Query: Path Planning through PSO Algorithm in Complex Environments

Relevant passages:
Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans <=SEP=> A mobile robot exploring an unknown environment has no absolute frame of reference for its position, other than features it detects through its sensors. Using distinguishable landmarks is one possible approach, but it requires solving the object recognition problem. In particular, when the robot uses two-dimensional laser range scans for localization, it is di cult to accurately detect and localize landmarks in the environment (such as corners and occlusions) from the range scans. In this paper, we develop two new iterative algorithms to register a range scan to a previous scan so as to compute relative robot positions in an unknown environment, that avoid the above problems. The rst algorithm is based on matching data points with tangent directions in two scans and minimizing a distance function in order to solve the displacement between the scans. The second algorithm establishes correspondences between points in the two scans and then solves the point-to-point least-squares problem to compute the relative pose of the two scans. Our methods work in curved environments and can handle partial occlusions by rejecting outliers.

Autonomous robot path planning in dynamic environment using a new optimization technique inspired by Bacterial Foraging technique <=SEP=> Path planning is one of the basic and interesting functions for a mobile robot. This paper explores the application of Bacterial Foraging Optimization to the problem of mobile robot navigation to determine shortest feasible path to move from any current position to target position in unknown environment with moving obstacles. It develops a new algorithm based on Bacterial Foraging Optimization (BFO) technique. This algorithm finds a path towards the target and avoiding the obstacles using particles which are randomly distributed on a circle around a robot. The criterion on which it selects the best particle is the distance to target and the Gaussian cost function of the particle. Then, a high level decision strategy is used for the selection and thus proceeds for the result. It works on local environment by using a simple robot sensor. So, it is free from having generated additional map which adds cost. Furthermore, it can be implemented without requirement to tuning algorithm and complex calculation. To simulate the algorithm, the program is written in C language and the environment is created by OpenGL. To test the efficiency of proposed technique, results are compared with Basic Bacterial Foraging Optimization (BFO) and another well-known algorithm called Particle Swarm Optimization (PSO). From the experimental result it can be told that the proposed method gives better path or optimal path.

Mobile Robot Path Planning with Randomly Moving Obstacles and Goal <=SEP=> This article presents the dynamic path planning for a mobile robot to track a randomly moving goal with avoidance of multiple randomly moving obstacles. The main feature of the developed scheme is its capability of dealing with the situation that the paths of both the goal and the obstacles are unknown a priori to the mobile robot. A new mathematical approach that is based on the concepts of 3-D geometry is proposed to generate the path of the mobile robot. The mobile robot decides its path in real time to avoid the randomly moving obstacles and to track the randomly moving goal. The developed scheme results in faster decision-making for successful goal tracking. 3-D simulations using MATLAB validate the developed scheme.

Bacterium-inspired robots for environmental monitoring <=SEP=> Locating gradient sources and tracking them over time has important applications to environmental monitoring and studies of the ecosystem. We present an approach, inspired by bacterial chemotaxis, for robots to navigate to sources using gradient measurements and a simple actuation strategy (biasing a random walk). Extensive simulations show the efficacy of the approach in varied conditions including multiple sources, dissipative sources, and noisy sensors and actuators. We also show how such an approach could be used for boundary finding. We validate our approach by testing it on a small robot (the robomote) in a phototaxis experiment. A comparison of our approach with gradient descent shows that while gradient descent is faster, our approach is better suited for boundary coverage, and performs better in the presence of multiple and dissipative sources.

Real-time obstacle avoidance for manipulators and mobile robots <=SEP=> 

Controlling electromagnetic fields. <=SEP=> Using the freedom of design that metamaterials provide, we show how electromagnetic fields can be redirected at will and propose a design strategy. The conserved fields-electric displacement field D, magnetic induction field B, and Poynting vector B-are all displaced in a consistent manner. A simple illustration is given of the cloaking of a proscribed volume of space to exclude completely all electromagnetic fields. Our work has relevance to exotic lens design and to the cloaking of objects from electromagnetic fields.

Brain activation and sexual arousal in healthy, heterosexual males. <=SEP=> Despite the brain's central role in sexual function, little is known about relationships between brain activation and sexual response. In this study, we employed functional MRI (fMRI) to examine relationships between brain activation and sexual arousal in a group of young, healthy, heterosexual males. Each subject was exposed to two sequences of video material consisting of explicitly erotic (E), relaxing (R) and sports (S) segments in an unpredictable order. Data on penile turgidity was collected using a custom-built pneumatic pressure cuff. Both traditional block analyses using contrasts between sexually arousing and non-arousing video clips and a regression using penile turgidity as the covariate of interest were performed. In both types of analyses, contrast images were computed for each subject and these images were subsequently used in a random effects analysis. Strong activations specifically associated with penile turgidity were observed in the right subinsular region including the claustrum, left caudate and putamen, right middle occipital/ middle temporal gyri, bilateral cingulate gyrus and right sensorimotor and pre-motor regions. Smaller, but significant activation was observed in the right hypothalamus. Few significant activations were found in the block analyses. Implications of the findings are discussed. Our study demonstrates the feasibility of examining brain activation/sexual response relationships in an fMRI environment and reveals a number of brain structures whose activation is time-locked to sexual arousal.

The Complexity of Relational Query Languages (Extended Abstract) <=SEP=> Two complexity measures for query languages are proposed. Data complexity is the complexity of evaluating a query in the language as a function of the size of the database, and expression complexity is the complexity of evaluating a query in the language as a function of the size of the expression defining the query. We study the data and expression complexity of logical languages - relational calculus and its extensions by transitive closure, fixpoint and second order existential quantification - and algebraic languages - relational algebra and its extensions by bounded and unbounded looping. The pattern which will be shown is that the expression complexity of the investigated languages is one exponential higher then their data complexity, and for both types of complexity we show completeness in some complexity class.

The emotional power of music: How music enhances the feeling of affective pictures <=SEP=> Music is an intriguing stimulus widely used in movies to increase the emotional experience. However, no brain imaging study has to date examined this enhancement effect using emotional pictures (the modality mostly used in emotion research) and musical excerpts. Therefore, we designed this functional magnetic resonance imaging study to explore how musical stimuli enhance the feeling of affective pictures. In a classical block design carefully controlling for habituation and order effects, we presented fearful and sad pictures (mostly taken from the IAPS) either alone or combined with congruent emotional musical excerpts (classical pieces). Subjective ratings clearly indicated that the emotional experience was markedly increased in the combined relative to the picture condition. Furthermore, using a second-level analysis and regions of interest approach, we observed a clear functional and structural dissociation between the combined and the picture condition. Besides increased activation in brain areas known to be involved in auditory as well as in neutral and emotional visual-auditory integration processes, the combined condition showed increased activation in many structures known to be involved in emotion processing (including for example amygdala, hippocampus, parahippocampus, insula, striatum, medial ventral frontal cortex, cerebellum, fusiform gyrus). In contrast, the picture condition only showed an activation increase in the cognitive part of the prefrontal cortex, mainly in the right dorsolateral prefrontal cortex. Based on these findings, we suggest that emotional pictures evoke a more cognitive mode of emotion perception, whereas congruent presentations of emotional visual and musical stimuli rather automatically evoke strong emotional feelings and experiences.

Low-Power and Area-Efficient Carry Select Adder <=SEP=> Carry Select Adder (CSLA) is one of the fastest adders used in many data-processing processors to perform fast arithmetic functions. From the structure of the CSLA, it is clear that there is scope for reducing the area and power consumption in the CSLA. This work uses a simple and efficient gate-level modification to significantly reduce the area and power of the CSLA. Based on this modification 8-, 16-, 32-, and 64-b square-root CSLA (SQRT CSLA) architecture have been developed and compared with the regular SQRT CSLA architecture. The proposed design has reduced area and power as compared with the regular SQRT CSLA with only a slight increase in the delay. This work evaluates the performance of the proposed designs in terms of delay, area, power, and their products by hand with logical effort and through custom design and layout in 0.18-μm CMOS process technology. The results analysis shows that the proposed CSLA structure is better than the regular SQRT CSLA.

Digital Media and Symptoms of Attention-Deficit/Hyperactivity Disorder in Adolescents. <=SEP=> In many regards, this trial represents the best of evidence-based quality improvement efforts—the improvement in health outcomes is often greater than what would have been expected from the individual components alone. An outcome of the trial by Wang et al is the proof that effective stroke interventions can be implemented in China where the burden of stroke is the highest in the world. Trials like this one leave a lasting legacy because the coaching and follow-up and the demonstration that data collection can lead to better outcomes with practice change will leave each of the intervention hospitals with a platform of good-quality stroke care and a mechanism to keep improving. Similar to the challenge of providing a novel drug to patients after a trial ends, a new dilemma that arises for the trialists is how to get the nonintervention hospitals up to the same level of care as the intervention hospitals. A major challenge is the sustainability of these kinds of interventions. Intensive quality improvement is just not practical for long periods; lower level embedded improvement needs to be built into the culture of a hospital, written into job descriptions, and identified as a deliverable in employment contracts to make continuous improvement possible and likely. In addition, the finer details of the intervention must be made available to others. A “teach the teacher” model may apply in distributing this kind of intervention widely. Overall, this randomized clinical trial by Wang et al has achieved its broad objective of both providing both stroke evidence and implanting practice change in 20 hospitals. The challenge now is to sustain that level of quality improvement at these hospitals, and expand this approach to other centers providing care for patients with acute stroke.

MobileDeepPill: A Small-Footprint Mobile Deep Learning System for Recognizing Unconstrained Pill Images <=SEP=> Correct identification of prescription pills based on their visual appearance is a key step required to assure patient safety and facilitate more effective patient care. With the availability of high-quality cameras and computational power on smartphones, it is possible and helpful to identify unknown prescription pills using smartphones. Towards this goal, in 2016, the U.S. National Library of Medicine (NLM) of the National Institutes of Health (NIH) announced a nationwide competition, calling for the creation of a mobile vision system that can recognize pills automatically from a mobile phone picture under unconstrained real-world settings. In this paper, we present the design and evaluation of such mobile pill image recognition system called MobileDeepPill. The development of MobileDeepPill involves three key innovations: a triplet loss function which attains invariances to real-world noisiness that deteriorates the quality of pill images taken by mobile phones; a multi-CNNs model that collectively captures the shape, color and imprints characteristics of the pills; and a Knowledge Distillation-based deep model compression framework that significantly reduces the size of the multi-CNNs model without deteriorating its recognition performance. Our deep learning-based pill image recognition algorithm wins the First Prize (champion) of the NIH NLM Pill Image Recognition Challenge. Given its promising performance, we believe MobileDeepPill helps NIH tackle a critical problem with significant societal impact and will benefit millions of healthcare personnel and the general public.

Fast-and-Light Stochastic ADMM <=SEP=> The alternating direction method of multipliers (ADMM) is a powerful optimization solver in machine learning. Recently, stochastic ADMM has been integrated with variance reduction methods for stochastic gradient, leading to SAG-ADMM and SDCA-ADMM that have fast convergence rates and low iteration complexities. However, their space requirements can still be high. In this paper, we propose an integration of ADMM with the method of stochastic variance reduced gradient (SVRG). Unlike another recent integration attempt called SCAS-ADMM, the proposed algorithm retains the fast convergence benefits of SAG-ADMM and SDCA-ADMM, but is more advantageous in that its storage requirement is very low, even independent of the sample size n. Experimental results demonstrate that it is as fast as SAG-ADMM and SDCA-ADMM, much faster than SCAS-ADMM, and can be used on much bigger data sets.

Low-Cost Printed Chipless RFID Humidity Sensor Tag for Intelligent Packaging <=SEP=> This paper presents a fully-printed chipless radio frequency identification sensor tag for short-range item identification and humidity monitoring applications. The tag consists of two planar inductor-capacitor resonators operating wirelessly through inductive coupling. One resonator is used to encode ID data based on frequency spectrum signature, and another one works as a humidity sensor, utilizing a paper substrate as a sensing material. The sensing performances of three paper substrates, including commercial packaging paper, are investigated. The use of paper provides excellent sensitivity and reasonable response time to humidity. The cheap and robust packaging paper, particularly, exhibits the largest sensitivity over the relative humidity range from 20% to 70%, which offers the possibility of directly printing the sensor tag on traditional packages to make the package intelligent at ultralow cost.

2.1 28Gb/s 560mW multi-standard SerDes with single-stage analog front-end and 14-tap decision-feedback equalizer in 28nm CMOS <=SEP=> A high-speed SerDes must meet multiple challenges including high-speed operation, intensive equalization technique, low power consumption, small area and robustness. In order to meet new standards, such a OIF CEI-25G-LR, CEI-28G-MR/SR/VSR, IEEE802.3bj and 32G-FC, data-rates are increased to 25 to 28Gb/s, which is more than 75% higher than the previous generation of SerDes. For SerDes applications with several hundreds of lanes integrated in single chip, power consumption is very important factor while maintaining high performance. There are several previous works at 28Gb/s or higher data-rate [1-2]. They use an unrolled DFE to meet the critical timing margin, but the unrolled DFE structure increases the number of DFE slicers, increasing the overall power and die area. In order to tackle these challenges, we introduce several circuits and architectural techniques. The analog front-end (AFE) uses a single-stage architecture and a compact on-chip passive inductor in the transimpedance amplifier (TIA), providing 15dB boost. The boost is adaptive and its adaptation loop is decoupled from the decision-feedback equalizer (DFE) adaptation loop by the use of a group-delay adaptation (GDA) algorithm. DFE has a half-rate 1-tap unrolled structure with 2 total error latches for power and area reduction. A two-stage sense-amplifier-based slicer achieves a sensitivity of 15mV and DFE timing closure. We also develop a high-speed clock buffer that uses a new active-inductor circuit. This active-inductor circuit has the capability to control output-common-mode voltage to optimize circuit operating points.

Making Decisions about Self-Disclosure in Online Social Networks <=SEP=> This paper explores privacy calculus decision making processes for online social networks (OSN). Content analysis method is applied to analyze data obtained from face-to-face interviews and online survey with open-ended questions of 96 OSN users from different countries. The factors users considered before self-disclosing are explored. The perceived benefits and risks of using OSN and their impact on self-disclosure are also identified. We determine that the perceived risks of OSN usage hinder selfdisclosure. It is not clear, however, whether the perceived benefits offset the impact of the risks on selfdisclosure behavior. The findings as a whole do not support privacy calculus in OSN settings.

Deep learning methods and applications - Classification of traffic signs and detection of Alzheimer's disease from images <=SEP=> In this thesis, the deep learning method convolutional neural networks (CNNs) has been used in an attempt to solve two classification problems, namely traffic sign recognition and Alzheimer’s disease detection. The two datasets used are from the German Traffic Sign Recognition Benchmark (GTSRB) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The final test results on the traffic sign dataset generated a classification accuracy of 98.81 %, almost as high as human performance on the same dataset, 98.84 %. Different parameter settings of the selected CNN structure have also been tested in order to see their impact on the classification accuracy. Trying to distinguish between MRI images of healthy brains and brains afflicted with Alzheimer’s disease gained only about 65 % classification accuracy. These results show that the convolutional neural network approach is very promising for classifying traffic signs, but more work needs to be done when working with the more complex problem of detecting Alzheimer’s disease.

Digital Clock and Data Recovery Circuits for Optical Links <=SEP=> Clock and Data Recovery (CDR) circuits perform the function of recovering clock and re-timing received data in optical links. These CDRs must be capable of tolerating large input jitter (high JTOL), filter input jitter (low JTRAN with no jitter peaking) and in burst-mode applications be capable of phase locking in a very short time. In this paper, we elucidate these design tradeoffs and present various CDR architectures that can overcome them. Specifically, D/PLL CDR architecture that achieves high JTOL, low JTRAN, and no jitter peaking is described. A new burst-mode CDR that can lock instantaneously while filtering input jitter is also discussed.

Can Structural Models Price Default Risk ? Evidence from Bond and Credit Derivative Markets ∗ <=SEP=> Using a set of structural models, we evaluate the price of default protection for a sample of US corporations. In contrast to previous evidence from corporate bond data, CDS premia are not systematically underestimated. In fact, one of our studied models has little difficulty on average in predicting their level. For robustness, we perform the same exercise for bond spreads by the same issuers on the same trading date. As expected, bond spreads relative to the Treasury curve are systematically underestimated. This is not the case when the swap curve is used as a benchmark, suggesting that previously documented underestimation results may be sensitive to the choice of risk free rate. ∗We are indebted to seminar participants at Wilfrid Laurier University, Carnegie Mellon University, the Bank of Canada, McGill University and Queen’s University for helpful discussions. In addition, we are grateful to participants of the 2005 EFA in Moscow, the 2005 NFA in Vancouver, the 2006 CFP Vallendar conference and the 2006 Moody’s NYU Credit Risk Conference. We are particularly grateful to Stephen Schaefer for comments. †Faculty of Management, McGill University, 1001 Sherbrooke Street West, Montreal QC, H3A 1G5 Canada. Tel +1 514 398-3186, Fax +1 514 398-3876, Email jan.ericsson@mcgill.ca. ‡Stockholm School of Economics, Department of Finance, Box 6501, S-113 83 Stockholm, Sweden. Tel: +46 8 736 9143, fax +46 8 312327. 1

To ban or not to ban: Differences in mobile phone policies at elementary, middle, and high schools <=SEP=> The present study was to examine differences in mobile phone policies at elementary, middle and high schools. We surveyed 245 elementary, middle and high schools teachers in Shenzhen of China, using a specially designed 18-item questionnaire. Teachers’ responses indicate that, across elementary, middle and high schools, significant differences exist in (1) students’ percentages of using mobile phones among students, (2) students’ dependence of mobile phones, (3) the number of schools banning students’ mobile phone use, (4) oral and written forms used by schools to ban students’ mobile phone use, and (5) policy reinforcement strategies used by schools. However, no school-level differences was found in (1) students’ fondness of using mobile phones, (2) teachers’ assessment of low-level effectiveness of mobile phone policies, and (3) teachers’ policy improvement recommendations. Significance and implications of the findings are discussed. 2014 Elsevier Ltd. All rights reserved.

Modeling and yield estimation of SRAM sub-system for different capacities subjected to parametric variations <=SEP=> Process variations have become a major challenge with the advancement in CMOS technologies. The performance of memory sub-systems such as Static Random Access Memory (SRAMs) is heavily dependent on these variations. Also, the VLSI industry requires the SRAM bit cell to qualify in the order of less than 0.1ppb to achieve higher Yield (Y). This paper proposes an efficient qualitative statistical analysis and Yield estimation method of SRAM sub-system which considers deviations due to variations in process parameters in bit line differential and input offset of sense amplifier (SA) all together. The Yield of SRAM is predicted for different capacities of SRAM array by developing a statistical model of memory sub-system in 65nm bulk CMOS technology. For the sub-system with 64 bit cells, it is estimated that the probability of failure is 4.802 ∗ 10−13 in a read cycle of frequency 1GHz. Furthermore, the probability of failure for 8MB capacity is 5.035 ∗ 10−7 while for 2GB capacity it increases to 1.289 ∗ 10−5. It is also observed that as the load on one SA per column is doubled, the probability of failure of memory slice increases by 70%. The proposed technique estimates the Yield(Y) for SRAM array to be more than 99.9999.

BigDebug: Debugging Primitives for Interactive Big Data Processing in Spark <=SEP=> Developers use cloud computing platforms to process a large quantity of data in parallel when developing big data analytics. Debugging the massive parallel computations that run in today's datacenters is time consuming and error-prone. To address this challenge, we design a set of interactive, real-time debugging primitives for big data processing in Apache Spark, the next generation data-intensive scalable cloud computing platform. This requires rethinking the notion of step-through debugging in a traditional debugger such as gdb, because pausing the entire computation across distributed worker nodes causes significant delay and naively inspecting millions of records using a watchpoint is too time consuming for an end user.
 First, BigDebug's simulated breakpoints and on-demand watchpoints allow users to selectively examine distributed, intermediate data on the cloud with little overhead. Second, a user can also pinpoint a crash-inducing record and selectively resume relevant sub-computations after a quick fix. Third, a user can determine the root causes of errors (or delays) at the level of individual records through a fine-grained data provenance capability. Our evaluation shows that BigDebug scales to terabytes and its record-level tracing incurs less than 25% overhead on average. It determines crash culprits orders of magnitude more accurately and provides up to 100% time saving compared to the baseline replay debugger. The results show that BigDebug supports debugging at interactive speeds with minimal performance impact.

The effect of different types of corrective feedback on ESL student writing <=SEP=> Debate about the value of providing corrective feedback on L2 writing has been prominent in recent years as a result of Truscott’s [Truscott, J. (1996). The case against grammar correction in L2 writing classes. Language Learning, 46, 327–369] claim that it is both ineffective and harmful and should therefore be abandoned. A growing body of empirical research is now investigating the agenda proposed by Ferris [Ferris, D.R. (1999). The case for grammar correction in L2 writing classes. A response to Truscott (1996). Journal of Second Language Writing, 8, 1–10, Ferris, D.R. (2004). The ‘‘Grammar Correction’’ debate in L2 writing: Where are we, and where do we go from here? (and what do we do in the meantime. . .?). Journal of Second Language Writing, 13, 49–62.]. Contributing to this research base, the study reported in this article investigated whether the type of feedback (direct, explicit written feedback and student–researcher 5 minute individual conferences; direct, explicit written feedback only; no corrective feedback) given to 53 adult migrant students on three types of error (prepositions, the past simple tense, and the definite article) resulted in improved accuracy in new pieces of writing over a 12 week period. The study found a significant effect for the combination of written and conference feedback on accuracy levels in the use of the past simple tense and the definite article in new pieces of writing but no overall effect on accuracy improvement for feedback types when the three error categories were considered as a single group. Significant variations in accuracy across the four pieces of writing support earlier SLA discoveries that L2 learners, in the process of acquiring new linguistic forms, may perform them with accuracy on one occasion but fail to do so on other similar occasions. # 2005 Elsevier Inc. All rights reserved.

Gut microbes and the brain: paradigm shift in neuroscience. <=SEP=> The discovery of the size and complexity of the human microbiome has resulted in an ongoing reevaluation of many concepts of health and disease, including diseases affecting the CNS. A growing body of preclinical literature has demonstrated bidirectional signaling between the brain and the gut microbiome, involving multiple neurocrine and endocrine signaling mechanisms. While psychological and physical stressors can affect the composition and metabolic activity of the gut microbiota, experimental changes to the gut microbiome can affect emotional behavior and related brain systems. These findings have resulted in speculation that alterations in the gut microbiome may play a pathophysiological role in human brain diseases, including autism spectrum disorder, anxiety, depression, and chronic pain. Ongoing large-scale population-based studies of the gut microbiome and brain imaging studies looking at the effect of gut microbiome modulation on brain responses to emotion-related stimuli are seeking to validate these speculations. This article is a summary of emerging topics covered in a symposium and is not meant to be a comprehensive review of the subject.

A Combined Model- and Learning-Based Framework for Interaction-Aware Maneuver Prediction <=SEP=> This paper presents a novel online-capable interaction-aware intention and maneuver prediction framework for dynamic environments. The main contribution is the combination of model-based interaction-aware intention estimation with maneuver-based motion prediction based on supervised learning. The advantages of this framework are twofold. On one hand, expert knowledge in the form of heuristics is integrated, which simplifies the modeling of the interaction. On the other hand, the difficulties associated with the scalability and data sparsity of the algorithm due to the so-called curse of dimensionality can be reduced, as a reduced feature space is sufficient for supervised learning. The proposed algorithm can be used for highly automated driving or as a prediction module for advanced driver assistance systems without the need of intervehicle communication. At the start of the algorithm, the motion intention of each driver in a traffic scene is predicted in an iterative manner using the game-theoretic idea of stochastic multiagent simulation. This approach provides an interpretation of what other drivers intend to do and how they interact with surrounding traffic. By incorporating this information into a Bayesian network classifier, the developed framework achieves a significant improvement in terms of reliable prediction time and precision compared with other state-of-the-art approaches. By means of experimental results in real traffic on highways, the validity of the proposed concept and its online capability is demonstrated. Furthermore, its performance is quantitatively evaluated using appropriate statistical measures.

Running on the bare metal with GeekOS <=SEP=> Undergraduate operating systems courses are generally taught using one of two approaches: abstract or concrete. In the abstract approach, students learn the concepts underlying operating systems theory, and perhaps apply them using user-level threads in a host operating system. In the concrete approach, students apply concepts by working on a real operating system kernel. In the purest manifestation of the concrete approach, students implement operating system projects that run on real hardware.GeekOS is an instructional operating system kernel which runs on real hardware. It provides the minimum functionality needed to schedule threads and control essential devices on an x86 PC. On this foundation, we have developed projects in which students build processes, semaphores, a multilevel feedback scheduler, paged virtual memory, a filesystem, and inter-process communication. We use the Bochs emulator for ease of development and debugging. While this approach (tiny kernel run on an emulator) is not new, we believe GeekOS goes further towards the goal of combining realism and simplicity than previous systems have.

The Reorganization of Human Brain Networks Modulated by Driving Mental Fatigue <=SEP=> The organization of the brain functional network is associated with mental fatigue, but little is known about the brain network topology that is modulated by the mental fatigue. In this study, we used the graph theory approach to investigate reconfiguration changes in functional networks of different electroencephalography (EEG) bands from 16 subjects performing a simulated driving task. Behavior and brain functional networks were compared between the normal and driving mental fatigue states. The scores of subjective self-reports indicated that 90 min of simulated driving-induced mental fatigue. We observed that coherence was significantly increased in the frontal, central, and temporal brain regions. Furthermore, in the brain network topology metric, significant increases were observed in the clustering coefficient (<italic>Cp</italic>) for beta, alpha, and delta bands and the character path length (<italic> Lp</italic>) for all EEG bands. The normalized measures <inline-formula><tex-math notation="LaTeX"> $\boldsymbol{\gamma}$</tex-math></inline-formula> showed significant increases in beta, alpha, and delta bands, and <inline-formula><tex-math notation="LaTeX">$\boldsymbol{\lambda}$</tex-math></inline-formula> showed similar patterns in beta and theta bands. These results indicate that functional network topology can shift the network topology structure toward a more economic but less efficient configuration, which suggests low wiring costs in functional networks and disruption of the effective interactions between and across cortical regions during mental fatigue states. Graph theory analysis might be a useful tool for further understanding the neural mechanisms of driving mental fatigue.

Automatic Image Annotation using Deep Learning Representations <=SEP=> We propose simple and effective models for the image annotation that make use of Convolutional Neural Network (CNN) features extracted from an image and word embedding vectors to represent their associated tags. Our first set of models is based on the Canonical Correlation Analysis (CCA) framework that helps in modeling both views - visual features (CNN feature) and textual features (word embedding vectors) of the data. Results on all three variants of the CCA models, namely linear CCA, kernel CCA and CCA with k-nearest neighbor (CCA-KNN) clustering, are reported. The best results are obtained using CCA-KNN which outperforms previous results on the Corel-5k and the ESP-Game datasets and achieves comparable results on the IAPRTC-12 dataset. In our experiments we evaluate CNN features in the existing models which bring out the advantages of it over dozens of handcrafted features. We also demonstrate that word embedding vectors perform better than binary vectors as a representation of the tags associated with an image. In addition we compare the CCA model to a simple CNN based linear regression model, which allows the CNN layers to be trained using back-propagation.

A round- and computation-efficient three-party authenticated key exchange protocol <=SEP=> In three-party authenticated key exchange protocols, each client shares a secret only with a trusted server with assists in generating a session key used for securely sending messages between two communication clients. Compared with two-party authenticated key exchange protocols where each pair of parties must share a secret with each other, a three-party protocol does not cause any key management problem for the parties. In the literature, mainly there exist three issues in three-party authenticated key exchange protocols are discussed that need to be further improved: (1) to reduce latency, communication steps in the protocol should be as parallel as possible; (2) as the existence of a security-sensitive table on the server side may cause the server to become compromised, the table should be removed; (3) resources required for computation should be as few as possible to avoid the protocol to become an efficiency bottleneck. In various applications over networks, a quick response is required especially by light-weight clients in the mobile e-commerce. In this paper, a roundand computation-efficient three-party authenticated key exchange protocol is proposed which fulfils all of the above mentioned requirements. 2007 Elsevier Inc. All rights reserved.

From eye movements to actions: how batsmen hit the ball <=SEP=> In cricket, a batsman watches a fast bowler's ball come toward him at a high and unpredictable speed, bouncing off ground of uncertain hardness. Although he views the trajectory for little more than half a second, he can accurately judge where and when the ball will reach him. Batsmen's eye movements monitor the moment when the ball is released, make a predictive saccade to the place where they expect it to hit the ground, wait for it to bounce, and follow its trajectory for 100–200 ms after the bounce. We show how information provided by these fixations may allow precise prediction of the ball's timing and placement. Comparing players with different skill levels, we found that a short latency for the first saccade distinguished good from poor batsmen, and that a cricket player's eye movement strategy contributes to his skill in the game.