nils-herrmann/clean_scirepeval huggingface dataset:
Prism adaptation and aftereffect: specifying the properties of a procedural memory system.

Prism adaptation, a form of procedural learning, is a phenomenon in which the motor system adapts to new visuospatial coordinates imposed by prisms that displace the visual field. Once the prisms are withdrawn, the degree and strength of the adaptation can be measured by the spatial deviation of the motor actions in the direction opposite to the visual displacement imposed by the prisms, a phenomenon known as aftereffect. This study was designed to define the variables that affect the acquisition and retention of the aftereffect. Subjects were required to throw balls to a target in front of them before, during, and after lateral displacement of the visual field with prismatic spectacles. The diopters of the prisms and the number of throws were varied among different groups of subjects. The results show that the adaptation process is dependent on the number of interactions between the visual and motor system, and not on the time spent wearing the prisms. The results also show that the magnitude of the aftereffect is highly correlated with the magnitude of the adaptation, regardless of the diopters of the prisms or the number of throws. Finally, the results suggest that persistence of the aftereffect depends on the number of throws after the adaptation is complete. On the basis of these results, we propose that the system underlying this kind of learning stores at least two different parameters, the contents (measured as the magnitude of displacement) and the persistence (measured as the number of throws to return to the baseline) of the learned information.
Activation of miR-9 by human papillomavirus in cervical cancer

Cervical cancer is the third most common cancer in women worldwide, leading to about 300,000 deaths each year. Most cervical cancers are caused by human papillomavirus (HPV) infection. However, persistent transcriptional activity of HPV oncogenes, which indicates active roles of HPV in cervical cancer maintenance and progression, has not been well characterized. Using our recently developed assays for comprehensive profiling of HPV E6/E7 transcripts, we have detected transcriptional activities of 10 high-risk HPV strains from 87 of the 101 cervical tumors included in the analysis. These HPV-positive patients had significantly better survival outcome compared with HPV-negative patients, indicating HPV transcriptional activity as a favorable prognostic marker for cervical cancer. Furthermore, we have determined microRNA (miRNA) expression changes that were correlated with tumor HPV status. Our profiling and functional analyses identified miR-9 as the most activated miRNA by HPV E6 in a p53-independent manner. Further target validation and functional studies showed that HPV-induced miR-9 activation led to significantly increased cell motility by downregulating multiple gene targets involved in cell migration. Thus, our work helps to understand the molecular mechanisms as well as identify potential therapeutic targets for cervical cancer and other HPV-induced cancers.
Time Management in The High Level Architecture

Time management is required in simulations to ensure temporal aspects of the system under investigation are correctly reproduced by the simulation model. This paper describes the time management services that have been defined in the High Level Architecture. The need for time management services is discussed, as well as design rationales that lead to the current definition of the HLA time management services. These services are described, highlighting information that must flow between federates and the Runtime Infrastructure (RTI) software in order to efficiently implement time management algorithms.
Composition of amino acids in feed ingredients for animal diets

Dietary amino acids (AA) are crucial for animal growth, development, reproduction, lactation, and health. However, there is a scarcity of information regarding complete composition of “nutritionally nonessential AA” (NEAA; those AA which can be synthesized by animals) in diets. To provide a much-needed database, we quantified NEAA (including glutamate, glutamine, aspartate, and asparagine) in feed ingredients for comparison with “nutritionally essential AA” (EAA; those AA whose carbon skeletons cannot be formed by animals). Except for gelatin and feather meal, animal and plant ingredients contained high percentages of glutamate plus glutamine, branched-chain AA, and aspartate plus asparagine, which were 10–32, 15–25, and 8–14% of total protein, respectively. In particular, leucine and glutamine were most abundant in blood meal and casein (13% of total protein), respectively. Notably, gelatin, feather meal, fish meal, meat and bone meal, and poultry byproduct had high percentages of glycine, proline plus hydroxyproline, and arginine, which were 10–35, 9.6–35, and 7.2–7.9% of total protein, respectively. Among plant products, arginine was most abundant in peanut meal and cottonseed meal (14–16% of total protein), whereas corn and sorghum had low percentages of cysteine, lysine, methionine, and tryptophan (0.9–3% of total protein). Overall, feed ingredients of animal origin (except for gelatin) are excellent sources of NEAA and EAA for livestock, avian, and aquatic species, whereas gelatin provides highest amounts of arginine, glycine, and proline plus hydroxyproline. Because casein, corn, soybean, peanut, fish, and gelatin are consumed by children and adults, our findings also have important implications for human nutrition.
Child and adolescent anxiety disorders and early attachment.

The major aim of this research is to determine whether infants who were anxiously/resistantly attached in infancy develop more anxiety disorders during childhood and adolescence than infants who were securely attached. To test different theories of anxiety disorders, newborn temperament and maternal anxiety were included in multiple regression analyses.



Infants participated in Ainsworth's Strange Situation Procedure at 12 months of age. The Schedule for Affective Disorders and Schizophrenia for School-Age Children was administered to the 172 children when they reached 17.5 years of age. Maternal anxiety and infant temperament were assessed near the time of birth.



The hypothesized relation between anxious/resistant attachment and later anxiety disorders was confirmed. No relations with maternal anxiety and the variables indexing temperament were discovered, except for a composite score of nurses' ratings designed to access "high reactivity," and the Neonatal Behavioral Assessment Scale clusters of newborn range of state and inability to habituate to stimuli. Anxious/resistant attachment continued to significantly predict child/adolescent anxiety disorders, even when entered last, after maternal anxiety and temperament, in multiple regression analyses.



The attachment relationship appears to play an important role in the development of anxiety disorders. Newborn temperament may also contribute.
Depth Imaging for Navigation in Challenging Environments

Depth sensors for robust navigation must measure scenes in darkness, bright light, and in scattering media. Scanning LIDAR devices are the most robust to these conditions, but capture sparse measurements, are slow, and expensive. Consumer depth cameras, on the other hand, are inexpensive and produce dense, high-rate depth measurements, but fail in bright ambient light, and are susceptible to effects of global light transport. Epipolar imaging devices combine the robustness of scanning LIDAR with the speed, sampling density, and potential price-point of consumer depth cameras, but no longer capture images with a global shutter. Due to the line-by-line sampling required by epipolar imaging, these type of depth cameras produce rolling shutter artifacts and are not amenable to traditional localization and mapping methods that pertain to LIDAR and RGB-D sensors. The goal of this thesis is to develop active illumination depth cameras, sensing methodologies, and navigation algorithms that produce accurate and detailed maps in real-world conditions that challenge traditional approaches. This thesis will exploit the unique and robust sensing characteristics of custom-designed depth cameras with novel sensing and mapping algorithms to generate reliable pose and map estimates in challenging environments. Of particular interest is their performance in uncontrived settings including those with strong ambient light, complete darkness, smoke, and underwater. There are three major accomplishments to date. The first is an empirical examination and dataset collection of common robotic sensing technologies deployed in smoke-filled environments. The second is a family of active illumination depth cameras based on epipolar imaging that efficiently capture depth images, are robust to global light transport, and work outdoors in bright sunlight. The third contribution is a prototype active illumination device that only images light from a programmable 3D surface. This device uses triangulation-based range-gating to create steerable curtains of light in the scene that provide inherent obstacle detection and are much less susceptible to scattering media than traditional active imaging techniques. Proposed research includes developing a continuous-time, spline-based localization and mapping method that overcomes the innate motion warp of rolling shutter epipolar depth cameras to sample the scene and generate dense maps and accurate pose estimates. In addition, a revised hardware prototype of the light curtain device will be developed that has improved frame-rate, range, and reliability in scattering media. These devices and methods will be evaluated in various challenging environments including smoke-filled, underwater, and bright outdoor scenes. The work proposed in this thesis could find applications in a diverse set of fields including mobile robotics, autonomous vehicles, and underwater inspection.
ClusterGAN : Latent Space Clustering in Generative Adversarial Networks

Generative Adversarial networks (GANs) have obtained remarkable success in many unsupervised learning tasks and unarguably, clustering is an important unsupervised learning problem. While one can potentially exploit the latentspace back-projection in GANs to cluster, we demonstrate that the cluster structure is not retained in the GAN latent space. In this paper, we propose ClusterGAN as a new mechanism for clustering using GANs. By sampling latent variables from a mixture of one-hot encoded variables and continuous latent variables, coupled with an inverse network (which projects the data to the latent space) trained jointly with a clustering specific loss, we are able to achieve clustering in the latent space. Our results show a remarkable phenomenon that GANs can preserve latent space interpolation across categories, even though the discriminator is never exposed to such vectors. We compare our results with various clustering baselines and demonstrate superior performance on both synthetic and real datasets.
The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology.

During the First International EEG Congress, London in 1947, it was recommended that Dr. Herbert H. Jasper study methods to standardize the placement of electrodes used in EEG (Jasper 1958). A report with recommendations was to be presented to the Second International Congress in Paris in 1949. The electrode placement systems in use at various centers were found to be similar, with only minor differences, although their designations, letters and numbers were entirely different. Dr. Jasper established some guidelines which would be established in recommending a speci®c system to the federation and these are listed below.
Expressive Singing Synthesis Based on Unit Selection for the Singing Synthesis Challenge 2016

Sample and statistically based singing synthesizers typically require a large amount of data for automatically generating expressive synthetic performances. In this paper we present a singing synthesizer that using two rather small databases is able to generate expressive synthesis from an input consisting of notes and lyrics. The system is based on unit selection and uses the Wide-Band Harmonic Sinusoidal Model for transforming samples. The first database focuses on expression and consists of less than 2 minutes of free expressive singing using solely vowels. The second one is the timbre database which for the English case consists of roughly 35 minutes of monotonic singing of a set of sentences, one syllable per beat. The synthesis is divided in two steps. First, an expressive vowel singing performance of the target song is generated using the expression database. Next, this performance is used as input control of the synthesis using the timbre database and the target lyrics. A selection of synthetic performances have been submitted to the Interspeech Singing Synthesis Challenge 2016, in which they are compared to other competing systems.
ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases

ConvNets and ImageNet have driven the recent success of deep learning for image classification. However, the marked slowdown in performance improvement combined with the lack of robustness of neural networks to adversarial examples and their tendency to exhibit undesirable biases question the reliability of these methods. This work investigates these questions from the perspective of the end-user by using human subject studies and explanations. The contribution of this study is threefold. We first experimentally demonstrate that the accuracy and robustness of ConvNets measured on ImageNet are vastly underestimated. Next, we show that explanations can mitigate the impact of misclassified adversarial examples from the perspective of the end-user. We finally introduce a novel tool for uncovering the undesirable biases learned by a model. These contributions also show that explanations are a valuable tool both for improving our understanding of ConvNets’ predictions and for designing more reliable models.
The impact of personality traits on users' information-seeking behavior

Although personality traits may influence information-seeking behavior, little is known about this topic. This study explored the impact of the Big Five personality traits on human online information seeking. For this purpose, it examined changes in eye-movement behavior in a sample of 75 participants (36 male and 39 female; age: 22–39 years; experience conducting online searches: 5–12 years) across three types of information-seeking tasks – factual, exploratory, and interpretive. The International Personality Item Pool Representation of the NEO PI-R TM (IPIP-NEO) was used to assess the participants’ personality profile. Hierarchical cluster analysis was used to categorize participants based on their personality traits. A three cluster solution was found (cluster one consists of participants who scored high in conscientiousness; cluster two consists of participants who scored high in agreeableness; and cluster three consists of participants who scored high in extraversion). Results revealed that individuals high in conscientiousness performed fastest in most information-seeking tasks, followed by those high in agreeableness and extraversion. This study has important practical implications for intelligent human – computer interfaces, personalization, and related applications.
Topology, connectedness, and modal logic

This paper presents a survey of topological spatial logics, taking as its point of departure the interpretation of the modal logic S4 due to McKinsey and Tarski. We consider the effect of extending this logic with the means to represent topological connectedness, focusing principally on the issue of computational complexity. In particular, we draw attention to the special problems which arise when the logics are interpreted not over arbitrary topological spaces, but over (low-dimensional) Euclidean spaces.
Deep fusion of hyperspectral and LiDAR data for thematic classification

Recently, the fusion of hyperspectral and light detection and ranging (LiDAR) data has obtained a great attention in the remote sensing community. In this paper, we propose a new feature fusion framework using deep neural network (DNN). The proposed framework employs a novel 3D convolutional neural network (CNN) to extract the spectral-spatial features of hyperspectral data, a deep 2D CNN to extract the elevation features of LiDAR data, and then a fully connected deep neural network to fuse the extracted features in the previous CNNs. Through the aforementioned three deep networks, one can extract the discriminant and invariant features of hyperspectral and LiDAR data. At last, logistic regression is used to produce the final classification results. The experimental results reveal that the proposed deep fusion model provides competitive results. Furthermore, the proposed deep fusion idea opens a new window for future research.
MDIS cloth system: virtual reality technology for firefighter training

Fire accidents can cause numerous casualties and heavy property losses, especially, in petrochemical industry, such accidents are likely to cause secondary disasters. However, common fire drill training would cause loss of resources and pollution. We designed a multi-dimensional interactive somatosensory (MDIS) cloth system based on virtual reality technology to simulate fire accidents in petrochemical industry. It provides a vivid visual and somatosensory experience. A thermal radiation model is built in a virtual environment, and it could predict the destruction radius of a fire. The participant position changes are got from Kinect, and shown in virtual environment synchronously. The somatosensory cloth, which could both heat and refrigerant, provides temperature feedback based on thermal radiation results and actual distance. In this paper, we demonstrate the details of the design, and then verified its basic function. Heating deviation from model target is lower than 3.3 °C and refrigerant efficiency is approximately two times faster than heating efficiency.
Dynamic modeling and stabilization of wheeled mobile robot

This paper presents the dynamic modeling of a nonholonomic mobile robot and the dynamic stabilization problem. The dynamic model is based on the kinematic one including nonholonomic constraints. The proposed control strategy allows to solve the control problem using linear controllers and only requires the robot localization coordinates. This strategy was tested by simulation using Matlab-Simulink.  Mobile robot, kinematic and dynamic modeling, simulation, point stabilization problem.
Phylogeny of the bee genus Lasioglossum ( Hymenoptera : Halictidae ) based on mitochondrial COI sequence data

The bee genus Lasioglossum Curtis is a model taxon for studying the evolutionary origins of and reversals in eusociality. This paper presents a phylogenetic analysis of Lasioglossum species and subgenera based on a data set consisting of 1240 bp of the mitochondrial cytochrome oxidase I (COI) gene for seventy-seven taxa (sixty-six ingroup and eleven outgroup taxa). Maximum parsimony was used to analyse the data set (using PAUP*4.0) by a variety of weighting methods, including equal weights, a priori weighting and a posteriori weighting. All methods yielded roughly congruent results. Michener's Hemihalictus series was found to be monophyletic in all analyses but one, while his Lasioglossum series formed a basal, paraphyletic assemblage in all analyses but one. Chilalictus was consistently found to be a basal taxon of Lasioglossum sensu lato and Lasioglossum sensu stricto was found to be monophyletic. Within the Hemihalictus series, major lineages included Dialictus + Paralictus, the acarinate Evylaeus + Hemihalictus + Sudila and the carinate Evylaeus + Sphecodogastra. Relationships within the Hemihalictus series were highly stable to altered weighting schemes, while relationships among the basal subgenera in the Lasioglossum series (Lasioglossum s.s., Chilalictus, Parasphecodes and Ctenonomia) were unclear. The social parasite of Dialictus, Paralictus, is consistently and unambiguously placed well within Dialictus, thus rendering Dialictus paraphyletic. The implications of this for understanding the origins of social parasitism are discussed.
Serratus anterior and lower trapezius muscle activities during multi-joint isotonic scapular exercises and isometric contractions.

Proper scapular function during humeral elevation, such as upward rotation, external rotation, and posterior tilting of the scapula, is necessary to prevent shoulder injury. However, the appropriate intensity of rehabilitation exercise for the periscapular muscles has yet to be clarified.



To identify the serratus anterior, lower trapezius, infraspinatus, and posterior deltoid muscle activities during 2 free-motion exercises using 3 intensities and to compare these muscle activities with isometric contractions during quadruped shoulder flexion and external rotation and abduction of the glenohumeral joint.



Cross-sectional study.



Health Science Laboratory.



A total of 16 uninjured, healthy, active, male college students (age = 19.5 ± 1.2 years, height = 173.1 ± 6.5 cm, weight = 68.8 ± 6.6 kg).



Mean electromyographic activity normalized by the maximal voluntary isometric contraction was analyzed across 3 intensities and 5 exercises. Intraclass correlation coefficients were calculated for electromyographic activity of the 4 muscles in each free-motion exercise.



Significant interactions in electromyographic activity were observed between intensities and exercises (P < .05). The quadruped shoulder-flexion exercise activated all 4 muscles compared with other exercises. Also, the modified robbery free-motion exercise activated the serratus anterior, lower trapezius, and infraspinatus compared with the lawn-mower free-motion exercise. However, neither exercise showed a difference in posterior deltoid electromyographic activity.



Three intensities exposed the nature of the periscapular muscle activities across the different exercises. The free-motion exercise in periscapular muscle rehabilitation may not modify serratus anterior, lower trapezius, and infraspinatus muscle activities unless knee-joint extension is limited.
Specification and design of an industrial manufacturing middleware

The European innovation project PERFoRM (Production harmonizEd Reconfiguration of Flexible Robots and Machinery) is aiming for a harmonized integration of research results in the area of flexible and reconfigurable manufacturing systems. Based on the cyber-physical system (CPS) paradigm, existing technologies and concepts are researched and integrated in an architecture which is enabling the application of these new technologies in real industrial environments. To implement such a flexible cyber-physical system, one of the core requirements for each involved component is a harmonized communication, which enables the capability to collaborate with each other in an intelligent way. But especially when integrating multiple already existing production components into such a cyber-physical system, one of the major issues is to deal with the various communication protocols and data representations coming with each individual cyber-physical component. To tackle this issue, the solution foreseen within PERFoRM's architecture is to use an integration platform, the PERFoRM Industrial Manufacturing Middleware, to enable all connected components to interact with each other through the Middleware and without having to implement new interfaces for each. This paper describes the basic requirements of such a Middleware and how it fits into the PERFoRM architecture and gives an overview about the internal design and functionality.