nils-herrmann/clean_scirepeval huggingface dataset:
Nonrigid Image Registration Using Multi-scale 3D Convolutional Neural Networks

In this paper we propose a method to solve nonrigid image registration through a learning approach, instead of via iterative optimization of a predefined dissimilarity metric. We design a Convolutional Neural Network (CNN) architecture that, in contrast to all other work, directly estimates the displacement vector field (DVF) from a pair of input images. The proposed RegNet is trained using a large set of artificially generated DVFs, does not explicitly define a dissimilarity metric, and integrates image content at multiple scales to equip the network with contextual information. At testing time nonrigid registration is performed in a single shot, in contrast to current iterative methods. We tested RegNet on 3D chest CT follow-up data. The results show that the accuracy of RegNet is on par with a conventional B-spline registration, for anatomy within the capture range. Training RegNet with artificially generated DVFs is therefore a promising approach for obtaining good results on real clinical data, thereby greatly simplifying the training problem. Deformable image registration can therefore be successfully casted as a learning problem.
Nonlinear Scale Space with Spatially Varying Stopping Time

A general scale space algorithm is presented for denoising signals and images with spatially varying dominant scales. The process is formulated as a partial differential equation with spatially varying time. The proposed adaptivity is semi-local and is in conjunction with the classical gradient-based diffusion coefficient, designed to preserve edges. The new algorithm aims at maximizing a local SNR measure of the denoised image. It is based on a generalization of a global stopping time criterion presented recently by the author and colleagues. Most notably, the method works well also for partially textured images and outperforms any selection of a global stopping time. Given an estimate of the noise variance, the procedure is automatic and can be applied well to most natural images.
Design of PI and PID controllers with transient performance specification

Proportional-integral-derivative (PID) controllers are widely used in industrial control systems because of the reduced number of parameters to be tuned. The most popular design technique is the Ziegler–Nichols method, which relies solely on parameters obtained from the plant step response. However, besides being suitable only for systems with monotonic step response, the compensated systems whose controllers are tuned in accordance with the Ziegler–Nichols method have generally a step response with a high-percent overshoot. In this paper, tuning methods for proportional-integral (PI) and PID controllers are proposed that, like the Ziegler–Nichols method, need only parameters obtained from the plant step response. The methodology also encompasses the design of PID controllers for plants with underdamped step response and provides the means for a systematic adjustment of the controller gain in order to meet transient performance specifications. In addition, since all the development of the methodology relies solely on concepts introduced in a frequency-domain-based control course, the paper has also a didactic contribution.
WLCp2-06: Modelling the Likelihood of Line-of-Sight for Air-to-Ground Radio Propagation in Urban Environments

The likelihood of line-of-sight (LoS) is an essential component in any radio channel model. It is particularly useful for radio network planning and urban coverage prediction. Empirical LoS models are hard to derive due to a strong dependency on local topology and the need for large measurement datasets. Since buildings are the major obstructions in a dense urban environment, we propose a new theoretical model to determine the LoS probability for air-to- ground channels based on local building geometry and knife-edge diffraction theory. The model takes into account key statistical parameters such as building height, building size, building coverage, street width and street angle distribution. The theoretical model is shown to agree well with ray tracing simulation results. The statistical parameters, such as mean building height, percentage of area covered by buildings (building coverage) and building density, can all be easily obtained for a specific location. We also derive equations for the likelihood of LoS for a direct slant path. These equations can be used in the analysis of air-to-ground channels.
SDN/NFV-enabled satellite communications networks: Opportunities, scenarios and challenges

In the context of next generation 5G networks, the satellite industry is clearly committed to revisit and revamp the role of satellite communications. As major drivers in the evolution of (terrestrial) fixed and mobile networks, Software Defined Networking (SDN) and Network Function Virtualisation (NFV) technologies are also being positioned as central technology enablers towards improved and more flexible integration of satellite and terrestrial segments, providing satellite network further service innovation and business agility by advanced network resources management techniques. Through the analysis of scenarios and use cases, this paper provides a description of the benefits that SDN/NFV technologies can bring into satellite communications towards 5G. Three scenarios are presented and analysed to delineate different potential improvement areas pursued through the introduction of SDN/NFV technologies in the satellite ground segment domain. Within each scenario, a number of use cases are developed to gain further insight into specific capabilities and to identify the technical challenges stemming from them.
Using Convolutional Neural Networks for Image Recognition By

A neural network is a system of interconnected artificial “neurons” that exchange messages between each other. The connections have numeric weights that are tuned during the training process, so that a properly trained network will respond correctly when presented with an image or pattern to recognize. The network consists of multiple layers of feature-detecting “neurons”. Each layer has many neurons that respond to different combinations of inputs from the previous layers. As shown in Figure 1, the layers are built up so that the first layer detects a set of primitive patterns in the input, the second layer detects patterns of patterns, the third layer detects patterns of those patterns, and so on. Typical CNNs use 5 to 25 distinct layers of pattern recognition.
Evaluation of Stereo Matching Costs on Images with Radiometric Differences

Stereo correspondence methods rely on matching costs for computing the similarity of image locations. We evaluate the insensitivity of different costs for passive binocular stereo methods with respect to radiometric variations of the input images. We consider both pixel-based and window-based variants like the absolute difference, the sampling-insensitive absolute difference, and normalized cross correlation, as well as their zero-mean versions. We also consider filters like LoG, mean, and bilateral background subtraction (BilSub) and nonparametric measures like Rank, SoftRank, Census, and Ordinal. Finally, hierarchical mutual information (HMI) is considered as pixelwise cost. Using stereo data sets with ground-truth disparities taken under controlled changes of exposure and lighting, we evaluate the costs with a local, a semiglobal, and a global stereo method. We measure the performance of all costs in the presence of simulated and real radiometric differences, including exposure differences, vignetting, varying lighting, and noise. Overall, the ranking of methods across all data sets and experiments appears to be consistent. Among the best costs are BilSub, which performs consistently very well for low radiometric differences; HMI, which is slightly better as pixelwise matching cost in some cases and for strong image noise; and Census, which showed the best and most robust overall performance.
You Are What You Post: What the Content of Instagram Pictures Tells About Users' Personality

Instagram is a popular social networking application that allows users to express themselves through the uploaded content and the different filters they can apply. In this study we look at the relationship between the content of the uploaded Instagram pictures and the personality traits of users. To collect data, we conducted an online survey where we asked participants to fill in a personality questionnaire, and grant us access to their Instagram account through the Instagram API. We gathered 54,962 pictures of 193 Instagram users. Through the Google Vision API, we analyzed the pictures on their content and clustered the returned labels with the k-means clustering approach. With a total of 17 clusters, we analyzed the relationship with users’ personality traits. Our findings suggest a relationship between personality traits and picture content. This allow for new ways to extract personality traits from social media trails, and new ways to facilitate personalized systems.
PyDial: A Multi-domain Statistical Dialogue System Toolkit

Statistical Spoken Dialogue Systems have been around for many years. However, access to these systems has always been difficult as there is still no publicly available end-to-end system implementation. To alleviate this, we present PyDial, an opensource end-to-end statistical spoken dialogue system toolkit which provides implementations of statistical approaches for all dialogue system modules. Moreover, it has been extended to provide multidomain conversational functionality. It offers easy configuration, easy extensibility, and domain-independent implementations of the respective dialogue system modules. The toolkit is available for download under the Apache 2.0 license.
RTextTools: A supervised learning package for text classification

Social scientists have long hand-labeled texts to create datasets useful for studying topics from congressional policymaking to media reporting. Many social scientists have begun to incorporate machine learning into their toolkits. RTextTools was designed to make machine learning accessible by providing a start-to-finish product in less than 10 steps. After installing RTextTools, the initial step is to generate a document term matrix. Second, a container object is created, which holds all the objects needed for further analysis. Third, users can use up to nine algorithms to train their data. Fourth, the data are classified. Fifth, the classification is summarized. Sixth, functions are available for performance evaluation. Seventh, ensemble agreement is conducted. Eighth, users can cross-validate their data. Finally, users write their data to a spreadsheet, allowing for further manual coding if required.
The primate basal ganglia: parallel and integrative networks

The basal ganglia and frontal cortex operate together to execute goal directed behaviors. This requires not only the execution of motor plans, but also the behaviors that lead to this execution, including emotions and motivation that drive behaviors, cognition that organizes and plans the general strategy, motor planning, and finally, the execution of that plan. The components of the frontal cortex that mediate these behaviors, are reflected in the organization, physiology, and connections between areas of frontal cortex and in their projections through basal ganglia circuits. This comprises a series of parallel pathways. However, this model does not address how information flows between circuits thereby developing new learned behaviors (or actions) from a combination of inputs from emotional, cognitive, and motor cortical areas. Recent anatomical evidence from primates demonstrates that the neuro-networks within basal ganglia pathways are in a position to move information across functional circuits. Two networks are: the striato-nigral-striatal network and the thalamo-cortical-thalamic network. Within each of these sets of connected structures, there are both reciprocal connections linking up regions associated with similar functions and non-reciprocal connections linking up regions that are associated with different cortical basal ganglia circuits. Each component of information (from limbic to motor outcome) sends both feedback connection, and also a feedforward connection, allowing the transfer of information. Information is channeled from limbic, to cognitive, to motor circuits. Action decision-making processes are thus influenced by motivation and cognitive inputs, allowing the animal to respond appropriate to environmental cues.
Wide-band microstrip antenna with an H-shaped coupling aperture

Theoretical and experimental results of a wide-band planar antenna are presented. This antenna can achieve a wide bandwidth, low cross-polarization levels, and low backward radiation levels. For wide bandwidth and easy integration with active circuits, it uses aperture-coupled stacked square patches. The coupling aperture is an H-shaped aperture. Based on the finite-difference time-domain method, a parametric study of the input impedance of the antenna is presented, and effects of each parameter on the antenna impedance are illustrated. One antenna is also designed, fabricated, and measured. The measured return loss exhibits an impedance bandwidth of 21.7%. The cross-polarization levels in both and planes are better than 23 dB. The front-to-back ratio of the antenna radiation pattern is better than 22 dB. Both theoretical and experimental results of parameters and radiation patterns are presented and discussed.
Crosslingual Distributed Representations of Words

Distributed representations of words have proven extremely useful in numerous natural language processing tasks. Their appeal is that they can help alleviate data sparsity problems common to supervised learning. Methods for inducing these representations require only unlabeled language data, which are plentiful for many natural languages. In this work, we induce distributed representations for a pair of languages jointly. We treat it as a multitask learning problem where each task corresponds to a single word, and task relatedness is derived from co-occurrence statistics in bilingual parallel data. These representations can be used for a number of crosslingual learning tasks, where a learner can be trained on annotations present in one language and applied to test data in another. We show that our representations are informative by using them for crosslingual document classification, where classifiers trained on these representations substantially outperform strong baselines when applied to a new language.
The Costs of Self-Driving Cars : Reconciling Freedom and Privacy with Tort Liability in Autonomous Vehicle Regulation

Nearly all of the literature on self-driving cars explores either their impact on social values, like freedom and privacy, or the questions they pose for legal liability. These lines of inquiry have developed largely in isolation, with little effort to examine how they might intersect and inform each other. This Article advances an integrated approach: regulators should consider freedom, privacy, and liability as interlocking pieces—not independent elements—of the puzzle of self-driving car regulation. Explorations into the laws of agency and product liability demonstrate that an actor’s post-sale control of and access to an autonomous vehicle may determine that actor’s liability for its accidents. As a result, the more that users want to preserve their freedom and privacy, the more liability they may end up retaining for the behavior of their self-driving cars. This Article then provides the first sustained inquiry into how different liability regimes for autonomous vehicles might generate sizable efficiencies in liability and insurance administration. The Article closes by making a normative appeal to regulators: only allow autonomous vehicles to infringe on user freedom and privacy to the extent that (1) reductions in freedom and privacy lead to equivalent reductions in liability for the users of self-driving cars; and (2) the social costs incurred by forfeiting these values will be outweighed by administrative efficiencies or other identifiable social benefits. By tying the reduction of user freedom and privacy to improvements in both individual and social welfare, this Article charts a possible course for regulators to reconcile freedom and privacy with tort liability in autonomous vehicle regulation.  J.D. Candidate, Yale Law School, Class of 2016.  The Costs of Self-Driving Cars: Reconciling Freedom and Privacy w Published by Yale Law School Legal Scholarship Repository, 2015 THE YALE JOURNAL OF LAW & TECHNOLOGY Vol.
A single-stage three-phase high power factor rectifier with high-frequency isolation and regulated DC-bus based on the DCM SEPIC converter

In this paper, the main concepts related to the development of a single-stage three-phase high power factor rectifier, with high-frequency isolation and regulated DC bus are described. The structure operation is presented, being based on the DC-DC SEPIC converter operating in the discontinuous conduction mode. This operational mode provides to the rectifier a high power factor feature, with sinusoidal input current, without the use of any current sensors and current loop control. A design example and simulation results are presented in order to validate the theoretical analysis.
CLASS-E HIGH-EFFICIENCY RF / MICROWAVE POWER AMPLIFIERS : PRINCIPLES OF OPERATION ,

Class-E power amplifiers [1]-[6] achieve significantly higher efficiency than for conventional Class-B or -C. Class E operates the transistor as an on/off switch and shapes the voltage and current waveforms to prevent simultaneous high voltage and high current in the transistor; that minimizes the power dissipation, especially during the switching transitions. In the published low-order Class-E circuit, a transistor performs well at frequencies up to about 70% of its frequency of good Class-B operation (an unpublished higher-order Class-E circuit operates well up to about double that frequency). This paper covers circuit operation, improved-accuracy explicit design equations for the published low-order Class E circuit, optimization principles, experimental results, tuning procedures, and gate/base driver circuits. Previously published analytically derived design equations did not include the dependence of output power (P) on loadnetwork loaded Q (QL ); as a result, the output power was 38% to 10% less than expected, for QL values in the usual range of 1.8 to 5. This paper includes an accurate new equation for P that includes the effect of QL .
Consumer Search Behavior in Online Shopping Environments

This paper explores search behavior of online shoppers. Information economics literature suggests that search cost in electronic markets has essentially been reduced to zero as consumers are able to use powerful search tools free of charge to easily find and compare product and shopping information on the Internet. In the present research, however, we present a research model proposing that users need to spend time and effort when completing search tasks resulting in significant search cost and a trade-off between search cost and search performance. Preliminary findings from an Internet experiment indicate that search task complexity, search engine capability, search strategy and user experience are important factors determining search cost and performance.
Web data mining trends and techniques

Web Services and Web-based applications are growing at an exponential rate. This is generating a huge amount of Web data having its own peculiar characteristics. This in turn makes research in the area of Web Data Mining more challenging. Web  Mining is an application of Data Mining which deals with extraction of interesting or hidden knowledge from the World Wide Web. Web Data Mining can be categorized into: Web Content Mining, Web Structure Mining, and Web Usage Mining. In this paper, we survey the state-of-the-art in each of these three types of Web Data Mining thereby describing a variety of specific trends and techniques. We also discuss different challenges and issues pertaining to Web Data Mining research.
Weighted graph algorithms with Python

Python implementation of selected weighted graph algorithms is presented. The minimal graph interface is defined together with several classes implementing this interface. Graph nodes can be any hashable Python objects. Directed edges are instances of the Edge class. Graphs are instances of the Graph class. It is based on the adjacency-list representation, but with fast lookup of nodes and neighbors (dict-of-dict structure). Other implementations of this class are also possible. In this work, many algorithms are implemented using a unified approach. There are separate classes and modules devoted to different algorithms. Three algorithms for finding a minimum spanning tree are implemented: the Bor ˙ uvka's algorithm, the Prim's algorithm (three implementations), and the Kruskal's algorithm. Three algorithms for solving the single-source shortest path problem are implemented: the dag shortest path algorithm, the Bellman-Ford algorithm, and the Dijkstra's algorithm (two implementations). Two algorithms for solving all-pairs shortest path problem are implemented: the Floyd-Warshall algorithm and the Johnson's algorithm. All algorithms were tested by means of the unittest module, the Python unit testing framework. Additional computer experiments were done in order to compare real and theoretical computational complexity. The source code is available from the public GitHub repository.
Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking

Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for research. We present a benchmark for Multiple Object Tracking launched in the late 2014, with the goal of creating a framework for the standardized evaluation of multiple object tracking methods. This paper collects the two releases of the benchmark made so far, and provides an in-depth analysis of almost 50 state-of-the-art trackers that were tested on over 11000 frames. We show the current trends and weaknesses of multiple people tracking methods, and provide pointers of what researchers should be focusing on to push the field forward.
Assessing forecast model performance in an ERP environment

The paper aims to describe and apply a commercially oriented method of forecast performance measurement (cost of forecast error – CFE) and to compare the results with commonly adopted statistical measures of forecast accuracy in an enterprise resource planning (ERP) environment.  The study adopts a quantitative methodology to evaluate the nine forecasting models (two moving average and seven exponential smoothing) of SAP’s ERP system. Event management adjustment and fitted smoothing parameters are also assessed. SAP is the largest European software enterprise and the third largest in the world, with headquarters in Walldorf, Germany.  The findings of the study support the adoption of CFE as a more relevant commercial decision-making measure than commonly applied statistical forecast measures.  The findings of the study provide forecast model selection guidance to SAP’s 12 þ million worldwide users. However, the CFE metric can be adopted in any commercial forecasting situation.  This study is the first published cost assessment of SAP’s forecasting models.