded Si Fan Out: A Low Cost Wafer Level Packaging Technology without Molding and De-Bonding Processes

abstract: The growing and diversifying system requirements have continued to drive the development of a variety of new package technologies and configurations: small form factor, low weight, low profile, high pin count and high speed and low cost. Embedded chip in EMC, also called fan-out wafer-level packaging (FOWLP), has been used in various products such as baseband, RF (radio frequency) transceiver, and PMICs (power management ICs). Currently, INFO technology developed by TSMCÂ®, NANIUMÂ® were in mass production for 3D integration for processor and memory, which inspires other packaging foundries to develop their own embedded FOWLP for the forecasted explosive growth of this market in the next few years. There are a number of challenges for FOWLP. For process point of view, temporary bonding and de-bonding are required. EMC wafers are difficult to handle due to its large warpage driven by the big CTE difference between the Si and molding material. In addition, the manufacturing of fine pitch RDL on EMC surface ...


title: Unsupervised Machine Translation Using Monolingual Corpora Only

abstract: Machine translation has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale parallel corpora. There have been numerous attempts to extend these successes to low-resource language pairs, yet requiring tens of thousands of parallel sentences. In this work, we take this research direction to the extreme and investigate whether it is possible to learn to translate even without any parallel data. We propose a model that takes sentences from monolingual corpora in two different languages and maps them into the same latent space. By learning to reconstruct in both languages from this shared feature space, the model effectively learns to translate without using any labeled data. We demonstrate our model on two widely used datasets and two language pairs, reporting BLEU scores up to 32.8, without using even a single parallel sentence at training time.


title: Optimization-based AMP for Phase Retrieval: The Impact of Initialization and $\ell_2$-regularization

abstract: We consider an l ::: 2 ::: -regularized non-convex optimization problem for recovering signals from their noisy phaseless observations. We design and study the performance of a message passing algorithm that aims to solve this optimization problem. We consider the asymptotic setting m, n → ∞, m/n → δ and obtain sharp performance bounds, where m is the number of measurements and n is the signal dimension. We show that for complex signals, the algorithm can perform accurate recovery with only m = ((64/π ::: 2 ::: ) - 4)n ≈ 2.5n measurements. Also, we provide a sharp analysis on the sensitivity of the algorithm to noise. We highlight the following facts about our message passing algorithm: 1) adding l ::: 2 ::: regularization to the non-convex loss function can be beneficial and 2) spectral initialization has a marginal impact on the performance of the algorithm. The sharp analyses, in this paper, not only enable us to compare the performance of our method with other phase recovery schemes but also shed light on designing better iterative algorithms for other non-convex optimization problems.


title: Instance Segmentation of Fibers from Low Resolution CT Scans via 3D Deep Embedding Learning

abstract: We propose a novel approach for automatic extraction (instance segmentation) of fibers from low resolution 3D X-ray computed tomography scans of short glass fiber reinforced polymers. We have designed a 3D instance segmentation architecture built upon a deep fully convolutional network for semantic segmentation with an extra output for embedding learning. We show that the embedding learning is capable of learning a mapping of voxels to an embedded space in which a standard clustering algorithm can be used to distinguish between different instances of an object in a volume. In addition, we discuss a merging post-processing method which makes it possible to process volumes of any size. The proposed 3D instance segmentation network together with our merging algorithm is the first known to authors knowledge procedure that produces results good enough, that they can be used for further analysis of low resolution fiber composites CT scans.


title: On Being Told How We Feel: How Algorithmic Sensor Feedback Influences Emotion Perception

abstract: Algorithms and sensors are increasingly deployed for highly personal aspects of our everyday lives. Recent work suggests people have imperfect understanding of system outputs, often assuming sophisticated capabilities and deferring to feedback. We explore how people construe algorithmic interpretations of emotional data in personal informatics systems. A survey (n=188) showed strong interest in automatic stress and emotion tracking, but many respondents expected these systems to provide objective measurements for their emotional experiences. A second study examined how algorithmic sensor feedback influences emotional self-judgments, by comparing three system framings of physiological ElectroDermal Activity data (EDA): Positive ("alert and engaged"), Negative ("stressed"), and Control (no frame) in a mixed-methods study with 64 participants. Despite users reporting strategies to test system outputs, users still deferred to feedback and their perceived emotions were significantly influenced by feedback frames. Some users overrode personal judgments, believing the system had access to privileged information about their emotions. Based on these findings, we explore design implications for personal informatics including risks of users trusting systems that seemingly "unlock" hidden aspects of the self. We propose design approaches that provide opportunities for future emotion-monitoring systems to exploit these framing effects, and for users to more actively construe emotional states.


title: Emotional agency in storygames

abstract: This paper explores the use of Weir's concept of emotional agency as a way to understand how agency can arise when players make meaningful decisions bearing emotional significance, regardless of whether these actions have major consequences. Through close readings of The Wolf Among Us, Kentucky Route Zero and Oxenfree, we suggest that emotional agency is linked to an illusion of agency on a local level, and a sense of affection towards the characters. However, emotional agency is potentially in conflict with global agency, when the desire to traverse other narrative routes is constrained by a player's sense of affection for the playable character. Based on these observations, we propose that emotional agency arises when moment-to-moment choices and responses, bearing sentimental value, accumulate to form a sense of attachment to characters in a game or interactive story.


title: Node classification for signed networks using diffuse interface methods

abstract: Signed networks contain both positive and negative kinds of interactions like friendship and enmity. The task of node classification in non-signed graphs has proven to be beneficial in many real world applications, yet extensions to signed networks remain largely unexplored. In this paper we introduce the first analysis of node classification in signed social networks via diffuse interface methods based on the Ginzburg-Landau functional together with different extensions of the graph Laplacian to signed networks. We show that blending the information from both positive and negative interactions leads to performance improvement in real signed social networks, consistently outperforming the current state of the art.


title: A Hybrid Global Maximum Power Point Tracking Technique With Fast Convergence Speed for Partial-Shaded PV Systems

abstract: Photovoltaic (PV) systems exhibit multiple local and one global maximum power points (MPPs) in their P – V and I – V curves during partial shading conditions (PSC). Thus, to improve the efficiency of the system, a global maximum power point tracking (GMPPT) algorithm is necessary. This paper presents a hybrid GMPPT algorithm for constant voltage load applications using a single current sensor. The proposed method combines single current sensor hill climbing (SSHC) and artificial bee colony (ABC) algorithms to track the GMPP. The SSHC algorithm detects the event of PSC and tracks the MPP during uniform insolation conditions. The output current of the power electronic interface is measured effectively at selective duty cycles to identify the type of P – V curve pattern and, thus, initiate either SSHC or ABC. The search space for the ABC algorithm is reduced in the proposed technique to improve the convergence speed. The proposed GMPPT technique is simulated in MATLAB and validated through experimental prototypes for various PSCs. The proposed algorithm tracks the GMPP with excellent efficiency and fast speed.


On Textual Analysis and Machine Learning for Cyberstalking Detection


Field-Robot-Based Agriculture : “ RemoteFarming . 1 ” and “ BoniRob-Apps


title: Compressing Convolutional Neural Networks in the Frequency Domain

abstract: Convolutional neural networks (CNN) are increasingly used in many areas of computer vision. They are particularly attractive because of their ability to "absorb" great quantities of labeled data through millions of parameters. However, as model sizes increase, so do the storage and memory requirements of the classifiers, hindering many applications such as image and speech recognition on mobile phones and other devices. In this paper, we present a novel net- work architecture, Frequency-Sensitive Hashed Nets (FreshNets), which exploits inherent redundancy in both convolutional layers and fully-connected layers of a deep learning model, leading to dramatic savings in memory and storage consumption. Based on the key observation that the weights of learned convolutional filters are typically smooth and low-frequency, we first convert filter weights to the frequency domain with a discrete cosine transform (DCT) and use a low-cost hash function to randomly group frequency parameters into hash buckets. All parameters assigned the same hash bucket share a single value learned with standard back-propagation. To further reduce model size, we allocate fewer hash buckets to high-frequency components, which are generally less important. We evaluate FreshNets on eight data sets, and show that it leads to better compressed performance than several relevant baselines.


title: Unsupervised DRG Upcoding Detection in Healthcare Databases

abstract: Diagnosis Related Group (DRG) upcoding is an anomaly in healthcare data that costs hundreds of millions of dollars in many developed countries. DRG upcoding is typically detected through resource intensive auditing. As supervised modeling of DRG upcoding is severely constrained by scope and timeliness of past audit data, we propose in this paper an unsupervised algorithm to filter data for potential identification of DRG upcoding. The algorithm has been applied to a hip replacement/revision dataset and a heart-attack dataset. The results are consistent with the assumptions held by domain experts.


title: A 320 GHz Frequency Quadrupler Based on 40 nm CMOS Technology

abstract: This letter presents a terahertz(THz) multiplier by four using two-stage push-push structure [1]. An 80 GHz Balun and a 160 GHz Balun are implemented by Marchand Balun structure and good performance of amplitude balance and phase balance are achieved as well as low insertion loss. The simulation results show that the output 3dB bandwidth is 70 GHz ranging from 270 GHz to 340 GHz. The quadruapler has a frequency conversion loss less than 13 dB. At the same time, good fundamental rejection greater than 30 dB and low DC power consumption 8 mW are achieved. The whole circuit is designed on 40 nm CMOS technology and occupies area of $\pmb{710 \mu\text{m} \times 400\mu \text{m}}$ including pads.


title: High-Rate Spatially Modulated Space Time Block Code

abstract: A high-rate spatially modulated space-time block code (HRSM-STBC) based on golden code is presented in which the spatial constellation (SC) codewords and the core STBC code matrix are designed for two active antennas. In addition to the high rate of the core STBC, the number of SC codewords offered by the HRSM-STBC scheme is the largest as compared to existing schemes that have the same antenna configuration. Consequently, the proposed scheme can provide the highest spectral efficiency to date. To maximize the achievable coding gain and diversity, the optimization of the HRSM-STBC is carried out. Performance advantages of our proposed scheme over its existing counterparts such as STBC-SM, H-STBC-SM, STBC-CSM, and DT-SM are demonstrated.


title: Automatically Detecting and Rating Product Aspects from Textual Customer Reviews

abstract: This paper proposes a new approach to aspect-based sentiment analysis. The goal of our algorithm is to obtain a summary of the most positive and the most negative aspects of a specific product, given a collection of free-text customer reviews. Our approach starts by matching handcrafted dependency paths in individual sentences to find opinions expressed towards candidate aspects. Then, it clusters together di?erent mentions of the same aspect by using a WordNet-based similarity measure. Finally, it computes a sentiment score for each aspect, which represents the overall emerging opinion of a group of customers towards a specific aspect of the product. Our approach does not require any seed word or domain-specific knowledge, as it only employs an off-the-shelf sentiment lexicon. We discuss encouraging preliminary results in detecting and rating aspects from on-line reviews of movies and MP3 players.


title: Interpolation of spatial data : some theory for kriging

abstract: 1 Linear Prediction.- 1.1 Introduction.- 1.2 Best linear prediction.- Exercises.- 1.3 Hilbert spaces and prediction.- Exercises.- 1.4 An example of a poor BLP.- Exercises.- 1.5 Best linear unbiased prediction.- Exercises.- 1.6 Some recurring themes.- The Matern model.- BLPs and BLUPs.- Inference for differentiable random fields.- Nested models are not tenable.- 1.7 Summary of practical suggestions.- 2 Properties of Random Fields.- 2.1 Preliminaries.- Stationarity.- Isotropy.- Exercise.- 2.2 The turning bands method.- Exercise.- 2.3 Elementary properties of autocovariance functions.- Exercise.- 2.4 Mean square continuity and differentiability.- Exercises.- 2.5 Spectral methods.- Spectral representation of a random field.- Bochner's Theorem.- Exercises.- 2.6 Two corresponding Hilbert spaces.- An application to mean square differentiability.- Exercises.- 2.7 Examples of spectral densities on 112.- Rational spectral densities.- Principal irregular term.- Gaussian model.- Triangular autocovariance functions.- Matern class.- Exercises.- 2.8 Abelian and Tauberian theorems.- Exercises.- 2.9 Random fields with nonintegrable spectral densities.- Intrinsic random functions.- Semivariograms.- Generalized random fields.- Exercises.- 2.10 Isotropic autocovariance functions.- Characterization.- Lower bound on isotropic autocorrelation functions.- Inversion formula.- Smoothness properties.- Matern class.- Spherical model.- Exercises.- 2.11 Tensor product autocovariances.- Exercises.- 3 Asymptotic Properties of Linear Predictors.- 3.1 Introduction.- 3.2 Finite sample results.- Exercise.- 3.3 The role of asymptotics.- 3.4 Behavior of prediction errors in the frequency domain.- Some examples.- Relationship to filtering theory.- Exercises.- 3.5 Prediction with the wrong spectral density.- Examples of interpolation.- An example with a triangular autocovariance function.- More criticism of Gaussian autocovariance functions.- Examples of extrapolation.- Pseudo-BLPs with spectral densities misspecified at high frequencies.- Exercises.- 3.6 Theoretical comparison of extrapolation and ointerpolation.- An interpolation problem.- An extrapolation problem.- Asymptotics for BLPs.- Inefficiency of pseudo-BLPs with misspecified high frequency behavior.- Presumed mses for pseudo-BLPs with misspecified high frequency behavior.- Pseudo-BLPs with correctly specified high frequency behavior.- Exercises.- 3.7 Measurement errors.- Some asymptotic theory.- Exercises.- 3.8 Observations on an infinite lattice.- Characterizing the BLP.- Bound on fraction of mse of BLP attributable to a set of frequencies.- Asymptotic optimality of pseudo-BLPs.- Rates of convergence to optimality.- Pseudo-BLPs with a misspecified mean function.- Exercises.- 4 Equivalence of Gaussian Measures and Prediction.- 4.1 Introduction.- 4.2 Equivalence and orthogonality of Gaussian measures.- Conditions for orthogonality.- Gaussian measures are equivalent or orthogonal.- Determining equivalence or orthogonality for periodic random fields.- Determining equivalence or orthogonality for nonperiodic random fields.- Measurement errors and equivalence and orthogonality.- Proof of Theorem 1.- Exercises.- 4.3 Applications of equivalence of Gaussian measures to linear prediction.- Asymptotically optimal pseudo-BLPs.- Observations not part of a sequence.- A theorem of Blackwell and Dubins.- Weaker conditions for asymptotic optimality of pseudo-BLPs.- Rates of convergence to asymptotic optimality.- Asymptotic optimality of BLUPs.- Exercises.- 4.4 Jeffreys's law.- A Bayesian version.- Exercises.- 5 Integration of Random Fields.- 5.1 Introduction.- 5.2 Asymptotic properties of simple average.- Results for sufficiently smooth random fields.- Results for sufficiently rough random fields.- Exercises.- 5.3 Observations on an infinite lattice.- Asymptotic mse of BLP.- Asymptotic optimality of simple average.- Exercises.- 5.4 Improving on the sample mean.- Approximating $$\int_0^1 {\exp } (ivt)dt$$.- Approximating $$\int_{{{[0,1]}^d}} {\exp (i{\omega ^T}x)} dx$$ in more than one dimension.- Asymptotic properties of modified predictors.- Are centered systematic samples good designs?.- Exercises.- 5.5 Numerical results.- Exercises.- 6 Predicting With Estimated Parameters.- 6.1 Introduction.- 6.2 Microergodicity and equivalence and orthogonality of Gaussian measures.- Observations with measurement error.- Exercises.- 6.3 Is statistical inference for differentiable processes possible?.- An example where it is possible.- Exercises.- 6.4 Likelihood Methods.- Restricted maximum likelihood estimation.- Gaussian assumption.- Computational issues.- Some asymptotic theory.- Exercises.- 6.5 Matern model.- Exercise.- 6.6 A numerical study of the Fisher information matrix under the Matern model.- No measurement error and?unknown.- No measurement error and?known.- Observations with measurement error.- Conclusions.- Exercises.- 6.7 Maximum likelihood estimation for a periodic version of the Matern model.- Discrete Fourier transforms.- Periodic case.- Asymptotic results.- Exercises.- 6.8 Predicting with estimated parameters.- Jeffreys's law revisited.- Numerical results.- Some issues regarding asymptotic optimality.- Exercises.- 6.9 An instructive example of plug-in prediction.- Behavior of plug-in predictions.- Cross-validation.- Application of Matern model.- Conclusions.- Exercises.- 6.10 Bayesian approach.- Application to simulated data.- Exercises.- A Multivariate Normal Distributions.- B Symbols.- References.


Nondegloving technique for Peyronie′s disease with penile prosthesis implantation and double dorsal-ventral patch graft


title: Could We Issue Driving Licenses to Autonomous Vehicles?

abstract: Many companies are studying autonomous vehicles. One trend in the development of control algorithms for autonomous vehicles is the use of deep-learning approaches. The general idea is to simulate a human driver’s decision-making and behavior in various scenarios without necessarily knowing why the decision is made. In this position paper, we first argue that traditional safety analysis methods need to be extended to verify deep-learning-based autonomous vehicles. Then, we propose borrowing ideas from the process of issuing driving licenses to human drivers to verify autonomous vehicles. Verification of autonomous vehicles could focus on sufficient training as well as mental and physical health checks. Based on this position, we list several challenges that need to be addressed.


title: 13.56-MHz Class-E RF power amplifier using normally-on GaN HEMT

abstract: Gallium nitride high-electron mobility transistors (GaN HEMTs) have attractive properties, low on-resistances and fast switching speeds. This paper presents the characteristics of a normally-on GaN HEMT that we fabricated. Further, the circuit operation of a Class-E amplifier is analyzed. Experimental results demonstrate the excellent performance of the gate drive circuit for the normally-on GaN HEMT and the 13.56MHz radio frequency (RF) power amplifier.


title: A Protocol Layer Trust-Based Intrusion Detection Scheme for Wireless Sensor Networks

abstract: This article proposes a protocol layer trust-based intrusion detection scheme for wireless sensor networks. Unlike existing work, the trust value of a sensor node is evaluated according to the deviations of key parameters at each protocol layer considering the attacks initiated at different protocol layers will inevitably have impacts on the parameters of the corresponding protocol layers. For simplicity, the paper mainly considers three aspects of trustworthiness, namely physical layer trust, media access control layer trust and network layer trust. The per-layer trust metrics are then combined to determine the overall trust metric of a sensor node. The performance of the proposed intrusion detection mechanism is then analyzed using the t-distribution to derive analytical results of false positive and false negative probabilities. Numerical analytical results, validated by simulation results, are presented in different attack scenarios. It is shown that the proposed protocol layer trust-based intrusion detection scheme outperforms a state-of-the-art scheme in terms of detection probability and false probability, demonstrating its usefulness for detecting cross-layer attacks.


title: Convergent innovation for sustainable economic growth and affordable universal health care: innovating the way we innovate.

abstract: This paper introduces convergent innovation (CI) as a form of meta-innovation-an innovation in the way we innovate. CI integrates human and economic development outcomes, through behavioral and ecosystem transformation at scale, for sustainable prosperity and affordable universal health care within a whole-of-society paradigm. To this end, CI combines technological and social innovation (including organizational, social process, financial, and institutional), with a special focus on the most underserved populations. CI takes a modular approach that convenes around roadmaps for real world change-a portfolio of loosely coupled complementary partners from the business community, civil society, and the public sector. Roadmaps serve as collaborative platforms for focused, achievable, and time-bound projects to provide scalable, sustainable, and resilient solutions to complex challenges, with benefits both to participating partners and to society. In this paper, we first briefly review the literature on technological innovation that sets the foundations of CI and motivates its feasibility. We then describe CI, its building blocks, and enabling conditions for deployment and scaling up, illustrating its operational forms through examples of existing CI-sensitive innovation.


title: Interactive Navigation of Open Data Linkages

abstract: We developed Toronto Open Data Search to support the ad hoc, interactive discovery of connections or linkages between datasets. It can be used to efficiently navigate through the open data cloud. Our system consists of three parts: a user-interface provided by a Web application; a scalable backend infrastructure that supports navigational queries; and a dynamic repository of open data tables. Our system uses LSH Ensemble, an efficient index structure, to compute linkages (attributes in two datasets with high containment score) in real time at Internet scale. Our application allows users to navigate along these linkages by joining datasets. ::: ::: LSH Ensemble is scalable, providing millisecond response times for linkage discovery queries even over millions of datasets. Our system offers users a highly interactive experience making unrelated (and unlinked) dynamic collections of datasets appear as a richly connected cloud of data that can be navigated and combined easily in real time.


title: Three-dimensional synthetic blood vessel generation using stochastic L-systems

abstract: Segmentation of blood vessels from magnetic resonance angiography (MRA) or computed tomography angiography (CTA) images is a complex process that usually takes a lot of computational resources. Also, most vascular ::: segmentation and detection algorithms do not work properly due to the wide architectural variability of the ::: blood vessels. Thus, the construction of convincing synthetic vascular trees makes it possible to validate new ::: segmentation methodologies. In this work, an extension to the traditional Lindenmayer system (L-system) that ::: generates synthetic 3D blood vessels by adding stochastic rules and parameters to the grammar is proposed. Towards this aim, we implement a parser and a generator of L-systems whose grammars simulate natural features ::: of real vessels such as the bifurcation angle, average length and diameter, as well as vascular anomalies, such as ::: aneurysms and stenoses. The resulting expressions are then used to create synthetic vessel images that mimic ::: MRA and CTA images. In addition, this methodology allows for vessel growth to be limited by arbitrary 3D ::: surfaces, and the vessel intensity profille can be tailored to match real angiographic intensities.


title: Quantitative electron microscopy for the nanoscale analysis of membrane lipid distribution

abstract: An important goal of membrane biology is to define the local heterogeneity of membrane lipid composition. Here we describe a quantitative electron microscopic method that enables the localization of specific membrane lipids at the nanometer scale. The method involves freezing cells rapidly to halt the molecular motion, physically stabilizing membrane molecules in the freeze-fracture replica by the deposition of evaporated platinum and carbon layers and labeling with specific probes for electron microscopic observation. Lipids in both the outer and inner membrane leaflets can thus be labeled, and their distributions can be analyzed quantitatively by statistical methods. A major advantage of this method is that it does not require the expression of artificial probes. Therefore, this method can be applied to any cell in vitro or in vivo, and the whole procedure can be completed in 1-2 d.


title: 3D CAD model retrieval based on the softassign quadratic assignment algorithm

abstract: A retrieval method for 3D CAD models based on the softassign quadratic assignment algorithm is presented in this paper. Firstly, retrieval models and target models are expressed as face adjacency graphs (FAGs) and thus 3D CAD model retrieval is turned to a graph matching problem. Secondly, vertex and edge compatibility matrices between the FAGs of a retrieval model and a target model are calculated. Then, an optimization objective function is created from compatibility matrices of a retrieval model and a target model, which serves as the similar metric for choosing vertex mapping matrix M of two models. Finally, the softassign quadratic assignment algorithm is introduced to find the optimal vertex mapping matrix M. Experimental results have shown that the proposed method supports 3D CAD model retrieval, and it is promising to meet the requirement of engineering applications.


title: Improved approximation algorithms for the vertex cover problem in graphs and hypergraphs

abstract: We obtain improved algorithms for finding small vertex covers in bounded degree graphs and hypergraphs. We use semidefinite programming to relax the problems and introduce new} rounding techniques for these relaxations. On graphs with maximum degree at most $\Delta$, the algorithm achieves a performance ratio of $2-(1-o(1))\frac{2 \ln \ln \Delta}{\ln \Delta}$ for large $\Delta$, which improves the previously known ratio of $2-\frac{\log \Delta + O(1)}{\Delta}$ obtained by Halld{orsson and Radhakrishnan. Using similar techniques, we also present improved approximations for the vertex cover problem in hypergraphs. For k-uniform hypergraphs with n vertices, we achieve a ratio of $k-(1-o(1))\frac{k\ln \ln n}{\ln n}$ for large n, and for k-uniform hypergraphs with maximum degree at most $\Delta$ the algorithm achieves a ratio of $k-(1-o(1))\frac{k(k-1)\ln \ln \Delta}{\ln \Delta}$ for large $\Delta$. These results considerably improve the previous best ratio of $k(1-c/\Delta^\frac{1}{k-1})$ for bounded degree k-uniform hypergraphs, and $k(1-c/n^\frac{k-1}{k})$ for general k-uniform hypergraphs, both obtained by Krivelevich. Using similar techniques, we also obtain an approximation algorithm for the weighted independent set problem, matching a recent result of Halldorsson.


title: Design of coupled power inductors with crossed anisotropy magnetic core for integrated power conversion

abstract: Design and partial microfabrication of a coupled power inductor is presented for use in high power-density integrated voltage regulators (IVR). The proposed inductor uses many laminations of uniaxial, high-permeability magnetic material where the orientation of anisotropy between successive laminations is rotated to provide an effectively isotropic core. The high permeability core allows for an inductance density of 200nH/mm2, while coupling between phases prevents magnetic saturation and allows a current density as high as 11A/mm2 according to quasi-static finite-element-analysis (FEA) simulations. The coupling factor, inductance and resistance of the device are optimized for operation in a four-phase integrated buck converter switching at 100MHz.


title: Efficient FFT Algorithm for Psychoacoustic Model of the MPEG-4 AAC

abstract: This paper proposes an efficient FFT algorithm for the Psycho-Acoustic Model (PAM) of MPEG-4 AAC. The proposed algorithm synthesizes FFT coefficients using MDCT and MDST coefficients through circular convolution. The complexity of the MDCT and MDST coefficients is approximately half of the original FFT. We also design a new PAM based on the proposed FFT algorithm, which has 15% lower computational complexity than the original PAM without degradation of sound quality. Subjective as well as objective test results are presented to confirm the efficiency of the proposed FFT computation algorithm and the PAM.


title: A Comparison of the Electroencephalogram between Institutionalized and Community Children in Romania

abstract: Electroencephalographic (EEG) data were collected from a sample of institutionalized infants and young children in Bucharest, Romania, and were compared with EEG data from age-matched children from the local community who had never been institutionalized and who were living with their families in the Bucharest area. Compared with the never-institutionalized group, the institutionalized group showed a pattern of increased low-frequency (theta) power in posterior scalp regions and decreased high-frequency (alpha and beta) power, particularly at frontal and temporal electrode sites. This finding is consistent with EEG studies of children facing environmental adversity and children with learning disorders. The institutionalized group also showed less marked hemispheric EEG asymmetries than the never-institutionalized group, particularly in the temporal region. The results are discussed in the context of two models: that the pattern of EEG in the institutionalized children reflects a maturational lag in nervous system development, or that it reflects tonic cortical hypoactivation.


title: Specification of concretization and symbolization policies in symbolic execution

abstract: Symbolic Execution (SE) is a popular and profitable approach to automatic code-based software testing. Concretization and symbolization (C/S) is a crucial part of modern SE tools, since it directly impacts the trade-offs between correctness, completeness and efficiency of the approach. Yet, C/S policies have been barely studied. We intend to remedy to this situation and to establish C/S policies on a firm ground. To this end, we propose a clear separation of concerns between C/S specification on one side, through the new rule-based description language CSml, and the algorithmic core of SE on the other side, revisited to take C/S policies into account. This view is implemented on top of an existing SE tool, demonstrating the feasibility and the benefits of the method. This work paves the way for more flexible SE tools with well-documented and reusable C/S policies, as well as for a systematic study of C/S policies.


title: Logistic Tensor Factorization for Multi-Relational Data

abstract: Tensor factorizations have become increasingly popular approaches for various learning tasks on structured data. In this work, we extend the RESCAL tensor factorization, which has shown state-of-the-art results for multi-relational learning, to account for the binary nature of adjacency tensors. We study the improvements that can be gained via this approach on various benchmark datasets and show that the logistic extension can improve the prediction results significantly.


Development of attachment orientations in response to childbirth: A longitudinal dyadic perspective.


title: A simple and efficient SNN and its performance&robustness evaluation method to enable hardware implementation

abstract: Spiking Neural Networks (SNN) are more closely related to brain-like computation and inspire hardware implementation. This is enabled by small networks that give high performance on standard classification problems. In literature, typical SNNs are deep and complex in terms of network structure, weight update rules and learning algorithms. This makes it difficult to translate them into hardware. In this paper, we first develop a simple 2-layered network in software which compares with the state of the art on four different standard data-sets within SNNs and has improved efficiency. For example, it uses lower number of neurons (3 x), synapses (3.5 x) and epochs for training (30 x) for the Fisher Iris classification problem. The efficient network is based on effective population coding and synapse-neuron co-design. Second, we develop a computationally efficient (15000 x) and accurate (correlation of 0.98) method to evaluate the performance of the network without standard recognition tests. Third, we show that the method produces a robustness metric that can be used to evaluate noise tolerance.


title: Minimal On-Road Time Route Scheduling on Time-Dependent Graphs

abstract: On time-dependent graphs, fastest path query is an important problem and has been well studied. It focuses on minimizing the total travel time (waiting time + on-road time) but does not allow waiting on any intermediate vertex if the FIFO property is applied. However, in practice, waiting on a vertex can reduce the time spent on the road (for example, resuming traveling after a traffic jam). In this paper, we study how to find a path with the minimal on-road time on time-dependent graphs by allowing waiting on some predefined parking vertices. The existing works are based on the following fact: the arrival time of a vertex v is determined by the arrival time of its in-neighbor u, which does not hold in our scenario since we also consider the waiting time on u if u allows waiting. Thus, determining the waiting time on each parking vertex to achieve the minimal on-road time becomes a big challenge, which further breaks FIFO property. To cope with this challenging problem, we propose two efficient algorithms using minimum on-road travel cost function to answer the query. The evaluations on multiple real-world time-dependent graphs show that the proposed algorithms are more accurate and efficient than the extensions of existing algorithms. In addition, the results further indicate, if the parking facilities are enabled in the route scheduling algorithms, the on-road time will reduce significantly compared to the fastest path algorithms.


title: Deep Orthogonal Representations: Fundamental Properties and Applications

abstract: Several representation learning and, more broadly, dimensionality reduction techniques seek to produce representations of the data that are orthogonal (uncorrelated). Examples include PCA, CCA, Kernel/Deep CCA, the ACE algorithm and correspondence analysis (CA). For a fixed data distribution, all finite variance representations belong to the same function space regardless of how they are derived. In this work, we present a theoretical framework for analyzing this function space, and demonstrate how a basis for this space can be found using neural networks. We show that this framework (i) underlies recent multi-view representation learning methods, (ii) enables classical exploratory statistical techniques such as CA to be scaled via neural networks, and (iii) can be used to derive new methods for comparing black-box models. We illustrate these applications empirically through different datasets.


title: Robust motion detection using histogram of oriented gradients for illumination variations

abstract: This paper proposes a robust motion detection method for illumination variations which uses histogram of oriented gradients. The detection process is divided into two phases: coarse detection and refinement. In the coarse detection phase, first, a texture-based background model is built which implements a group of adaptive histograms of oriented gradients; then, by comparing the histogram of oriented gradients of each pixel between current frame and background model, a foreground is segmented based on texture feature which is not susceptible to illumination variations, whereas some missing foreground regions exist; finally, the result based on texture is optimized by combining the pixel-wise detection result produced by Gaussian Mixture Model (GMM) algorithm, which greatly improves the detection performance by incorporating efficient morphological operations. In the refinement phase, the above detection result is refined based on the distinction in color feature to eliminate errors like shadows, noises, redundant contour, etc. Experimental results show the effectiveness and robustness of our approach in detecting moving objects in varying illumination conditions.


title: Query-oriented multi-document summarization via unsupervised deep learning

abstract: Extractive style query oriented multi document summarization generates the summary by extracting a proper set of sentences from multiple documents based on the pre given query. This paper proposes a novel multi document summarization framework via deep learning model. This uniform framework consists of three parts: concepts extraction, summary generation, and reconstruction validation, which work together to achieve the largest coverage of the documents content. A new query oriented extraction technique is proposed to concentrate distributed information to hidden units layer by layer. Then, the whole deep architecture is fine tuned by minimizing the information loss of reconstruction validation. According to the concentrated information, dynamic programming is used to seek most informative set of sentences as the summary. Experiments on three bench mark datasets demonstrate the effectiveness of the proposed framework and algorithms.


Characterizing Taxi Flows in New York City


title: PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls

abstract: Chromatin immunoprecipitation (ChIP) followed by tag sequencing (ChIP-seq) using high-throughput next-generation instrumentation is fast, replacing chromatin immunoprecipitation followed by genome tiling array analysis (ChIP-chip) as the preferred approach for mapping of sites of transcription-factor binding and chromatin modification. Using two deeply sequenced data sets for human RNA polymerase II and STAT1, each with matching input-DNA controls, we describe a general scoring approach to address unique challenges in ChIP-seq data analysis. Our approach is based on the observation that sites of potential binding are strongly correlated with signal peaks in the control, likely revealing features of open chromatin. We develop a two-pass strategy called PeakSeq to compensate for this. A two-pass strategy compensates for signal caused by open chromatin, as revealed by inclusion of the controls. The first pass identifies putative binding sites and compensates for genomic variation in the 'mappability' of sequences. The second pass filters out sites not significantly enriched compared to the normalized control, computing precise enrichments and significances. Our scoring procedure enables us to optimize experimental design by estimating the depth of sequencing required for a desired level of coverage and demonstrating that more than two replicates provides only a marginal gain in information.


title: Hearing Silences: Human Auditory Processing Relies on Preactivation of Sound-Specific Brain Activity Patterns

abstract: The remarkable capabilities displayed by humans in making sense of an overwhelming amount of sensory information cannot be explained easily if perception is viewed as a passive process. Current theoretical and computational models assume that to achieve meaningful and coherent perception, the human brain must anticipate upcoming stimulation. But how are upcoming stimuli predicted in the brain? We unmasked the neural representation of a prediction by omitting the predicted sensory input. Electrophysiological brain signals showed that when a clear prediction can be formulated, the brain activates a template of its response to the predicted stimulus before it arrives to our senses.


title: Attendance checker for students of Mapúa University

abstract: Attendance checking can be tedious as there are hundreds or even thousands of students to be checked every day whether they attend class or not and instances where checking attendances manually just makes it worse. This design aims to create a method of checking the attendances of students without the use of the traditional pen and paper method. The design made use of the RFID tags within the school ID of the students, Raspberry Pi as the processing unit of the system, and an android application that operates the entire system. The results include successes in the functionalities of the design including registration of students and checking of the attendance itself. It can be concluded that the design's effectiveness enables the professor to check the attendance of the students without the use of the pen and paper method.


title: The resource‐based view within the conversation of strategic management

abstract: The resource-based approach is an emerging framework that has stimulated discussion between scholars from three research perspectives. First, the resource-based theory incorporates traditional strategy insights concerning a firm's distinctive competencies and heterogeneous capabilities. The resource-based approach also provides value-added theoretical propositions that are testable within the diversification strategy literature. Second, the resource-based view fits comfortably within the organizational economics paradigm. Third, the resource-based view is complementary to industrial organization research. The resource-based view provides a framework for increasing dialogue between scholars from these important research areas within the conversation of strategic management. Resource-based studies that give simultaneous attention to each of these research programs are suggested.


title: CNN Feature boosted SeqSLAM for Real-Time Loop Closure Detection

abstract: Loop closure detection (LCD) is an indispensable part of simultaneous localization and mapping systems (SLAM); it enables robots to produce a consistent map by recognizing previously visited places. When robots operate over extended periods, robustness to viewpoint and condition changes as well as satisfactory real-time performance become essential requirements for a practical LCD system. This paper presents an approach to directly utilize the outputs at the intermediate layer of a pre-trained convolutional neural network (CNN) as image descriptors. The matching location is determined by matching the image sequences through a method called SeqCNNSLAM. The utility of SeqCNNSLAM is comprehensively evaluated in terms of viewpoint and condition invariance. Experiments show that SeqCNNSLAM outperforms state-of-the-art LCD systems, such as SeqSLAM and Change Removal, in most cases. To allow for the real-time performance of SeqCNNSLAM, an acceleration method, A-SeqCNNSLAM, is established. This method exploits the location relationship between the matching images of adjacent images to reduce the matching range of the current image. Results demonstrate that acceleration of 4-6 is achieved with minimal accuracy degradation, and the method's runtime satisfies the real-time demand. To extend the applicability of A-SeqCNNSLAM to new environments, a method called O-SeqCNNSLAM is established for the online adjustment of the parameters of A-SeqCNNSLAM.


title: On Architecture Design, Congestion Notification, TCP Incast and Power Consumption in Data Centers

abstract: With rapid deployment of modern data center networks (DCNs), many problems with respect to DCN architecture, congestion notification, TCP Incast, and power consumption have been observed. In this paper, we provide a comprehensive survey of the most recent research activities in DCNs, with emphasis on the network architecture design, congestion notification algorithms, TCP Incast, and power consumption. We describe in detail the architecture of a typical modern DCN. Furthermore, we investigate the challenges of the typical tree-based hierarchical DCN architecture encountered today and the requirements for the optimal DCN architecture, and we classify the proposed DCN architectures into switch-centric topology and server-centric topology. A section is also devoted to describe some newly proposed DCN architectures. We present a brief overview of the TCP Incast problem along with previously proposed solutions. We also review the recent energy-efficient solutions to minimize the power consumption in DCNs. Finally, we outline possible future research topics in DCNs.


title: Simplified Tightly-Coupled Cross-Dipole Arrangement for Base Station Applications

abstract: The electromagnetic fundamentals that govern the performance characteristics of dual-polarized tightly coupled cross-dipoles that are widely used in cellular base station applications are investigated. The mutual coupling effects and their impact on standard performance indices are stressed. A model is developed that considers this type of cross-dipole as an array. Links between the physical dimensions of the components of these model and key radiation characteristics, including directivity, half-power-beam width, and cross polarization discrimination levels, are established. The model guides the introduction and optimization of a simplified cross-dipole structure that exhibits excellent performance. A prototype was fabricated, assembled, and tested. The measured results are in good agreement with their simulated values, validating the model, and its governing principles.


title: A modeling ontology for integrating vulnerabilities into security requirements conceptual foundations

abstract: Vulnerabilities are weaknesses in the requirements, design, and implementation, which attackers exploit to compromise the system. This paper proposes a vulnerability-centric modeling ontology, which aims to integrate empirical knowledge of vulnerabilities into the system development process. In particular, we identify the basic concepts for modeling and analyzing vulnerabilities and their effects on the system. These concepts drive the definition of criteria that make it possible to compare and evaluate security frameworks based on vulnerabilities. We show how the proposed modeling ontology can be adopted in various conceptual modeling frameworks through examples.


title: An application of cascaded 3D fully convolutional networks for medical image segmentation

abstract: Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of volumetric images. In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of several anatomical structures (ranging from the large organs to thin vessels) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training class-specific models. To this end, we propose a two-stage, coarse-to-fine approach that will first use a 3D FCN to roughly define a candidate region, which will then be used as input to a second 3D FCN. This reduces the number of voxels the second FCN has to classify to ~10% and allows it to focus on more detailed segmentation of the organs and vessels. We utilize training and validation sets consisting of 331 clinical CT images and test our models on a completely unseen data collection acquired at a different hospital that includes 150 CT scans, targeting three anatomical organs (liver, spleen, and pancreas). In challenging organs such as the pancreas, our cascaded approach improves the mean Dice score from 68.5 to 82.2%, achieving the highest reported average score on this dataset. We compare with a 2D FCN method on a separate dataset of 240 CT scans with 18 classes and achieve a significantly higher performance in small organs and vessels. Furthermore, we explore fine-tuning our models to different datasets. Our experiments illustrate the promise and robustness of current 3D FCN based semantic segmentation of medical images, achieving state-of-the-art results. Our code and trained models are available for download: https://github.com/holgerroth/3Dunet_abdomen_cascade.


title: Minutiae Extraction From Level 1 Features of Fingerprint

abstract: Fingerprint features can be divided into three major categories based on the granularity at which they are extracted: level 1, level 2, and level 3 features. Orientation field, ridge frequency field, and minutiae set are three fundamental components of fingerprint, where the orientation field and ridge frequency field are regarded as level 1 features and minutiae set as level 2 features. It is generally believed that level 1 features, especially orientation field, can be reconstructed from level 2 features, i.e., minutiae. However, it is still a question that if minutiae can be extracted from level 1 features. In this paper, we analyze the relations between level 1 and level 2 features using the frequency modulation (FM) model and propose an approach to extract minutiae from level 1 features (i.e., orientation field and frequency field). The proposed algorithm is evaluated on NIST SD27 and FVC2002 DB1 databases. The true detection rate (TDR) and false detection rate (FDR) of minutiae detection on NIST SD27 and FVC2002 DB1 are about 45% and 30% compared with manually marked minutiae, respectively, with level 1 features extracted at a block size of 16 pixels. When pixelwise orientation and frequency fields are available, TDR and FDR can reach 70% and 25%, respectively. With a smaller block size, the minutiae recovering accuracy can be even higher. Our quantitative and experimental results show the deep relationship between level 1 and level 2 features of a fingerprint.


title: Nonlinear disparity mapping for stereoscopic 3D

abstract: This paper addresses the problem of remapping the disparity range of stereoscopic images and video. Such operations are highly important for a variety of issues arising from the production, live broadcast, and consumption of 3D content. Our work is motivated by the observation that the displayed depth and the resulting 3D viewing experience are dictated by a complex combination of perceptual, technological, and artistic constraints. We first discuss the most important perceptual aspects of stereo vision and their implications for stereoscopic content creation. We then formalize these insights into a set of basic disparity mapping operators. These operators enable us to control and retarget the depth of a stereoscopic scene in a nonlinear and locally adaptive fashion. To implement our operators, we propose a new strategy based on stereoscopic warping of the input video streams. From a sparse set of stereo correspondences, our algorithm computes disparity and image-based saliency estimates, and uses them to compute a deformation of the input views so as to meet the target disparities. Our approach represents a practical solution for actual stereo production and display that does not require camera calibration, accurate dense depth maps, occlusion handling, or inpainting. We demonstrate the performance and versatility of our method using examples from live action post-production, 3D display size adaptation, and live broadcast. An additional user study and ground truth comparison further provide evidence for the quality and practical relevance of the presented work.


title: An efficient visual loop closure detection method in a map of 20 million key locations

abstract: An important problem in robot simultaneous localization and mapping (SLAM) is loop closure detection. Recent studies of the problem have led to successful development of methods that are based on images captured by the robot. These methods tackle the issue of efficiency through data structures such as indexing and hierarchical (tree) organization of the image data that represent the robot map. In this paper, we offer an alternative approach and present a novel method for visual loop-closure detection. Our approach uses an extremely simple image representation, namely, a down-sampled binarized version of the original image, combined with a highly efficient image similarity measure mutual information. As a result, our method is able to perform loop closure detection in a map with 20 million key locations in about 2.38 seconds on a commodity computer. The excellent performance of our method in terms of its low complexity and accuracy in experiments establishes it as a promising solution to loop closure detection in large-scale robot maps.


title: Argumentation Mining: Exploiting Multiple Sources and Background Knowledge

abstract: The field of Argumentation Mining has arisen from the need of determining the underlying causes from an expressed opinion and the urgency to develop the established fields of Opinion Mining and Sentiment Analysis. The recent progress in the wider field of Artificial Intelligence in combination with the available data through Social Web has create great potential for every sub-field of Natural Language Process including Argumentation Mining.


title: The Immediate Effect of Therapeutic Touch and Deep Touch Pressure on Range of Motion, Interoceptive Accuracy and Heart Rate Variability: A Randomized Controlled Trial With Moderation Analysis

abstract: Background. There is paucity in the literature regarding the role of the interoceptive pathway through the insular cortex (IC), as well as heart rate variability (HRV) in relation to Osteopathic Manipulative Therapy (OMT) and deep-touch. Aims. The present study investigated whether both OMT treatment and deep-touch (a newly hypothesised treatment option) was effective at altering the interoceptive pathway and heart rate variability (HRV), whilst OMT was only expected to be effective for increasing Range of Motion (ROM). Method. Thirty-five healthy volunteers were randomly allocated into three conditions in a repeated measures crossover design; a control (laying supine on a plinth); deep-touch (head cradling); and an osteopathic mobilisation therapeutic technique on the temporomandibular joint (TMJ). Interoceptive accuracy (IAc), HRV, as well as range of motion (ROM) for the TMJ area as well as the cervical spine (Csp) right and left measures were taken pre and post each condition setting. Results. Significant condition effects emerged from the deep-touch and mobilisation interventions for IAc where increases were identified through planned comparisons. For the HRV measure (RMSSD), a significant effect emerged in the deep-touch condition (increase) but not in the mobilisation or control conditions. ROM did not increase for any condition. IAc correlated with post-ROM outcomes in many cases and HRV moderated some of these relations. Conclusions. These results are discussed in the context of clinical practice, where cranial deep-touch maybe an effective treatment and modulator of the parasympathetic nervous systems, as well as the interoceptive system.


title: Relational Object Maps for Mobile Robots

abstract: Mobile robot map building is the task of generating a model of an environment from sensor data. Most existing approaches to mobile robot mapping either build topological representations or generate accurate, metric maps of an environment. In this paper we introduce relational object maps, a novel approach to building metric maps that represent individual objects such as doors or walls. We show how to extend relational Markov networks in order to reason about a hierarchy of objects and the spatial relationships between them. Markov chain Monte Carlo is used for efficient inference and to learn the parameters of the model. We show that the spatial constraints modeled by our mapping technique yield drastic improvements for labeling line segments extracted from laser range-finders.


title: An automated satellite image classification design using object-oriented segmentation algorithms: A move towards standardization

abstract: Abstract Numerous segmentation algorithms have been developed, many of them highly specific and only applicable to a reduced class of problems and image data. Without an additional source of knowledge, automatic image segmentation based on low level image features seemed unlikely to succeed in extracting semantic objects in generic images. A new region-merging segmentation technique has recently been developed which incorporates the spectral and textural properties of the objects to be detected and also their different size and behaviour at different stages of scale, respectively. Linking this technique with the FAO Land Cover Land Use classification system resulted in the development of an automated, standardized classification methodology. Testing on Landsat and Aster images resulted in mutually exclusive classes with clear and unambiguous class definitions. The error matrix based on field samples showed overall accuracy values of 92% for Aster image and 89% for Landsat. The KIA values were 88% for Aster images and 84% for the Landsat image.


title: Real-Time Facial Segmentation and Performance Capture from RGB Input

abstract: We introduce the concept of unconstrained real-time 3D facial performance capture through explicit semantic segmentation in the RGB input. To ensure robustness, cutting edge supervised learning approaches rely on large training datasets of face images captured in the wild. While impressive tracking quality has been demonstrated for faces that are largely visible, any occlusion due to hair, accessories, or hand-to-face gestures would result in significant visual artifacts and loss of tracking accuracy. The modeling of occlusions has been mostly avoided due to its immense space of appearance variability. To address this curse of high dimensionality, we perform tracking in unconstrained images assuming non-face regions can be fully masked out. Along with recent breakthroughs in deep learning, we demonstrate that pixel-level facial segmentation is possible in real-time by repurposing convolutional neural networks designed originally for general semantic segmentation. We develop an efficient architecture based on a two-stream deconvolution network with complementary characteristics, and introduce carefully designed training samples and data augmentation strategies for improved segmentation accuracy and robustness. We adopt a state-of-the-art regression-based facial tracking framework with segmented face images as training, and demonstrate accurate and uninterrupted facial performance capture in the presence of extreme occlusion and even side views. Furthermore, the resulting segmentation can be directly used to composite partial 3D face models on the input images and enable seamless facial manipulation tasks, such as virtual make-up or face replacement.


title: POSTER: Towards Compiler-Assisted Taint Tracking on the Android Runtime (ART)

abstract: Dynamic analysis and taint tracking on Android was typically implemented by instrumenting the Dalvik Virtual Machine. However, the new Android Runtime (ART) introduced in Android 5 replaces the interpreter with an on-device compiler suite. Therefore as of Android 5, the applicability of interpreter instrumentation-based approaches like TaintDroid is limited to Android versions up to 4.4 Kitkat. In this poster, we present ongoing work on re-enabling taint tracking for apps by instrumenting the Optimizing backend, used by the new ART compiler suite for code generation. As Android now compiles apps ahead-of-time from dex bytecode to platform specific native code on the device itself, an instrumented compiler provides the opportunity to emit additional instructions that enable the actual taint tracking. The result is a custom compiler that takes arbitrary app APKs and transforms them into self-taint tracking native code, executable by the Android Runtime.


title: Flipped classroom — Students as producers

abstract: Flipped classroom is something that more and more teachers add to their teaching plans. To use video recordings of their lectures as a support for the students and then focus more on working with the curriculum in class has become a method that is adopted by an increasing number of lecturers. In higher education the students are adults. This implies that it needs to be a form of lecturing adapted to adults. From the area of organizational learning, and from andragogy, the key to learning lies in motivation and the motivation is triggered by engagement that in its turn stems from involvement. However, involving the learner in their own learning process is also about “letting go” of the teachers' full control. But is it necessary to maintain control? Is it possible to view the undertaking as a learning experience also for the teacher/lecturer? What control should be executed and what can one let go of? The research done at Hedmark University of Applied Sciences, show some interesting features. The courses have been “Learning Organizations” (autumn) and “Knowledge Management” (spring). The lectures have been in the form of streaming video and the course is organized as three full day seminars each semester/course. Each day has had a similar approach: a browse through the different chapters that are going to be discussed. Then follows solving assignments related to the presented topics, first in small groups, then in plenary. Before the lunch break, the students present suggestions towards possible new assignments. During the lunch break, the lecturer writes up the assignment using the input from the students. There is a quality check regarding the topics being within the scope of the seminar. After the lunch break, the students solve the assignment, first in small groups, then in plenary. The assignment and solution(s) are discussed using the following standard: 1. What did we learn from the assignment? 2. What did we learn from making the assignment? 3. Which issues raised in the assignment could be elaborated further, either as a mandatory assignment (fall) or an exam (fall and spring)? It is important to be clear and unambiguous about the learning objective of the course. It is also important to keep the scope within the limitations of the main literature. (This does, however, not exclude added resources like research papers, external links, etc.) Note also that there is a balance between the literature of the curriculum and the way the courses are taught. The course on “Learning Organizations” includes a section on how adults learn, and thus they are “convinced” about the method of teaching. The course “Knowledge Management” has the course “Learning Organizations” as a prerequisite, so it also “inherits” the way of lecturing/teaching/learning. This involvement, the students claim, has contributed to enhancing their learning outcomes. The students seem to grow accustomed to the organization and “expectations” the program. Also the average grades from last fall have improved from an average grade of C to an average grade of B. The activity in the classroom has shifted from the front of the classroom to the whole classroom. A few Observations made during this process are: 1. The students are far more strict than the lecturer 2. The students suggest wider assignments than suggested by lecturer 3. The lecturer receives numerous tips and hints to support creating new assignments 4. Even if it is not 6 hours lecturing, it is a demanding task to secure that the assignments and solutions at all times are within the framework of the learning objective 5. For the second and the third seminar, it is important to seek to include at least parts of the previous literature. The paper will detail the different issues tied to the process of this “flip” and seek to explain the findings using relevant theory.


title: Using Encyclopedic Knowledge For Named Entity Disambiguation

abstract: We present a new method for detecting and disambiguating named entities in open domain text. A disambiguation SVM kernel is trained to exploit the high coverage and rich structure of the knowledge encoded in an online encyclopedia. The resulting model significantly outperforms a less informed baseline.


title: An Equivalence of Fully Connected Layer and Convolutional Layer

abstract: This article demonstrates that convolutional operation can be converted to matrix multiplication, which has the same calculation way with fully connected layer. The article is helpful for the beginners of the neural network to understand how fully connected layer and the convolutional layer work in the backend. To be concise and to make the article more readable, we only consider the linear case. It can be extended to the non-linear case easily through plugging in a non-linear encapsulation to the values like this $\sigma(x)$ denoted as $x^{\prime}$.


title: Beyond SumBasic: Task-focused summarization with sentence simplification and lexical expansion

abstract: In recent years, there has been increased interest in topic-focused multi-document summarization. In this task, automatic summaries are produced in response to a specific information request, or topic, stated by the user. The system we have designed to accomplish this task comprises four main components: a generic extractive summarization system, a topic-focusing component, sentence simplification, and lexical expansion of topic words. This paper details each of these components, together with experiments designed to quantify their individual contributions. We include an analysis of our results on two large datasets commonly used to evaluate task-focused summarization, the DUC2005 and DUC2006 datasets, using automatic metrics. Additionally, we include an analysis of our results on the DUC2006 task according to human evaluation metrics. In the human evaluation of system summaries compared to human summaries, i.e., the Pyramid method, our system ranked first out of 22 systems in terms of overall mean Pyramid score; and in the human evaluation of summary responsiveness to the topic, our system ranked third out of 35 systems.


title: Calibration of Ultrawide Fisheye Lens Cameras by Eigenvalue Minimization

abstract: We present a new technique for calibrating ultrawide fisheye lens cameras by imposing the constraint that collinear points be rectified to be collinear, parallel lines to be parallel, and orthogonal lines to be orthogonal. Exploiting the fact that line fitting reduces to an eigenvalue problem in 3D, we do a rigorous perturbation analysis to obtain a practical calibration procedure. Doing experiments, we point out that spurious solutions exist if collinearity and parallelism alone are imposed. Our technique has many desirable properties. For example, no metric information is required about the reference pattern or the camera position, and separate stripe patterns can be displayed on a video screen to generate a virtual grid, eliminating the grid point extraction processing.


title: DARTS: Deceiving Autonomous Cars with Toxic Signs

abstract: Sign recognition is an integral part of autonomous cars. Any misclassification of traffic signs can potentially lead to a multitude of disastrous consequences, ranging from a life-threatening accident to even a large-scale interruption of transportation services relying on autonomous cars. In this paper, we propose and examine security attacks against sign recognition systems for Deceiving Autonomous caRs with Toxic Signs (we call the proposed attacks DARTS). In particular, we introduce two novel methods to create these toxic signs. First, we propose Out-of-Distribution attacks, which expand the scope of adversarial examples by enabling the adversary to generate these starting from an arbitrary point in the image space compared to prior attacks which are restricted to existing training/test data (In-Distribution). Second, we present the Lenticular Printing attack, which relies on an optical phenomenon to deceive the traffic sign recognition system. We extensively evaluate the effectiveness of the proposed attacks in both virtual and real-world settings and consider both white-box and black-box threat models. Our results demonstrate that the proposed attacks are successful under both settings and threat models. We further show that Out-of-Distribution attacks can outperform In-Distribution attacks on classifiers defended using the adversarial training defense, exposing a new attack vector for these defenses.


Domain Decomposition Method for Stokes Problem 2 with Tresca Friction


title: 5.6 A 0.13μm fully digital low-dropout regulator with adaptive control and reduced dynamic stability for ultra-wide dynamic range

abstract: An increasing number of power domains and of power states per domain, as well as decreasing decoupling capacitance per local grid and ultra-wide current dynamic range of digital load circuits (for low power on one end while maintaining performance at another) necessitate the design of high-efficiency, compact on-die voltage regulators providing ultra-fine grained spatio-temporal voltage distribution [1,2]. Digitally implementable linear regulators operated in low-dropout (LDO) mode, based on continuous time or discrete time control, exhibit process and voltage scalability [3-5], thus supplementing their analog counterparts [6].


Change of Lipovitellin during in vitro Oocyte Maturation in Japanese Flounder Paralichthys olivaceus


title: Dynamical Models and Tracking Regret in Online Convex Programming

abstract: This paper describes a new online convex optimization method which incorporates a family of candidate dynamical models and establishes novel tracking regret bounds that scale with the comparator's deviation from the best dynamical model in this family. Previous online optimization methods are designed to have a total accumulated loss comparable to that of the best comparator sequence, and existing tracking or shifting regret bounds scale with the overall variation of the comparator sequence. In many practical scenarios, however, the environment is nonstationary and comparator sequences with small variation are quite weak, resulting in large losses. The proposed Dynamic Mirror Descent method, in contrast, can yield low regret relative to highly variable comparator sequences by both tracking the best dynamical model and forming predictions based on that model. This concept is demonstrated empirically in the context of sequential compressive observations of a dynamic scene and tracking a dynamic social network.


title: A Virtual Museum for Design: New forms of interactive fruition

abstract: The project “Virtual Museum of Design” explored the possible fruition modalities of a product having a high cultural content starting, on the one hand, from the analysis of the Design Museum concept, and the way this is interpreted in the different international experiences, and, on the other hand, of the Virtual Museum concept, leading to implementations very different from each others. This study has made evident interesting possibilities showing the potentiality of this approach on a limited number of museum items, narrated through the assistance of virtual models and their interactions with images, drawings, and multimedia materials.


title: Mobile medical and health apps: state of the art, concerns, regulatory control and certification

abstract: This paper examines the state of the art in mobile clinical and health-related apps. A 2012 estimate puts the number of health-related apps at no fewer than 40,000, as healthcare professionals and consumers continue to express concerns about the quality of many apps, calling for some form of app regulatory control or certification to be put in place. We describe the range of apps on offer as of 2013, and then present a brief survey of evaluation studies of medical and health-related apps that have been conducted to date, covering a range of clinical disciplines and topics. Our survey includes studies that highlighted risks, negative issues and worrying deficiencies in existing apps. We discuss the concept of ‘apps as a medical device’ and the relevant regulatory controls that apply in USA and Europe, offering examples of apps that have been formally approved using these mechanisms. We describe the online Health Apps Library run by the National Health Service in England and the calls for a vetted medical and health app store. We discuss the ingredients for successful apps beyond the rather narrow definition of ‘apps as a medical device’. These ingredients cover app content quality, usability, the need to match apps to consumers’ general and health literacy levels, device connectivity standards (for apps that connect to glucometers, blood pressure monitors, etc.), as well as app security and user privacy. ‘Happtique Health App Certification Program’ (HACP), a voluntary app certification scheme, successfully captures most of these desiderata, but is solely focused on apps targeting the US market. HACP, while very welcome, is in ways reminiscent of the early days of the Web, when many “similar” quality benchmarking tools and codes of conduct for information publishers were proposed to appraise and rate online medical and health information. It is probably impossible to rate and police every app on offer today, much like in those early days of the Web, when people quickly realised the same regarding informational Web pages. The best first line of defence was, is, and will always be to educate consumers regarding the potentially harmful content of (some) apps.


title: Differential coherent algorithm based on fast navigation-bit correction for airborne GNSS-R software receivers

abstract: Signals from the Global Navigation Satellite System (GNSS) scatter over the sea surface resulting in relatively low Signal-to-Noise Ratios (SNR). A differential coherent algorithm is given here to improve the SNR and reduce the performance degradation due to the Squaring-Loss and the navigation-bit effect. The algorithm uses fast navigation-bit correction for Delay-Doppler Maps (DDM) in airborne Global Navigation Satellite Signal Reflectometry (GNSS-R) software receivers. The system model is introduced with an analysis of the statistical properties with simulations to support the theoretical analysis. Field experiments with real airborne receivers then demonstrate the effectiveness of this algorithm. Comparisons with test results show that this algorithm offers a significant SNR gain over conventional algorithms.


title: A systematic review of implementation strategies for assessment, prevention, and management of ICU delirium and their effect on clinical outcomes

abstract: INTRODUCTION ::: Despite recommendations from professional societies and patient safety organizations, the majority of ICU patients worldwide are not routinely monitored for delirium, thus preventing timely prevention and management. The purpose of this systematic review is to summarize what types of implementation strategies have been tested to improve ICU clinicians' ability to effectively assess, prevent and treat delirium and to evaluate the effect of these strategies on clinical outcomes. ::: ::: ::: METHOD ::: We searched PubMed, Embase, PsychINFO, Cochrane and CINAHL (January 2000 and April 2014) for studies on implementation strategies that included delirium-oriented interventions in adult ICU patients. Studies were suitable for inclusion if implementation strategies' efficacy, in terms of a clinical outcome, or process outcome was described. ::: ::: ::: RESULTS ::: We included 21 studies, all including process measures, while 9 reported both process measures and clinical outcomes. Some individual strategies such as "audit and feedback" and "tailored interventions" may be important to establish clinical outcome improvements, but otherwise robust data on effectiveness of specific implementation strategies were scarce. Successful implementation interventions were frequently reported to change process measures, such as improvements in adherence to delirium screening with up to 92%, but relating process measures to outcome changes was generally not possible. In meta-analyses, reduced mortality and ICU length of stay reduction were statistically more likely with implementation programs that employed more (six or more) rather than less implementation strategies and when a framework was used that either integrated current evidence on pain, agitation and delirium management (PAD) or when a strategy of early awakening, breathing, delirium screening and early exercise (ABCDE bundle) was employed. Using implementation strategies aimed at organizational change, next to behavioral change, was also associated with reduced mortality. ::: ::: ::: CONCLUSION ::: Our findings may indicate that multi-component implementation programs with a higher number of strategies targeting ICU delirium assessment, prevention and treatment and integrated within PAD or ABCDE bundle have the potential to improve clinical outcomes. However, prospective confirmation of these findings is needed to inform the most effective implementation practice with regard to integrated delirium management and such research should clearly delineate effective practice change from improvements in clinical outcomes.


title: Patterns in Bathyal Demersal Biodiversity and Community Composition Around Archipelagos in the Tropical Eastern Pacific

abstract: Deep-sea fauna of the Tropical Eastern Pacific (TEP) have remained largely un-described because exploration has historically been a challenge in these remote locations. Consequently, little is currently known about deep-ocean biodiversity in the TEP. An enriched understanding of biogeographic patterns and the factors that influence them is crucial to the success of efforts to conserve deep-sea communities. The objectives of this research were to provide the first assessment of biodiversity and community composition of bathyal demersal (200 - 3,000m) fauna across multiple ocean/island archipelagos in the TEP. To better understand biodiversity and its drivers in the region, we tested the hypotheses that biodiversity and faunal assemblage composition vary with location and environmental drivers (e.g bathymetry, oxygen concentration). Using Deep-Ocean Dropcams, we collected video footage from Clipperton, Galapagos, Revillagigedos, and Malpelo, in depths ranging from 200 to 3,000 m. Taxa richness, Shannon diversity, and Simpson diversity were calculated for each location. In general, biodiversity values were high closer to the equator and closer to the continent, and low in the locations farther north and farther west. Fish family biodiversity was highest in the Galapagos, followed by Malpelo. In contrast, Clipperton had significantly lower fish family richness compared to all other locations. For invertebrate orders, the Revillagigedos were significantly lower in biodiversity compared to the Galapagos and Malpelo. Broad-scale Bathymetric Position Index (BPI) and dissolved oxygen had a positive effect on fish diversity metrics. Both fish and invertebrate assemblages were significantly different among locations. Around each of the island groups are vastly different current patterns, which likely accounts for some of the variation we observed in biodiversity and community composition. Due to the longitudinal East Pacific Rise, which limits population connectivity by creating a spatial barrier to gene flow and dispersal among marine organisms, the TEP is largely isolated from the wider Pacific. The islands and archipelagos likely represent important stepping-stones and corridors for species connectivity across the TEP and the wider Pacific. Therefore, the preservation of these intact ecosystems is crucial for maintaining biodiversity both within the TEP as well as across the wider Pacific region.


title: A Fast Algorithm for Streaming Betweenness Centrality

abstract: Analysis of social networks is challenging due to the rapid changes of its members and their relationships. For many cases it impractical to recompute the metric of interest, therefore, streaming algorithms are used to reduce the total runtime following modifications to the graph. Centrality is often used for determining the relative importance of a vertex or edge in a graph. The vertex Between ness Centrality is the fraction of shortest paths going through a vertex among all shortest paths in the graph. Vertices with a high between ness centrality are usually key players in a social network or a bottleneck in a communication network. Evaluating the between ness centrality for a graph G=(V, E) is computationally demanding and the best known algorithm for unweighted graphs has an upper bound time complexity of O(V^2+VE). Consequently, it is desirable to find a way to avoid a full re-computation of between ness centrality when a new edge is inserted into the graph. In this work, we give a novel algorithm that reduces computation for the insertion of an edge into the graph. This is the first algorithm for the computation of between ness centrality in a streaming graph. While the upper bound time complexity of the new algorithm is the same as the upper bound for the static graph algorithm, we show significant speedups for both synthetic and real graphs. For synthetic graphs the speedup varies depending on the type of graph and the graph size. For synthetic graphs with 16384 vertices the average speedup is between 100X-400X. For five different real world collaboration networks the average speedup per graph is in range of 36X-148X.


title: Data Augmentation for Time Series Classification using Convolutional Neural Networks

abstract: Time series classification has been around for decades in the data-mining and machine learning communities. In this paper, we investigate the use of convolutional neural networks (CNN) for time series classification. Such networks have been widely used in many domains like computer vision and speech recognition, but only a little for time series classification. We design a convolu-tional neural network that consists of two convolutional layers. One drawback with CNN is that they need a lot of training data to be efficient. We propose two ways to circumvent this problem: designing data-augmentation techniques and learning the network in a semi-supervised way using training time series from different datasets. These techniques are experimentally evaluated on a benchmark of time series datasets.


title: Restoration of Endodontically Treated Molars Using All Ceramic Endocrowns

abstract: Clinical success of endodontically treated posterior teeth is determined by the postendodontic restoration. Several options have been proposed to restore endodontically treated teeth. Endocrowns represent a conservative and esthetic restorative alternative to full coverage crowns. The preparation consists of a circular equigingival butt-joint margin and central retention cavity into the entire pulp chamber constructing both the crown and the core as a single unit. The case reports discussed here are moderately damaged endodontically treated molars restored using all ceramic endocrowns fabricated using two different systems, namely, CAD/CAM and pressed ceramic.


title: Anatomical analysis of afferent projections to the medial prefrontal cortex in the rat

abstract: The medial prefrontal cortex (mPFC) has been associated with diverse functions including attentional processes, visceromotor activity, decision making, goal directed behavior, and working memory. Using retrograde tracing techniques, we examined, compared, and contrasted afferent projections to the four divisions of the mPFC in the rat: the medial (frontal) agranular (AGm), anterior cingulate (AC), prelimbic (PL), and infralimbic (IL) cortices. Each division of the mPFC receives a unique set of afferent projections. There is a shift dorsoventrally along the mPFC from predominantly sensorimotor input to the dorsal mPFC (AGm and dorsal AC) to primarily ‘limbic’ input to the ventral mPFC (PL and IL). The AGm and dorsal AC receive afferent projections from widespread areas of the cortex (and associated thalamic nuclei) representing all sensory modalities. This information is presumably integrated at, and utilized by, the dorsal mPFC in goal directed actions. In contrast with the dorsal mPFC, the ventral mPFC receives significantly less cortical input overall and afferents from limbic as opposed to sensorimotor regions of cortex. The main sources of afferent projections to PL/IL are from the orbitomedial prefrontal, agranular insular, perirhinal and entorhinal cortices, the hippocampus, the claustrum, the medial basal forebrain, the basal nuclei of amygdala, the midline thalamus and monoaminergic nuclei of the brainstem. With a few exceptions, there are few projections from the hypothalamus to the dorsal or ventral mPFC. Accordingly, subcortical limbic information mainly reaches the mPFC via the midline thalamus and basal nuclei of amygdala. As discussed herein, based on patterns of afferent (as well as efferent) projections, PL is positioned to serve a direct role in cognitive functions homologous to dorsolateral PFC of primates, whereas IL appears to represent a visceromotor center homologous to the orbitomedial PFC of primates.


title: CSI: A Hybrid Deep Model for Fake News Detection

abstract: The topic of fake news has drawn attention both from the public and the academic communities. Such misinformation has the potential of affecting public opinion, providing an opportunity for malicious parties to manipulate the outcomes of public events such as elections. Because such high stakes are at play, automatically detecting fake news is an important, yet challenging problem that is not yet well understood. Nevertheless, there are three generally agreed upon characteristics of fake news: the text of an article, the user response it receives, and the source users promoting it. Existing work has largely focused on tailoring solutions to one particular characteristic which has limited their success and generality. In this work, we propose a model that combines all three characteristics for a more accurate and automated prediction. Specifically, we incorporate the behavior of both parties, users and articles, and the group behavior of users who propagate fake news. Motivated by the three characteristics, we propose a model called CSI which is composed of three modules: Capture, Score, and Integrate. The first module is based on the response and text; it uses a Recurrent Neural Network to capture the temporal pattern of user activity on a given article. The second module learns the source characteristic based on the behavior of users, and the two are integrated with the third module to classify an article as fake or not. Experimental analysis on real-world data demonstrates that CSI achieves higher accuracy than existing models, and extracts meaningful latent representations of both users and articles.


title: Social Ties in Organizational Crowdfunding: Benefits of Team-Authored Proposals

abstract: Social ties have been hypothesized to help people to gain support in achieving collaborative goals. We test this hypothesis in a study of organizational crowdfunding (or 'crowdfunding behind the firewall'). 201 projects were proposed for peer-crowdfunding in a large international corporation. The crowdfunding website allowed people to join a project as Co-Proposers. We analyzed the funding success of 114 projects as a function of the number of (Co-)Proposers. Projects that had more co-proposers were more likely to reach their funding targets. Using data from an organizational social-networking service, we show how employees' social ties were associated with these success patterns. Our results have implications for theories of collaboration in social networks, and the design of crowdfunding websites.


title: THE FUTURE OF EMPLOYMENT: HOW SUSCEPTIBLE ARE JOBS TO COMPUTERISATION? ∗

abstract: We examine how susceptible jobs are to computerisation. To assess this, we begin by implementing a novel methodology to estimate the probability of computerisation for 702 detailed occupations, using a Gaussian process classifier. Based on these estimates, we examine expected impacts of future computerisation on US labour market outcomes, with the primary objective of analysing the number of jobs at risk and the relationship between an occupations probability of computerisation, wages and educational attainment.


title: Channel Selection and Feature Projection for Cognitive Load Estimation Using Ambulatory EEG

abstract: We present an ambulatory cognitive state classification system to assess the subject's mental load based on EEG measurements. The ambulatory cognitive state estimator is utilized in the context of a real-time augmented cognition (AugCog) system that aims to enhance the cognitive performance of a human user through computer-mediated assistance based on assessments of cognitive states using physiological signals including, but not limited to, EEG. This paper focuses particularly on the offline channel selection and feature projection phases of the design and aims to present mutual-information-based techniques that use a simple sample estimator for this quantity. Analyses conducted on data collected from 3 subjects performing 2 tasks (n-back/Larson) at 2 difficulty levels (low/high) demonstrate that the proposed mutual-information-based dimensionality reduction scheme can achieve up to 94% cognitive load estimation accuracy.


title: Online simultaneous localization and mapping in dynamic environments

abstract: We propose an on-line algorithm for simultaneous localization and mapping of dynamic environments. Our algorithm is capable of differentiating static and dynamic parts of the environment and representing them appropriately on the map. Our approach is based on maintaining two occupancy grids. One grid models the static parts of the environment, and the other models the dynamic parts of the environment. The union of the two provides a complete description of the environment over time. We also maintain a third map containing information about static landmarks detected in the environment. These landmarks provide the robot with localization. Results in simulation and with physical robots show the efficiency of our approach and show how the differentiation of dynamic and static entities in the environment and SLAM can be mutually beneficial.


title: Realization of a motion control IC for electric-powered wheelchair

abstract: This work is mainly to implement a motion control IC for electric wheelchair based on the novel FPGA (Field Programmable Gate Array) technology, which a Nios ll soft core processor can be embedded into FPGA to construct an SoPC (System on a Programmable Chip) environment. This electric wheelchair herein is driven by two DC rim motors and a fuzzy controller is applied in speed loop of each motor drive to cope with the system uncertainty and external load problems. The motion control IC has two modules. One is Nios II processor IP (Intelligent properties) which is used to perform the function of motion trajectory for electric-powered wheelchair and the data communication with control panel; and the other is an application IP which is utilized to execute the function of PWM (Pulse width modulation) generation, QEP (Quadrature encoder pulse) signal detection, and speed loop fuzzy controller for two DC rim motors. Therefore, a fully digital motion controller for electric wheelchair can be realized within one FPGA chip. Finally, an experimental system, which is composed of the DE2 (CycloneII EP2C35F672C6) with FPGA basis, two inverters, and one platform for electric wheelchair, is set up to prove the efficiency and correctness of the proposed motion control IC.