nils-herrmann/clean_scirepeval huggingface dataset:
Screening for Dyslexia Using Eye Tracking during Reading

Dyslexia is a neurodevelopmental reading disability estimated to affect 5-10% of the population. While there is yet no full understanding of the cause of dyslexia, or agreement on its precise definition, it is certain that many individuals suffer persistent problems in learning to read for no apparent reason. Although it is generally agreed that early intervention is the best form of support for children with dyslexia, there is still a lack of efficient and objective means to help identify those at risk during the early years of school. Here we show that it is possible to identify 9-10 year old individuals at risk of persistent reading difficulties by using eye tracking during reading to probe the processes that underlie reading ability. In contrast to current screening methods, which rely on oral or written tests, eye tracking does not depend on the subject to produce some overt verbal response and thus provides a natural means to objectively assess the reading process as it unfolds in real-time. Our study is based on a sample of 97 high-risk subjects with early identified word decoding difficulties and a control group of 88 low-risk subjects. These subjects were selected from a larger population of 2165 school children attending second grade. Using predictive modeling and statistical resampling techniques, we develop classification models from eye tracking records less than one minute in duration and show that the models are able to differentiate high-risk subjects from low-risk subjects with high accuracy. Although dyslexia is fundamentally a language-based learning disability, our results suggest that eye movements in reading can be highly predictive of individual reading ability and that eye tracking can be an efficient means to identify children at risk of long-term reading difficulties.
Hindi handwritten character recognition using multiple classifiers

Humans can easily recognize handwritten words, after gaining basic knowledge of languages. This knowledge needs to be transferred to computers for automatic character recognition. The work proposed in this paper tries to automate recognition of handwritten hindi isolated characters using multiple classifiers. For feature extraction, it uses histogram of oriented gradients as one feature and profile projection histogram as another feature. The performance of various classifiers has been evaluated using theses features experimentally and quadratic SVM has been found to produce better results.
A+D Net: Training a Shadow Detector with Adversarial Shadow Attenuation

We propose a novel GAN-based framework for detecting shadows in images, in which a shadow detection network (D-Net) is trained together with a shadow attenuation network (A-Net) that generates adversarial training examples. The A-Net modifies the original training images constrained by a simplified physical shadow model and is focused on fooling the D-Net’s shadow predictions. Hence, it is effectively augmenting the training data for D-Net with hard-to-predict cases. The D-Net is trained to predict shadows in both original images and generated images from the A-Net. Our experimental results show that the additional training data from A-Net significantly improves the shadow detection accuracy of D-Net. Our method outperforms the stateof-the-art methods on the most challenging shadow detection benchmark (SBU) and also obtains state-of-the-art results on a cross-dataset task, testing on UCF. Furthermore, the proposed method achieves accurate real-time shadow detection at 45 frames per second.
Learning through inquiry: student difficulties with online course-based Material

This study investigates the case-based learning experience of 133 undergraduate veterinarian science students. Using qualitative methodologies from relational Student Learning Research, variation in the quality of the learning experience was identified, ranging from coherent, deep, quality experiences of the cases, to experiences that separated significant aspects, such as the online case histories, laboratory test results, and annotated images emphasizing symptoms, from the meaning of the experience. A key outcome of this study was that a significant percentage of the students surveyed adopted a poor approach to learning with online resources in a blended experience even when their overall learning experience was related to cohesive conceptions of veterinary science, and that the difference was even more marked for less successful students. The outcomes from the study suggest that many students are unsure of how to approach the use of online resources in ways that are likely to maximise benefits for learning in blended experiences, and that the benefits from case-based learning such as authenticity and active learning can be threatened if issues closely associated with qualitative variation arising from incoherence in the experience are not addressed.
Matte painting in the digital age

the turn of the century, matte paintings have been a mainstay in the filmmaker's repertoire of visual effects. These intricate landscapes, rendered on glass by talented artists, have long been used by filmmakers to create realistic illusions without exceeding the limits of a reasonable budget. But today, many are beginning to question whether this technique is still a viable option in the digital age. Like many other filmmaking techniques, traditional matte painting is being completely redefined by computer-generated imagery. Traditional matte painting is quickly becoming a lost art, eclipsed by digital backlots and virtual sets. Today's new digital capabilities vastly expand the ability of filmmakers to create realistic landscapes that do not exist in the real world. They can now quickly and easily darken a sky, add or remove a mountain, or test multiple color combinations. Before the digital era, it took weeks to re-paint a scene for the same effect. Digital technologies can enhance locations and sets in ways that would be too costly to construct by conventional means. Digital techniques also free filmmakers from the two-dimensional limitations of traditional matte paintings. Now, for the first time, 3D rendering techniques allow the filmmaker complete freedom of camera movement through digitally generated environments. But while new digital capabilities offer many exciting possibilities , they can never replace the artist. The tools may have changed from paint brushes to graphics tablets, but the artist's ability to visualize and mimic reality is still in high demand as a way to offset the enormous costs required to produce films today. In fact, some traditional techniques often go unused even when the offer the possibility of superior results because the next generation of artists lacks the skills and training to use them. And the digital tools that artists need to paint on the computer today are still relatively crude when compared to traditional paints and brushes. While working on effects for "Titanic," our studio had less than two weeks to complete a shot of the rescue ship Carpathia. It would have taken too long to render a 3D model of the ship, but one of our artists was able to paint it in less than a week. That traditional matte painting was then com-posited digitally into the scene. Digital techniques also have the potential to save filmmakers time and money, but this is not always the case in practice. The time saved can easily …
Gated-Attention Architectures for Task-Oriented Language Grounding

To perform tasks specified by natural language instructions, autonomous agents need to extract semantically meaningful representations of language and map it to visual elements and actions in the environment. This problem is called task-oriented language grounding. We propose an end-to-end trainable neural architecture for task-oriented language grounding in 3D environments which assumes no prior linguistic or perceptual knowledge and requires only raw pixels from the environment and the natural language instruction as input. The proposed model combines the image and text representations using a Gated-Attention mechanism and learns a policy to execute the natural language instruction using standard reinforcement and imitation learning methods. We show the effectiveness of the proposed model on unseen instructions as well as unseen maps, both quantitatively and qualitatively. We also introduce a novel environment based on a 3D game engine to simulate the challenges of task-oriented language grounding over a rich set of instructions and environment states.
Supervising Unsupervised Learning

We introduce a framework to transfer knowledge acquired from a repository of (heterogeneous) supervised datasets to new unsupervised datasets. Our perspective avoids the subjectivity inherent in unsupervised learning by reducing it to supervised learning, and provides a principled way to evaluate unsupervised algorithms. We demonstrate the versatility of our framework via rigorous agnostic bounds on a variety of unsupervised problems. In the context of clustering, our approach helps choose the number of clusters and the clustering algorithm, remove the outliers, and provably circumvent Kleinberg’s impossibility result. Experiments across hundreds of problems demonstrate improvements in performance on unsupervised data with simple algorithms despite the fact our problems come from heterogeneous domains. Additionally, our framework lets us leverage deep networks to learn common features across many small datasets, and perform zero shot learning.
Quantum Information Medicine : Bit as It — The Future Direction of Medical Science : Antimicrobial and Other Potential Nontoxic Treatments

Experimental evidence has accumulated to suggest that biologically efficacious informational effects can be derived mimicking active compounds solely through electromagnetic distribution upon aqueous systems affecting biological systems. Empirically rigorous demonstrations of antimicrobial agent associated electromagnetic informational inhibition of MRSA, Entamoeba histolytica, Trichomonas vaginalis, Candida albicans and a host of other important and various reported effects have been evidenced, such as the electro-informational transfer of retinoic acid influencing human neuroblastoma cells and stem teratocarcinoma cells. Cell proliferation and differentiation effects from informationally affected fields interactive with aqueous systems are measured via microscopy, statistical analysis, reverse transcription polymerase chain reaction and other techniques. Information associated with chemical compounds affects biological aqueous systems, sans direct systemic exposure to the source molecule. This is a quantum effect, based on the interactivity between electromagnetic fields, and aqueous ordered coherence domains. The encoding of aqueous systems and tissue by photonic transfer and instantiation of information rather than via direct exposure to potentially toxic drugs and physical substances holds clear promise of creating inexpensive non-toxic medical treatments. Corresponding author.
Far voxels: a multiresolution framework for interactive rendering of huge complex 3D models on commodity graphics platforms

We present an efficient approach for end-to-end out-of-core construction and interactive inspection of very large arbitrary surface models. The method tightly integrates visibility culling and out-of-core data management with a level-of-detail framework. At preprocessing time, we generate a coarse volume hierarchy by binary space partitioning the input triangle soup. Leaf nodes partition the original data into chunks of a fixed maximum number of triangles, while inner nodes are discretized into a fixed number of cubical voxels. Each voxel contains a compact direction dependent approximation of the appearance of the associated volumetric subpart of the model when viewed from a distance. The approximation is constructed by a visibility aware algorithm that fits parametric shaders to samples obtained by casting rays against the full resolution dataset. At rendering time, the volumetric structure, maintained off-core, is refined and rendered in front-to-back order, exploiting vertex programs for GPU evaluation of view-dependent voxel representations, hardware occlusion queries for culling occluded subtrees, and asynchronous I/O for detecting and avoiding data access latencies. Since the granularity of the multiresolution structure is coarse, data management, traversal and occlusion culling cost is amortized over many graphics primitives. The efficiency and generality of the approach is demonstrated with the interactive rendering of extremely complex heterogeneous surface models on current commodity graphics platforms.
Alternative educational models for interdisciplinary student teams.

Few studies compare instructor-modeled learning with modified debriefing to self-directed learning with facilitated debriefing during team-simulated clinical scenarios.



: To determine whether self-directed learning with facilitated debriefing during team-simulated clinical scenarios (group A) has better outcomes compared with instructor-modeled learning with modified debriefing (group B).



This study used a convenience sample of students. The four tools used assessed pre/post knowledge, satisfaction, technical, and team behaviors. Thirteen interdisciplinary student teams participated: seven in group A and six in group B. Student teams consisted of one nurse practitioner student, one registered nurse student, one social work student, and one respiratory therapy student. The Knowledge Assessment Tool was analyzed by student profession.



There were no statistically significant differences within each student profession group on the Knowledge Assessment Tool. Group B was significantly more satisfied than group A (P = 0.01). Group B registered nurses and social worker students were significantly more satisfied than group A (30.0 +/- 0.50 vs. 26.2 +/- 3.0, P = 0.03 and 28.0 +/- 2.0 vs. 24.0 +/- 3.3, P = 0.04, respectively). Group B had significantly better scores than group A on 8 of the 11 components of the Technical Evaluation Tool; group B intervened more quickly. Group B had significantly higher scores on 8 of 10 components of the Behavioral Assessment Tool and overall team scores.



The data suggest that instructor-modeling learning with modified debriefing is more effective than self-directed learning with facilitated debriefing during team-simulated clinical scenarios.
Tumbler: An Effective Load-Balancing Technique for Multi-CPU Multicore Systems

Schedulers used by modern OSs (e.g., Oracle Solaris 11™ and GNU/Linux) balance load by balancing the number of threads in run queues of different cores. While this approach is effective for a single CPU multicore system, we show that it can lead to a significant load imbalance across CPUs of a multi-CPU multicore system. Because different threads of a multithreaded application often exhibit different levels of CPU utilization, load cannot be measured in terms of the number of threads alone. We propose Tumbler that migrates the threads of a multithreaded program across multiple CPUs to balance the load across the CPUs. While Tumbler distributes the threads equally across the CPUs, its assignment of threads to CPUs is aimed at minimizing the variation in utilization of different CPUs to achieve load balance. We evaluated Tumbler using a wide variety of 35 multithreaded applications, and our experimental results show that Tumbler outperforms both Oracle Solaris 11™ and GNU/Linux.
Bridging the Virtualization Performance Gap for HPC Using SR-IOV for InfiniBand

This paper shows that using SRIOV for InfiniBand can enable virtualized HPC, but only if the NIC tunable parameters are set appropriately. In particular, contrary to common belief, our results show that the default policy of aggressive use of interrupt moderation can have a negative impact on the performance of InfiniBand platforms virtualized using SR-IOV. Careful tuning of interrupt moderation benefits both Native and VM platforms and helps to bridge the gap between native and virtualized performance. For some workloads, the performance gap is reduced by 15-30%.
Text to Scene conversion for smart learning

Text to Scene conversion system automatically converts the given natural language text in to its visual representation such as readymade image, composite scene or animation. Natural language is a language in which we, humans interact with each other. We have built a system which converts the natural language text into the composite scene. It basically converts the sentences with spatial relationships into virtual scene. It involves the natural language processing and 3D computer graphics techniques. System consists of two main modules: text processing module and graphics module. Text processing module plays an important role. It processes the text given by user and finds the objects to create the scene. Graphics module takes this information as input and generates scene by placing those objects in relative position. As students can understand the concepts easily with the visualization, we have proposed a system of text to scene conversion to visualize maths problems, which can be useful to understand the problem statement and solve it. Application of this system in education domain can be useful in developing efficient methodology.
Germline KRAS mutations cause Noonan syndrome

Noonan syndrome (MIM 163950) is characterized by short stature, facial dysmorphism and cardiac defects. Heterozygous mutations in PTPN11, which encodes SHP-2, cause ∼50% of cases of Noonan syndrome. The SHP-2 phosphatase relays signals from activated receptor complexes to downstream effectors, including Ras. We discovered de novo germline KRAS mutations that introduce V14I, T58I or D153V amino acid substitutions in five individuals with Noonan syndrome and a P34R alteration in a individual with cardio-facio-cutaneous syndrome (MIM 115150), which has overlapping features with Noonan syndrome. Recombinant V14I and T58I K-Ras proteins show defective intrinsic GTP hydrolysis and impaired responsiveness to GTPase activating proteins, render primary hematopoietic progenitors hypersensitive to growth factors and deregulate signal transduction in a cell lineage–specific manner. These studies establish germline KRAS mutations as a cause of human disease and infer that the constellation of developmental abnormalities seen in Noonan syndrome spectrum is, in large part, due to hyperactive Ras.
Single-stage single-switch PFC converter with extreme step-down voltage conversion ratio

This paper presents a new single-stage singleswitch (S4) high power factor correction (PFC) AC/DC converter suitable for low power applications (< 150 W) with a universal input voltage range (90–265 Vrms). The proposed topology integrates a buck-boost input current shaper followed by a buck and a buck-boost converter, respectively. As a result, the proposed converter can operate with larger duty cycles compared to the exiting S4 topologies; hence, making them suitable for extreme step-down voltage conversion applications. Several desirable features are gained when the three integrated converter cells operate in discontinuous conduction mode (DCM). These features include low semiconductor voltage stress, zero-current switch at turn-on, and simple control with a fast well-regulated output voltage. A detailed circuit analysis is performed to derive the design equations. The theoretical analysis and effectiveness of the proposed approach are confirmed by experimental results obtained from a 35-W/12-Vdc laboratory prototype.
Markov Influence Diagrams.

Markov influence diagrams (MIDs) are a new type of probabilistic graphical model that extends influence diagrams in the same way that Markov decision trees extend decision trees. They have been designed to build state-transition models, mainly in medicine, and perform cost-effectiveness analyses. Using a causal graph that may contain several variables per cycle, MIDs can model various patient characteristics without multiplying the number of states; in particular, they can represent the history of the patient without using tunnel states. OpenMarkov, an open-source tool, allows the decision analyst to build and evaluate MIDs-including cost-effectiveness analysis and several types of deterministic and probabilistic sensitivity analysis-with a graphical user interface, without writing any code. This way, MIDs can be used to easily build and evaluate complex models whose implementation as spreadsheets or decision trees would be cumbersome or unfeasible in practice. Furthermore, many problems that previously required discrete event simulation can be solved with MIDs; i.e., within the paradigm of state-transition models, in which many health economists feel more comfortable.
A simulation method to predict the turn-to-turn voltage spikes in a PWM fed motor winding

This paper proposes a numerical simulation method dedicated to design assistance of high reliability machines fed by PWM inverters. It allows the computation of winding turn-to-turn maximum voltage stress for any combination of the wire positions in the stator slot. With such a tool, it is possible to design coils with the best wire arrangement for any fast-fronted pulses. An equivalent circuit is used to simulate the voltage distribution among the turns of the winding. A finite-element analysis simulation package is used to estimate the high-frequency distributed-circuit parameters of the winding. In order to validate the simulation results, they are compared with experimental results.
Can We Gain More from Orthogonality Regularizations in Training Deep CNNs?

OVERVIEW • We develop novel orthogonality regularizations on training deep CNNs, by borrowing ideas and tools from sparse optimization. • These plug-and-play regularizations can be conveniently incorporated into training almost any CNN without extra hassle. • The proposed regularizations can consistently improve the performances of baseline deep networks on CIFAR-10/100, ImageNet and SVHN datasets, based on intensive empirical experiments, as well as accelerate/stabilize the training curves. • The proposed orthogonal regularizations outperform existing competitors.