nils-herrmann/clean_scirepeval huggingface dataset:
StructSLAM: Visual SLAM With Building Structure Lines

We propose a novel 6-degree-of-freedom (DoF) visual simultaneous localization and mapping (SLAM) method based on the structural regularity of man-made building environments. The idea is that we use the building structure lines as features for localization and mapping. Unlike other line features, the building structure lines encode the global orientation information that constrains the heading of the camera over time, eliminating the accumulated orientation errors and reducing the position drift in consequence. We extend the standard extended Kalman filter visual SLAM method to adopt the building structure lines with a novel parameterization method that represents the structure lines in dominant directions. Experiments have been conducted in both synthetic and real-world scenes. The results show that our method performs remarkably better than the existing methods in terms of position error and orientation error. In the test of indoor scenes of the public RAWSEEDS data sets, with the aid of a wheel odometer, our method produces bounded position errors about 0.79 m along a 967-m path although no loop-closing algorithm is applied.
Model-Based Methods For Steganography And Steganalysis

This paper presents methods for performing steganography and steganalysis using a statistical model of the cover medium. The methodology is general, and can be applied to virtually any type of media. It provides answers for some fundamental questions that have not been fully addressed by previous steganographic methods, such as how large a message can be hidden without risking detection by certain statistical methods, and how to achieve this maximum capacity. Current steganographic methods have been shown to be insecure against simple statistical attacks. Using the model-based methodology, an example steganography method is proposed for JPEG images that achieves a higher embedding efficiency and message capacity than previous methods while remaining secure against first order statistical attacks. A method is also described for defending against “blockiness” steganalysis attacks. Finally, a model-based steganalysis method is presented for estimating the length of messages hidden with Jsteg in JPEG images.
Open issues in genetic programming

It is approximately 50 years since the first computational experiments were conducted in what has become known today as the field of Genetic Programming (GP), twenty years since John Koza named and popularised the method, and ten years since the first issue appeared of the Genetic Programming & Evolvable Machines journal. In particular, during the past two decades there has been a significant range and volume of development in the theory and application of GP, and in recent years the field has become increasingly applied. There remain a number of significant open issues despite the successful application of GP to a number of challenging real-world problem domains and progress in the development of a theory explaining the behavior and dynamics of GP. These issues must be addressed for GP to realise its full potential and to become a trusted mainstream member of the computational problem solving toolkit. In this paper we outline some of the challenges and open issues that face researchers and practitioners of GP. We hope this overview will stimulate debate, focus the direction of future research to deepen our understanding of GP, and further the development of more powerful problem solving algorithms.
Learning to decompose for object detection and instance segmentation

Although deep convolutional neural networks(CNNs) have achieved remarkable results on object detection and segmentation, preand post-processing steps such as region proposals and non-maximum suppression(NMS), have been required. These steps result in high computational complexity and sensitivity to hyperparameters, e.g. thresholds for NMS. In this work, we propose a novel end-to-end trainable deep neural network architecture, which consists of convolutional and recurrent layers, that generates the correct number of object instances and their bounding boxes (or segmentation masks) given an image, using only a single network evaluation without any preor post-processing steps. We have tested on detecting digits in multi-digit images synthesized using MNIST, automatically segmenting digits in these images, and detecting cars in the KITTI benchmark dataset. The proposed approach outperforms a strong CNN baseline on the synthesized digits datasets and shows promising results on KITTI car detection.
Non linear time series a dynamical Non linear time series a dynamical system approach pdf system approach pdf Non linear time series a dynamical system approach

Non-Linear Time Series: A Dynamical System Approach Oxford Statistical Science Series, 6 9780198523000: Howell Tong: Books.nonlinear time series model from a nonlinear stochastic dynamical system. Only the approach of Smith 1977 based on piecewise linear.Time series analysis for nonlinear dynamical systems with applications to. Vides a computationally viable alternative to a state-of-the-art nitro pdf reader 1 2 0 11 nissan td27 engine manual pdf beta Bayesian approach.nonlinear dynamical systems that exhibit deterministic behavior. Main steps of the proposed approach for time series synthesis. And kernel.A new approach based on Wasserstein distances, which are numerical costs. Shows the functional relationships between the dynamical systems under study. The focus in nonlinear time series analysis lies therefore not on predicting single.Non-Linear Time Series. A Dynamical System.smoother long memory Markov chain Monte Carlo nonlinear dynamical systems nonlinear state space models nonlinear time series nonparametrics panel of time. NTSA and chaos, the nonparametricsemiparametric approach, nonlinear.one coordinate of the dynamical system to a fixed value TISEAN program: poincare. One of the objectives of time series analysis is the characterisation of the. Presented rudiments of the approach based on piecewise linearization. That a limit cycle can be generated by a dynamical system only if there is a balance. E.g. Tong, 1990 pressed home the case for nonlinear time series mod. Probability density function pdf, say Πx, assumed to exist, of a.He is the father of the threshold time series models, which have extensive applications in ecology. Non-linear Time Series: A Dynamical System Approach. I reflect upon the development of nonlinear nntt r g b playboy playmate calendar 2012 usa pdf time series analysis since 1990 by. Approach, nonlinear state space modelling, financial time series and nonlinear modelling of panels of time series. Markov chain Monte Carlo nonlinear dynamical systems nonlinear state.Often, a time series is observed at discrete time intervals. What is a nonlinear time series model. Non-linear Time Series: A Dynamical System Approach.This is a preliminary, very brief summary of nonlinear time series useful for finance.
Measurement of user experience: A Spanish language version of the user experience questionnaire (UEQ)

To improve a product you will need most likely developers, managers and user feedback. Besides the basic software qualities other important properties are usability and user experience for developing a good product. Usability is well known and can be tested with e.g. a usability test or an expert review. In contrast user experience describes the whole impact a product has on the end-user. The timeline goes from before, while and after the use of a product. We present a tool that allows to evaluate the user experience of a product with little effort. We show in addition how this tool can be used for a continuous user experience assessment.
Detecting semantic events in soccer games: towards a complete solution

This paper presents a new method for detecting semantic events which may happen in a soccer game. Our method requires the position information of the players and ball as input, which can be provided by a tracking system from processing either microwave signals or video sequences. Based on the tracking data, the method detects a wide range of player actions and game events using a set of heuristic rules which are derived from a hierarchical entity-relationship model representing the prior knowledge of soccer events. We have applied the proposed method as a key enabling module in a realworld sports entertainment application and the experimental results are given in the paper.
Instance Normalization: The Missing Ingredient for Fast Stylization

It this paper we revisit the fast stylization method introduced in Ulyanov et al. (2016). We show how a small change in the stylization architecture results in a significant qualitative improvement in the generated images. The change is limited to swapping batch normalization with instance normalization, and to apply the latter both at training and testing times. The resulting method can be used to train high-performance architectures for real-time image generation. The code will be made available at https://github.com/DmitryUlyanov/texture_nets.
LEADERSHIP DEVELOPMENT FROM A COMPLEXITY PERSPECTIVE

Intentional change theory (ICT) explains sustainable leadership development in terms of the essential components of behavior, thoughts, feelings, and perceptions related to leadership effectiveness as a complex system (Boyatzis, 2001, 2006a, 2006b). This article reviews previous studies and expands the interpretation using complexity theory concepts focused on leadership development. Sustained, desired change represents a metamorphosis in actions, habits, or competencies associated with leadership effectiveness. It may be in dreams or aspirations. It may be in the way someone acts in certain situations. A person may refine her sensitivity to others (Boyatzis, in press), become more optimistic (Seligman & Csikszentmihalyi, 2000), or learn how to articulate a shared vision for those in her organization (Bennis & Nanus, 1985). These changes are desired in that the person thinks, feels, or acts in a specified manner. They are sustainable in that they endure. A sustained, desired change may also include the wish to maintain a current state, relationship, or habit, but maintaining the current state appears to require an investment of energy. In either situation, it requires intentional effort.
BundleFusion: Real-Time Globally Consistent 3D Reconstruction Using On-the-Fly Surface Reintegration

Real-time, high-quality, 3D scanning of large-scale scenes is key to mixed reality and robotic applications. However, scalability brings challenges of drift in pose estimation, introducing significant errors in the accumulated model. Approaches often require hours of offline processing to globally correct model errors. Recent online methods demonstrate compelling results but suffer from (1) needing minutes to perform online correction, preventing true real-time use; (2) brittle frame-to-frame (or frame-to-model) pose estimation, resulting in many tracking failures; or (3) supporting only unstructured point-based representations, which limit scan quality and applicability. We systematically address these issues with a novel, real-time, end-to-end reconstruction framework. At its core is a robust pose estimation strategy, optimizing per frame for a global set of camera poses by considering the complete history of RGB-D input with an efficient hierarchical approach. We remove the heavy reliance on temporal tracking and continually localize to the globally optimized frames instead. We contribute a parallelizable optimization framework, which employs correspondences based on sparse features and dense geometric and photometric matching. Our approach estimates globally optimized (i.e., bundle adjusted) poses in real time, supports robust tracking with recovery from gross tracking failures (i.e., relocalization), and re-estimates the 3D model in real time to ensure global consistency, all within a single framework. Our approach outperforms state-of-the-art online systems with quality on par to offline methods, but with unprecedented speed and scan completeness. Our framework leads to a comprehensive online scanning solution for large indoor environments, enabling ease of use and high-quality
A Wideband Transmitarray Using Dual-Resonant Double Square Rings

A four-layer transmitarray operating at 30 GHz is designed using a dual-resonant double square ring as the unit cell element. The two resonances of the double ring are used to increase the per-layer phase variation while maintaining a wide transmission magnitude bandwidth of the unit cell. The design procedure for both the single-layer unit cell and the cascaded connection of four layers is described and it leads to a 50% increase in the -1 dB gain bandwidth over that of previous transmitarrays. Results of a 7.5% -1 dB gain bandwidth and 47% radiation efficiency are reported.
Neural Syntax: Cell Assemblies, Synapsembles, and Readers

A widely discussed hypothesis in neuroscience is that transiently active ensembles of neurons, known as "cell assemblies," underlie numerous operations of the brain, from encoding memories to reasoning. However, the mechanisms responsible for the formation and disbanding of cell assemblies and temporal evolution of cell assembly sequences are not well understood. I introduce and review three interconnected topics, which could facilitate progress in defining cell assemblies, identifying their neuronal organization, and revealing causal relationships between assembly organization and behavior. First, I hypothesize that cell assemblies are best understood in light of their output product, as detected by "reader-actuator" mechanisms. Second, I suggest that the hierarchical organization of cell assemblies may be regarded as a neural syntax. Third, constituents of the neural syntax are linked together by dynamically changing constellations of synaptic weights ("synapsembles"). The existing support for this tripartite framework is reviewed and strategies for experimental testing of its predictions are discussed.
Gaussian Process Regression-Based Video Anomaly Detection and Localization With Hierarchical Feature Representation

This paper presents a hierarchical framework for detecting local and global anomalies via hierarchical feature representation and Gaussian process regression (GPR) which is fully non-parametric and robust to the noisy training data, and supports sparse features. While most research on anomaly detection has focused more on detecting local anomalies, we are more interested in global anomalies that involve multiple normal events interacting in an unusual manner, such as car accidents. To simultaneously detect local and global anomalies, we cast the extraction of normal interactions from the training videos as a problem of finding the frequent geometric relations of the nearby sparse spatio-temporal interest points (STIPs). A codebook of interaction templates is then constructed and modeled using the GPR, based on which a novel inference method for computing the likelihood of an observed interaction is also developed. Thereafter, these local likelihood scores are integrated into globally consistent anomaly masks, from which anomalies can be succinctly identified. To the best of our knowledge, it is the first time GPR is employed to model the relationship of the nearby STIPs for anomaly detection. Simulations based on four widespread datasets show that the new method outperforms the main state-of-the-art methods with lower computational burden.
A 10.1" 56-channel, 183 uW/electrode, 0.73 mm2/sensor high SNR 3D hover sensor based on enhanced signal refining and fine error calibrating techniques

This paper presents a high SNR self-capacitance sensing 3D hover sensor that does not use panel offset cancelation blocks. Not only reducing noise components, but increasing the signal components together, this paper achieved a high SNR performance while consuming very low power and die-area. Thanks to the proposed separated structure between driving and sensing circuits of the self-capacitance sensing scheme (SCSS), the signal components are increased without using high-voltage MOS sensing amplifiers which consume big die-area and power and badly degrade SNR. In addition, since a huge panel offset problem in SCSS is solved exploiting the panel's natural characteristics, other costly resources are not required. Furthermore, display noise and parasitic capacitance mismatch errors are compressed. We demonstrate a 39dB SNR at a 1cm hover point under 240Hz scan rate condition with noise experiments, while consuming 183uW/electrode and 0.73mm2/sensor, which are the power per electrode and the die-area per sensor, respectively.
Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches

0950-7051/$ see front matter 2013  A http://dx.doi.org/10.1016/j.knosys.2013.01.018 ⇑ Corresponding author.  213016; fax: E-mail addresses: alberto.fernandez@ujaen.es (A. ugr.es (V. López), mikel.galar@unavarra.es (M. Galar Jesus), herrera@decsai.ugr.es (F. Herrera). The imbalanced class problem is related to the real-world application of classification in engineering. It is characterised by a very different distribution of examples among the classes. The condition of multiple imbalanced classes is more restrictive when the aim of the final system is to obtain the most accurate precision for each of the concepts of the problem. The goal of this work is to provide a thorough experimental analysis that will allow us to determine the behaviour of the different approaches proposed in the specialised literature. First, we will make use of binarization schemes, i.e., one versus one and one versus all, in order to apply the standard approaches to solving binary class imbalanced problems. Second, we will apply several ad hoc procedures which have been designed for the scenario of imbalanced data-sets with multiple classes. This experimental study will include several well-known algorithms from the literature such as decision trees, support vector machines and instance-based learning, with the intention of obtaining global conclusions from different classification paradigms. The extracted findings will be supported by a statistical comparative analysis using more than 20 data-sets from the KEEL repository.
Effect size estimates: current use, calculations, and interpretation.

The Publication Manual of the American Psychological Association (American Psychological Association, 2001,  2010) calls for the reporting of effect sizes and their confidence intervals. Estimates of effect size are useful for determining the practical or theoretical importance of an effect, the relative contributions of factors, and the power of an analysis. We surveyed articles published in 2009 and 2010 in the Journal of Experimental Psychology: General, noting the statistical analyses reported and the associated reporting of effect size estimates. Effect sizes were reported for fewer than half of the analyses; no article reported a confidence interval for an effect size. The most often reported analysis was analysis of variance, and almost half of these reports were not accompanied by effect sizes. Partial η2 was the most commonly reported effect size estimate for analysis of variance. For t tests, 2/3 of the articles did not report an associated effect size estimate; Cohen's d was the most often reported. We provide a straightforward guide to understanding, selecting, calculating, and interpreting effect sizes for many types of data and to methods for calculating effect size confidence intervals and power analysis.
Large-Scale Kernel Machines

One long-term goal of machine learning research is to produc e methods that are applicable to highly complex tasks, such as perception ( vision, audition), reasoning, intelligent control, and other artificially intell igent behaviors. We argue that in order to progress toward this goal, the Machine Learn ing community must endeavor to discover algorithms that can learn highly compl ex functions, with minimal need for prior knowledge, and with minimal human interv ention. We present mathematical and empirical evidence suggesting that many p opular approaches to non-parametric learning, particularly kernel methods, are fundamentally limited in their ability to learn complex high-dimensional fun ctions. Our analysis focuses on two problems. First, kernel machines are shallow architectures , in which one large layer of simple template matchers i followed by a single layer of trainable coefficients. We argue that shallow architectu res can be very inefficient in terms of required number of computational elements a d examples. Second, we analyze a limitation of kernel machines with a local k ernel, linked to the curse of dimensionality, that applies to supervised, unsup ervised (manifold learning) and semi-supervised kernel machines. Using empirical esults on invariant image recognition tasks, kernel methods are compared with deep architectures , in which lower-level features or concepts are progressively c ombined into more abstract and higher-level representations. We argue that dee p architectures have the potential to generalize in non-local ways, i.e., beyond imm ediate neighbors, and that this is crucial in order to make progress on the kind of co mplex tasks required for artificial intelligence.
The Blurring Line Between Electronic and Physical Channels: Reconceptualising Multichannel Commerce

This paper proposes a revision of the multichannel concept as it has been applied in previous studies on multichannel commerce. Digitalization and technological innovations have blurred the line between physical and electronic channels. A structured literature review on multichannel consumer and firm behaviour is conducted to reveal the established view on multichannel phenomena. By providing empirical evidence on market offerings and consumer perceptions, we expose a significant mismatch between the dominant conceptualization of multichannel commerce applied in research and today’s market realities. This tension highlights the necessity for a changed view on multichannel commerce to study and understand phenomena in converging sales channels. Therefore, an extended conceptualization of multichannel commerce, named the multichannel continuum, is proposed. This is the first study that considers the broad complexity of integrated multichannel decisions. It aims at contributing to the literature on information systems and channel choice by developing a reference frame for studies on how technological advancements that allow the integration of different channels shape consumer and firm decision making in multichannel commerce. Accordingly, a brief research agenda contrasts established findings with unanswered questions, challenges and opportunities that arise in this more complex multichannel market environment.
How many vehicles in the LTE-V2V awareness range with half or full duplex radios?

Given the increasing interest for future connected vehicles, the long term evolution (LTE) specifications are being enhanced by 3GPP to cope with the vehicle-to-everything (V2X) scenarios starting from the upcoming Release 14, which will include, among others, vehicle-to-vehicle (V2V) direct communications. Since the main service expected for connected vehicles is the cooperative awareness, in this work we aim at deriving the number of neighbors that can be managed by LTE-V2V. In addition to the normal half duplex (HD) radios, which strictly limit the granularity of resource allocation, advanced full duplex (FD) radios are also considered. Results show that LTE-V2V with HD radios is able to manage up to few tens of neighbors, whereas FD can increase this limit significantly at the expense of an increase of complexity.