Query: Redirected Walking in Virtual Reality

Relevant passages:
Walking > Walking-in-Place > Flying, in Virtual Environments <=SEP=> A study by Slater, et al., [1995] indicated that naive subjects in an immersive virtual environment experience a higher subjective sense of presence when they locomote by walking-in-place (virtual walking) than when they push-button-fly (along the floor plane). We replicated their study, adding real walking as a third condition. Our study confirmed their findings. We also found that real walking is significantly better than both virtual walking and flying in ease (simplicity, straightforwardness, naturalness) as a mode of locomotion. The greatest difference in subjective presence was between flyers and both kinds of walkers. In addition, subjective presence was higher for real walkers than virtual walkers, but the difference was statistically significant only in some models. Follow-on studies show virtual walking can be substantially improved by detecting footfalls with a head accelerometer. As in the Slater study, subjective presence significantly correlated with subjects’ degree of association with their virtual bodies (avatars). This, our strongest statistical result, suggests that substantial potential presence gains can be had from tracking all limbs and customizing avatar appearance. An unexpected by-product was that real walking through our enhanced version of Slater’s visual-cliff virtual environment (Figure 1) yielded a strikingly compelling virtual experience—the strongest we and most of our visitors have yet experienced. The most needed system improvement is the substitution of wireless technology for all links to the user. CR

Point & Teleport Locomotion Technique for Virtual Reality <=SEP=> With the increasing popularity of virtual reality (VR) and new devices getting available with relatively lower costs, more and more video games have been developed recently. Most of these games use first person interaction techniques since it is more natural for Head Mounted Displays (HMDs). One of the most widely used interaction technique in VR video games is locomotion that is used to move user's viewpoint in virtual environments. Locomotion is an important component of video games since it can have a strong influence on user experience. In this study, a new locomotion technique we called "Point & Teleport" is described and compared with two commonly used VR locomotion techniques of walk-in-place and joystick. In this technique, users simply point where they want to be in virtual world and they are teleported to that position. As a major advantage, it is not expected to introduce motion sickness since it does not involve any visible translational motion. In this study, two VR experiments were designed and performed to analyze the Point & Teleport technique. In the first experiment, Point & Teleport was compared with walk-in-place and joystick locomotion techniques. In the second experiment, a direction component was added to the Point & Teleport technique so that the users could specify their desired orientation as well. 16 users took part in both experiments. Results indicated that Point & Teleport is a fun and user friendly locomotion method whereas the additional direction component degraded the user experience.

The "visual cliff". <=SEP=> This simple apparatus is used to investigate depth perception in different animals. All species thus far tested seem able to perceive and avoid a sharp drop as soon as they can move about. Human infants at the creeping and toddling stage are notoriously prone to falls from more or less high places. They must be kept from going over the brink by side panels on their cribs, gates on stairways and the vigilance of adults. As their muscular coordination matures they begin to avoid such accidents on their own. Common sense might suggest that the child learns to recognize falling-off places by experience–that is, by falling and hurting himself. But is experience really the teacher? Or is the ability to perceive and avoid a brink part of the child's original endowment? Answers to these questions will throw light on the genesis of space perception in general. Height perception is a special case of distance perception: information in the light reaching the eye provides stimuli that can be utilized for the discrimination both of depth and of receding distance on the level. At what stage of development can an animal respond effectively to these stimuli? Does the onset of such response vary with animals of different species and habitats? At Cornell University we have been investigating these problems by means of a simple experimental setup that we call a visual cliff. The cliff is a simulated one and hence makes it possible not only to control the optical and other stimuli (auditory and tactual, for instance) but also to protect the experimental subjects. It consists of a board laid across a large sheet of heavy glass which is supported a foot or more above the floor. On one side of the board a sheet of patterned material is placed flush against the undersurface of the glass, giving the glass the appearance as well as the substance of solidity. On the other side a sheet of the same material is laid upon the floor; this side of the board thus becomes the visual cliff (Fig. 1). The Classic Visual Cliff Experiment This young explorer has the good sense not to crawl out onto an apparently unsupported surface, even when Mother beckons from the other side. Rats, pups, kittens, and chicks also will not try to walk across to the other side. (So don't bother asking why the chicken crossed the visual cliff.) …

Improved Redirection with Distractors: A large-scale-real-walking locomotion interface and its effect on navigation in virtual environments <=SEP=> Users in virtual environments often find navigation more difficult than in the real world. Our new locomotion interface, Improved Redirection with Distractors (IRD), enables users to walk in larger-than-tracked space VEs without predefined waypoints. We compared IRD to the current best interface, really walking, by conducting a user study measuring navigational ability. Our results show that IRD users can really walk through VEs that are larger than the tracked space and can point to targets and complete maps of VEs no worse than when really walking.

Quantifying immersion in virtual reality <=SEP=> Virtual Reality (VR) has generated much excitement but little formal proof that it is useful. Because VR interfaces are difficult and expensive to build, the computer graphics community needs to be able to predict which applications will benefit from VR. In this paper, we show that users with a VR interface complete a search task faster than users with a stationary monitor and a hand-based input device. We placed users in the center of the virtual room shown in Figure 1 and told them to look for camouflaged targets. VR users did not do significantly better than desktop users. However, when asked to search the room and conclude if a target existed, VR users were substantially better at determining when they had searched the entire room. Desktop users took 41% more time, re-examining areas they had already searched. We also found a positive transfer of training from VR to stationary displays and a negative transfer of training from stationary displays to VR.

When is it biased?: assessing the representativeness of twitter's streaming API <=SEP=> Twitter shares a free 1% sample of its tweets through the "Streaming API". Recently, research has pointed to evidence of bias in this source. The methodologies proposed in previous work rely on the restrictive and expensive Firehose to find the bias in the Streaming API data. We tackle the problem of finding sample bias without costly and restrictive Firehose data. We propose a solution that focuses on using an open data source to find bias in the Streaming API.

Information security culture: A management perspective <=SEP=> Information technology has become an integral part of modern life. Today, the use of information permeates every aspect of both business and private lives. Most organizations need information systems to survive and prosper and thus need to be serious about protecting their information assets. Many of the processes needed to protect these information assets are, to a large extent, dependent on human cooperated behavior. Employees, whether intentionally or through negligence, often due to a lack of knowledge, are the greatest threat to information security. It has become widely accepted that the establishment of an organizational sub-culture of information security is key to managing the human factors involved in information security. This paper briefly examines the generic concept of corporate culture and then borrows from the management and economical sciences to present a conceptual model of information security culture. The presented model incorporates the concept of elasticity from the economical sciences in order to show how various variables in an information security culture influence each other. The purpose of the presented model is to facilitate conceptual thinking and argumentation about information security culture. a 2009 Elsevier Ltd. All rights reserved.

Designing Anthropomorphic Robot Hand With Active Dual-Mode Twisted String Actuation Mechanism and Tiny Tension Sensors <=SEP=> In this letter, using the active dual-mode twisted string actuation (TSA) mechanism and tiny tension sensors on the tendon strings, an anthropomorphic robot hand is newly designed in a compact manner. Thanks to the active dual-mode TSA mechanism, which is a miniaturized transmission, the proposed robot hand dose has a wide range of operation in terms of the grasping force and speed. It experimentally produces maximally the fingertip force 31.3 N and minimally the closing time of 0.5 s in average. Also, tiny tension sensor with the dimension of 4.7 (width) × 4.0 (height) × 10.75 (length) mm is newly presented and embedded at the fingertips in order to measure the tension on the tendon strings, which would allow the grasping force control. The kinetic and kinematic analyses are performed and the performance is verified by experiments.

Control of a Quadrotor Using a Smart Self-Tuning Fuzzy PID Controller <=SEP=> This paper deals with the modelling, simulation-based controller design and path planning of a four rotor helicopter known as a quadrotor. All the drags, aerodynamic, coriolis and gyroscopic effect are neglected. A Newton-Euler formulation is used to derive the mathematical model. A smart self-tuning fuzzy PID controller based on an EKF algorithm is proposed for the attitude and position control of the quadrotor. The PID gains are tuned using a self-tuning fuzzy algorithm. The self-tuning of fuzzy parameters is achieved based on an EKF algorithm. A smart selection technique and exclusive tuning of active fuzzy parameters is proposed to reduce the computational time. Dijkstra’s algorithm is used for path planning in a closed and known environment filled with obstacles and/or boundaries. The Dijkstra algorithm helps avoid obstacle and find the shortest route from a given initial position to the final position.

A Flooding Warning System based on RFID Tag Array for Energy Facility <=SEP=> Passive radio-frequency identification (RFID) tags are widely used due to its economic cost and satisfactory performance. So far, passive RFID tags are mostly applied to identify certain objects, such as underground pipe identification under buried conditions. However, there is lack of study on the application of buried tags for further application. In this paper, the performances of buried RFID tags are studied to develop a flooding warning system based on RFID tag array for energy facility such as power stations. In this study, the corresponding signal strength received by the RFID reader is evaluated when the RFID tags are buried by seven materials respectively. The results show that flood warning detector can be constructed using passive RFID tag array and reader.

Cognitive Control Deficits in Schizophrenia: Mechanisms and Meaning <=SEP=> Although schizophrenia is an illness that has been historically characterized by the presence of positive symptomatology, decades of research highlight the importance of cognitive deficits in this disorder. This review proposes that the theoretical model of cognitive control, which is based on contemporary cognitive neuroscience, provides a unifying theory for the cognitive and neural abnormalities underlying higher cognitive dysfunction in schizophrenia. To support this model, we outline converging evidence from multiple modalities (eg, structural and functional neuroimaging, pharmacological data, and animal models) and samples (eg, clinical high risk, genetic high risk, first episode, and chronic subjects) to emphasize how dysfunction in cognitive control mechanisms supported by the prefrontal cortex contribute to the pathophysiology of higher cognitive deficits in schizophrenia. Our model provides a theoretical link between cellular abnormalities (eg, reductions in dentritic spines, interneuronal dysfunction), functional disturbances in local circuit function (eg, gamma abnormalities), altered inter-regional cortical connectivity, a range of higher cognitive deficits, and symptom presentation (eg, disorganization) in the disorder. Finally, we discuss recent advances in the neuropharmacology of cognition and how they can inform a targeted approach to the development of effective therapies for this disabling aspect of schizophrenia.

Tampering Detection in Low-Power Smart Cameras <=SEP=> A desirable feature in smart cameras is the ability to autonomously detect any tampering event/attack that would prevent a clear view over the monitored scene. No matter whether tampering is due to atmospheric phenomena (e.g., few rain drops over the camera lens) or to malicious attacks (e.g., occlusions or device displacements), these have to be promptly detected to possibly activate countermeasures. Tampering detection is particularly challenging in battery-powered cameras, where it is not possible to acquire images at full-speed frame-rates, nor use sophisticated image-analysis algorithms. We here introduce a tampering-detection algorithm specifically designed for low-power smart cameras. The algorithm leverages very simple indicators that are then monitored by an outlier-detection scheme: any frame yielding an outlier is detected as tampered. Core of the algorithm is the partitioning of the scene into adaptively defined regions, that are preliminarily defined by segmenting the image during the algorithmconfiguration phase, and which shows to improve the detection of camera displacements. Experiments show that the proposed algorithm can successfully operate on sequences acquired at very low-frame rate, such as one frame every minute, with a very small computational complexity.

W-BAND MICROSTRIP-TO-WAVEGUIDE TRANSITION USING VIA FENCES <=SEP=> Abstract—The paper presents integrated probe for direct coupling to the WR-10 waveguide with the use of metal filled vias on both sides of the microstrip line. Design and optimization of this novel microstripto-waveguide transition has been performed using 3-D finite element method based software HFSS (High Frequency Structure Simulator). A back-to-back transition has been fabricated and measured between 75–110GHz. The measured return loss is higher than 10 dB and the insertion loss for a single microstrip-to-waveguide transition is about 1.15 dB.

Hierarchical Discrete Distribution Decomposition for Match Density Estimation <=SEP=> Existing deep learning methods for pixel correspondence output a point estimate of the motion field, but do not represent the full match distribution. Explicit representation of a match distribution is desirable for many applications as it allows direct representation of the correspondence probability. The main difficulty of estimating a full probability distribution with a deep network is the high computational cost of inferring the entire distribution. In this paper, we propose Hierarchical Discrete Distribution Decomposition, dubbed HD, to learn probabilistic point and region matching. Not only can it model match uncertainty, but also region propagation. To achieve this, we estimate the hierarchical distribution of pixel correspondences at different image scales without multi-hypotheses ensembling. Despite its simplicity, our method can achieve competitive results for both optical flow and stereo matching on established benchmarks, while the estimated uncertainty is a good indicator of errors. Furthermore, the point match distribution within a region can be grouped together to propagate the whole region even if the area changes across images.

A meta-analysis of working memory impairments in children with attention-deficit/hyperactivity disorder. <=SEP=> OBJECTIVE
To determine the empirical evidence for deficits in working memory (WM) processes in children and adolescents with attention-deficit/hyperactivity disorder (ADHD).


METHOD
Exploratory meta-analytic procedures were used to investigate whether children with ADHD exhibit WM impairments. Twenty-six empirical research studies published from 1997 to December, 2003 (subsequent to a previous review) met our inclusion criteria. WM measures were categorized according to both modality (verbal, spatial) and type of processing required (storage versus storage/manipulation).


RESULTS
Children with ADHD exhibited deficits in multiple components of WM that were independent of comorbidity with language learning disorders and weaknesses in general intellectual ability. Overall effect sizes for spatial storage (effect size = 0.85, CI = 0.62 - 1.08) and spatial central executive WM (effect size = 1.06, confidence interval = 0.72-1.39) were greater than those obtained for verbal storage (effect size = 0.47, confidence interval = 0.36-0.59) and verbal central executive WM (effect size = 0.43, confidence interval = 0.24-0.62).


CONCLUSION
Evidence of WM impairments in children with ADHD supports recent theoretical models implicating WM processes in ADHD. Future research is needed to more clearly delineate the nature, severity, and specificity of the impairments to ADHD.

Terpene synthases of oregano (Origanum vulgare L.) and their roles in the pathway and regulation of terpene biosynthesis <=SEP=> The aroma, flavor and pharmaceutical value of cultivated oregano (Origanum vulgare L.) is a consequence of its essential oil which consists mostly of monoterpenes and sesquiterpenes. To investigate the biosynthetic pathway to oregano terpenes and its regulation, we identified and characterized seven terpene synthases, key enzymes of terpene biosynthesis, from two cultivars of O. vulgare. Heterologous expression of these enzymes showed that each forms multiple mono- or sesquiterpene products and together they are responsible for the direct production of almost all terpenes found in O. vulgare essential oil. The correlation of essential oil composition with relative and absolute terpene synthase transcript concentrations in different lines of O. vulgare demonstrated that monoterpene synthase activity is predominantly regulated on the level of transcription and that the phenolic monoterpene alcohol thymol is derived from γ-terpinene, a product of a single monoterpene synthase. The combination of heterologously-expressed terpene synthases for in vitro assays resulted in blends of mono- and sesquiterpene products that strongly resemble those found in vivo, indicating that terpene synthase expression levels directly control the composition of the essential oil. These results will facilitate metabolic engineering and directed breeding of O. vulgare cultivars with higher quantity of essential oil and improved oil composition.

A wireless slanted optrode array with integrated micro leds for optogenetics <=SEP=> This paper presents a wireless-enabled, flexible optrode array with multichannel micro light-emitting diodes (μ-LEDs) for bi-directional wireless neural interface. The array integrates wirelessly addressable μ-LED chips with a slanted polymer optrode array for precise light delivery and neural recording at multiple cortical layers simultaneously. A droplet backside exposure (DBE) method was developed to monolithically fabricate varying-length optrodes on a single polymer platform. In vivo tests in rat brains demonstrated that the μ-LEDs were inductively powered and controlled using a wireless switched-capacitor stimulator (SCS), and light-induced neural activity was recorded with the optrode array concurrently.

Efficient projections onto the l1-ball for learning in high dimensions <=SEP=> We describe efficient algorithms for projecting a vector onto the l1-ball. We present two methods for projection. The first performs exact projection in O(n) expected time, where n is the dimension of the space. The second works on vectors k of whose elements are perturbed outside the l1-ball, projecting in O(k log(n)) time. This setting is especially useful for online learning in sparse feature spaces such as text categorization applications. We demonstrate the merits and effectiveness of our algorithms in numerous batch and online learning tasks. We show that variants of stochastic gradient projection methods augmented with our efficient projection procedures outperform interior point methods, which are considered state-of-the-art optimization techniques. We also show that in online settings gradient updates with l1 projections outperform the exponentiated gradient algorithm while obtaining models with high degrees of sparsity.

Integrated on-chip inductors with electroplated magnetic yokes ( invited ) <=SEP=> J. Appl. Phys. 111, 07E328 (2012) A single-solenoid pulsed-magnet system for single-crystal scattering studies Rev. Sci. Instrum. 83, 035101 (2012) Solution to the problem of E-cored coil above a layered half-space using the method of truncated region eigenfunction expansion J. Appl. Phys. 111, 07E717 (2012) Array of 12 coils to measure the position, alignment, and sensitivity of magnetic sensors over temperature J. Appl. Phys. 111, 07E501 (2012) Skin effect suppression for Cu/CoZrNb multilayered inductor J. Appl. Phys. 111, 07A501 (2012)

Slow Flow: Exploiting High-Speed Cameras for Accurate and Diverse Optical Flow Reference Data <=SEP=> Existing optical flow datasets are limited in size and variability due to the difficulty of capturing dense ground truth. In this paper, we tackle this problem by tracking pixels through densely sampled space-time volumes recorded with a high-speed video camera. Our model exploits the linearity of small motions and reasons about occlusions from multiple frames. Using our technique, we are able to establish accurate reference flow fields outside the laboratory in natural environments. Besides, we show how our predictions can be used to augment the input images with realistic motion blur. We demonstrate the quality of the produced flow fields on synthetic and real-world datasets. Finally, we collect a novel challenging optical flow dataset by applying our technique on data from a high-speed camera and analyze the performance of the state-of-the-art in optical flow under various levels of motion blur.

Dermoscopy of cutaneous leishmaniasis. <=SEP=> BACKGROUND
Dermoscopy has been proposed as a diagnostic tool in the case of skin infections and parasitosis but no specific dermoscopic criteria have been described for cutaneous leishmaniasis (CL).


OBJECTIVES
To describe the dermoscopic features of CL.


METHODS
Dermoscopic examination (using the DermLite Foto; 3Gen, LLC, Dana Point, CA, U.S.A.) of 26 CL lesions was performed to evaluate specific dermoscopic criteria.


RESULTS
We observed the following dermoscopic features: generalized erythema (100%), 'yellow tears' (53%), hyperkeratosis (50%), central erosion/ulceration (46%), erosion/ulceration associated with hyperkeratosis (38%) and 'white starburst-like pattern' (38%). Interestingly, at least one vascular structure described in skin neoplasms was observed in all cases: comma-shaped vessels (73%), linear irregular vessels (57%), dotted vessels (53%), polymorphous/atypical vessels (26%), hairpin vessels (19%), arborizing telangiectasia (11%), corkscrew vessels (7%) and glomerular-like vessels (7%). Combination of two or more different types of vascular structures was present in 23 of 26 CL lesions (88%), with a combination of two vascular structures in 13 cases (50%) and three or more in 10 cases (38%).


CONCLUSIONS
Characteristic dermoscopic structures have been identified in CL. Important vascular patterns seen in melanocytic and nonmelanocytic tumours are frequently observed in this infection.

Detecting and Tracking Political Abuse in Social Media <=SEP=> We study astroturf political campaigns on microblogging platforms: politically-motivated individuals and organizations that use multiple centrally-controlled accounts to create the appearance of widespread support for a candidate or opinion. We describe a machine learning framework that combines topological, content-based and crowdsourced features of information diffusion networks on Twitter to detect the early stages of viral spreading of political misinformation. We present promising preliminary results with better than 96% accuracy in the detection of astroturf content in the run-up to the 2010 U.S. midterm elections.

Path Planning and Controlled Crash Landing of a Quadcopter in case of a Rotor Failure <=SEP=> This paper presents a framework for controlled emergency landing of a quadcopter, experiencing a rotor failure, away from sensitive areas. A complete mathematical model capturing the dynamics of the system is presented that takes the asymmetrical aerodynamic load on the propellers into account. An equilibrium state of the system is calculated around which a linear time-invariant control strategy is developed to stabilize the system. By utilizing the proposed model, a specific configuration for a quadcopter is introduced that leads to the minimum power consumption during a yaw-rateresolved hovering after a rotor failure. Furthermore, given a 3D representation of the environment, an optimal flight trajectory towards a safe crash landing spot, while avoiding collision with obstacles, is developed using an RRT* approach. The cost function for determining the best landing spot consists of: (i) finding the safest landing spot with the largest clearance from the obstacles; and (ii) finding the most energy-efficient trajectory towards the landing spot. The performance of the proposed framework is tested via simulations.

A Bibliometric Study on Culture Research in International Business <=SEP=> National cultures and cultural differences provide a crucial component of the context of international business (IB) research. We conducted a bibliometric study of the articles published in seven leading IB journals, over a period of three decades, to analyze how “national culture” has been impacting in IB research. Co-citation mappings permit us to identify the ties binding works dealing with culture and cultural issues in IB. We identify two main clusters of research each comprising two sub-clusters, with Hofstede’s (1980) work setting much of the conceptual and empirical approach on culture-related studies. One main cluster entails works on the conceptualization of culture and its dimensions and other cluster on cultural distance. This conceptual framework captures the extant IB research incorporating culture-related concepts and influences.

Ensemble Algorithms in Reinforcement Learning <=SEP=> This paper describes several ensemble methods that combine multiple different reinforcement learning (RL) algorithms in a single agent. The aim is to enhance learning speed and final performance by combining the chosen actions or action probabilities of different RL algorithms. We designed and implemented four different ensemble methods combining the following five different RL algorithms: Q-learning, Sarsa, actor-critic (AC), QV-learning, and AC learning automaton. The intuitively designed ensemble methods, namely, majority voting (MV), rank voting, Boltzmann multiplication (BM), and Boltzmann addition, combine the policies derived from the value functions of the different RL algorithms, in contrast to previous work where ensemble methods have been used in RL for representing and learning a single value function. We show experiments on five maze problems of varying complexity; the first problem is simple, but the other four maze tasks are of a dynamic or partially observable nature. The results indicate that the BM and MV ensembles significantly outperform the single RL algorithms.

Memory Interference as a Determinant of Language Comprehension <=SEP=> The parameters of the human memory system constrain the operation of language comprehension processes. In the memory literature, both decay and interference have been proposed as causes of forgetting; however, while there is a long history of research establishing the nature of interference effects in memory, the effects of decay are much more poorly supported. Nevertheless, research investigating the limitations of the human sentence processing mechanism typically focus on decay-based explanations, emphasizing the role of capacity, while the role of interference has received comparatively little attention. This paper reviews both accounts of difficulty in language comprehension by drawing direct connections to research in the memory domain. Capacity-based accounts are found to be untenable, diverging substantially from what is known about the operation of the human memory system. In contrast, recent research investigating comprehension difficulty using a retrieval-interference paradigm is shown to be wholly consistent with both behavioral and neuropsychological memory phenomena. The implications of adopting a retrieval-interference approach to investigating individual variation in language comprehension are discussed.

Multiresolution Tree Networks for 3D Point Cloud Processing <=SEP=> We present multiresolution tree-structured networks to process point clouds for 3D shape understanding and generation tasks. Our network represents a 3D shape as a set of locality-preserving 1D ordered list of points at multiple resolutions. This allows efficient feed-forward processing through 1D convolutions, coarse-to-fine analysis through a multi-grid architecture, and it leads to faster convergence and small memory footprint during training. The proposed treestructured encoders can be used to classify shapes and outperform existing pointbased architectures on shape classification benchmarks, while tree-structured decoders can be used for generating point clouds directly and they outperform existing approaches for image-to-shape inference tasks learned using the ShapeNet dataset. Our model also allows unsupervised learning of point-cloud based shapes by using a variational autoencoder, leading to higher-quality generated shapes.

An application of explicit model predictive control to electric power assisted steering systems <=SEP=> This paper presents explicit model predictive control (MPC) for electric power assisted steering (EPAS) systems. Explicit MPC has the ability to alleviate the computational burdens of MPC, which is an online optimization technique, using multi-parametric quadratic programming (mp-QP). EPAS systems are being used more frequently for passenger cars because of their advantages over hydraulic steering systems in terms of performance and cost. The main objective of EPAS systems is to provide a desired motor torque; therefore, to handle this problem, explicit MPC is employed owing to the ability of MPC to deal with constraints on controls and states and of explicit MPC to do the same work offline. This paper summarizes a formulation of explicit MPC and introduces a straight-line boost curve, which is used to determine desired motor torques. A mechanical model, motion equations and the state-space form of a column-type EPAS system are also described in this paper. An linear-quadratic regulator (LQR) controller is designed so that the explicit MPC controller could be compared against it. The parameter values are respectively varied to demonstrate the robustness of the proposed explicit MPC controller. The proposed control strategy shows better performance than that of the LQR as well as a reduction in the required computational complexity.

Neural Semantic Encoders <=SEP=> We present a memory augmented neural network for natural language understanding: Neural Semantic Encoders. NSE is equipped with a novel memory update rule and has a variable sized encoding memory that evolves over time and maintains the understanding of input sequences through read, compose and write operations. NSE can also access multiple and shared memories. In this paper, we demonstrated the effectiveness and the flexibility of NSE on five different natural language tasks: natural language inference, question answering, sentence classification, document sentiment analysis and machine translation where NSE achieved state-of-the-art performance when evaluated on publically available benchmarks. For example, our shared-memory model showed an encouraging result on neural machine translation, improving an attention-based baseline by approximately 1.0 BLEU.

CCDN: Content-Centric Data Center Networks <=SEP=> Data center networks continually seek higher network performance to meet the ever increasing application demand. Recently, researchers are exploring the method to enhance the data center network performance by intelligent caching and increasing the access points for hot data chunks. Motivated by this, we come up with a simple yet useful caching mechanism for generic data centers, i.e., a server caches a data chunk after an application on it reads the chunk from the file system, and then uses the cached chunk to serve subsequent chunk requests from nearby servers. To turn the basic idea above into a practical system and address the challenges behind it, we design content-centric data center networks CCDNs, which exploits an innovative combination of content-based forwarding and location [Internet Protocol IP]-based forwarding in switches, to correctly locate the target server for a data chunk on a fully distributed basis. Furthermore, CCDN enhances traditional content-based forwarding to determine the nearest target server, and enhances traditional location IP-based forwarding to make high utilization of the precious memory space in switches. Extensive simulations based on real-world workloads and experiments on a test bed built with NetFPGA prototypes show that, even with a small portion of the server’s storage as cache e.g., 3% and with a modest content forwarding information base size e.g., 1000 entries in switches, CCDN can improve the average throughput to get data chunks by 43% compared with a pure Hadoop File System HDFS system in a real data center.