nils-herrmann/clean_scirepeval huggingface dataset:
Numerical simulations of multicomponent ecological models with adaptive methods
Venue: Theoretical Biology and Medical Modelling

BackgroundThe study of dynamic relationship between a multi species models has gained a huge amount of scientific interest over the years and will continue to maintain its dominance in both ecology and mathematical ecology in the years to come due to its practical relevance and universal existence. Some of its emergence phenomena include spatiotemporal patterns, oscillating solutions, multiple steady states and spatial pattern formation.MethodsMany time dependent partial differential equations are found combining low order nonlinear with higher order linear terms. In attempt to obtain a reliable results of such problems, it is desirable to use higher order methods in both space and time. Most computations heretofore are restricted to second order in time due to some difficulties introduced by the combination of stiffness and nonlinearity. Hence, the dynamics of a reaction diffusion models considered in this paper permit the use of two classic mathematical ideas. As a result, we introduce higher order finite difference approximation for the spatial discretization, and advance the resulting system of ODE with a family of exponential time differencing schemes. We present the stability properties of these methods along with the extensive numerical simulations for a number of multi species models.ResultsWhen the diffusivity is small many of the models considered in this paper are found to exhibit a form of localized spatiotemporal patterns. Such patterns are correctly captured in the local analysis of the model equations. An extended 2D results that are in agreement with Turing typical patterns such as stripes and spots, as well as irregular snakelike structures are presented. We finally show that the designed schemes are dynamically consistent.ConclusionThe dynamic complexities of some ecological models are studied by considering their linear stability analysis. Based on the choices of parameters in transforming the system into a dimensionless form, we were able to obtain a well balanced system that is biologically meaningful. The accuracy and reliability of the schemes are justified via the computational results presented for each of the diffusive multi species models.
Learning Temporal Regularity in Video Sequences
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Perceiving meaningful activities in a long video sequence is a challenging problem due to ambiguous definition of 'meaningfulness' as well as clutters in the scene. We approach this problem by learning a generative model for regular motion patterns (termed as regularity) using multiple sources with very limited supervision. Specifically, we propose two methods that are built upon the autoencoders for their ability to work with little to no supervision. We first leverage the conventional handcrafted spatio temporal local features and learn a fully connected autoencoder on them. Second, we build a fully convolutional feed forward autoencoder to learn both the local features and the classifiers as an end to end learning framework. Our model can capture the regularities from multiple datasets. We evaluate our methods in both qualitative and quantitative ways showing the learned regularity of videos in various aspects and demonstrating competitive performance on anomaly detection datasets as an application.
Rosenblatt's Transactional Theory and Its Implementation in the Teaching of Integrated Reading

The transactional theory signifies that both the reader and the text play important roles in the formation of meaning. Meaning is produced by continuous transaction between the reader and the text, employing the meaning potential of the text and the reader's experiential reservoir. While the reader is active in selecting and synthesizing the potential in his reservoir, the text contributes to the shaping of his selection and hypotheses, resulting in an interplay between them. Reading and writing are interrelated skills in which the transactional theory is also applicable. The transactional theory provide some implications for the classroom language instruction. Then, an experience in doing journal writing for an English reading class, as one way to put the theory into practice, is presented.
Autonomous navigation of UAV by using real time model based reinforcement learning
Venue: 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV)

Autonomous navigation in an unknown or uncertain environment is one of the challenging tasks for unmanned aerial vehicles (UAVs) In order to address this challenge, it is necessary to have sophisticated high level control methods that can learn and adapt themselves to changing conditions. One of the most promising frameworks for such a purpose is reinforcement learning. In this paper, a novel model based reinforcement learning algorithm, TEXPLORE, is developed as a high level control method for autonomous navigation of UAVs. The developed approach has been extensively tested with a quadcopter UAV in ROS Gazebo environment. The experimental results show that our method is able to learn an efficient trajectory in a few iterations and perform actions in real time. Moreover, we show that our approach significantly outperforms Q learning based method. To the best of our knowledge, this is the first time that TEXPLORE has been developed to achieve autonomous navigation of UAVs.
Malocclusion in the primary dentition in children with and without deformational plagiocephaly
Venue: Clinical Oral Investigations

ObjectivesAsymmetries of the jaw and orthodontic abnormalities are suspected as long term consequences of positional cranial deformity. But only few data exist on this issue. As plagiocephaly is a common problem in infancy, potential functional impairments should be investigated to initiate appropriate measures if necessary. The aim of our study was to compare the orthodontic situation in primary dentition of children with positional plagiocephaly and children without cranial deformities. children treated by helmet therapy for plagiocephaly and 50 non affected children (age 1.98 5.69 years) were examined in a cross sectional study. Orthodontic parameters of all dimensions were assessed and analyzed.ResultsChildren of the plagiocephalic group showed more often orthodontic alterations compared to the others. Especially the frequencies of a class II malocclusion (36 vs. 14 an edge to edge bite (28 vs. 12 and deviations of the midline (38 vs. 16 were conspicuous. However, none of the differences was significant (p 0.003) Of all observed mandibular asymmetries, 69 appeared as a shift to the contralateral side of the former flattened occipital region.ConclusionPositional head deformity might be associated in some cases with a higher prevalence of occlusal abnormalities in primary dentition.Clinical relevancePositional plagiocephaly interfaces medicine and dentistry. As it is a common disorder, this etiology has to be considered in the prevention and therapy of malocclusion.
Acquisition of Hovering by Actual UAV Using Reinforcement Learning
Venue: 2016 3rd International Conference on Information Science and Control Engineering (ICISCE)

In this study, we applied reinforcement learning to actual quadrotor unmanned aerial vehicles (UAVs) It is expected that UAVs will contribute to multiple fields (e.g. rescue, sports, and entertainment) However, the autonomous control of UAVs is still a difficult problem to solve. We applied reinforcement learning to a UAV to achieve stable hovering. Q learning, a common reinforcement learning method, was used in our study. Several previous studies on learning controllers for UAVs have been conducted. However, most of these studies only carried out computer simulations to verify the effectiveness of the learning method. Conversely, we conducted our experiment using an actual quadrotor UAV. The experimental results demonstrate that the UAV can acquire knowledge to achieve stable hovering over a marker placed on the ground. We also compared the performances of a trained UAV (using the learning method) and a UAV controlled by a PID controller. The result indicates that a trained UAV can hover as stably as one controlled by the PID.
Global gene expression analysis using RNA seq uncovered a new role for SR1/CAMTA3 transcription factor in salt stress
Venue: Scientific reports

Abiotic and biotic stresses cause significant yield losses in all crops. Acquisition of stress tolerance in plants requires rapid reprogramming of gene expression. SR1/CAMTA3, a member of signal responsive transcription factors (TFs) functions both as a positive and a negative regulator of biotic stress responses and as a positive regulator of cold stress induced gene expression. Using high throughput RNA seq, we identified ~3000 SR1 regulated genes. Promoters of about 60% of the differentially expressed genes have a known DNA binding site for SR1, suggesting that they are likely direct targets. Gene ontology analysis of SR1 regulated genes confirmed previously known functions of SR1 and uncovered a potential role for this TF in salt stress. Our results showed that SR1 mutant is more tolerant to salt stress than the wild type and complemented line. Improved tolerance of sr1 seedlings to salt is accompanied with the induction of salt responsive genes. Furthermore, ChIP PCR results showed that SR1 binds to promoters of several salt responsive genes. These results suggest that SR1 acts as a negative regulator of salt tolerance by directly repressing the expression of salt responsive genes. Overall, this study identified SR1 regulated genes globally and uncovered a previously uncharacterized role for SR1 in salt stress response.
Robust object tracking based on local region sparse appearance model
Venue: Neurocomputing

We propose a robust object tracking algorithm based on local region sparse appearance model in this paper. In this algorithm, the object is divided into several sub regions, and the sparse dictionaries are obtained by clustering in each sub region. Therefore spatial structure information of the object can be captured well, and the change of object appearance can be also resisted effectively. First, the object is divided into many small patches. Then the object is divided into several sub regions according to patch distribution again. The establishment of object dictionary base is based on combination of the dictionaries from all the sub regions, and then space alignment between different parts of the object can be achieved. Meanwhile, noise removal and other operations in the existing sparse reconstruction error maps are performed to retain valuable information. In the updating framework, a novel flexible template set update mechanism is introduced in this paper. In this update mechanism, valuable object samples will be put into the template set. If samples are not valuable, they should not be put into the template set, even when the template set is not full. Then we use patch sparse coefficient histogram of updated templates to extract time domain information of the object in the form of weighted sum. Therefore, it can provide a reliable template basis for obtaining good candidate object. In addition, when tracking result deviates from the actual position of the object, we use a dynamic sub region resampling method based on cosine angle to correct the position deviation timely. Therefore this method can effectively prevent the object from being completely lost. Both qualitative and quantitative evaluations on challenging video sequences demonstrate that the proposed tracking algorithm performs favorably against several state of the art methods.
A simple approach to synthesize novel sulfur/graphene oxide/multiwalled carbon nanotube composite cathode for high performance lithium/sulfur batteries
Venue: Ionics

A sulfur/graphene oxide/multiwalled carbon nanotube (S/GO/MWNT) composite was synthesized via a simple ultrasonic mixing method followed by heat treatment. By taking advantage of this solution based self assembly synthesis route, poisonous and noxious reagents and complicated fabrication processes are rendered unnecessary, thereby simplifying its manufacturing and decreasing the cost of the final product. Transmission and scanning electronic microscopy observations indicated the formation of the three dimensional interconnected S/GO/MWNT composite through the environmentally friendly process. The GO layers and long MWNTs synergistically constructed hierarchical electron/ion pathways, favoring the ion transport and electrolyte diffusion. The interlaced network can serve as sponges to physically absorb polysulfides to their wrinkled surface and porous structure. In addition, GO could confine the polysulfides' dissolution through chemical absorption by the functional groups on GO layers. Therefore, the resulting S/GO/MWNT composite exhibits good rate capability and highly stable specific discharge capacity of 773 mA h g 1 after 100 cycles at 0.1 C.
Toward Operational Compensation of Ionospheric Effects in SAR Interferograms: The Split Spectrum Method
Venue: IEEE Transactions on Geoscience and Remote Sensing

The differential ionospheric path delay is a major error source in L band interferograms. It is superimposed to topography and ground deformation signals, hindering the measurement of geophysical processes. In this paper, we proceed toward the realization of an operational processor to compensate the ionospheric effects in interferograms. The processor should be robust and accurate to meet the scientific requirements for the measurement of geophysical processes, and it should be applicable on a global scale. An implementation of the split spectrum method, which will be one element of the processor, is presented in detail, and its performance is analyzed. The method is based on the dispersive nature of the ionosphere and separates the ionospheric component of the interferometric phase from the nondispersive component related to topography, ground motion, and tropospheric path delay. We tested the method using various Advanced Land Observing Satellite Phased Array type L band synthetic aperture radar interferometric pairs with different characteristics: high to low coherence, moving and nonmoving terrains, with and without topography, and different ionosphere states. Ionospheric errors of almost 1 m have been corrected to a centimeter or a millimeter level. The results show how the method is able to systematically compensate the ionospheric phase in interferograms, with the expected accuracy, and can therefore be a valid element of the operational processor.
Prompt penetration electric fields and the extreme topside ionospheric response to the June 22 23, 2015 geomagnetic storm as seen by the Swarm constellation
Venue: Earth, Planets and Space

Using data from the three Swarm satellites, we study the ionospheric response to the intense geomagnetic storm of June 22 23, 2015. With the minimum SYM H excursion of 207 nT, this storm is so far the second strongest geomagnetic storm in the current 24th solar cycle. A specific configuration of the Swarm satellites allowed investigation of the evolution of the storm time ionospheric alterations on the day and the nightside quasi simultaneously. With the development of the main phase of the storm, a significant dayside increase of the vertical total electron content (VTEC) and electron density Ne was first observed at low latitudes on the dayside. From ~22 UT of 22 June to ~1 UT of 23 June, the dayside experienced a strong negative ionospheric storm, while on the nightside an extreme enhancement of the topside VTEC occurred at mid latitudes of the northern hemisphere. Our analysis of the equatorial electrojet variations obtained from the magnetic Swarm data indicates that the storm time penetration electric fields were, most likely, the main driver of the observed ionospheric effects at the initial phase of the storm and at the beginning of the main phase. The dayside ionosphere first responded to the occurrence of the strong eastward equatorial electric fields. Further, penetration of westward electric fields led to gradual but strong decrease of the plasma density on the dayside in the topside ionosphere. At this stage, the disturbance dynamo could have contributed as well. On the nightside, the observed extreme enhancement of the Ne and VTEC in the northern hemisphere (i.e. the summer hemisphere) in the topside ionosphere was most likely due to the combination of the prompt penetration electric fields, disturbance dynamo and the storm time thermospheric circulation. From ~2.8 UT, the ionospheric measurements from the three Swarm satellites detected the beginning of the second positive storm on the dayside, which was not clearly associated with electrojet variations. We find that this second storm might be provoked by other drivers, such as an increase in the thermospheric composition.Graphical AbstractA scheme of the topside ionosphere sounding by the three Swarm satellites during the June 22 23, 2015 geomagnetic storm.
Pinning Controllability Scheme of Directed Complex Delayed Dynamical Networks via Periodically Intermittent Control

This paper studies the pinning controllability of directed complex delayed dynamical networks by using periodic intermittent control scheme. The general and low dimensional pinning synchronization criteria are derived to illustrate the design of periodic intermittent control scheme. According to our low dimensional pinning criterion, especially, the constraint condition of coupling strength is obtained when the network structure and amounts of pinned nodes are fixed. An algorithm is presented to determine the amounts of periodically intermittent controllers and locate these intermittent controllers in a directed network, in which the significance of nodes out (in degree in pinning control of complex network is also illustrated. Finally, a directed network consisting of 12 coupled delayed Chua oscillators is designed as numerical example to verify the effectiveness of the theoretical analysis.
The caudate nucleus mediates learning of stimulus control state associations.
Venue: The Journal of neuroscience the official journal of the Society for Neuroscience

A longstanding dichotomy in cognitive psychology and neuroscience pits controlled, top down driven against associative, bottom up driven behavior, where cognitive control processes allow us to override well learned stimulus response (S R) associations. By contrast, some recent studies have raised the intriguing possibility of an integration between associative and controlled processing in the form of stimulus control state (S C) associations, the learned linkage of specific stimuli to particular control states, such as high attentional selectivity. The neural machinery mediating S C learning remains poorly understood, however. Here, we combined functional neuroimaging (fMRI) with a recently developed Stroop protocol that allowed us to dissociate reductions in Stroop interference based on S R learning from those based on S C learning in humans. We modeled subjects' acquisition of S C and S R associations using an associative learning model and then employed trial by trial S C and S R prediction error (PE) estimates in model based behavioral and fMRI analyses. We found that PE estimates derived from S C and S R associations accounted for the reductions in behavioral Stroop interference effects in the S C and S R learning conditions, respectively. Moreover, model based fMRI analyses identified the caudate nucleus as the key structure involved in selectively updating stimulus control state associations. Complementary analyses also revealed a greater reliance on parietal cortex when utilizing the learned S R versus S C associations to minimize Stroop interference. These results support the emerging view that generalizable control states can become associated with specific bottom up cues, and they place the caudate nucleus of the dorsal striatum at the center of the neural stimulus control learning machinery.  Recent behavioral studies have demonstrated that control states, for instance, heightened attentional selectivity, can become directly associated with and subsequently retrieved by particular stimuli, thus breaking down the traditional dichotomy between top down and bottom up driven behavior. However, the neural mechanisms underlying this type of stimulus control learning remain poorly understood. We therefore combined non invasive human neuroimaging with a task that allowed us to dissociate the acquisition of stimulus control associations from that of stimulus response associations. The results revealed the caudate nucleus as the key brain structure involved in selectively driving stimulus control learning. These data represent the first identification of the neural mechanisms of stimulus specific control associations, and they significantly extend current conceptions of the type of learning processes mediated by the caudate.
The Stroop effect at 80: The competition between stimulus control and cognitive control.
Venue: Journal of the experimental analysis of behavior

For more than 80 years, researchers have examined the interference between automatic processing of stimuli, such as the meaning of color words, on performance of a controlled processing task such as naming the color in which words are printed. The Stroop effect and its many variations provide an ideal test platform for examining the competition between stimulus control and cognitive control of attention, as reflected in behavior. The two experiments reported here show that rhesus monkeys, like human adults, show interference from incongruous stimulus conditions in a number Stroop task, and that the monkeys may be particularly susceptible to influence from response strength and less able, relative to human adults, of using executive attention to minimize this interference.
Deep, Convolutional, and Recurrent Models for Human Activity Recognition Using Wearables
Venue: IJCAI

Human activity recognition (HAR) in ubiquitous computing is beginning to adopt deep learning to substitute for well established analysis techniques that rely on hand crafted feature extraction and classification techniques. From these isolated applications of custom deep architectures it is, however, difficult to gain an overview of their suitability for problems ranging from the recognition of manipulative gestures to the segmentation and identification of physical activities like running or ascending stairs. In this paper we rigorously explore deep, convolutional, and recurrent approaches across three representative datasets that contain movement data captured with wearable sensors. We describe how to train recurrent approaches in this setting, introduce a novel regularisation approach, and illustrate how they outperform the state of the art on a large benchmark dataset. Across thousands of recognition experiments with randomly sampled model configurations we investigate the suitability of each model for different tasks in HAR, explore the impact of hyperparameters using the fANOVA framework, and provide guidelines for the practitioner who wants to apply deep learning in their problem setting.