nils-herrmann/clean_scirepeval huggingface dataset:
The Rosenberg Self-Esteem Scale: translation and validation in university students.

The aim of this study was to translate into Spanish and to validate the Rosenberg Self-Esteem Scale (RSES), completed by 420 university students. Confirmatory factor analysis revealed that the model that best fit the data, both in the total sample and in the male and female subsamples, was the one-factor structure with method effects associated with positively worded items. The results indicated high, positive correlations between self-esteem and the five dimensions of self-concept. The scale showed satisfactory levels of internal consistency and temporal stability over a four-week period. Lastly, gender differences were obtained. These findings support the use of the RSES for the assessment of self-esteem in higher education.
MRI in the diagnosis of diphallia

Diphallia or duplication of the penis is an extremely rare but well-documented anomaly. According to the presence of one or two corpora cavernosa in each of the penises, diphallia is classified into two major groups of bifid phallus or true diphallia, respectively. We report a 5-year-old boy with duplication of the penis. Significant separation of the penises and their morphology made them appear as true complete phalluses, but MRI established the definitive diagnosis of bifid phallus by demonstrating the presence of one corpus cavernosum in each penis. MRI is a valuable method for achieving the accurate diagnosis of these anomalies and associated malformations. It also provides the appropriate knowledge regarding anatomical detail and assists the surgeon in decision making and preoperative planning for the optimal surgical approach.
Robust Cross-Domain Sentiment Analysis for Low-Resource Languages

Friday June 27, 2014 (8:30) Opening Remarks (8:35) Invited talk: Dr. Saif Mohammad Words: Evaluative, Emotional, Colourful, Musical! Saif Mohammad (9:10) Session 1: Cross-domain and Multilingual Sentiment Analysis 9:10 Robust Cross-Domain Sentiment Analysis for Low-Resource Languages Jakob Elming, Barbara Plank and Dirk Hovy 9:35 An Investigation for Implicatures in Chinese : Implicatures in Chinese and in English are similar ! Lingjia Deng and Janyce Wiebe 10:00 Inducing Domain-specific Noun Polarity Guided by Domain-independent Polarity Preferences of Adjectives Manfred Klenner, Michael Amsler and Nora Hollenstein 10:15 Aspect-Level Sentiment Analysis in Czech Josef Steinberger, Tomáš Brychcín and Michal Konkol (10:30) Break (10:50) Invited talk: Dr. Myle Ott 10:50 Linguistic Models of Deceptive Opinion Spam Myle Ott
THE REPRESENTING BRAIN : NEURAL CORRELATES OF MOTOR INTENTION AND IMAGERY

This target article concerns how motor actions are neurally represented and coded. Action planning and motor preparation can be studied using motor imagery. A close functional equivalence between motor imagery and motor preparation is suggested by the positive effects of imagining movements on motor learning, the similarity between the neural structures involved, and the similar physiological correlates observed in both imagining and preparing. The content of motor representations can be inferred from motor images at a macroscopic level: from global aspects of the action (the duration and amount of effort involved) and from the motor rules and constraints which predict the spatial path and kinematics of movements. A microscopic neural account of the represenation of object-oriented action is described. Object attributes are processed in different neural pathways depending on the kind of task the subject is performing. During object-oriented action, a pragmatic representation is activated in which object affordances are transformed into specific motor schemata independently of other tasks such as object recognition. Animal as well as clinical data implicate posterior parietal and premotor cortical areas in schema instantiation. A mechanism is proposed that is able to encode the desired goal of the action and is applicable to different levels of representational organization.
Training Generative Adversarial Networks with Binary Neurons by End-to-end Backpropagation

We propose the BinaryGAN, a novel generative adversarial network (GAN) that uses binary neurons at the output layer of the generator. We employ the sigmoidadjusted straight-through estimators to estimate the gradients for the binary neurons and train the whole network by end-to-end backpropogation. The proposed model is able to directly generate binary-valued predictions at test time. We implement such a model to generate binarized MNIST digits and experimentally compare the performance for different types of binary neurons, GAN objectives and network architectures. Although the results are still preliminary, we show that it is possible to train a GAN that has binary neurons and that the use of gradient estimators can be a promising direction for modeling discrete distributions with GANs. For reproducibility, the source code is available at https://github.com/salu133445/binarygan.
Computer aided inbetweening

The production of inbetweens is a tedious task for animators and a complicated one for algorithms. In this paper, an algorithm for computer aided inbetweening and its integration in a pen-based graphical user interface are presented.The algorithm is layer-based, assuming an invariant layering order. It is applicable to animations in a style similar to paper cut out, in which the drawings on the cut-out pieces are inbetweened as well. The content of each key drawing is analysed and classified into strokes, chains of strokes and relations that hold among them. Rules decide what parts of different drawings may be matched. These rules specify allowed changes between relations in key drawings. A cost function based approach determines the correct matching of strokes. Generated animation paths between corresponding strokes determine the resulting inbetweens.To hold for possible mismatchings and to allow for artistic control over the results, the inbetweening algorithm is embedded in a free form graphic user interface. Thus artists are enabled to focus on the part of the inbetweening task computers are not able to solve.
Influence of Some Parameters on the Effectiveness of Induction Heating

We have investigated the effectiveness of heating conductive plates by induction in low frequencies analytically and numerically, in relation to the shape of the plate, the area of the region exposed to the magnetic flux, and the position of the exposed region with respect to the center of the plate. We considered both uniform and nonuniform magnetic fields. For plates with equivalent area exposed to the same uniform magnetic flux, the one with the most symmetrical shape is heated most effectively. If a coil is employed for the excitation, the results depend on the shape of the plate, the shape of the coil section, and the coil lift-off. When only the central region of a plate is exposed to a variable magnetic flux, there is a specific value of the exposed area for which the power dissipated in the plate material reaches a maximum. The most effective heating of a plate partially exposed occurs when the axis of the exciting coil is at the plate center.
LINE-mediated retrotransposition of marked Alu sequences

Alu elements are the most successful transposons in humans. They are 300-bp non-coding sequences transcribed by RNA polymerase III (Pol III) and are expected to retrotranspose with the aid of reverse transcriptases of cellular origin. We previously showed that human LINEs can generate cDNA copies of any mRNA transcript by means of a retroposition process involving reverse transcription and integration by the LINE-encoded endonuclease and reverse transcriptase. Here we show mobility of marked Alu sequences in human HeLa cells with the canonical features of a retrotransposition process, including splicing out of an autocatalytic intron introduced into the marked sequence, target site duplications of varying lengths and integrations into consensus A-rich sequences. We further show that the poly-A stretch at the Alu 3′ end is essential for mobility, that LINEs are required for transposition and that the rate of retroposition is 100–1,000 times higher for Alu transcripts than for control mRNAs, thus accounting for the high mutational activity of these elements observed in humans.
Efficient projections onto the l1-ball for learning in high dimensions

We describe efficient algorithms for projecting a vector onto the l1-ball. We present two methods for projection. The first performs exact projection in O(n) expected time, where n is the dimension of the space. The second works on vectors k of whose elements are perturbed outside the l1-ball, projecting in O(k log(n)) time. This setting is especially useful for online learning in sparse feature spaces such as text categorization applications. We demonstrate the merits and effectiveness of our algorithms in numerous batch and online learning tasks. We show that variants of stochastic gradient projection methods augmented with our efficient projection procedures outperform interior point methods, which are considered state-of-the-art optimization techniques. We also show that in online settings gradient updates with l1 projections outperform the exponentiated gradient algorithm while obtaining models with high degrees of sparsity.
All-printed diode operating at 1.6 GHz.

Printed electronics are considered for wireless electronic tags and sensors within the future Internet-of-things (IoT) concept. As a consequence of the low charge carrier mobility of present printable organic and inorganic semiconductors, the operational frequency of printed rectifiers is not high enough to enable direct communication and powering between mobile phones and printed e-tags. Here, we report an all-printed diode operating up to 1.6 GHz. The device, based on two stacked layers of Si and NbSi2 particles, is manufactured on a flexible substrate at low temperature and in ambient atmosphere. The high charge carrier mobility of the Si microparticles allows device operation to occur in the charge injection-limited regime. The asymmetry of the oxide layers in the resulting device stack leads to rectification of tunneling current. Printed diodes were combined with antennas and electrochromic displays to form an all-printed e-tag. The harvested signal from a Global System for Mobile Communications mobile phone was used to update the display. Our findings demonstrate a new communication pathway for printed electronics within IoT applications.
Application of Artificial Neural Networks to Multiple Criteria Inventory Classification

Original scientific paper Inventory classification is a very important part of inventory control which represents the technique of operational research discipline. A systematic approach to the inventory control and classification may have a significant influence on company competitiveness. The paper describes the results obtained by investigating the application of neural networks in multiple criteria inventory classification. Various structures of a back-propagation neural network have been analysed and the optimal one with the minimum Root Mean Square error selected. The predicted results are compared to those obtained by the multiple criteria classification using the analytical hierarchy process.
On the achievable throughput of a multiantenna Gaussian broadcast channel

A Gaussian broadcast channel (GBC) with r single-antenna receivers and t antennas at the transmitter is considered. Both transmitter and receivers have perfect knowledge of the channel. Despite its apparent simplicity, this model is, in general, a nondegraded broadcast channel (BC), for which the capacity region is not fully known. For the two-user case, we find a special case of Marton's (1979) region that achieves optimal sum-rate (throughput). In brief, the transmitter decomposes the channel into two interference channels, where interference is caused by the other user signal. Users are successively encoded, such that encoding of the second user is based on the noncausal knowledge of the interference caused by the first user. The crosstalk parameters are optimized such that the overall throughput is maximum and, surprisingly, this is shown to be optimal over all possible strategies (not only with respect to Marton's achievable region). For the case of r>2 users, we find a somewhat simpler choice of Marton's region based on ordering and successively encoding the users. For each user i in the given ordering, the interference caused by users j>i is eliminated by zero forcing at the transmitter, while interference caused by users j<i is taken into account by coding for noncausally known interference. Under certain mild conditions, this scheme is found to be throughput-wise asymptotically optimal for both high and low signal-to-noise ratio (SNR). We conclude by providing some numerical results for the ergodic throughput of the simplified zero-forcing scheme in independent Rayleigh fading.
Assessing patient suitability for short-term cognitive therapy with an interpersonal focus

In the current study, the development and initial validation of the Suitability for Short-Term Cognitive Therapy (SSCT) interview procedure is reported. The SSCT is an interview and rating procedure designed to evaluate the potential appropriateness of patients for short-term cognitive therapy with an interpersonal focus. It consists of a 1-hour, semistructured interview, focused on eliciting information from the patient relevant to nine selection criteria. The procedures involved in the development of this scale are described in detail, and preliminary evidence suggesting that the selection criteria can be rated reliably is presented. In addition, data indicating that scores on the SSCT scale predict the outcome of short-term cognitive therapy on multiple dependent measures, including both therapist and patient perspectives, are reported. It is concluded that the SSCT is a potentially useful scale for identifying patients who may be suitable, or unsuitable, for the type of short-term cognitive therapy administered in the present study.
Intuitive visualization of Pareto Frontier for multi-objective optimization in n-dimensional performance space

A visualization methodology is presented in which a Pareto Frontier can be visualized in an intuitive and straightforward manner for an n-dimensional performance space. Based on this visualization, it is possible to quickly identify ‘good’ regions of the performance and optimal design spaces for a multi-objective optimization application, regardless of space complexity. Visualizing Pareto solutions for more than three objectives has long been a significant challenge to the multi-objective optimization community. The Hyper-space Diagonal Counting (HSDC) method described here enables the lossless visualization to be implemented. The proposed method requires no dimension fixing. In this paper, we demonstrate the usefulness of visualizing n-f space (i.e. for more than three objective functions in a multiobjective optimization problem). The visualization is shown to aid in the final decision of what potential optimal design point should be chosen amongst all possible Pareto solutions.
Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications

Consumer-grade range cameras such as the Kinect sensor have the potential to be used in mapping applications where accuracy requirements are less strict. To realize this potential insight into the geometric quality of the data acquired by the sensor is essential. In this paper we discuss the calibration of the Kinect sensor, and provide an analysis of the accuracy and resolution of its depth data. Based on a mathematical model of depth measurement from disparity a theoretical error analysis is presented, which provides an insight into the factors influencing the accuracy of the data. Experimental results show that the random error of depth measurement increases with increasing distance to the sensor, and ranges from a few millimeters up to about 4 cm at the maximum range of the sensor. The quality of the data is also found to be influenced by the low resolution of the depth measurements.
An integrated charger using segmented windings of interior permanent magnet motor based on 3 phase with 9 windings

Connecting the plug of electric vehicles (EVs) into the grid source directly means to charge the battery. The electric power generated from the grid goes through the electric motor, crosses the bidirectional inverter used as three-boost rectifier and is received by the load (battery). An innovative fast-integrated charger based on segmented windings of permanent magnet synchronous motor (PMSM) is introduced for plug-in electric vehicles (PEV). The three-phase with nine windings of PMSM is used during the traction and charging modes. Three (or single) phases of grid source are directly connected to the neutral points of segmented PMSM windings to provide high power density to the battery. The system configuration and operation of charging mode are explained in detail for proposed integrated on-board charger. The simulation models are designed by using Ansoft Maxwell, Ansys Maxwell circuit editor, and Matlab/Simulink softwares, and simulation results are presented to verify the performance of the proposed system. In addition, the experimental setup of the integrated system is under progress.
Changes of resting state brain networks in amyotrophic lateral sclerosis

The defining feature of amyotrophic lateral sclerosis is degeneration of upper and lower motor neurons but extramotor involvement, evidenced for example by executive dysfunction, has also been demonstrated. Here we employed a novel functional imaging approach, the analysis of resting state activity, followed by the definition of functionally connected brain networks by independent component analysis (ICA) to assess differences between ALS patients (n=20) and healthy controls (n=20). ICA analysis revealed 5 typical brain networks among which the so-called default mode network and the sensori-motor network showed distinct differences between patients and controls. The default mode network showed less activation in patients in several regions including the ventral anterior cingulate cortex, posterior cingulate cortex and the left and right inferior parietal cortex, regions that have been linked previously to executive functions. The sensori-motor network showed group differences in the premotor cortex. We propose that resting state analysis affords a new and simple means to assess disease-related neurofunctional alterations in widespread brain networks. A decisive advantage is that no task is demanded from the subjects and, thus, the problem of differential task difficulty and effort between groups is circumvented.
Stacking classifiers for anti-spam filtering of e-mail

We evaluate empirically a scheme for combining classifiers, known as stacked generalization, in the context of anti-spam filtering, a novel cost-sensitive application of text categorization. Unsolicited commercial email, or “spam”, floods mailboxes, causing frustration, wasting bandwidth, and exposing minors to unsuitable content. Using a public corpus, we show that stacking can improve the efficiency of automatically induced anti-spam filters, and that such filters can be used in reallife applications.
A performance prediction model for the CUDA GPGPU platform

The significant growth in computational power of modern Graphics Processing Units (GPUs) coupled with the advent of general purpose programming environments like NVIDIA's CUDA, has seen GPUs emerging as a very popular parallel computing platform. Till recently, there has not been a performance model for GPGPUs. The absence of such a model makes it difficult to definitively assess the suitability of the GPU for solving a particular problem and is a significant impediment to the mainstream adoption of GPUs as a massively parallel (super)computing platform. In this paper we present a performance prediction model for the CUDA GPGPU platform. This model encompasses the various facets of the GPU architecture like scheduling, memory hierarchy, and pipelining among others. We also perform experiments that demonstrate the effects of various memory access strategies. The proposed model can be used to analyze pseudo code for a CUDA kernel to obtain a performance estimate, in a way that is similar to performing asymptotic analysis. We illustrate the usage of our model and its accuracy with three case studies: matrix multiplication, list ranking, and histogram generation.
Computer generated animation of faces

This paper describes the representation, animation and data collection techniques that have been used to produce "realistic" computer generated half-tone animated sequences of a human face changing expression. It was determined that approximating the surface of a face with a polygonal skin containing approximately 250 polygons defined by about 400 vertices is sufficient to achieve a realistic face. Animation was accomplished using a cosine interpolation scheme to fill in the intermediate frames between expressions. This approach is good enough to produce realistic facial motion. The three-dimensional data used to describe the expressions of the face was obtained photogrammetrically using pairs of photographs.
DS-Index: Ranking Authors Distinctively in an Academic Network

The impact and productivity of researchers are assessed using bibliometric parameters, such as the number of publications and citation analysis. A number of indices exist that use these parameters, but almost all of them overlook citation pattern of the researchers, which results in assigning the same index value to the two different authors with different citation patterns. In this paper, a new index called DS-index is proposed, which differentiates among the authors having even a very small change in the citation pattern of their publications. It uniquely identifies the different index values and thus the proper ranking order for authors. The index is applied to the self-developed large DBLP data set having publication data of over 50 years. The results compared with the existing indices using the standard performance evaluation measures confirm that the proposed index performs better by ranking the authors in a distinctive order.
Virtual channel space-time processing with dual-polarization discrete lens antenna arrays

This paper discusses channel capacity improvements with optimal multiantenna wireless communications using a discrete lens array (DLA) at the analog front end. It is first shown that optimal signaling over multiantenna channels via virtual spatial models (VSMs) has a direct hardware correspondence in a multibeam spatially fed antenna architecture. A multibeam dual-polarized DLA is designed and characterized, and the effects of measured antenna radiation patterns on calculated capacity are quantified. In particular, the paper examines: 1) effects of cross-polarization for a dual-polarized multibeam array; 2) effects of beam overlap in a multibeam array; and 3) effects of gain variations for different scan angles. It is demonstrated that adding an ideal dual-polarized array achieves a capacity improvement of up to 88% compared to the single-polarization case. In practice, the two polarizations are weakly coupled, and the measured cross-polarization level of -14 dB decreases the capacity of the system by less than 2%. Furthermore, it is shown that a dual-polarized multibeam DLA is nearly insensitive to polarization rotation in a rich scattering environment, whereas single-polarized antennas incur a capacity degradation of up to 24%. Finally, capacity decrease due to measured nonuniform amplitudes on the antenna aperture is shown to be less than 10%.