nils-herrmann/clean_scirepeval huggingface dataset:
Frequency conversion of structured light
Venue: Scientific reports

Coherent frequency conversion of structured light, i.e. the ability to manipulate the carrier frequency of a wave front without distorting its spatial phase and intensity profile, provides the opportunity for numerous novel applications in photonic technology and fundamental science. In particular, frequency conversion of spatial modes carrying orbital angular momentum can be exploited in sub wavelength resolution nano optics and coherent imaging at a wavelength different from that used to illuminate an object. Moreover, coherent frequency conversion will be crucial for interfacing information stored in the high dimensional spatial structure of single and entangled photons with various constituents of quantum networks. In this work, we demonstrate frequency conversion of structured light from the near infrared (803 nm) to the visible (527 nm) The conversion scheme is based on sum frequency generation in a periodically poled lithium niobate crystal pumped with a 1540 nm Gaussian beam. We observe frequency converted fields that exhibit a high degree of similarity with the input field and verify the coherence of the frequency conversion process via mode projection measurements with a phase mask and a single mode fiber. Our results demonstrate the suitability of exploiting the technique for applications in quantum information processing and coherent imaging.
On the use of structured light in nonlinear optics studies of the symmetry group of a crystal
Venue: Scientific reports

We put forward a technique that allows to extract information about the symmetry group to which certain nonlinear crystals belong using a single illuminating beam. It provides such information by considering the outcome of a nonlinear optics process characterized by the electric nonlinear susceptibility tensor, whose structure is dictated by such symmetry group. As an example, we consider the process of spontaneous parametric down conversion, when it is pumped with a special type of Bessel beam. The observation of the spatial angular dependence of the lower frequency generated light provides direct information about the symmetry group of the crystal. We should stress that the choice of the appropriate illumination is of paramount importance for unveiling the sought after information.
A Multi criteria Intelligent Control for Traffic Lights Using Reinforcement Learning

Traffic signal control plays a crucial role in traffic management and operation practices. In the past decade, adaptive signal control systems, capable of adjusting control schemes in response to traffic patterns, have shown the abilities to improve traffic mobility. On the other hand, the negative impacts on environments by increased vehicles attract increased attentions from traffic stakeholders and the general public. Most of the prevalent adaptive signal control systems do not address energy and environmental issues. The present paper proposes an adaptive signal control system capable of taking multi criteria strategies into account. A general multi agent framework is introduced for modeling signal control operations. The behavior of each cognitive agent is modeled by a Constrained Markov Decision Process (CMDP) Reinforcement learning algorithms are applied to solve the MDP problem. As a result, the signal controller makes intelligent timing decisions according to a pre defined policy goal. A case study is carried out for the stage based control scheme to investigate the effectiveness of the adaptive signal control system from two perspectives, traffic mobility and energy efficiency. The control approach can be further applied to a large network in a decentralized manner.
A Web services accessible database of turbulent channel flow and its use for testing a new integral wall model for LES

The output from a direct numerical simulation (DNS) of turbulent channel flow at Ret 1000 is used to construct a publicly and Web services accessible, spatio temporal database for this flow. The simulated channel has a size of 8ph x 2h x 3ph, where h is the channel half height. Data are stored at 2048 x 512 x 1536 spatial grid points for a total of 4000 time samples every 5 time steps of the DNS. These cover an entire channel flow through time, i.e. the time it takes to traverse the entire channel length 8ph at the mean velocity of the bulk flow. Users can access the database through an interface that is based on the Web services model and perform numerical experiments on the slightly over 100 terabytes (TB) DNS data on their remote platforms, such as laptops or local desktops. Additional technical details about the pressure calculation, database interpolation, and differentiation tools are provided in several appendices. As a sample application of the channel flow database, we use it to conduct an a priori test of a recently introduced integral wall model for large eddy simulation of wall bounded turbulent flow. The results are compared with those of the equilibrium wall model, showing the strengths of the integral wall model as compared to the equilibrium model.
Advances in Chaos Theory and Intelligent Control
Venue: Advances in Chaos Theory and Intelligent Control

The book reports on the latest advances in and applications of chaos theory and intelligent control. Written by eminent scientists and active researchers andusing a clear, matter of fact style, it covers advanced theories, methods, and applications in a varietyof research areas, and explains key concepts in modeling, analysis, and control of chaotic and hyperchaotic systems. Topics include fractional chaotic systems, chaos control, chaos synchronization, memristors, jerk circuits, chaotic systems with hidden attractors, mechanical and biological chaos, and circuit realization of chaotic systems. The book furthercovers fuzzy logic controllers, evolutionary algorithms, swarm intelligence, and petri nets among other topics. Not only does it provide the readers with chaos fundamentals and intelligent control based algorithms; it also discusses key applications of chaos as well as multidisciplinary solutions developed via intelligent control. The book is a timely and comprehensive reference guide for graduate students, researchers, and practitioners in the areas of chaos theory and intelligent control.
Landslide displacement prediction using discrete wavelet transform and extreme learning machine based on chaos theory
Venue: Environmental Earth Sciences

Landslide displacement system is generally characterized by non stationary and nonlinear characteristics. Traditionally, many artificial neural network (ANN) models have been proposed to forecast landslide displacement. However, the underlying non stationary characteristics in the landslide displacement are not captured, and the input output variables of the ANN models are not selected nonlinearly. To overcome these drawbacks, this paper proposes the chaos theory based discrete wavelet transform (DWT) extreme learning machine (ELM) model to predict landslide displacement. The DWT method is adopted to decompose the landslide displacement into several low and high frequency components to address the non stationary characteristics. And chaos theory is used to determine the input output variables of the ELM model. The cumulative displacement time series of the Baishuihe and Baijiabao landslides in the Three Gorges Reservoir Area, China, are used as data sets. The results show that the chaotic DWT ELM model accurately predicts landslide displacement. The chaotic DWT support vector machine (SVM) chaotic DWT back propagation neural network (BPNN) and single chaotic ELM models are used for comparisons. The comparison results show that the chaotic DWT ELM model achieves higher prediction accuracy than do the chaotic DWT SVM, chaotic DWT BPNN and the single chaotic ELM models.
Hand eye calibration of SCARA robots using dual quaternions
Venue: Pattern Recognition and Image Analysis

In SCARA robots, which are often used in industrial applications, all joint axes are parallel, covering three degrees of freedom in translation and one degree of freedom in rotation. Therefore, conventional approaches for the hand eye calibration of articulated robots cannot be used for SCARA robots. In this paper, we present a new linear method that is based on dual quaternions and extends the work of Daniilid is 1999 (IJRR) for SCARA robots. To improve the accuracy, a subsequent nonlinear optimization is proposed. We address several practical implementation issues and show the effectiveness of the method by evaluating it on synthetic and real data.
High Rank Matrix Completion and Clustering under Self Expressive Models
Venue: NIPS

We propose efficient algorithms for simultaneous clustering and completion of incomplete high dimensional data that lie in a union of low dimensional subspaces. We cast the problem as finding a completion of the data matrix so that each point can be reconstructed as a linear or affine combination of a few data points. Since the problem is NP hard, we propose a lifting framework and reformulate the problem as a group sparse recovery of each incomplete data point in a dictionary built using incomplete data, subject to rank one constraints. To solve the problem efficiently, we propose a rank pursuit algorithm and a convex relaxation. The solution of our algorithms recover missing entries and provides a similarity matrix for clustering. Our algorithms can deal with both low rank and high rank matrices, does not suffer from initialization, does not need to know dimensions of subspaces and can work with a small number of data points. By extensive experiments on synthetic data and real problems of video motion segmentation and completion of motion capture data, we show that when the data matrix is low rank, our algorithm performs on par with or better than low rank matrix completion methods, while for high rank data matrices, our method significantly outperforms existing algorithms.
Operational Modal Analysis and Model Updating of Riveted Steel Bridge

Riveted steel bridges are common in the Norwegian and European railway network. Assessment of the current state is important to ensure continuous and safe operation of the railway. Furthermore, the prediction of remaining service life of these bridges is essential to allocate limited funds and resources to the most critical points in the infrastructure. Reliable and accurate numerical models are necessary tools to assess the current state and predict the remaining service life of these bridges.This paper presents work carried out in obtaining a validated finite element model of the Lerelva railway bridge. System identification was carried out with data driven stochastic subspace identification using data with both initial conditions from train passage and ambient excitation. The modal data from the system identification procedure was utilized in updating the finite element model.
Theories Of Human Communication

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Turn taking in Human Communication Origins and Implications for Language Processing
Venue: Trends in Cognitive Sciences

Most language usage is interactive, involving rapid turn taking. The turn taking system has a number of striking properties: turns are short and responses are remarkably rapid, but turns are of varying length and often of very complex construction such that the underlying cognitive processing is highly compressed. Although neglected in cognitive science, the system has deep implications for language processing and acquisition that are only now becoming clear. Appearing earlier in ontogeny than linguistic competence, it is also found across all the major primate clades. This suggests a possible phylogenetic continuity, which may provide key insights into language evolution.
Human Communication Principles And Contexts

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Essentials Of Human Communication

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Pattern recognition for electroencephalographic signals based on continuous neural networks.
Venue: Neural networks the official journal of the International Neural Network Society

This study reports the design and implementation of a pattern recognition algorithm to classify electroencephalographic (EEG) signals based on artificial neural networks (NN) described by ordinary differential equations (ODEs) The training method for this kind of continuous NN (CNN) was developed according to the Lyapunov theory stability analysis. A parallel structure with fixed weights was proposed to perform the classification stage. The pattern recognition efficiency was validated by two methods, a generalization regularization and a k fold cross validation (k=5) The classifier was applied on two different databases. The first one was made up by signals collected from patients suffering of epilepsy and it is divided in five different classes. The second database was made up by 90 single EEG trials, divided in three classes. Each class corresponds to a different visual evoked potential. The pattern recognition algorithm achieved a maximum correct classification percentage of 97.2% using the information of the entire database. This value was similar to some results previously reported when this database was used for testing pattern classification. However, these results were obtained when only two classes were considered for the testing. The result reported in this study used the whole set of signals (five different classes) In comparison with similar pattern recognition methods that even considered less number of classes, the proposed CNN proved to achieve the same or even better correct classification results.
A pseudoinverse incremental algorithm for fast training deep neural networks with application to spectra pattern recognition
Venue: 2016 International Joint Conference on Neural Networks (IJCNN)

Deep learning scheme has received significant attention during these years, particularly as a way of building hierarchical representations from unlabeled data for a variety of signal and information processing tasks. However, deep neural networks suffer from slow learning speed since most used training algorithms are based on variations of the gradient descent algorithms which require iterative optimization and thus are time consuming. In addition, a series of control parameters need to be specified empirically which lacks of the theoretical guidance, and current learning algorithms for deep networks are not very suitable to incremental learning scenario. To address these issues, we propose a fast learning scheme in this paper. The basic idea of our approach is to pre train basic units such as auto encoders of the deep architecture in an analytical way without any iterative optimization procedure. This scheme is also extended to an incremental learning version. The experimental result shows the superiority of our approach over the state of the art gradient descent based algorithms. To demonstrate the impact of our algorithm on complicated real world applications, we give an example of its performance in astronomical spectra pattern recognition.
Questioning the evidence for a Central Asian domestication origin of dogs
Venue: Proceedings of the National Academy of Sciences

A recent study by Shannon et al. (1) proposes a Central Asian domestication origin of dogs, based upon the finding that Central Asia had the lowest short range linkage disequilibrium (LD) among village dogs across the globe. A reanalysis of their data, however, suggests that these conclusions may require revision. First, Shannon et al.'s (1) definition of "Central Asia" is questionable. Mongolia and Nepal [the two regions assigned to Central Asia by Shannon et al. (1) are not associated with the common definition of Central Asia that includes the region bounded by the Caspian Sea, Russia, China, and Afghanistan, but are located in East Asia and South Asia, respectively. Although Mongolia is occasionally considered a part of Central Asia (e.g. by the United Nations Educational, Scientific, and Cultural Organization) Nepal is not, but is considered [1]1To whom correspondence should be addressed.  #xref corresp 1 1
Genomic and archaeological evidence suggest a dual origin of domestic dogs
Venue: Science

A dogged investigation of domestication The history of how wolves became our pampered pooches of today has remained controversial. Frantz et al. describe high coverage sequencing of the genome of an Irish dog from the Bronze Age as well as ancient dog mitochondrial DNA sequences. Comparing ancient dogs to a modern worldwide panel of dogs shows an old, deep split between East Asian and Western Eurasian dogs. Thus, dogs were domesticated from two separate wolf populations on either side of the Old World. Science, this issue p. 1228 Dogs may have been domesticated independently in Eastern and Western Eurasia from distinct wolf populations. The geographic and temporal origins of dogs remain controversial. We generated genetic sequences from 59 ancient dogs and a complete (28x) genome of a late Neolithic dog (dated to ~4800 calendar years before the present) from Ireland. Our analyses revealed a deep split separating modern East Asian and Western Eurasian dogs. Surprisingly, the date of this divergence ~14,000 to 6400 years ago) occurs commensurate with, or several millennia after, the first appearance of dogs in Europe and East Asia. Additional analyses of ancient and modern mitochondrial DNA revealed a sharp discontinuity in haplotype frequencies in Europe. Combined, these results suggest that dogs may have been domesticated independently in Eastern and Western Eurasia from distinct wolf populations. East Eurasian dogs were then possibly transported to Europe with people, where they partially replaced European Paleolithic dogs.
Game User Experience Evaluation
Venue: CHI Extended Abstracts

In a nutshell: This course comprehensively covers important user experience (UX) evaluation methods as well as opportunities and challenges of UX evaluation in the area of entertainment and games. The course is an ideal forum for attendees to gain insight into state of the art user experience evaluation methods going way beyond standard usability and user experience evaluation approaches in the area of human computer interaction. It surveys and assesses the efforts of user experience evaluation of the gaming and human computer interaction communities during the last 15 years.
PAPER: A Practical Framework for Measuring Higher Order Cognitive Constructs: An Application to Measuring Nursing Clinical Judgment

Testing professionals encounter numerous challenges in developing reliable and valid measurements to capture higher order cognitive constructs, such as information integration, critical thinking and clinical judgment.  A framework consisting of four models provides a modularized yet integrated way for measuring high order cognitive constructs. To provide a concrete context for this paper session, nursing clinical judgment (NCJ) is chosen to be the construct of interest.  The paper explores three competing theories for NCJ and describes the process of building a conceptual model as the initial step of the framework. The conceptual model provides theoretical foundations for assessing the construct. The conceptual model specifies what should be measured but not how to measure. This is where a multilayer assessment model comes into play: the assessment model renders the conceptual model into a psychometric model that becomes amenable to measurement. The assessment model plays a major role in this framework in that it provides a psychometric foundation upon which the remaining models in the paper are developed. After that, the paper describes building a task model. The task model fleshes out lower layer components in the assessment model (e.g. connecting the lower layer components to knowledge, skills, and abilities identified from a competency study) A well developed task model functions as a blueprint for developing structured items that elicit responses from test takers consistently. Finally, the paper presents a scoring model and discusses validation processes of the framework. The scoring model assigns numeric values to test takers' responses to items developed from the task model. Both qualitative and quantitative validation processes are introduced that can be employed to evaluate the assessment, task, and scoring models.  The framework is flexible enough to be adapted by testing professionals who are considering developing measurements for high order cognitive constructs.