title: Integration and relative value of biomarkers for prediction of MCI to AD progression: Spatial patterns of brain atrophy, cognitive scores, APOE genotype and CSF biomarkers

abstract: This study evaluates the individual, as well as relative and joint value of indices obtained from magnetic resonance imaging (MRI) patterns of brain atrophy (quantified by the SPARE-AD index), cerebrospinal fluid (CSF) biomarkers, APOE genotype, and cognitive performance (ADAS-Cog) in progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) within a variable follow-up period up to 6 years, using data from the Alzheimer's Disease Neuroimaging Initiative-1 (ADNI-1). SPARE-AD was first established as a highly sensitive and specific MRI-marker of AD vs. cognitively normal (CN) subjects (AUC = 0.98). Baseline predictive values of all aforementioned indices were then compared using survival analysis on 381 MCI subjects. SPARE-AD and ADAS-Cog were found to have similar predictive value, and their combination was significantly better than their individual performance. APOE genotype did not significantly improve prediction, although the combination of SPARE-AD, ADAS-Cog and APOE e4 provided the highest hazard ratio estimates of 17.8 (last vs. first quartile). In a subset of 192 MCI patients who also had CSF biomarkers, the addition of Aβ1–42, t-tau, and p-tau181p to the previous model did not improve predictive value significantly over SPARE-AD and ADAS-Cog combined. Importantly, in amyloid-negative patients with MCI, SPARE-AD had high predictive power of clinical progression. Our findings suggest that SPARE-AD and ADAS-Cog in combination offer the highest predictive power of conversion from MCI to AD, which is improved, albeit not significantly, by APOE genotype. The finding that SPARE-AD in amyloid-negative MCI patients was predictive of clinical progression is not expected under the amyloid hypothesis and merits further investigation.


title: A proposed spam detection approach for Arabic social networks content

abstract: Data collected from social networks can be used to discover useful information by mining users' opinions for different knowledge fields, such as marketing, politics or social studies. Nowadays, online social networks and reviews platforms become a prime target to different types of malicious and opportunistic actions by creating spam content which can skew the results of an automatic study on user opinion trends. We propose in this paper a spam detection system to process Arabic content generated on social networks. Our approach is based on a set of selected features which characterize Arabic spam content. Our initial tests by using WEKA software show the effectiveness (91.73 % of precision) of our approach and the accurate choice of features.


title: Layer-crossing overhead and information spreading in multiplex social networks

abstract: We propose and study a model of information or disease spreading on multiplex social networks, in which agents interact or communicate through multiple channels (layers), say online vs. offline, and there exists a layer-switching overhead for transmission across the interaction layers. The model is characterized by the path-dependent transmissibility over a contact, that is dynamically determined dependent on both incoming and outgoing transmission layers due to the switching overhead. We formulate a generalized theory with a mapping to deal with such a path-dependent transmissibility, and demonstrate that network multiplexity can bring about non-additive and non-monotonic effects in spreading dynamics. Our results suggest that explicit consideration of multiplexity can be crucial in realistic modeling of spreading processes on social networks in an era of ever-diversifying social interaction layers.


title: Packed Ciphertexts in LWE-based Homomorphic Encryption

abstract: In this short note we observe that the Peikert-Vaikuntanathan-Waters (PVW) method of packing many plaintext elements in a single Regev-type ciphertext, can be used for performing SIMD homomorphic operations on packed ciphertext. This provides an alternative to the Smart-Vercauteren (SV) ciphertext-packing technique that relies on polynomial-CRT. While the SV technique is only applicable to schemes that rely on ring-LWE (or other hardness assumptions in ideal lattices), the PVW method can be used also for cryptosystems whose security is based on standard LWE (or more broadly on the hardness of “General-LWE”).


Defocus Magnification


title: Antenna avoidance in layer assignment

abstract: The sustained progress of very-large-scale-integration (VLSI) technology has dramatically increased the likelihood of the antenna problem in the manufacturing process and calls for corresponding considerations in the routing stage. In this paper, the authors propose a technique that can handle the antenna problem during the layer-assignment (LA) stage, which is an important step between global routing and detailed routing. The antenna-avoidance problem is modeled as a tree-partitioning problem with a linear-time-optimal-algorithm solution. This algorithm is customized to guide antenna avoidance in the LA stage. A linear-time optimal jumper-insertion algorithm is also derived. Experimental results on benchmark circuits show that the proposed techniques can lead to an average of 76% antenna-violation reduction and 99% via-violation reduction.


title: Discriminative learning with latent variables for cluttered indoor scene understanding

abstract: We address the problem of understanding an indoor scene from a single image in terms of recovering the room geometry (floor, ceiling, and walls) and furniture layout. A major challenge of this task arises from the fact that most indoor scenes are cluttered by furniture and decorations, whose appearances vary drastically across scenes, thus can hardly be modeled (or even hand-labeled) consistently. In this paper we tackle this problem by introducing latent variables to account for clutter, so that the observed image is jointly explained by the room and clutter layout. Model parameters are learned from a training set of images that are only labeled with the layout of the room geometry. Our approach enables taking into account and inferring indoor clutter without hand-labeling of the clutter in the training set, which is often inaccurate. Yet it outperforms the state-of-the-art method of Hedau et al. that requires clutter labels. As a latent variable based method, our approach has an interesting feature that latent variables are used in direct correspondence with a concrete visual concept (clutter in the room) and thus interpretable.


title: Altitude optimization of Airborne Wind Energy systems: A Bayesian Optimization approach

abstract: This study presents a data-driven approach for optimizing the operating altitude of Airborne Wind Energy (AWE) systems to maximize net energy production. Determining the optimal operating altitude of an AWE system is challenging, as the wind speed constantly varies with both time and altitude. Furthermore, without expensive auxiliary equipment, the wind speed is only measurable at the AWE system's operating altitude. The work presented in this paper shows how tools from machine learning can be blended with real-time control to optimize the AWE system's operating altitude efficiently, without the use of auxiliary wind profiling equipment. Specifically, Bayesian Optimization, which is a data-driven technique for finding the optimum of an unknown and expensive-to-evaluate objective function, is applied to the real-time control of an AWE system. The underlying objective function is modeled by a Gaussian Process (GP); then, Bayesian Optimization utilizes the predictive uncertainty information from the GP to decide the best subsequent operating altitude. In the AWE application, conventional Bayesian Optimization is extended to handle the time-varying nature of the wind shear profile (wind speed vs. time). Using real wind data, our method is validated against three baseline approaches. Our simulation results show that the Bayesian Optimization method is successful in dramatically increasing power production over these baselines.


Solving Sudoku Using Probabilistic Graphical Models


Real-Time Rendering of Wet Fur


title: A Compact CPW-Fed Curved Meander Line Monopole Antenna (MLMA) for GSM Application

abstract: Monopole antenna is widely used in many communication systems especially in broadcasting where omnidirectional pattern allow the 360-degree coverage. However, at low frequency the conventional design may require miniaturization to fit in versatile spaces. Thus, this paper discusses a low cost, compact CPW-fed curved meander line monopole antenna (MLMA) designed to operate at 0.9 GHz GSM band. The overall dimension is 25mm x 80mm. The antenna is well matched at required GSM band with the bandwidth from 0.88 GHz to 0.93 GHz. Comparison between the conventional MLMA and curved (MLMA) is made in term of return loss and gain. It was found that the curved MLMA has a better gain compared to the conventional MLMA which is 1.472 dB.


title: Blazing Fast Time Series Segmentation Based on Update Techniques for Polynomial Approximations

abstract: Segmentation is an important step in processing and analyzing time series. In this article, we present an approach to speed up some standard time series segmentation techniques. Often, time series segmentation is based on piecewise polynomial approximations of the time series (including piecewise constant or linear approximations as special cases). Basically, a least-squares fit with a polynomial has a computational complexity that depends on the number of observations, i.e., the length of the time series. To improve the computational complexity of segmentation techniques we exploit the fact that approximations have to be repeated in sliding (moving) or growing time windows. Therefore, we suggest to use update techniques for the approximations that determine the approximating polynomial in a sliding or growing time window from an already existing one with a computational complexity that is independent of the number of observations, i.e., the length of the window. For that purpose bases of orthogonal polynomials must be used instead of standard bases such as monomials. We take two standard techniques for segmentation - the on-line algorithm SWAB (Sliding Window And Bottom-up) and the off-line technique OptSeg (Optimal Segmentation) - and show that the run-times can be reduced substantially for a given polynomial degree. If run-time constraints are given, e.g., in real-time applications, it would also be possible to adapt the degree of the approximating polynomials. Higher polynomial degrees typically result in lower modeling errors or longer segments. The various properties of the new realizations of segmentation techniques are outlined by means of some benchmark time series. The experimental results show that, depending on the chosen parameterization, OptSeg can be accelerated by some orders of magnitude, SWAB by a factor of up to about ten.


title: Document embeddings learned on various types of n-grams for cross-topic authorship attribution

abstract: Recently, document embeddings methods have been proposed aiming at capturing hidden properties of the texts. These methods allow to represent documents in terms of fixed-length, continuous and dense feature vectors. In this paper, we propose to learn document vectors based on n-grams and not only on words. We use the recently proposed Paragraph Vector method. These n-grams include character n-grams, word n-grams and n-grams of POS tags (in all cases with n varying from 1 to 5). We considered the task of Cross-Topic Authorship Attribution and made experiments on The Guardian corpus. Experimental results show that our method outperforms word-based embeddings and character n-gram based linear models, which are among the most effective approaches for identifying the writing style of an author.


title: Access control for SDN controllers

abstract: Based on the OpenFlow model, we propose an access control scheme for SDN controllers. Our scheme accounts for the different network resources, multiple security requirements, conflicts originating from the reconfiguration of network components, and the delegation of access permissions.


title: Gate oxide failures due to anomalous stress from HBM ESD testers

abstract: An unexpected and serious effect from the ESD HBM tester causing gate oxide failure in input buffers is reported in this paper. The most significant finding is that this unwarranted stress comes from the tester relay and gives rise to false HBM evaluation. In this paper we investigate this new effect on gate oxide reliability and establish the safe control limits for proper output of the state-of-the-art ESD simulator waveforms.


title: Measuring two-dimensional movements using a single InSAR pair

abstract: [1] We present a new method to extract along-track displacements from InSAR data, based on split-beam InSAR processing, to create forward- and backward-looking interferograms. The phase difference between the two modified interferograms provides the along-track displacement component. Thus, from each conventional InSAR pair we extract two components of the displacement vector: one along the line of sight, the other in the along-track direction. We analyze the precision of the new method by comparing our solution to GPS and offset-derived along-track displacements in interferograms of the 1999, Hector Mine earthquake. The RMS error between GPS displacements and our results ranges from 5 to 8.8cm. Our method is consistent with along-track displacements derived by pixel-offsets, which are limited to 12–15cm precision. The theoretical precision of the new method depends on SNR and coherence. For a signal to noise ratio of 30 the expected precisions are 3, 11cm for coherence of 0.8, 0.4, respectively.


title: Mask-Specific Inpainting with Deep Neural Networks

abstract: Most inpainting approaches require a good image model to infer the unknown pixels. In this work, we directly learn a mapping from image patches, corrupted by missing pixels, onto complete image patches. This mapping is represented as a deep neural network that is automatically trained on a large image data set. In particular, we are interested in the question whether it is helpful to exploit the shape information of the missing regions, i.e. the masks, which is something commonly ignored by other approaches. In comprehensive experiments on various images, we demonstrate that our learning-based approach is able to use this extra information and can achieve state-of-the-art inpainting results. Furthermore, we show that training with such extra information is useful for blind inpainting, where the exact shape of the missing region might be uncertain, for instance due to aliasing effects.


title: Retaining Valued Employees

abstract: How Turnover Can Be Costly to Organizations Job Enrichment Realistic Job Previews Socialization and Supervision Employee Selection Using Employee Surveys to Predict and Diagnose Turnover Causes Compensation and Rewards Reducing Turnover among Special Groups: Minorities and Women Managing Exiting Employees Appendix: Sample Employee Surveys


title: Scalability and Robustness of Time-Series Databases for Cloud-Native Monitoring of Industrial Processes

abstract: Today's industrial control systems store large amounts of monitored sensor data in order to optimize industrial processes. In the last decades, architects have designed such systems mainly under the assumption that they operate in closed, plant-side IT infrastructures without horizontal scalability. Cloud technologies could be used in this context to save local IT costs and enable higher scalability, but their maturity for industrial applications with high requirements for responsiveness and robustness is not yet well understood. We propose a conceptual architecture as a basis to designing cloud-native monitoring systems. As a first step we benchmarked three open source time-series databases (OpenTSDB, KairosDB and Databus) on cloud infrastructures with up to 36 nodes with workloads from realistic industrial applications. We found that at least KairosDB fulfills our initial hypotheses concerning scalability and reliability.


title: Wideband In-Phase and Out-of-Phase Balanced Power Dividing and Combining Networks

abstract: Two new wideband in-phase and out-of-phase balanced power dividing/combining networks are proposed in this paper. Based on matrix transformation, the differential-mode and common-mode equivalent circuits of the two wideband in-phase and out-of-phase networks can be easily deduced. A patterned ground-plane technique is used to realize the strong coupling of the shorted coupled lines for the differential mode. Two planar wideband in-phase and out-of-phase balanced networks with bandwidths of 55.3% and 64.4% for the differential mode with wideband common-mode suppression are designed and fabricated. The theoretical and measured results agree well with each other and show good in-band performances.


title: Triangulating a cognitive control network using diffusion-weighted magnetic resonance imaging (MRI) and functional

abstract: The ability to stop motor responses depends critically on the right inferior frontal cortex (IFC) and also engages a midbrain region consistent with the subthalamic nucleus (STN). Here we used diffusion-weighted imaging (DWI) tractography to show that the IFC and the STN region are connected via a white matter tract, which could underlie a "hyperdirect" pathway for basal ganglia control. Using a novel method of "triangulation" analysis of tractography data, we also found that both the IFC and the STN region are connected with the presupplementary motor area (preSMA). We hypothesized that the preSMA could play a conflict detection/resolution role within a network between the preSMA, the IFC, and the STN region. A second experiment tested this idea with functional magnetic resonance imaging (fMRI) using a conditional stop-signal paradigm, enabling examination of behavioral and neural signatures of conflict-induced slowing. The preSMA, IFC, and STN region were significantly activated the greater the conflict-induced slowing. Activation corresponded strongly with spatial foci predicted by the DWI tract analysis, as well as with foci activated by complete response inhibition. The results illustrate how tractography can reveal connections that are verifiable with fMRI. The results also demonstrate a three-way functional-anatomical network in the right hemisphere that could either brake or completely stop responses.


title: Performance Comparison Between Surface-Mounted and Interior PM Motor Drives for Electric Vehicle Application

abstract: Electric vehicles make use of permanent-magnet (PM) synchronous traction motors for their high torque density and efficiency. A comparison between interior PM and surface-mounted PM (SPM) motors is carried out, in terms of performance at given inverter ratings. The results of the analysis, based on a simplified analytical model and confirmed by finite element (FE) analysis, show that the two motors have similar rated power but that the SPM motor has barely no overload capability, independently of the available inverter current. Moreover, the loss behavior of the two motors is rather different in the various operating ranges with the SPM one better at low speed due to short end connections but penalized at high speed by the need of a significant deexcitation current. The analysis is validated through FE simulation of two actual motor designs.


title: Differentiable Scheduled Sampling for Credit Assignment

abstract: We demonstrate that a continuous relaxation of the argmax operation can be used to create a differentiable approximation to greedy decoding for sequence-to-sequence (seq2seq) models. By incorporating this approximation into the scheduled sampling training procedure (Bengio et al., 2015)--a well-known technique for correcting exposure bias--we introduce a new training objective that is continuous and differentiable everywhere and that can provide informative gradients near points where previous decoding decisions change their value. In addition, by using a related approximation, we demonstrate a similar approach to sampled-based training. Finally, we show that our approach outperforms cross-entropy training and scheduled sampling procedures in two sequence prediction tasks: named entity recognition and machine translation.


title: Miniaturization of top-loaded monopole antennas using Peano-curves

abstract: Many wireless applications require planar antennas but with monopole-like, vertically polarized radiation, a design challenge that cannot be easily achieved by using conventional microstrip patch or other types of printed antennas. In addition, while there have been significant advances in design of low-profile printed patch-type antennas in the frequency ranges of L-band and above, the patch antennas are not suitable for lower frequency applications, such as those in VHF and UHF bands, because of their relatively large dimensions. These requirements provide challenging problems in optimization and design of the future generation of low-profile reconfigurable antenna systems. In the present work, we discuss the use of printed metallic paths in the form of Peano and Hilbert space-filling curves (H. Sagan, 1994) to provide top-loading properties that miniaturizes monopole antenna elements. The full-wave simulation together with measured results shows that the proposed electrically small antenna results in a perfect mono-polar radiation pattern with relatively wideband characteristics. Some initial numerical results of this work were recently presented in J. McVay and A. Hoorfar, (2006)


title: Medicinal mushroom modulators of molecular targets as cancer therapeutics

abstract: Empirical approaches to discover anticancer drugs and cancer treatments have made limited progress in the past several decades in finding a cure for cancer. The expanded knowledge of the molecular basis of tumorigenesis and metastasis, together with the inherently vast structural diversity of natural compounds found in mushrooms, provided unique opportunities for discovering new drugs that rationally target the abnormal molecular and biochemical signals leading to cancer. This review focuses on mushroom low-molecular-weight secondary metabolites targeting processes such as apoptosis, angiogenesis, metastasis, cell cycle regulation, and signal transduction cascades. Also discussed in this review are high-molecular-weight polysaccharides or polysaccharide-protein complexes from mushrooms that appear to enhance innate and cell-mediated immune responses, exhibit antitumor activities in animals and humans, and demonstrate the anticancer properties of selenium compounds accumulated in mushrooms.


title: Lossless and lossy memory I/O link compression for improving performance of GPGPU workloads

abstract: State-of-the-art graphic processing units (GPUs) provide very high memory bandwidth, but the performance of many general-purpose GPU (GPGPU) workloads is still bounded by memory bandwidth. Although compression techniques have been adopted by commercial GPUs, they are only used for compressing texture and color data, not data for GPGPU workloads. Furthermore, the microarchitectural details of GPU compression are proprietary and its performance benefits have not been previously published. In this paper, we first investigate required microarchitectural changes to support lossless compression techniques for data transferred between the GPU and its off-chip memory to provide higher effective bandwidth. Second, by exploiting some characteristics of floating-point numbers in many GPGPU workloads, we propose to apply lossless compression to floating-point numbers after truncating their least-significant bits (i.e., lossy compression). This can reduce the bandwidth usage even further with very little impact on overall computational accuracy. Finally, we demonstrate that a GPU with our lossless and lossy compression techniques can improve the performance of memory-bound GPGPU workloads by 26% and 41% on average.


title: Detection strategies: metrics-based rules for detecting design flaws

abstract: In order to support the maintenance of an object-oriented software system, the quality of its design must be evaluated using adequate quantification means. In spite of the current extensive use of metrics, if used in isolation metrics are oftentimes too fine grained to quantify comprehensively an investigated design aspect (e.g., distribution of system's intelligence among classes). To help developers and maintainers detect and localize design problems in a system, we propose a novel mechanism - called detection strategy - for formulating metrics-based rules that capture deviations from good design principles and heuristics. Using detection strategies an engineer can directly localize classes or methods affected by a particular design flaw (e.g., God Class), rather than having to infer the real design problem from a large set of abnormal metric values. We have defined such detection strategies for capturing around ten important flaws of object-oriented design found in the literature and validated the approach experimentally on multiple large-scale case-studies.


title: An ultra-low voltage self-startup charge pump for energy harvesting applications

abstract: An ultra-low voltage, self-starting, switched-capacitor based charge pump is proposed for energy harvesting applications. The integrated linear charge pump topology presented in this work has been optimized for low-voltage start-up. The control signals for the charge-transfer switches (CTS), generated using two clock phases, reduce reverse currents and thus improve the efficiency of the converter. Adiabatic switching techniques have been employed to reduce the switching losses associated with the CTS gate control. This design has been implemented in a 130-nm CMOS process. Simulation results demonstrate a low startup voltage of 125 mV with efficiency of 62 % for a static current load of 0.1 µA.


title: A practical traffic management system for integrated LTE-WiFi networks

abstract: Mobile operators are leveraging WiFi to relieve the pressure posed on their networks by the surging bandwidth demand of applications. However, operators often lack intelligent mechanisms to control the way users access their WiFi networks. This lack of sophisticated control creates poor network utilization, which in turn degrades the quality of experience (QoE). To meet user traffic demands, it is evident that operators need solutions that optimally balance user traffic across cellular and WiFi networks. Motivated by the lack of practical solutions in this space, we design and implement ATOM - an end-to-end system for adaptive traffic offloading for WiFi-LTE deployments. ATOM has two novel components: (i) A network interface selection algorithm that maps user traffic across WiFi and LTE to optimize user QoE and (ii) an interface switching service that seamlessly re-directs ongoing user sessions in a cost-effective and standards-compatible manner. Our evaluations on a real LTE-WiFi testbed using YouTube traffic reveals that ATOM reduces video stalls by 3-4 times compared to naive solutions.


title: Substrate-currents-aware characterization of an integrated buck DC/DC converter for floor-plan optimization

abstract: The performance of integrated DC/DC converters is affected by substrate parasitic devices, such as bipolar transistors and thyristors: these devices cause enhanced substrate noise that can lead to the malfunctioning of the control circuitry, they degrade the efficiency, and thyristors can even cause the failure of the converter by latch-up. Due to the lack of software tools for the evaluation of the effects of such parasitic devices in the design phase, often only qualitative counter-measures are taken. This can lead to sub-optimal floor-plans in terms of substrate noise immunity of the control circuitry, power efficiency and area consumption. Being the effects of the substrate parasitic devices often overlooked in the characterization phase, in this work we propose a systematic approach to evaluate such effects in a buck converter. Such method can be used to determine the best floor-plan among several options. Finally, we apply the method to compare two different floor-plans of an integrated buck converter designed in a 130 nm technology.


title: Novel LTPS-TFT Pixel Circuit with OLED Luminance Compensation for 3D AMOLED Displays

abstract: A novel low-temperature polycrystalline-silicon thin-film-transistor pixel circuit for 3D active-matrix organic light-emitting diode (AMOLED) displays is presented in this work. The proposed pixel circuit employs high frame rate (240 Hz) emission driving scheme and only needs 3.5 $~\mu\hbox{s}$ for input data period. Thus, 3D AMOLED displays can be realized under high speed operations. The simulation results demonstrate excellent stability in the proposed pixel circuit. The relative current error rate is only 0.967% under the threshold voltage deviation ( $\Delta {{\rm V}_{{\rm TH}\_{\rm DTFT}}} = {\pm} \hbox{0.33 V}$ ) of driving TFT. With an OLED threshold voltage detecting architecture, the OLED current can be increased with the increased OLED threshold voltage to compensate for the OLED luminance degradation. The proposed pixel circuit can therefore effectively compensate for the DTFT threshold voltage shift and OLED electric degradation at the same time.


title: Temperature-compensated lumped element tunable bandpass filter

abstract: This paper presents and compares two techniques for compensating temperature effects in varactor diode-based lumped element tunable bandpass filters (BPFs). Temperature compensation relies on first measuring the temperature and subsequently re-biasing the varactors of the filter based on pre-calculated lookup table. The lookup table is calibrated using two different methods: a) constant varactor capacitance, and b) constant filter response. Both approaches are evaluated when the filter is subjected to temperatures ranging from −40 to 100°C. In the first approach the capacitance of each of the filter's varactors is held constant at the value found to yield the desired filter response at room temperature. On the other hand, the second approach utilizes a global optimization scheme that re-biases all varactors until the filter yields the desired transfer function. The results from both methods are measured and compared for a third-order BPF tunable from 226 MHz to 333 MHz. While the first method is simpler to implement and may appear intuitive, it actually causes a worse temperature-induced drift because it ignores substrate and inductor effects. Specifically, the resulting temperature drift of the center frequency with the first method is 4.3% as compared to 3.2% when no compensation is applied. Optimal results are achieved with the second approach where the filter's center frequency remains constant to within 0.1% over the −40 to 100°C temperature range.


Analysis of Hand Synergies for Inverse Kinematics Hand Tracking


title: Everyday robotics: robots as everyday objects

abstract: Why are we not living yet with robots? If robots are not common everyday objects, it is maybe because we have looked for robotic applications without considering with sufficient attention what could be the experience of interacting with a robot. This article introduces the idea of a value profile, a notion intended to capture the general evolution of our experience with different kinds of objects. After discussing value profiles of commonly used objects, it offers a rapid outline of the challenging issues that must be investigated concerning immediate, short-term and long-term experience with robots. Beyond science-fiction classical archetypes, the picture emerging from this analysis is the one of versatile everyday robots, autonomously developing in interaction with humans, communicating with one another, changing shape and body in order to be adapted to their various context of use. To become everyday objects, robots will not necessary have to be useful, but they will have to be at the origins of radically new forms of experiences.


title: Mobile Cloud Computing Research - Issues, Challenges and Needs

abstract: The rapid advance of mobile computing technology and wireless networking, there is a significant increase of mobile subscriptions. This drives a strong demand for mobile cloud applications and services for mobile device users. This brings out a great business and research opportunity in mobile cloud computing (MCC). This paper first discusses the market trend and related business driving forces and opportunities. Then it presents an overview of MCC in terms of its concepts, distinct features, research scope and motivations, as well as advantages and benefits. Moreover, it discusses its opportunities, issues and challenges. Furthermore, the paper highlights a research roadmap for MCC.


title: Classification of potential nuclei in prostate histology images using shape manifold learning

abstract: The demanding step in the development of ancillary systems for the diagnosis of cancer and other diseases based on nuclear morphometry is the delineation of nuclei in the images of stained tissue sections. Various constituents of the tissue section such as cellular and extra-cellular elements, staining artefacts, debris of nuclei, and clusters of overlapping nuclei apart from the image acquisition noise to name a few contribute to in the complexity of the task. In this paper, we pose the problem of selection of nuclei in tissue section as classification of shapes using manifold learning on training images followed by out-of-sample extension for unknown test images. Experimental results demonstrate the effectiveness of the proposed algorithm.


title: Higher-Order Inference for Multi-class Log-Supermodular Models

abstract: Higher-order models have been shown to be very useful for a plethora of computer vision tasks. However, existing techniques have focused mainly on MAP inference. In this paper, we present the first efficient approach towards approximate Bayesian marginal inference in a general class of high-order, multi-label attractive models, where previous techniques slow down exponentially with the order (clique size). We formalize this task as performing inference in log-supermodular models under partition constraints, and present an efficient variational inference technique. The resulting optimization problems are convex and yield bounds on the partition function. We also obtain a fully factorized approximation to the posterior, which can be used in lieu of the true complicated distribution. We empirically demonstrate the performance of our approach by comparing it to traditional inference methods on a challenging high-fidelity multi-label image segmentation dataset. We obtain state-of-the-art classification accuracy for MAP inference, and substantially improved ROC curves using the approximate marginals.


title: Methods of Hierarchical Clustering

abstract: We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations that are available in R and other software environments. We look at hierarchical self-organizing maps, and mixture models. We review grid-based clustering, focusing on hierarchical density-based approaches. Finally we describe a recently developed very efficient (linear time) hierarchical clustering algorithm, which can also be viewed as a hierarchical grid-based algorithm.


title: Brain Points: A Deeper Look at a Growth Mindset Incentive Structure for an Educational Game

abstract: Student retention is a central challenge in systems for learning at scale. It has been argued that educational video games could improve student retention by providing engaging experiences and informing the design of other online learning environments. However, educational games are not uniformly effective. Our recent research shows that player retention can be increased by using a brain points incentive structure that rewards behaviors associated with growth mindset, or the belief that intelligence can grow. In this paper, we expand on our prior work by providing new insights into how growth mindset behaviors can be effectively promoted in the educational game Refraction. We present results from an online study of 25,000 children who were exposed to five different versions of the brain points intervention. We find that growth mindset animations cause a large number of players to quit, while brain points encourage persistence. Most importantly, we find that awarding brain points randomly is ineffective; the incentive structure is successful specifically because it rewards desirable growth mindset behaviors. These findings have important implications that can support the future generalization of the brain points intervention to new educational contexts.


title: Grid resource selection model based on trust moment

abstract: The research on resource selection in grid most focus on QoS property of resource, but the requirement preference of consumer is ignored. Using trust behavior experience in human society, this paper introduced a new concept of trust moment to analyze the trust relationship between transaction partners. Furthermore, trust moment is influenced by multi-factors, such as trust gravitation and trust radius, we use TOPSIS method to implement resource selection based on the requirement preference of consumer. Finally, the effectiveness and feasibility of this model is illustrated by the experiments.


title: Automated Vehicle Overtaking based on a Multiple-Goal Reinforcement Learning Framework

abstract: In this paper, we propose a reinforcement learning multiple-goal framework to solve the automated vehicle overtaking problem. Here, the overtaking problem is solved by considering the destination seeking goal and collision avoidance goal simultaneously. The host vehicle uses Double-action Q-Learning for collision avoidance and Q-learning for destination seeking by learning to react with different motions carried out by a leading vehicle. Simulations show that the proposed method performs well disregarding whether the vehicle to be overtaken holds a steady or un-steady course. Given the promising results, better navigation is expected if additional goals such as lane following is introduced in the multiple-goal framework.


title: Intrusion detection in connected cars

abstract: Automobiles are becoming increasingly connected and therefore are vulnerable to cyber attacks. Intrusion detection systems are a well established technique in cyber security and are now being deployed to detect attacks on cars by filtering the data streams which are exchanged by the car and the outside world as well within the automotive bus systems. The paper gives a brief overview of intrusion detection systems and discusses some of the major cyber security threats arising in the world of connected cars. We also show how intrusion detection systems can be implemented by artificial neural networks.


title: Modeling and Analyzing the Video Game Live-Streaming Community

abstract: In parallel to the exponential growth of the gaming industry, video game live-streaming is rising as a major form of online entertainment. Gathering a heterogeneous community, the popularity of this new media led to the creation of web services just for streaming video games, such as Twitch. TV. In this paper, we propose a model to characterize how streamers and spectators behave, based on their possible actions in Twitch and, using it, we perform a case study on the Star craft II streamers and spectators. In the case study we analyze a large amount of data collected in Twitch. TV's chat in order to better understand how streamers behave, and how this new form of online entertainment is different from previous ones. Based on this analysis, we were able to better understand channel switching, channel surfing, and to create a model for predicting the number of chat messages based on the number of spectators. We were also able to describe behavioral patterns, such as the mass evasion of spectators before the end of a streaming section in a channel.


title: Virtual Heliodon: Spatially Augmented Reality for Architectural Daylighting Design

abstract: We present an application of interactive global illumination and spatially augmented reality to architectural daylight modeling that allows designers to explore alternative designs and new technologies for improving the sustainability of their buildings. Images of a model in the real world, captured by a camera above the scene, are processed to construct a virtual 3D model. To achieve interactive rendering rates, we use a hybrid rendering technique, leveraging radiosity to simulate the inter-reflectance between diffuse patches and shadow volumes to generate per-pixel direct illumination. The rendered images are then projected on the real model by four calibrated projectors to help users study the daylighting illumination. The virtual heliodon is a physical design environment in which multiple designers, a designer and a client, or a teacher and students can gather to experience animated visualizations of the natural illumination within a proposed design by controlling the time of day, season, and climate. Furthermore, participants may interactively redesign the geometry and materials of the space by manipulating physical design elements and see the updated lighting simulation.


title: Economic prediction using neural networks: the case of IBM daily stock returns

abstract: A report is presented of some results of an ongoing project using neural-network modeling and learning techniques to search for and decode nonlinear regularities in asset price movements. The author focuses on the case of IBM common stock daily returns. Having to deal with the salient features of economic data highlights the role to be played by statistical inference and requires modifications to standard learning techniques which may prove useful in other contexts. >


title: Integrated Grasp and motion planning

abstract: In this work, we present an integrated planner for collision-free single and dual arm grasping motions. The proposed Grasp-RRT planner combines the three main tasks needed for grasping an object: finding a feasible grasp, solving the inverse kinematics and searching a collision-free trajectory that brings the hand to the grasping pose. Therefore, RRT-based algorithms are used to build a tree of reachable and collision-free configurations. During RRT-generation, potential grasping positions are generated and approach movements toward them are computed. The quality of reachable grasping poses is scored with an online grasp quality measurement module which is based on the computation of applied forces in order to diminish the net torque.We also present an extension to a dual arm planner which generates bimanual grasps together with corresponding dual arm grasping motions. The algorithms are evaluated with different setups in simulation and on the humanoid robot ARMAR-III.


title: Comparison of Joint Strength and Fracture Energy of Lead-free Solder Balls in High Speed Ball Shear/Pull Tests and their Correlation with Board Level Drop Test

abstract: A comprehensive solder joint reliability study was conducted using both high-speed solder ball shear/pull and board level drop testing (BLDT). The samples were divided into groups which were subject to various periods of thermal aging (125degC, up to 500 hours) in order to accelerate the formation of intermetallic compound (IMC) in the solder joints. The ball shear tests ranged from 10 mm/s to 3,000 mm/s while the ball pull tests ranged from 5 mm/s to 500 mm/s. A total of 6 unique package constructions were evaluated, ranging from a 316 PBGA (27 mm, sq) to a 2,395 CBGA (51 mm, sq). The samples used SAC lead-free solder balls and a variety of pad surface finishes. Detailed failure analyses were performed to identify the failed solder joints and corresponding failure modes. The failure modes and loading speeds of ball shear and ball pull tests were cross-referenced with the mechanical drop tests for comparison. Correlation models were established relating drops-to-failure with both solder ball fracture force and energy.


title: VMAR: Optimizing I/O Performance and Resource Utilization in the Cloud

abstract: A key enabler for standardized cloud services is the encapsulation of software and data into VM images. With the rapid evolution of the cloud ecosystem, the number of VM images is growing at high speed. These images, each containing gigabytes or tens of gigabytes of data, create heavy disk and network I/O workloads in cloud data centers. Because these images contain identical or similar OS, middleware, and applications, there are plenty of data blocks with duplicate content among the VM images. However, current deduplication techniques cannot efficiently capitalize on this content similarity due to their warmup delay, resource overhead and algorithmic complexity.


title: TART: Timed-Automata to Real-Time Java Tool

abstract: In previous work, we have proposed a model based approach to developing real-time Java programs from timed automata. This approach allows us to verify the timed automata model mechanically by using current real-time model checking tools. Programs are then derived from the model by following a systematic approach. TART (timed automata to RTSJ Tool) is a prototype tool to support this approach. This paper presents TART, including its limitations, and discusses its application on four examples.


title: Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection

abstract: Grammatical error correction, like other machine ::: learning tasks, greatly benefits from ::: large quantities of high quality training data, ::: which is typically expensive to produce. While ::: writing a program to automatically generate ::: realistic grammatical errors would be difficult, ::: one could learn the distribution of naturallyoccurring ::: errors and attempt to introduce them ::: into other datasets. Initial work on inducing ::: errors in this way using statistical machine ::: translation has shown promise; we investigate ::: cheaply constructing synthetic samples, given ::: a small corpus of human-annotated data, using ::: an off-the-rack attentive sequence-to-sequence ::: model and a straight-forward post-processing ::: procedure. Our approach yields error-filled artificial ::: data that helps a vanilla bi-directional ::: LSTM to outperform the previous state of the ::: art at grammatical error detection, and a previously ::: introduced model to gain further improvements ::: of over 5% F0.5 score. When attempting ::: to determine if a given sentence is ::: synthetic, a human annotator at best achieves ::: 39.39 ::: F1 score, indicating that our model generates ::: mostly human-like instances.


title: An Ensemble Model Using Face and Body Tracking for Engagement Detection

abstract: Precise detection and localization of learners' engagement levels are useful for monitoring their learning quality. In the emotiW Challenge's engagement detection task, we proposed a series of novel improvements, including (a) a cluster-based framework for fast engagement level predictions, (b) a neural network using the attention pooling mechanism, (c) heuristic rules using body posture information, and (d) model ensemble for more accurate and robust predictions. Our experimental results suggest that our proposed methods effectively improved engagement detection performance. On the validation set, our system can reduce the baseline Mean Squared Error (MSE) by about 56%. On the final test set, our system yielded a competitively low MSE of 0.081.


title: Functional-Based Acoustic Group Feature Selection for Automatic Recognition of Eating Condition

abstract: This paper presents the novel Functional-based acoustic Group Feature Selection (FGFS) method for automatic eating condition recognition addressed in the ICMI 2018 Eating Analysis and Tracking Challenge's Food-type Sub-Challenge. The Food-type Sub-Challenge employs the audiovisual iHEARu-EAT database and attempts to classify which of six food types, or none, is being consumed by subjects while speaking. The approach proposed by the FGFS method uses the audio mode and considers the acoustic feature space in groups rather than individually. Each group is comprised of acoustic features generated by the application of a statistical functional to a specified set of the low-level descriptors of the audio data. The FGFS method provides information about the degree of relevance of the statistical functionals to the task. In addition, the partitioning of features into groups allows for more rapid processing of the official Sub-Challenge's large acoustic feature set. The FGFS-based system achieves 2.8% relative Unweighted Average Recall performance improvement over the official Food-type Sub-Challenge baseline on iHEARu-EAT test data.


title: Data Security and Privacy in Fog Computing

abstract: Cloud computing is now a popular computing paradigm that can provide end users access to configurable resources on any device, from anywhere, at any time. During the past years, cloud computing has been developed dramatically. However, with the development of the Internet of Things, the disadvantages (such as high latency) of cloud computing are gradually revealed due to the long distance between the cloud and end users. Fog computing is proposed to solve this problem by extending the cloud to the edge of the network. In particular, fog computing introduces an intermediate layer called fog that is designed to process the communication data between the cloud and end users. Hence, fog computing is usually considered as an extension of cloud computing. In this article, we discuss the design issues for data security and privacy in fog computing. Specially, we present the unique data security and privacy design challenges presented by the fog layer and highlight the reasons why the data protection techniques in cloud computing cannot be directly applied in fog computing.


title: Development and validation of a learning analytics framework: Two case studies using support vector machines

abstract: Interest in collecting and mining large sets of educational data on student background and performance to conduct research on learning and instruction has developed as an area generally referred to as learning analytics. Higher education leaders are recognizing the value of learning analytics for improving not only learning and teaching but also the entire educational arena. However, theoretical concepts and empirical evidence need to be generated within the fast evolving field of learning analytics. The purpose of the two reported cases studies is to identify alternative approaches to data analysis and to determine the validity and accuracy of a learning analytics framework and its corresponding student and learning profiles. The findings indicate that educational data for learning analytics is context specific and variables carry different meanings and can have different implications across educational institutions and area of studies. Benefits, concerns, and challenges of learning analytics are critically reflected, indicating that learning analytics frameworks need to be sensitive to idiosyncrasies of the educational institution and its stakeholders.


title: Enhancing virtual immersion through tactile feedback

abstract: The lack of tangibility while interacting with virtual objects can be compensated by adding haptic and/or tactile devices or actuators to enhance the user experience. In this demonstration, we present two scenarios that consist of perceiving moving objects on the human body (insects) and feeling physical sensations of virtual thermal objects.


title: An Entropy-Based Approach to Detecting Covert Timing Channels

abstract: The detection of covert timing channels is of increasing interest in light of recent exploits of covert timing channels over the Internet. However, due to the high variation in legitimate network traffic, detecting covert timing channels is a challenging task. Existing detection schemes are ineffective at detecting most of the covert timing channels known to the security community. In this paper, we introduce a new entropy-based approach to detecting various covert timing channels. Our new approach is based on the observation that the creation of a covert timing channel has certain effects on the entropy of the original process, and hence, a change in the entropy of a process provides a critical clue for covert timing channel detection. Exploiting this observation, we investigate the use of entropy and conditional entropy in detecting covert timing channels. Our experimental results show that our entropy-based approach is sensitive to the current covert timing channels and is capable of detecting them in an accurate manner.


title: A survey on abstractive text summarization

abstract: Text Summarization is the task of extracting salient information from the original text document. In this process, the extracted information is generated as a condensed report and presented as a concise summary to the user. It is very difficult for humans to understand and interpret the content of the text. In this paper, an exhaustive survey on abstractive text summarization methods has been presented. The two broad abstractive summarization methods are structured based approach and semantic based approach. This paper collectively summarizes and deciphers the various methodologies, challenges and issues of abstractive summarization. State of art benchmark datasets and their properties are being explored. This survey portrays that most of the abstractive summarization methods produces highly cohesive, coherent, less redundant summary and information rich.


title: Cache invalidation strategies for internet-based mobile ad hoc networks

abstract: Internet-based mobile ad hoc network (Imanet) is an emerging technique that combines a mobile ad hoc network (Manet) and the Internet to provide universal information accessibility. Although caching frequently accessed data items in mobile terminals (MTs) improves the communication performance in an Imanet, it brings a critical design issue when data updates. In this paper, we analyze several push and pull-based cache invalidation strategies for Imanets. A global positioning system (GPS) based connectivity estimation (GPSCE) scheme is first proposed to assess the connectivity of an MT for supporting cache invalidation mechanisms. Then, we propose a pull-based approach, called aggregate cache based on demand (ACOD) scheme that uses an efficient search algorithm for finding the queried data items. In addition, we modify two push-based cache invalidation strategies, proposed for cellular networks, to work in Imanets. They are called modified timestamp (MTS) scheme and MTS with updated invalidation report (MTS+UIR) scheme, respectively. We compare the performance of all these schemes as a function of query interval, cache update interval, and cache size through extensive simulation. Simulation results indicate that the ACOD scheme provides high throughput, low query latency, and low communication overhead, and thus, is a viable approach for implementation in Imanets.


title: Cloudlets: bringing the cloud to the mobile user

abstract: Although mobile devices are gaining more and more capabilities (i.e. CPU power, memory, connectivity, ...), they still fall short to execute complex rich media and data analysis applications. Offloading to the cloud is not always a solution, because of the high WAN latencies, especially for applications with real-time constraints such as augmented reality. Therefore the cloud has to be moved closer to the mobile user in the form of cloudlets. Instead of moving a complete virtual machine from the cloud to the cloudlet, we propose a more fine grained cloudlet concept that manages applications on a component level. Cloudlets do not have to be fixed infrastructure close to the wireless access point, but can be formed in a dynamic way with any device in the LAN network with available resources. We present a cloudlet architecture together with a prototype implementation, showing the advantages and capabilities for a mobile real-time augmented reality application.


title: One extra bit of download ensures perfectly private information retrieval

abstract: Private information retrieval (PIR) systems allow a user to retrieve a record from a public database without revealing to the server which record is being retrieved. The literature on PIR considers only replication-based systems, wherein each storage node stores a copy of the entire data. However, systems based on erasure codes are gaining increasing popularity due to a variety of reasons. This paper initiates an investigation into PIR in erasure-coded systems by establishing its capacity and designing explicit codes and algorithms. The notion of privacy considered here is information-theoretic, and the metric optimized is the amount of data downloaded by the user during PIR. In this paper, we present four main results. First, we design an explicit erasure code and PIR algorithm that requires only one extra bit of download to provide perfect privacy. In contrast, all existing PIR algorithms require a download of at least twice the size of the requisite data. Second, we derive lower bounds proving the necessity of downloading at least one additional bit. This establishes the precise capacity of PIR with respect to the metric of download. These results are also applicable to PIR in replication-based systems, which are a special case of erasure codes. Our third contribution is a negative result showing that capacity-achieving codes necessitate super-linear storage overheads. This motivates the fourth contribution of this paper: an erasure code and PIR algorithm that requires a linear storage overhead, provides high reliability to the data, and is a small factor away from the capacity.


title: Quantifying and Comparing Centrality Measures for Network Individuals as Applied to the Enron Corpus

abstract: The ever increasing body of social networks creates an opportunity for extensive network analysis and investigations of communications, cliques, and network contributions. In this study, we focus our attention on the Enron email corpus and the corresponding network of employees, attempting to gather information from the email communications. Methods of data reduction on the email corpus were used to create a weighted adjacency matrix in which each i, j-entry corresponds to a weighted count of correspondences from employee i to employee j. While there are many ways to measure importance within a corporate network, of which job title constitutes one such measure, our study focuses on five primary measures: eigenvector centrality, row-sums of a topological overlap matrix, closeness, betweenness, and Opsahl metric. These network analysis metrics were applied to the weighted adjacency matrix to calculate the centrality measures for each individual employee, which were subsequently compiled into ordinally ranked lists of employees for each centrality measure based on decreasing importance. Additionally, the centrality data was visualized using the DataDriven Documents (D3) javascript library, allowing for network visualization in terms of department job title and number of emails sent. In applying the centrality measures to network data, we explore the differences inherent in each measure and work to compare them as well as the corresponding employee importance rankings for each. The metrics in our analysis determined individual importance of employees by applying significant weight to various aspects of the employees’ network roles. By identifying employees that are connected to a large number of individuals and simultaneously have extensive correspondences with those individuals, the Opsahl score combines the other measures, proving to be the most useful metric in exploring Enron’s inner-corporate structure.


title: Similarity of color images

abstract: We describe two new color indexing techniques. The first one is a more robust version of the commonly used color histogram indexing. In the index we store the cumulative color histograms. The L1-, L2-, L(infinity )-distance between two cumulative color histograms can be used to define a similarity measure of these two color distributions. We show that this method produces slightly better results than color histogram methods, but it is significantly more robust with respect to the quantization parameter of the histograms. The second technique is an example of a new approach to color indexing. Instead of storing the complete color distributions, the index contains only their dominant features. We implement this approach by storing the first three moments of each color channel of an image in the index, i.e., for a HSV image we store only 9 floating point numbers per image. The similarity function which is used for the retrieval is a weighted sum of the absolute differences between corresponding moments. Our tests clearly demonstrate that a retrieval based on this technique produces better results and runs faster than the histogram-based methods.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.


title: Neural-body coupling for emergent locomotion: A musculoskeletal quadruped robot with spinobulbar model

abstract: To gain a synthetic understanding of how the body and nervous system co-create animal locomotion, we propose an investigation into a quadruped musculoskeletal robot with biologically realistic morphology and a nervous system. The muscle configuration and sensory feedback of our robot are compatible with the mono- and bi-articular muscles of a quadruped animal and with its muscle spindles and Golgi tendon organs. The nervous system is designed with a biologically plausible model of the spinobulbar system with no pre-defined gait patterns such that mutual entrainment is dynamically created by exploiting the physics of the body. In computer simulations, we found that designing the body and the nervous system of the robot with the characteristics of biological systems increases information regularities in sensorimotor flows by generating complex and coordinated motor patterns. Furthermore, we found similar results in robot experiments with the generation of various coordinated locomotion patterns created in a self-organized manner. Our results demonstrate that the dynamical interaction between the physics of the body with the neural dynamics can shape behavioral patterns for adaptive locomotion in an autonomous fashion.


title: A Tool for Automated Iptables Firewall Analysis

abstract: We describe ITVal, a tool that enables the efficient analysis of an iptables-based firewall. The underlying basis of ITVal is a library for the efficient manipulation of multi-way decision diagrams. We represent iptables rule sets and queries about the firewall defined by those rule sets as multi-way decision diagrams, and determine answers for the queries by manipulating the diagrams. In addition to discussing the design and implementation of ITVal, we describe how it can be used to detect and correct common firewall errors.


title: A Joint Model for Topic-Sentiment Evolution over Time

abstract: Most existing topic models focus either on extracting static topic-sentiment conjunctions or topic-wise evolution over time leaving out topic-sentiment dynamics and missing the opportunity to provide a more in-depth analysis of textual data. In this paper, we propose an LDA-based topic model for analyzing topic-sentiment evolution over time by modeling time jointly with topics and sentiments. We derive inference algorithm based on Gibbs Sampling process. Finally, we present results on reviews and news datasets showing interpretable trends and strong correlation with ground truth in particular for topic-sentiment evolution over time.


title: Parameterized linear matrix inequality techniques in fuzzy control system design

abstract: This paper proposes different parameterized linear matrix inequality (PLMI) characterizations for fuzzy control systems. These PLMI characterizations are, in turn, relaxed into pure LMI programs, which provides tractable and effective techniques for the design of suboptimal fuzzy control systems. The advantages of the proposed methods over earlier ones are then discussed and illustrated through numerical examples and simulations.


title: Design of SIW variable phase shifter for beam steering antenna application

abstract: This paper presents a variable phase shifter in Substrate Intergrated Waveguide (SIW) technology for beam steering antenna application. With this design, the phase difference between antenna array elements can be varied which results in changing the direction of maximum radiation. The proposed structure is designed by interfacing of a 3 dB directional coupler and — 300 phase shifter. The optimized design produces 30% bandwidth at 10 GHz operating frequency. In the 8.5–11.5 GHz band, isolation between input ports is higher than 20 dB and the return loss is better than 15 dB. By changing the feeding port, the phase difference of 600±20 and 1190±1.50 between the direct and coupled port are achieved. This circuit is useful in beam steering radar application.


title: Evolution of Convolutional Highway Networks

abstract: Convolutional highways are deep networks based on multiple stacked convolutional layers for feature preprocessing. We introduce an evolutionary algorithm (EA) for optimization of the structure and hyperparameters of convolutional highways and demonstrate the potential of this optimization setting on the well-known MNIST data set. The (1+1)-EA employs Rechenberg's mutation rate control and a niching mechanism to overcome local optima adapts the optimization approach. An experimental study shows that the EA is capable of improving the state-of-the-art network contribution and of evolving highway networks from scratch.


title: Conditional must not aliasing for static race detection

abstract: Race detection algorithms for multi-threaded programs using the common lock-based synchronization idiom must correlate locks with the memory locations they guard. The heart of a proof of race freedom is showing that if two locks are distinct, then the memory locations they guard are also distinct. This is an example of a general property we call conditional must not aliasing: Under the assumption that two objects are not aliased, prove that two other objects are not aliased. This paper introduces and gives an algorithm for conditional must not alias analysis and discusses experimental results for sound race detection of Java programs.


title: Dual band interleaved base station phased array antenna with optimized cross-dipole and EBG/AMC structure

abstract: In this work, a new base station antenna is proposed. Two separate frequency bands with separate radiating elements are used in each band. The frequency band separation ratio is about 1.3:1. These elements are arranged with different spacing (wider spacing for the lower frequency band, and narrower spacing for the higher frequency band). Isolation between bands inherently exists in this approach. This avoids the grating lobe effect, and mitigates the beam narrowing (dispersion) seen with fixed element spacing covering the whole wide bandwidth. A new low-profile cross dipole is designed, which is integrated in the array with an EBG/AMC structure for reducing the size of low band elements and decreasing coupling at high band.


title: MVSNet: Depth Inference for Unstructured Multi-view Stereo

abstract: We present an end-to-end deep learning architecture for depth map inference from multi-view images. In the network, we first extract deep visual image features, and then build the 3D cost volume upon the reference camera frustum via the differentiable homography warping. Next, we apply 3D convolutions to regularize and regress the initial depth map, which is then refined with the reference image to generate the final output. Our framework flexibly adapts arbitrary N-view inputs using a variance-based cost metric that maps multiple features into one cost feature. The proposed MVSNet is demonstrated on the large-scale indoor DTU dataset. With simple post-processing, our method not only significantly outperforms previous state-of-the-arts, but also is several times faster in runtime. We also evaluate MVSNet on the complex outdoor Tanks and Temples dataset, where our method ranks first before April 18, 2018 without any fine-tuning, showing the strong generalization ability of MVSNet.


title: Fuzzy Logic Control of Doubly Fed Induction Generator Wind Turbine 1 H. Karimi-Davijani,

abstract: This paper presents fuzzy logic control of Doubly Fed Induction Generator (DFIG) wind turbine in a sample power system. DFIG consists of a common induction generator with slip ring and a partial scale power electronic converter. Fuzzy logic controller is applied to rotor side converter for active power control and voltage regulation of wind turbine. Wind turbine and its control unit are described in details. All power system components are simulated in PSCAD/EMTDC software and for fuzzy control, using a user defined block, this software is linked to MATLAB. For studying the performance of controller, different abnormal conditions are applied even the worst case. Simulation results prove the excellent performance of fuzzy control unit as improving power quality and stability of wind turbine.


title: A Single-Switch High Step-Up DC–DC Converter Based on Quadratic Boost

abstract: This paper proposes a novel high step-up nonisolated single switch dc–dc converter suitable for regulating dc bus in various microsources especially for photovoltaic (PV) sources. Quadratic boost and switched-capacitor technique are used as primary and secondary circuits, respectively. A coupled inductor is applied to make a connection between them, so a high dc voltage gain is achieved. High efficiency is yield where voltage stress on active switch is alleviated by clamped capacitor; consequently, smaller $\rm{R}_{\rm{DS(ON)}}$ for power switch is required. On the other hand, input current of the proposed converter is continued, hence stress on the input source is reduced. The operating principles and steady-state analyses are discussed in detail for both continuous and discontinuous conduction modes. Also, the boundary condition is computed. To verify the performance of the proposed converter and theoretical calculations, a 250-W prototype converter is implemented with an input voltage of 24 V and an output voltage of 400 V designed especially for PV sources in continuous conduction mode operation. Finally, simulation results are confirmed by experimental results; maximum efficiency is occurred at 150 W and full-load efficiency is 92.96%.


title: Performance enhancement of IEEE 802.11p system using a novel channel estimation scheme

abstract: Vehicular channels are among the most challenging wireless channels due to its high time varying characteristics. However, in the IEEE 802.11p standard which designed for vehicle to vehicle (VTV) communication, the preamble based channel estimation scheme cannot guarantee a desirable equalization under urban and highway scenarios. In this paper, we first propose a novel channel estimation scheme which focus on both BER and PER improvement of IEEE 802.11p. Our scheme exploits the time domain correlation of the data pilots reconstructed by the Viterbi decoded bits and builds a reliable data pilots set (RDPS), then the frequency domain interpolation is applied to RDPS to complete the channel estimation which exploiting the frequency domain correlation of VTV channel. Furthermore, to improve the performance of IEEE 802.11p in highway scenarios, we propose a novel receiver design which combine our channel estimation scheme and spatial diversity in frequency domain to combat fast fading. Simulation results demonstrate the superior performance improvement of the proposed channel estimation scheme and excellent adaptability of our spatial diversity receiver design under highway scenarios.


title: Comparing Stochastic and Deterministic Computing

abstract: Technology scaling has raised the specter of myriads of cheap, but unreliable and/or stochastic devices that must be creatively combined to create a reliable computing system. This has renewed the interest in computing that exploits stochasticity—embracing, not combating the device physics. If a stochastic representation is used to implement a programmable general-purpose architecture akin to CPUs, GPUs, or FPGAs, the preponderance of evidence indicates that most of the system energy will be expended in communication and storage as opposed to computation. This paper presents an analytical treatment of the benefits and drawbacks of adopting a stochastic approach by examining the cost of representing a value. We show both scaling laws and costs for low precision representations. We also analyze the cost of multiplication implemented using stochastic versus deterministic approaches, since multiplication is the prototypical inexpensive stochastic operation. We show that the deterministic approach compares favorably to the stochastic approach when holding precision and reliability constant.


title: Bilingualism, Aging, and Cognitive Control: Evidence From the Simon Task

abstract: Previous work has shown that bilingualism is associated with more effective controlled processing in children; the assumption is that the constant management of 2 competing languages enhances executive functions (E. Bialystok, 2001). The present research attempted to determine whether this bilingual advantage persists for adults and whether bilingualism attenuates the negative effects of aging on cognitive control in older adults. Three studies are reported that compared the performance of monolingual and bilingual middle-aged and older adults on the Simon task. Bilingualism was associated with smaller Simon effect costs for both age groups; bilingual participants also responded more rapidly to conditions that placed greater demands on working memory. In all cases the bilingual advantage was greater for older participants. It appears, therefore, that controlled processing is carried out more effectively by bilinguals and that bilingualism helps to offset age-related losses in certain executive processes.


title: Improved CCG Parsing with Semi-supervised Supertagging

abstract: Current supervised parsers are limited by the size of their labelled training data, making improving them with unlabelled data an important goal. We show how a state-of-the-art CCG parser can be enhanced, by predicting lexical categories using unsupervised vector-space embeddings of words. The use of word embeddings enables our model to better generalize from the labelled data, and allows us to accurately assign lexical categories without depending on a POS-tagger. Our approach leads to substantial improvements in dependency parsing results over the standard supervised CCG parser when evaluated on Wall Street Journal (0.8%), Wikipedia (1.8%) and biomedical (3.4%) text. We compare the performance of two recently proposed approaches for classification using a wide variety of word embeddings. We also give a detailed error analysis demonstrating where using embeddings outperforms traditional feature sets, and showing how including POS features can decrease accuracy.


title: A cluster-based opinion leader discovery in social network

abstract: Recently, opinion leader discovery has drawn much attention due to its widespread applicability. By identifying the opinion leader, companies or governments can manipulate the selling or guiding public opinion, respectively. However, mining opinion leader is a challenge task because of the complexity of processing social graph and analyzing leadership quality. In this study, a novel method, TCOL-Miner, is proposed to efficiently find the opinion leaders from a huge social network. We integrate the clustering and semantic analysis methods with some pruning strategies to tackle the influence overlapping issue and the potential leadership of individuals. The experimental results show that the proposed TCOL-Miner can effectively discover the influenced opinion leaders in different real social networks with efficiency.


Issues in Cloud Computing


title: A novel grey wolf optimizer for global optimization problems

abstract: Grey wolf optimizer (GWO) is a recently proposed intelligent optimization method inspired by hunting behavior of grey wolves. In GWO algorithm, the parameter of a is decreased from 2 to 0 to balance exploitation and exploration, respectively. A novel time-varying parameter of a decreasing linearly is used to enhance the performance of GWO algorithm. In order to enhance the global convergence, when generating the initial population, the good-point-set method is employed. The simulation results tested on 10 standard unconstrained functions demonstrate that the proposed method has fine solution quality and convergence performance comparing to standard GWO method and performs superior to the other intelligent optimization method in most functions.


abstract: Switching noise and shoot-through current reduction techniques for switched-capacitor voltage doublers based on cross-coupled structure are presented. The intuitive analysis of the shoot-through current and switching noise generation processes in the doubler is first reported. Break-before-make mechanism is adopted to minimize the shoot-through current, thereby greatly reducing the no-load supply current dissipation and improving the light-load power efficiency of the voltage doubler. In addition, by employing gate-slope reduction technique at the serial power transistor during turn-on, the switching noise of the voltage doubler is significantly lowered. Two voltage doublers with and without the proposed circuit techniques have been fabricated in a 0.6-/spl mu/m CMOS process. Experimental results verify that the total supply current at no-load condition of the proposed voltage doubler is reduced by two fold and its switching noise is decreased by 2.5 times.


title: Constructing efficient information extraction pipelines

abstract: Information Extraction (IE) pipelines analyze text through several stages. The pipeline's algorithms determine both its effectiveness and its run-time efficiency. In real-world tasks, however, IE pipelines often fail acceptable run-times because they analyze too much task-irrelevant text. This raises two interesting questions: 1) How much "efficiency potential" depends on the scheduling of a pipeline's algorithms? 2) Is it possible to devise a reliable method to construct efficient IE pipelines? Both questions are addressed in this paper. In particular, we show how to optimize the run-time efficiency of IE pipelines under a given set of algorithms. We evaluate pipelines for three algorithm sets on an industrially relevant task: the extraction of market forecasts from news articles. Using a system-independent measure, we demonstrate that efficiency gains of up to one order of magnitude are possible without compromising a pipeline's original effectiveness.


title: Unsupervised Concept-to-text Generation with Hypergraphs

abstract: Concept-to-text generation refers to the task of automatically producing textual output from non-linguistic input. We present a joint model that captures content selection ("what to say") and surface realization ("how to say") in an unsupervised domain-independent fashion. Rather than breaking up the generation process into a sequence of local decisions, we define a probabilistic context-free grammar that globally describes the inherent structure of the input (a corpus of database records and text describing some of them). We represent our grammar compactly as a weighted hypergraph and recast generation as the task of finding the best derivation tree for a given input. Experimental evaluation on several domains achieves competitive results with state-of-the-art systems that use domain specific constraints, explicit feature engineering or labeled data.


title: TDOA/AOD/AOA localization in NLOS environments

abstract: In this paper we present a novel localization algorithm for use in non-line-of-sight environments. We use the single bounce scattering model to model the non-line-of-sight propagations and estimate the position of a mobile station when the observations are the time-difference-of-arrival, the angle-of-departure, and the angle-of-arrival. The proposed algorithm uses the geometry of the radio propagation paths to estimate the position of the mobile station. An iterative localization algorithm based on the linearization of the observation function using a first order Taylor series is also discussed. The lower bound of the variance of an unbiased mobile station position estimator is determined using the Cramer-Rao lower bound. The performance of the proposed algorithm is analyzed using measurements from a real world indoor localization scenario and using Monte-Carlo simulations. It is shown that the proposed algorithms yield a satisfactory performance.


title: AUV based mobile fluorometers: System for underwater oil-spill detection and quantification

abstract: The tragic Deepwater Horizon accident in the Gulf of Mexico in 2010 as well as increase in deepwater offshore activity have increased public interest in counter-measures available for sub-surface releases of hydrocarbons. Available remote-sensing techniques are efficient and well developed for surface disasters but they are not useful underwater. Along these lines, this paper analyzes application of Autonomous Underwater Vehicles (AUV) with integrated submersible fluorometer for underwater detection of hydrocarbons. Experiments with rhodamine, which was used as a replacement for oil, showed that the proposed system can be efficiently used both as an input into numerical model and consequent visualization of spatial distribution of pollutant.


title: Inverse color to black-and-white halftone conversion via dictionary learning and color mapping

abstract: This paper challenges the problem of estimating the original red-green-blue (RGB) image from a black-and-white (B&W) halftone image with homogeneously distributed dot patterns. To achieve this goal, training RGB images are converted into color-embedded gray images using the conventional reversible color to gray conversion method, and then converted into halftone images using error diffusion in order to produce the corresponding B&W halftone images. The proposed method is composed of two processing steps: (1) restoring the color-embedded gray image from an input B&W halftone image using a sparse linear representation between the image patch pairs obtained from the images and (2) restoring the original colors from the color-embedded gray image using the reversible color to gray conversion and linear color mapping methods. The proposed method successfully demonstrates the recovery of colors similar to the originals. The experimental results indicate that the proposed method outperforms the conventional methods. It is suggested that our method is not only successfully applied for the color recovery of the B&W halftone image, but that it can also be extended to various applications including color restoration of printed image, hardcopy data hiding, and halftone color compression.


title: Wavelet-Based EEG Preprocessing for Biometric Applications

abstract: EEG signals, measuring transient brain activities, can be used as a source of biometric information with potential application in high-security person recognition scenarios. However, due to the inherent nature of these signals and the process used for their acquisition, their effective preprocessing is critical for their successful utilisation. In this paper we compare the effectiveness of different wavelet-based noise removal methods and propose an EEG-based biometric identification system which combines two such de-noising methods to enhance the signal preprocessing stage. In tests using 50 subjects from a public database, the proposed new approach is shown to provide improved identification performance over alternative techniques. Another important preprocessing consideration is the segmentation of the EEG record prior to de-noising. Different segmentation approaches were investigated and the trade-off between performance and computation time is explored. Finally the paper reports on the impact of the choice of wavelet function used for feature extraction on system performance.


title: Influence of Hypoxia and Hypercapnia on Sleep State-Dependent Heart Rate Variability Behavior in Newborn Lambs

abstract: STUDY OBJECTIVES: Although hypercapnia and/or hypoxia are frequently present during chronic lung disease of infancy and have also been implicated in sudden infant death syndrome (SIDS), their effect on cardiac autonomic regulation remains unclear. The authors' goal is to test that hypercapnia and hypoxia alter sleep-wake cycle-dependent heart rate variability (HRV) in the neonatal period. DESIGN: Experimental study measuring HRV during sleep states in lambs randomly exposed to hypercapnia, hypoxia, or air. SETTING: University center for perinatal research in ovines (Sherbrooke, Canada). INSERM-university research unit for signal processing (Rennes, France). PARTICIPANTS: Six nonsedated, full-term lambs. INTERVENTIONS: Each lamb underwent polysomnographic recordings while in a chamber flowed with either air or 21% O(2) + 5% CO(2) (hypercapnia) or 10% O(2) + 0% CO(2) (hypoxia) on day 3, 4, and 5 of postnatal age. MEASUREMENTS AND RESULTS: Hypercapnia increased the time spent in wakefulness and hypoxia the time spent in quiet sleep (QS). The state of alertness was the major determinant of HRV characterized with linear or nonlinear methods. Compared with QS, active sleep (AS) was associated with an overall increase in HRV magnitude and short-term self-similarity and a decrease in entropy of cardiac cycle length in air. This AS-related HRV pattern persisted in hypercapnia and was even more pronounced in hypoxia. CONCLUSION: Enhancement of AS-related sympathovagal coactivation in hypoxia, together with increased heart rate regularity, may be evidence that AS + hypoxia represent a particularly vulnerable state in early life. This should be kept in mind when deciding the optimal arterial oxygenation target in newborns and when investigating the potential involvement of hypoxia in SIDS pathogenesis.


title: RoboCup: A Challenge Problem for AI and Robotics

abstract: RoboCup is an attempt to foster AI and intelligent robotics research by providing a standard problem where wide range of technologies can be integrated and examined. The first RoboCup competition was held at IJCAI-97, Nagoya. In order for a robot team to actually perform a soccer game, various technologies must be incorporated including: design principles of autonomous agents, multi-agent collaboration, strategy acquisition, real-time reasoning, robotics, and sensorfusion. RoboCup is a task for a team of multiple fast-moving robots under a dynamic environment. Although RoboCup's final target is a world cup with real robots, RoboCup offers a software platform for research on the software aspects of RoboCup. This paper describes technical challenges involved in RoboCup, rules, and simulation environment.


title: Reappraisal of spontaneous stereotypy in the deer mouse as an animal model of obsessive-compulsive disorder (OCD): Response to escitalopram treatment and basal serotonin transporter (SERT) density

abstract: Obsessive-compulsive disorder (OCD) is characterized by recurrent thoughts and repetitive motor actions. Hyposerotonergic signalling in the cortico-striatal circuitry is believed to be central to the pathology of OCD, while many patients only respond to chronic treatment with high dose selective serotonin (5HT) reuptake inhibitors (SSRIs). Confined deer mice spontaneously develop two forms of stereotypy, namely vertical jumping and pattern running. The purpose of this investigation was to reappraise these behaviours and strengthen the validity of deer mouse stereotypy as an animal model of OCD within a framework of three study questions: (1) can the time spent executing stereotypical behaviours be employed as a measure of extent of stereotypy, (2) does deer mouse stereotypy only respond to chronic, but not sub-chronic treatment with a high-dose SSRI, and (3) is deer mouse stereotypy associated with altered cortico-striatal 5HT transporter (SERT) binding? The current study demonstrates that treatment naive high stereotypical (HS) deer mice spend significantly more time executing stereotypical behaviours while significantly less time is spent indulging in stereotypy following chronic, but not sub-chronic, treatment with escitalopram. Furthermore, HS deer mice present with a significant decrease in striatal SERT density compared to non-stereotypical (NS) controls. Building on previous validation studies, we conclude that deer mouse stereotypy is a valid naturalistic animal model of OCD with robust face, construct and predictive validity.


title: Low losses printed distribution network technologies for planar antennas in Ka band

abstract: Study of Substrate Integrated Waveguide and Gap Waveguide technologies used to manufacture some demonstration prototypes at Ka band for a low losses distribution network in a low profile antenna. A transmission losses comparison is presented using lossy and lossless dielectric substrates in the simulations for the different technologies at 30 GHz. Various structures such as transitions from WR-28, bends, and power dividers have been designed and simulated. The simulations of different models are compared with the manufactured prototypes in metallized 3D-printed plastic technology and in RO4350B and FR-4 substrates for a band from 28 to 30 GHz.


title: Nyström method vs random fourier features: A theoretical and empirical comparison

abstract: Both random Fourier features and the Nystrom method have been successfully applied to efficient kernel learning. In this work, we investigate the fundamental difference between these two approaches, and how the difference could affect their generalization performances. Unlike approaches based on random Fourier features where the basis functions (i.e., cosine and sine functions) are sampled from a distribution independent from the training data, basis functions used by the Nystrom method are randomly sampled from the training examples and are therefore data dependent. By exploring this difference, we show that when there is a large gap in the eigen-spectrum of the kernel matrix, approaches based on the Nystrom method can yield impressively better generalization error bound than random Fourier features based approach. We empirically verify our theoretical findings on a wide range of large data sets.


title: Random-Scrambling Tunable POTDR for Distributed Measurement of Cumulative PMD

abstract: We provide the theoretical framework for a new method of distributed measurement of cumulative PMD along single-mode optical fiber links. Independent pairs of optical pulses are launched into the fiber, where each pair consists of two pulses having the same state of polarization (SOP) but two different, closely spaced wavelengths. The key aspect is that both the SOP and center wavelength of a given pair need not be correlated with those of any other pair. This random-scrambling approach is well suited for an economically designed instrument that is more robust for field applications than previously reported approaches. Since no assumed model of the distributed birefringence behavior is required, the method is suitable for any actual single-mode telecom fiber span encompassing many fiber sections of different types and origins.


title: Coupling matrix synthesis for a new class of microwave filter configuration

abstract: In this paper a new approach to the synthesis of coupling matrices for microwave filters is presented. The new approach represents an advance on existing direct and optimization methods for coupling matrix synthesis in that it will exhaustively discover all possible coupling matrix solutions for a network if more than one exists. This enables a selection to be made of the set of coupling values, resonator frequency offsets, parasitic coupling tolerance etc that will be best suited to the technology it is intended to realize the microwave filter with. To demonstrate the use of the method, the case of the recently - introduced 'extended box' (EB) coupling matrix configuration is taken. The EB represents a new class of filter configuration featuring a number of important advantages, one of which is the existence of multiple coupling matrix solutions for each prototype filtering function, eg 16 for 8 th degree cases. This case is taken as an example to demonstrate the use of the synthesis method - yielding one solution suitable for dual-mode realization and one where some couplings are small enough to neglect.


title: THE [WIKIPEDIA] WORLD IS NOT FLAT: ON THE ORGANIZATIONAL STRUCTURE OF ONLINE PRODUCTION COMMUNITIES

abstract: The creation and maintenance of online production communities depend on the complex ecology created by the interaction of social roles, and these roles are essential for the governance of the community. This study investigates the organizational structure of one of the most notable peerproduction projects: Wikipedia. While online communities have often been depicted as ‘flat’ and egalitarian, recent studies of Wikipedia suggest that it has developed a cumbersome beaurocratic structure that includes a hierarchy of organizational role. The objective of this study is, thus, to empirically study the organization of roles in Wikipedia and the hierarchy formed through their power relationships. Our research method employs Wikipedia’s formal set of access privileges as indicators of roles, and analyses all 4,902,643 Wikipedia members (of which 10,496 hold special access privileges). Applying statistical techniques traditionally employed to validate the psychometric properties of scales, we find that Wikipedia has an intricate ecology of roles. Our analysis of power relationships within these twelve roles reveals Wikipedia’s organizational hierarchy. Implications for theory and practice are discussed.


title: Critical success factors in enterprise resource planning implementation

abstract: Purpose – The purpose of this paper is to consolidate the critical success factors (CSFs) as published in enterprise resource planning (ERP) implementation case studies. The authors perform the analysis and propose the final CSFs based on the reported ERP implementation process stages. Design/methodology/approach – The paper follows the eight category coding steps proposed by Carley (1993) and utilizes only ERP implementation case studies to identify a distinct set of critical success factors. The 37 case studies used in this paper provide a reasonable sample from different countries and contexts. Two methodologies were followed, one for the literature review process and the other for the analysis and synthesis. Findings – Out of 64 reported CSFs that were extracted from the literature and subsequent detailed analysis and synthesis the authors found a total of 22 factors that are distinct. These factors which encompass change management, are proposed with five ERP implementation stages. Research limitatio...


title: Clustering Data of Mixed Categorical and Numerical Type With Unsupervised Feature Learning

abstract: Mixed-type categorical and numerical data are a challenge in many applications. This general area of mixed-type data is among the frontier areas, where computational intelligence approaches are often brittle compared with the capabilities of living creatures. In this paper, unsupervised feature learning (UFL) is applied to the mixed-type data to achieve a sparse representation, which makes it easier for clustering algorithms to separate the data. Unlike other UFL methods that work with homogeneous data, such as image and video data, the presented UFL works with the mixed-type data using fuzzy adaptive resonance theory (ART). UFL with fuzzy ART (UFLA) obtains a better clustering result by removing the differences in treating categorical and numeric features. The advantages of doing this are demonstrated with several real-world data sets with ground truth, including heart disease, teaching assistant evaluation, and credit approval. The approach is also demonstrated on noisy, mixed-type petroleum industry data. UFLA is compared with several alternative methods. To the best of our knowledge, this is the first time UFL has been extended to accomplish the fusion of mixed data types.


title: An 84.7% Efficiency 100-MHz Package Bondwire-Based Fully Integrated Buck Converter With Precise DCM Operation and Enhanced Light-Load Efficiency

abstract: A 100-MHz PWM fully integrated buck converter utilizing standard package bondwire as power inductor with enhanced light-load efficiency which occupies 2.25 mm2 in 0.13-μm CMOS is presented. Standard package bondwire instead of on-chip spiral metal or special spiral bondwire is implemented as power inductor to minimize the cost and the conduction loss of an integrated inductor. The accuracy requirement of bondwire inductance is relaxed by an extra discontinuous-conduction-mode (DCM) calibration loop, which solves the precise DCM operation issue of fully integrated converters and eliminates the reverse current-related loss, thus enabling the use of standard package bondwire inductor with various packaging techniques. Optimizations of the power transistors, the input decoupling capacitor (CI), and the controller are also presented to achieve an efficient and robust high-frequency design. With all three major power losses, conduction loss, switching loss, and reverse current related loss, optimized or eliminated, the efficiency is significantly improved. An efficiency of 74.8% is maintained at 10 mA, and a peak efficiency of 84.7% is measured at nominal operating conditions with a voltage conversion of 1.2 to 0.9 V. Converters with various bondwire inductances from 3 to 8.5 nH are measured to verify the reliability and compatibility of different packaging techniques.