Query: State of the art of virtual reality technology

Relevant passages:
Three-dimensional menus: A survey and taxonomy <=SEP=> Various interaction techniques have been developed in the field of virtual and augmented reality. Whereas techniques for object selection, manipulation, travel, and wayfinding have already been covered in existing taxonomies in some detail, application control techniques have not yet been sufficiently considered. However, they are needed by almost every mixed reality application, e.g. for choosing from alternative objects or options. For this purpose a great variety of distinct three-dimensional (3D) menu selection techniques is available. This paper surveys existing 3D menus from the corpus of literature and classifies them according to various criteria. The taxonomy introduced here assists developers of interactive 3D applications to better evaluate their options when choosing, optimizing, and implementing a 3D menu technique. Since the taxonomy spans the design space for 3D menu solutions, it also aids researchers in identifying opportunities to improve or create novel virtual menu techniques. r 2006 Elsevier Ltd. All rights reserved.

ShoeSoleSense: proof of concept for a wearable foot interface for virtual and real environments <=SEP=> ShoeSoleSense is a proof of concept, novel body worn interface - an insole that enables location independent hands-free interaction through the feet. Forgoing hand or finger interaction is especially beneficial when the user is engaged in real world tasks. In virtual environments as moving through safety training applications is often conducted via finger input, which is not very suitable. To enable a more intuitive interaction, alternative control concepts utilize gesture control, which is usually tracked by statically installed cameras in CAVE-like-installations. Since tracking coverage is limited, problems may also occur. The introduced prototype provides a novel control concept for virtual reality as well as real life applications. Demonstrated functions include movement control in a virtual reality installation such as moving straight, turning and jumping. Furthermore the prototype provides additional feedback by heating up the feet and vibrating in dedicated areas on the surface of the insole.

A discussion of cybersickness in virtual environments <=SEP=> An important and troublesome problem with current virtual environment (VE) technology is the tendency for some users to exhibit symptoms that parallel symptoms of classical motion sickness both during and after the VE experience. This type of sickness, cybersickness, is distinct from motion sickness in that the user is often stationary but has a compelling sense of self motion through moving visual imagery. Unfortunately, there are many factors that can cause cybersickness and there is no foolproof method for eliminating the problem. In this paper, I discuss a number of the primary factors that contribute to the cause of cybersickness, describe three conflicting cybersickness theories that have been postulated, and discuss some possible methods for reducing cybersickness in VEs.

ChairIO--the Chair-Based Interface <=SEP=> 

Cone Trees: animated 3D visualizations of hierarchical information <=SEP=> The task of managing and accessing large information spaces is a problem in large scale cognition. Emerging technologies for 3D visualization and interactive animation offer potential solutions to this problem, especially when the structure of the information can be visualized. We describe one of these Information Visualization techniques, called the Cone Tree, which is used for visualizing hierarchical information structures. The hierarchy is presented in 3D to maximize effective use of available screen space and enable visualization of the whole structure. Interactive animation is used to shift some of the user's cognitive load to the human perceptual system.

Multi-agent quadrotor testbed control design: integral sliding mode vs. reinforcement learning <=SEP=> The Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC) is a multi-vehicle testbed currently comprised of two quadrotors, also called X4-flyers, with capacity for eight. This paper presents a comparison of control design techniques, specifically for outdoor altitude control, in and above ground effect, that accommodate the unique dynamics of the aircraft. Due to the complex airflow induced by the four interacting rotors, classical linear techniques failed to provide sufficient stability. Integral sliding mode and reinforcement learning control are presented as two design techniques for accommodating the nonlinear disturbances. The methods both result in greatly improved performance over classical control techniques.

Crowdsourcing of Pollution Data using Smartphones <=SEP=> In this paper we present our research into participatory sensing based solutions for the collection of data on urban pollution and nuisance. In the past 2 years we have been involved in the NoiseTube project which explores a crowdsourcing approach to measuring and mapping urban noise pollution using smartphones. By involving the general public and using off-the-shelf smartphones as noise sensors, we seek to provide a low cost solution for citizens to measure their personal exposure to noise in their everyday environment and participate in the creation of collective noise maps by sharing their geo-localized and annotated measurements with the community. We believe our work represents an interesting example of the novel mobile crowdsourcing applications which are enabled by ubiquitous computing systems. Furthermore we believe the NoiseTube system, and the currently ongoing validation experiments, provide an illustrative context for some of the open challenges faced by creators of ubiquitous crowdsourcing applications and services in general. We will also take the opportunity to present the insights we gained into some of the challenges.

Design guidelines for wireless sensor networks: communication, clustering and aggregation <=SEP=> When sensor nodes are organized in clusters, they could use either single hop or multi-hop mode of communication to send their data to their respective cluster heads. We present a systematic cost-based analysis of both the modes, and provide results that could serve as guidelines to decide which mode should be used for given settings. We determine closed form expressions for the required number of cluster heads and the required battery energy of nodes for both the modes. We also propose a hybrid communication mode which is a combination of single hop and multi-hop modes, and which is more cost-effective than either of the two modes. Our problem formulation also allows for the application to be taken into account in the overall design problem through a data aggregation model. 2003 Elsevier B.V. All rights reserved.

Grounded Learning of Color Semantics with Autoencoders <=SEP=> Humans learn language by grounding word meaning in the physical world. Recent efforts in natural language processing have attempted to model such multimodal learning by incorporating visual information into word and sentence representations. Here, we explore the task of grounding lexical color descriptions in their visual referents. We propose an RNN-based autoencoder model to learn vector representations of sentences that reflect their associated color values. Our model effectively learns a joint visual-lexical space that demonstrates compositionality and generalizes to unseen color names. As a demonstration of such a space learned, we show that our model can predict captions from color representations and color representations from captions. In addition to successfully modeling color language, this work provides a novel framework for grounded language learning.

Behavior recognition based on machine learning algorithms for a wireless canine machine interface <=SEP=> Training and handling working dogs is a costly process and requires specialized skills and techniques. Less subjective and lower-cost training techniques would not only improve our partnership with these dogs but also enable us to benefit from their skills more efficiently. To facilitate this, we are developing a canine body-area-network (cBAN) to combine sensing technologies and computational modeling to provide handlers with a more accurate interpretation for dog training. As the first step of this, we used inertial measurement units (IMU) to remotely detect the behavioral activity of canines. Decision tree classifiers and Hidden Markov Models were used to detect static postures (sitting, standing, lying down, standing on two legs and eating off the ground) and dynamic activities (walking, climbing stairs and walking down a ramp) based on the heuristic features of the accelerometer and gyroscope data provided by the wireless sensing system deployed on a canine vest. Data was collected from 6 Labrador Retrievers and a Kai Ken. The analysis of IMU location and orientation helped to achieve high classification accuracies for static and dynamic activity recognition.

Encoding User as More Than the Sum of Their Parts: Recurrent Neural Networks and Word Embedding for People-to-people Recommendation <=SEP=> Neural networks and word embeddings are powerful tools to capture latent factors. These tools can provide effective measures of similarities between users or items in the context of sparse data. We propose a novel approach that relies on neural networks and word embeddings to the problem of matching a learner looking for mentoring, and a tutor that is willing to provide this mentoring. Tutors and learners can issue multiple offers/requests on different topics. The approach matches over the whole array of topics specified by learners and tutors. Its performance for tutor-learner matching is compared with the state of the art. It yields similar results in terms of precision, but improves the recall.

MATLAB / SIMULINK Based Modelling Photovoltaic Array Fed T-Source Inverter <=SEP=> In order to utilize the solar energy for industrial, commercial and domestic applications the power conversion schemes plays an important role. The problem exists in conventional power conversion schemes are low efficiency, poor transient response, low voltage gain and more reactive components are being used .This paper proposes a single stage power conversion scheme called T-source inverter(TSI) to overcome these drawbacks. T-Source inverter (TSI) with simple boost control scheme is used as interface circuit between PV array and load. The PV array is analyzed under different irradiation and temperature value. The mathematical equations are verified with simulation and hardware. The verification shows the voltage gain of TSI was comparatively higher than ZSI. The reactive components in the circuit are less, fast transient response and low output ripple.

A near-threshold 7T SRAM cell with high write and read margins and low write time for sub-20 nm FinFET technologies <=SEP=> In this paper, a 7T SRAM cell with differential write and single ended read operations working in the near-threshold region is proposed. The structure is based on modifying a recently proposed 5T cell which uses high and low VTH transistors to improve the read and write stability. To enhance the read static noise margin (RSNM) while keeping the high write margin and low write time, an extra access transistor is used and the threshold voltages of the SRAM transistors are appropriately set. In addition, to maintain the low leakage power of the cell and increase the Ion/Ioff ratio of its access transistors, a high VTH transistor is used in the pull down path of the cell. To assess the efficacy of the proposed cell, its characteristics are compared with those of 5T, 6T, 8T, and 9T SRAM cells. The characteristics are obtained from HSPICE simulations using 20 nm, 16 nm, 14 nm, 10 nm, and 7 nm FinFET technologies assuming a supply voltage of 500 mV. The results reveal high write and read margins, the highest Ion/Ioff ratio, a fast write, and ultra-low leakage power in the hold “0” state for the cell. Therefore, the suggested 7T cell may be considered as one of the better design choices for both high performance and low power applications. Also, the changes of cell parameters when the temperature rises from 40 1C to 100 1C are investigated. Finally, the write margin as well as the read and hold SNMs of the cell in the presence of the process variations are studied at two supply voltages of 400 mV and 500 mV. The study shows that the proposed cell meets the required cell sigma value (6σ) under all conditions. & 2015 Elsevier B.V. All rights reserved.

Microcontroller-based quadratic buck converter used as led lamp driver <=SEP=> This paper presents a new proposal for driving LED lamps through the use of a micro controlled quadratic buck DC/DC converter. The main focus is to improve the LED driver's characteristics using the microcontroller PIC16F873A, thus achieving greater durability and efficiency. Mathematical analyses and experimental results are presented in this paper.

Integrating Grid-Based and Topological Maps for Mobile Robot Navigation <=SEP=> Research on mobile robot navigation has produced two major paradigms for mapping indoor environments: grid-based and topological. While grid-based methods produce accurate metric maps, their complexity often prohibits efficient planning and problem solving in large-scale indoor environments. Topological maps, on the other hand, can be used much more efficiently, yet accurate and consistent topological maps are considerably difficult to learn in large-scale environments. This paper describes an approach that integrates both paradigms: grid-based and topological. Grid-based maps are learned using artificial neural networks and Bayesian integration. Topological maps are generated on top of the grid-based maps, by partitioning the latter into coherent regions. By combining both paradigms—grid-based and topological—, the approach presented here gains the best of both worlds: accuracy/consistency and efficiency. The paper gives results for autonomously operating a mobile robot equipped with sonar sensors in populated multi-room envi-

Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation <=SEP=> Providing model-generated explanations in recommender systems is important to user experience. State-of-the-art recommendation algorithms — especially the collaborative filtering (CF)-based approaches with shallow or deep models — usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured knowledge bases were considered in content-based approaches, they have been largely ignored recently due to the availability of vast amounts of data and the learning power of many complex models. However, structured knowledge bases exhibit unique advantages in personalized recommendation systems. When the explicit knowledge about users and items is considered for recommendation, the system could provide highly customized recommendations based on users’ historical behaviors and the knowledge is helpful for providing informed explanations regarding the recommended items. A great challenge for using knowledge bases for recommendation is how to integrate large-scale structured and unstructured data, while taking advantage of collaborative filtering for highly accurate performance. Recent achievements in knowledge-base embedding (KBE) sheds light on this problem, which makes it possible to learn user and item representations while preserving the structure of their relationship with external knowledge for explanation. In this work, we propose to explain knowledge-base embeddings for explainable recommendation. Specifically, we propose a knowledge-base representation learning framework to embed heterogeneous entities for recommendation, and based on the embedded knowledge base, a soft matching algorithm is proposed to generate personalized explanations for the recommended items. Experimental results on real-world e-commerce datasets verified the superior recommendation performance and the explainability power of our approach compared with state-of-the-art baselines.

Seasonal Effect on Tree Species Classification in an Urban Environment Using Hyperspectral Data, LiDAR, and an Object-Oriented Approach <=SEP=> The objective of the current study was to analyze the seasonal effect on differentiating tree species in an urban environment using multi-temporal hyperspectral data, Light Detection And Ranging (LiDAR) data, and a tree species database collected from the field. Two Airborne Imaging Spectrometer for Applications (AISA) hyperspectral images were collected, covering the Summer and Fall seasons. In order to make both datasets spatially and spectrally compatible, several preprocessing steps, including band reduction and a spatial degradation, were performed. An object-oriented classification was performed on both images using training data collected randomly from the tree species database. The seven dominant tree species (Gleditsia triacanthos, Acer saccharum, Tilia Americana, Quercus palustris, Pinus strobus and Picea glauca) were used in the classification. The results from this analysis did not show any major difference in overall accuracy between the two seasons. Overall accuracy was approximately 57% for the Summer dataset and 56% for the Fall dataset. However, the Fall dataset provided more consistent results for all tree species while the Summer dataset had a few higher individual class accuracies. Further, adding LiDAR into the classification improved the results by 19% for both fall and summer. This is mainly due to the removal of shadow effect and the addition of elevation data to separate low and high vegetation.

Gamification solutions for software acceptance: A comparative study of Requirements Engineering and Organizational Behavior techniques <=SEP=> Gamification is a powerful paradigm and a set of best practices used to motivate people carrying out a variety of ICT-mediated tasks. Designing gamification solutions and applying them to a given ICT system is a complex and expensive process (in time, competences and money) as software engineers have to cope with heterogeneous stakeholder requirements on one hand, and Acceptance Requirements on the other, that together ensure effective user participation and a high level of system utilization. As such, gamification solutions require significant analysis and design as well as suitable supporting tools and techniques. In this work, we compare concepts, tools and techniques for gamification design drawn from Software Engineering and Human and Organizational Behaviors. We conduct a comparison by applying both techniques to the specific Meeting Scheduling exemplar used extensively in the Requirements Engineering literature.

Organic food: buying more safety or just peace of mind? A critical review of the literature. <=SEP=> Consumer concern over the quality and safety of conventional food has intensified in recent years, and primarily drives the increasing demand for organically grown food, which is perceived as healthier and safer. Relevant scientific evidence, however, is scarce, while anecdotal reports abound. Although there is an urgent need for information related to health benefits and/or hazards of food products of both origins, generalized conclusions remain tentative in the absence of adequate comparative data. Organic fruits and vegetables can be expected to contain fewer agrochemical residues than conventionally grown alternatives; yet, the significance of this difference is questionable, inasmuch as actual levels of contamination in both types of food are generally well below acceptable limits. Also, some leafy, root, and tuber organic vegetables appear to have lower nitrate content compared with conventional ones, but whether or not dietary nitrate indeed constitutes a threat to human health is a matter of debate. On the other hand, no differences can be identified for environmental contaminants (e.g. cadmium and other heavy metals), which are likely to be present in food from both origins. With respect to other food hazards, such as endogenous plant toxins, biological pesticides and pathogenic microorganisms, available evidence is extremely limited preventing generalized statements. Also, results for mycotoxin contamination in cereal crops are variable and inconclusive; hence, no clear picture emerges. It is difficult, therefore, to weigh the risks, but what should be made clear is that 'organic' does not automatically equal 'safe.' Additional studies in this area of research are warranted. At our present state of knowledge, other factors rather than safety aspects seem to speak in favor of organic food.

Induction of pluripotent stem cells from fibroblast cultures <=SEP=> Clinical application of embryonic stem (ES) cells faces difficulties regarding use of embryos, as well as tissue rejection after implantation. One way to circumvent these issues is to generate pluripotent stem cells directly from somatic cells. Somatic cells can be reprogrammed to an embryonic-like state by the injection of a nucleus into an enucleated oocyte or by fusion with ES cells. However, little is known about the mechanisms underlying these processes. We have recently shown that the combination of four transcription factors can generate ES-like pluripotent stem cells directly from mouse fibroblast cultures. The cells, named induced pluripotent stem (iPS) cells, can be differentiated into three germ layers and committed to chimeric mice. Here we describe detailed methods and tips for the generation of iPS cells.

Foundations of Augmented Cognition: Neuroergonomics and Operational Neuroscience <=SEP=> This work evaluates the feasibility of a motor imagery-based optical brain-computer interface (BCI) for humanoid robot control. The functional near-infrared spectroscopy (fNIRS) based BCI-robot system developed in this study operates through a high-level control mechanism where user specifies a target action through the BCI and the robot performs the set of micro operations necessary to fulfill the identified goal. For the evaluation of the system, four motor imagery tasks (left hand, right hand, left foot, and right foot) were mapped to operational commands (turn left, turn right, walk forward, walk backward) that were sent to the robot in real time to direct the robot navigating a small room. An ecologically valid offline analysis with minimal preprocessing shows that seven subjects could achieve an average accuracy of 32.5 %. This was increased to 43.6 % just by including calibration data from the same day of the robot control using the same cap setup, indicating that day-of calibration following the initial training may be important for BCI control.

Understanding Availability <=SEP=> This paper addresses a simple, yet fundamental question in the design of peer-to-peer systems: What does it mean when we say “availability” and how does this understanding impact the engineering of practical systems? We argue that existing measurements and models do not capture the complex timevarying nature of availability in today’s peer-to-peer environments. Further, we show that unforeseen methodological shortcomings have dramatically biased previous analyses of this phenomenon. As the basis of our study, we empirically characterize the availability of a large peer-to-peer system over a period of 7 days, analyze the dependence of the underlying availability distributions, measure host turnover in the system, and discuss how these results may affect the design of high-availability peer-to-peer services.

Entity Extraction: From Unstructured Text to DBpedia RDF triples <=SEP=> In this paper, we describe an end-to-end system that automatically extracts RDF triples describing entity relations and properties from unstructured text. This system is based on a pipeline of text processing modules that includes a semantic parser and a coreference solver. By using coreference chains, we group entity actions and properties described in different sentences and convert them into entity triples. We applied our system to over 114,000 Wikipedia articles and we could extract more than 1,000,000 triples. Using an ontology-mapping system that we bootstrapped using existing DBpedia triples, we mapped 189,000 extracted triples onto the DBpedia namespace. These extracted entities are available online in the N-Triple format1.

Photonic Crystal-Structures for THz Vacuum Electron Devices <=SEP=> The technology of photonic crystals (PhCs) is investigated here to improve the performance of THz vacuum electron devices. Compared with conventional metallic waveguides, the PhC arrangement alleviates typical issues in THz vacuum electron tubes, i.e. difficult vacuum pumping process and assembling, and improves the input/output coupling. A slow-wave structure (SWS) based on a corrugated waveguide assisted by PhC lateral walls and the efficient design of a PhC coupler for sheet-beam interaction devices are demonstrated. Based on the proposed technology, a backward-wave oscillator (BWO) is designed in this paper. Cold parameters of the novel PhC SWS as well as 3-D particle-in-cell simulations of the overall BWO are investigated, obtaining more than 70-mW-peak output power at 0.650 THz for beam voltage of 11 kV and beam current of 6 mA.

Presenting system uncertainty in automotive UIs for supporting trust calibration in autonomous driving <=SEP=> To investigate the impact of visualizing car uncertainty on drivers' trust during an automated driving scenario, a simulator study was conducted. A between-group design experiment with 59 Swedish drivers was carried out where a continuous representation of the uncertainty of the car's ability to autonomously drive during snow conditions was displayed to one of the groups, whereas omitted for the control group. The results show that, on average, the group of drivers who were provided with the uncertainty representation took control of the car faster when needed, while they were, at the same time, the ones who spent more time looking at other things than on the road ahead. Thus, drivers provided with the uncertainty information could, to a higher degree, perform tasks other than driving without compromising with driving safety. The analysis of trust shows that the participants who were provided with the uncertainty information trusted the automated system less than those who did not receive such information, which indicates a more proper trust calibration than in the control group.

Kernel-Based Learning of Hierarchical Multilabel Classification Models <=SEP=> We present a kernel-based algorithm for hierarchical text c lassification where the documents are allowed to belong to more than one category at a time. The clas sification model is a variant of the Maximum Margin Markov Network framework, where the classifi cation hierarchy is represented as a Markov tree equipped with an exponential family defined o n the edges. We present an efficient optimization algorithm based on incremental conditi onal gradient ascent in single-example subspaces spanned by the marginal dual variables. The optim ization is facilitated with a dynamic programming based algorithm that computes best update dire ctions in the feasible set. Experiments show that the algorithm can feasibly optimize t raining sets of thousands of examples and classification hierarchies consisting of hundreds of nodes. Training of the full hierarchical model is as efficient as training independent SVM-light clas sifiers for each node. The algorithm’s predictive accuracy was found to be competitive with other r ecently introduced hierarchical multicategory or multilabel classification learning algorithms .

A novel ultra-wideband 80 GHz FMCW radar system for contactless monitoring of vital signs <=SEP=> In this paper an ultra-wideband 80 GHz FMCW-radar system for contactless monitoring of respiration and heart rate is investigated and compared to a standard monitoring system with ECG and CO2 measurements as reference. The novel FMCW-radar enables the detection of the physiological displacement of the skin surface with submillimeter accuracy. This high accuracy is achieved with a large bandwidth of 10 GHz and the combination of intermediate frequency and phase evaluation. This concept is validated with a radar system simulation and experimental measurements are performed with different radar sensor positions and orientations.

Optimizing F-measure: A Tale of Two Approaches <=SEP=> F-measures are popular performance metrics, particularly for tasks with imbalanced data sets. Algorithms for learning to maximize F-measures follow two approaches: the empirical utility maximization (EUM) approach learns a classifier having optimal performance on training data, while the decision-theoretic approach learns a probabilistic model and then predicts labels with maximum expected F-measure. In this paper, we investigate the theoretical justifications and connections for these two approaches, and we study the conditions under which one approach is preferable to the other using synthetic and real datasets. Given accurate models, our results suggest that the two approaches are asymptotically equivalent given large training and test sets. Nevertheless, empirically, the EUM approach appears to be more robust against model misspecification, and given a good model, the decision-theoretic approach appears to be better for handling rare classes and a common domain adaptation scenario.

Diffusion maps for high-dimensional single-cell analysis of differentiation data <=SEP=> MOTIVATION
Single-cell technologies have recently gained popularity in cellular differentiation studies regarding their ability to resolve potential heterogeneities in cell populations. Analyzing such high-dimensional single-cell data has its own statistical and computational challenges. Popular multivariate approaches are based on data normalization, followed by dimension reduction and clustering to identify subgroups. However, in the case of cellular differentiation, we would not expect clear clusters to be present but instead expect the cells to follow continuous branching lineages.


RESULTS
Here, we propose the use of diffusion maps to deal with the problem of defining differentiation trajectories. We adapt this method to single-cell data by adequate choice of kernel width and inclusion of uncertainties or missing measurement values, which enables the establishment of a pseudotemporal ordering of single cells in a high-dimensional gene expression space. We expect this output to reflect cell differentiation trajectories, where the data originates from intrinsic diffusion-like dynamics. Starting from a pluripotent stage, cells move smoothly within the transcriptional landscape towards more differentiated states with some stochasticity along their path. We demonstrate the robustness of our method with respect to extrinsic noise (e.g. measurement noise) and sampling density heterogeneities on simulated toy data as well as two single-cell quantitative polymerase chain reaction datasets (i.e. mouse haematopoietic stem cells and mouse embryonic stem cells) and an RNA-Seq data of human pre-implantation embryos. We show that diffusion maps perform considerably better than Principal Component Analysis and are advantageous over other techniques for non-linear dimension reduction such as t-distributed Stochastic Neighbour Embedding for preserving the global structures and pseudotemporal ordering of cells.


AVAILABILITY AND IMPLEMENTATION
The Matlab implementation of diffusion maps for single-cell data is available at https://www.helmholtz-muenchen.de/icb/single-cell-diffusion-map.


CONTACT
fbuettner.phys@gmail.com, fabian.theis@helmholtz-muenchen.de


SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.

Algorithms for the Assignment and Transiortation Troblems* <=SEP=> In this paper we presen algorithms for the solution of the general assignment and transportation problems. In Section 1, a statement of the algorithm for the assignment problem appears, along with a proof for the correctness of the algorithm. The remarks which constitute the proof are incorporated parenthetically into the statement of the algorithm. Following this appears a discussion of certain theoretical aspects of the problem. In Section 2, the algorithm is generalized to one for the transportation problem. The algorithm of that section is stated as concisely as possible, with theoretical remarks omitted.