nils-herrmann/clean_scirepeval huggingface dataset:
Continuum Robot Dynamics Utilizing the Principle of Virtual Power

Efficient formulations for the dynamics of continuum robots are necessary to enable accurate modeling of the robot's shape during operation. Previous work in continuum robotics has focused on low-fidelity lumped parameter models, in which actuated segments are modeled as circular arcs, or computationally intensive high-fidelity distributed parameter models, in which continuum robots are modeled as a parameterized spatial curve. In this paper, a novel dynamic modeling methodology is studied that captures curvature variations along a segment using a finite set of kinematic variables. This dynamic model is implemented using the principle of virtual power (also called Kane's method) for a continuum robot. The model is derived to account for inertial, actuation, friction, elastic, and gravitational effects. The model is inherently adaptable for including any type of external force or moment, including dissipative effects and external loading. Three case studies are simulated on a cable-driven continuum robot structure to study the dynamic properties of the numerical model. Cross validation is performed in comparison to both experimental results and finite-element analysis.
Kinetic and dimensional optimization for a tendon-driven gripper

We design, optimize and demonstrate the behavior of a tendon-driven robotic gripper performing fingertip and enveloping grasps. The gripper consists of two fingers, each with two links, and is actuated using a single active tendon. During unobstructed closing, the distal links remain parallel, creating exact fingertip grasps. Conversely, if the proximal links are stopped by contact with an object, the distal links start flexing, creating a stable enveloping grasp. We optimize the route of the active tendon and the parameters of the springs providing passive extension forces in order to achieve this behavior. We show how an additional passive tendon can be used as a constraint preventing the gripper from entering undesirable parts of the joint workspace. Finally, we introduce a method for optimizing the dimensions of the links in order to achieve enveloping grasps of a large range of objects, and apply it to a set of common household objects.
Data Visualization and Feature Selection: New Algorithms for Nongaussian Data

Data visualization and feature selection methods are proposed based on the )oint mutual information and ICA. The visualization methods can find many good 2-D projections for high dimensional data interpretation, which cannot be easily found by the other existing methods. The new variable selection method is found to be better in eliminating redundancy in the inputs than other methods based on simple mutual information. The efficacy of the methods is illustrated on a radar signal analysis problem to find 2-D viewing coordinates for data visualization and to select inputs for a neural network classifier.
The genome of a songbird

The zebra finch is an important model organism in several fields with unique relevance to human neuroscience. Like other songbirds, the zebra finch communicates through learned vocalizations, an ability otherwise documented only in humans and a few other animals and lacking in the chicken—the only bird with a sequenced genome until now. Here we present a structural, functional and comparative analysis of the genome sequence of the zebra finch (Taeniopygia guttata), which is a songbird belonging to the large avian order Passeriformes. We find that the overall structures of the genomes are similar in zebra finch and chicken, but they differ in many intrachromosomal rearrangements, lineage-specific gene family expansions, the number of long-terminal-repeat-based retrotransposons, and mechanisms of sex chromosome dosage compensation. We show that song behaviour engages gene regulatory networks in the zebra finch brain, altering the expression of long non-coding RNAs, microRNAs, transcription factors and their targets. We also show evidence for rapid molecular evolution in the songbird lineage of genes that are regulated during song experience. These results indicate an active involvement of the genome in neural processes underlying vocal communication and identify potential genetic substrates for the evolution and regulation of this behaviour.
Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents

Psychlab is a simulated psychology laboratory inside the first-person 3D game world of DeepMind Lab (Beattie et al., 2016). Psychlab enables implementations of classical laboratory psychological experiments so that they work with both human and artificial agents. Psychlab has a simple and flexible API that enables users to easily create their own tasks. As examples, we are releasing Psychlab implementations of several classical experimental paradigms including visual search, change detection, random dot motion discrimination, and multiple object tracking. We also contribute a study of the visual psychophysics of a specific state-of-the-art deep reinforcement learning agent: UNREAL (Jaderberg et al., 2016). This study leads to the surprising conclusion that UNREAL learns more quickly about larger target stimuli than it does about smaller stimuli. In turn, this insight motivates a specific improvement in the form of a simple model of foveal vision that turns out to significantly boost UNREAL’s performance, both on Psychlab tasks, and on standard DeepMind Lab tasks. By open-sourcing Psychlab we hope to facilitate a range of future such studies that simultaneously advance deep reinforcement learning and improve its links with cognitive science.
Cross-Dataset Adaptation for Visual Question Answering

We investigate the problem of cross-dataset adaptation for visual question answering (Visual QA). Our goal is to train a Visual QA model on a source dataset but apply it to another target one. Analogous to domain adaptation for visual recognition, this setting is appealing when the target dataset does not have a sufficient amount of labeled data to learn an "in-domain" model. The key challenge is that the two datasets are constructed differently, resulting in the cross-dataset mismatch on images, questions, or answers. We overcome this difficulty by proposing a novel domain adaptation algorithm. Our method reduces the difference in statistical distributions by transforming the feature representation of the data in the target dataset. Moreover, it maximizes the likelihood of answering questions (in the target dataset) correctly using the Visual QA model trained on the source dataset. We empirically studied the effectiveness of the proposed approach on adapting among several popular Visual QA datasets. We show that the proposed method improves over baselines where there is no adaptation and several other adaptation methods. We both quantitatively and qualitatively analyze when the adaptation can be mostly effective.
Blind deconvolution using a normalized sparsity measure

Blind image deconvolution is an ill-posed problem that requires regularization to solve. However, many common forms of image prior used in this setting have a major drawback in that the minimum of the resulting cost function does not correspond to the true sharp solution. Accordingly, a range of additional methods are needed to yield good results (Bayesian methods, adaptive cost functions, alpha-matte extraction and edge localization). In this paper we introduce a new type of image regularization which gives lowest cost for the true sharp image. This allows a very simple cost formulation to be used for the blind deconvolution model, obviating the need for additional methods. Due to its simplicity the algorithm is fast and very robust. We demonstrate our method on real images with both spatially invariant and spatially varying blur.
Infant-Guided, Co-Regulated Feeding in the Neonatal Intensive Care Unit. Part II: Interventions to Promote Neuroprotection and Safety.

Feeding skills of preterm neonates in a neonatal intensive care unit are in an emergent phase of development and require careful support to minimize stress. The underpinnings that influence and enhance both neuroprotection and safety were discussed in Part I. An infant-guided, co-regulated approach to feeding can protect the vulnerable neonate's neurologic development, support the parent-infant relationship, and prevent feeding problems that may endure. Contingent interventions are used to maintain subsystem stability and enhance self-regulation, development, and coping skills. This co-regulation between caregiver and neonate forms the foundation for a positive infant-guided feeding experience. Caregivers select evidence-based interventions contingent to the newborn's communication. When these interventions are then titrated from moment to moment, neuroprotection and safety are fostered.
Template Adaptation for Face Verification and Identification

Face recognition performance evaluation has traditionally focused on one-to-one verification, popularized by the Labeled Faces in the Wild dataset  for imagery and the YouTubeFaces dataset [2] for videos. In contrast, the newly released IJB-A face recognition dataset  unifies evaluation of one-to-many face identification with one-to-one face verification over templates, or sets of imagery and videos for a subject. In this paper, we study the problem of template adaptation, a form of transfer learning to the set of media in a template. Extensive performance evaluations on IJB-A show a surprising result, that perhaps the simplest method of template adaptation, combining deep convolutional network features with template specific linear SVMs, outperforms the state-of-the-art by a wide margin. We study the effects of template size, negative set construction and classifier fusion on performance, then compare template adaptation to convolutional networks with metric learning, 2D and 3D alignment. Our unexpected conclusion is that these other methods, when combined with template adaptation, all achieve nearly the same top performance on IJB-A for templatebased face verification and identification.
The finding probably will support those ... Lol , u think im being there ...

In this work, we study the problem of partof-speech tagging for Tweets. In contrast to newswire articles, Tweets are usually informal and contain numerous out-ofvocabulary words. Moreover, there is a lack of large scale labeled datasets for this domain. To tackle these challenges, we propose a novel neural network to make use of out-of-domain labeled data, unlabeled in-domain data, and labeled indomain data. Inspired by adversarial neural networks, the proposed method tries to learn common features through adversarial discriminator. In addition, we hypothesize that domain-specific features of target domain should be preserved in some degree. Hence, the proposed method adopts a sequence-to-sequence autoencoder to perform this task. Experimental results on three different datasets show that our method achieves better performance than state-of-the-art methods.
Autonomous MAV flight in indoor environments using single image perspective cues

We consider the problem of autonomously flying Miniature Aerial Vehicles (MAVs) in indoor environments such as home and office buildings. The primary long range sensor in these MAVs is a miniature camera. While previous approaches first try to build a 3D model in order to do planning and control, our method neither attempts to build nor requires a 3D model. Instead, our method first classifies the type of indoor environment the MAV is in, and then uses vision algorithms based on perspective cues to estimate the desired direction to fly. We test our method on two MAV platforms: a co-axial miniature helicopter and a toy quadrotor. Our experiments show that our vision algorithms are quite reliable, and they enable our MAVs to fly in a variety of corridors and staircases.
Learning Interpretable Spatial Operations in a Rich 3D Blocks World

In this paper, we study the problem of mapping natural language instructions to complex spatial actions in a 3D blocks world. We first introduce a new dataset that pairs complex 3D spatial operations to rich natural language descriptions that require complex spatial and pragmatic interpretations such as “mirroring”, “twisting”, and “balancing”. This dataset, built on the simulation environment of Bisk, Yuret, and Marcu (2016), attains language that is significantly richer and more complex, while also doubling the size of the original dataset in the 2D environment with 100 new world configurations and 250,000 tokens. In addition, we propose a new neural architecture that achieves competitive results while automatically discovering an inventory of interpretable spatial operations .
Successful implementation of e-Learning Pedagogical considerations

Many institutions of Higher Education and Corporate Training Institutes are resorting to e-Learning as a means of solving authentic learning and performance problems, while other institutions are hopping onto the bandwagon simply because they do not want to be left behind. Success is crucial because an unsuccessful effort to implement e-Learning will be clearly reflected in terms of the return of investment. One of the most crucial prerequisites for successful implementation of e-Learning is the need for careful consideration of the underlying pedagogy, or how learning takes place online. In practice, however, this is often the most neglected aspect in any effort to implement e-Learning. The purpose of this paper is to identify the pedagogical principles underlying the teaching and learning activities that constitute effective e-Learning. An analysis and synthesis of the principles and ideas by the practicing e-Learning company employing the author will also be presented, in the perspective of deploying an effective Learning Management Systems (LMS). D 2002
Statistical Modeling Approaches for PM 10 Prediction in Urban Areas ; A Review of 21 st-Century Studies

PM10 prediction has attracted special legislative and scientific attention due to its harmful effects on human health. Statistical techniques have the potential for high-accuracy PM10 prediction and accordingly, previous studies on statistical methods for temporal, spatial and spatio-temporal prediction of PM10 are reviewed and discussed in this paper. A review of previous studies demonstrates that Support Vector Machines, Artificial Neural Networks and hybrid techniques show promise for suitable temporal PM10 prediction. A review of the spatial predictions of PM10 shows that the LUR (Land Use Regression) approach has been successfully utilized for spatial prediction of PM10 in urban areas. Of the six introduced approaches for spatio-temporal prediction of PM10, only one approach is suitable for high-resolved prediction (Spatial resolution < 100 m; Temporal resolution ď 24 h). In this approach, based upon the LUR modeling method, short-term dynamic input variables are employed as explanatory variables alongside typical non-dynamic input variables in a non-linear modeling procedure.
Crime Mapping through Geo-Spatial Social Media Activity

The presence of crime is one of the major challenges for societies all over the World, especially in metropolitan areas. As indicated by prior research, Information Systems can contribute greatly to cope with the complex factors that influence the emergence and location of delinquencies. In this work, we combine commonly used approaches of static environmental characteristics with Social Media. We expect that blending in such dynamic information of public behavior is a valuable addition to explain and predict criminal activity. Consequently, we employ Zero-Inflated Poisson Regressions and Geographically Weighted Regressions to examine how suitable Social Media data actually is for this purpose. Our results unveil geographic variation of explanatory power throughout a metropolitan area. Furthermore, we find that Social Media works exceptionally well for description of certain crime types and thus is also likely to enhance the accuracy of delinquency prediction.
Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations

Classical numerical methods for solving partial differential equations suffer from the curse dimensionality mainly due to their reliance on meticulously generated spatio-temporal grids. Inspired by modern deep learning based techniques for solving forward and inverse problems associated with partial differential equations, we circumvent the tyranny of numerical discretization by devising an algorithm that is scalable to high-dimensions. In particular, we approximate the unknown solution by a deep neural network which essentially enables us to benefit from the merits of automatic differentiation. To train the aforementioned neural network we leverage the well-known connection between high-dimensional partial differential equations and forwardbackward stochastic differential equations. In fact, independent realizations of a standard Brownian motion will act as training data. We test the effectiveness of our approach for a couple of benchmark problems spanning a number of scientific domains including Black-Scholes-Barenblatt and HamiltonJacobi-Bellman equations, both in 100-dimensions.
A literature review on the state-ofthe-art in patent analysis

The rapid growth of patent documents has called for the development of sophisticated patent analysis tools. Currently, there are various tools that are being utilized by organizations for analyzing patents. These tools are capable of performing wide range of tasks, such as analyzing and forecasting future technological trends, conducting strategic technology planning, detecting patent infringement, determining patents quality and the most promising patents, and identifying technological hotspots and patent vacuums. This literature review presents the state-of-the-art in patent analysis and also presents taxonomy of patent analysis techniques. Moreover, the key features and weaknesses of the discussed tools and techniques are presented and several directions for future research are highlighted. The literature review will be helpful for the researchers in finding the latest research efforts pertaining to the patent analysis in a unified form.
Consistent feature attribution for tree ensembles

It is critical in many applications to understand what features are important for a model, and why individual predictions were made. For tree ensemble methods these questions are usually answered by attributing importance values to input features, either globally or for a single prediction. Here we show that current feature attribution methods are inconsistent, which means changing the model to rely more on a given feature can actually decrease the importance assigned to that feature. To address this problem we develop fast exact solutions for SHAP (SHapley Additive exPlanation) values, which were recently shown to be the unique additive feature attribution method based on conditional expectations that is both consistent and locally accurate. We integrate these improvements into the latest version of XGBoost, demonstrate the inconsistencies of current methods, and show how using SHAP values results in significantly improved supervised clustering performance. Feature importance values are a key part of understanding widely used models such as gradient boosting trees and random forests. We believe our work improves on the state-of-the-art in important ways, and so impacts any current user of tree ensemble methods.
Overweight/obesity among school aged children in Bahir Dar City: cross sectional study

Developing countries, including Ethiopia are experiencing a double burden of malnutrition. There is limited information about prevalence of overweight/obesity among school aged children in Ethiopia particularly in Bahir Dar city. Hence this study aimed to assess the prevalence of overweight/obesity and associated factors among school children aged 6-12 years at Bahir Dar City, Northwest Ethiopia.



A school based cross-sectional study was carried out. A total of 634 children were included in the study. Multi stage systematic random sampling technique was used. A multivariable logistic regression analysis was used to identify factors associated with overweight/obesity. The association between dependent and independent variables were assessed using odds ratio with 95% confidence interval and p-value ≤0.05 was considered statistically significant.



The overall prevalence of overweight and/or obesity was 11.9% (95% CI, 9.3, 14.4) (out of which 8.8% were overweight and 3.1% were obese). Higher wealth status[adjusted OR = 3.14, 95% CI:1.17, 8.46], being a private school student [AOR = 2.21, 95% CI:1.09, 4.49], use of transportation to and from school [AOR = 2.53, 95% CI: 1.26,5.06], fast food intake [AOR = 3.88, 95% CI: 1.42,10.55], lack of moderate physical activity [AOR = 2.87, 95% CI: 1.21,6.82], low intake of fruit and vegetable [AOR = 6.45, 95% CI:3.19,13.06] were significant factors associated with overweight and obesity.



This study revealed that prevalence of overweight/obesity among school aged children in Bahir Dar city is high. Thus, promoting healthy dietary habit, particularly improving fruit and vegetable intake is essential to reduce the burden of overweight and obesity. Furthermore, it is important to strengthen nutrition education about avoiding junk food consumption and encouraging regular physical activity.
Explainable Recommendation: A Survey and New Perspectives

Explainable Recommendation refers to the personalized recommendation algorithms that address the problem of why they not only provide users with the recommendations, but also provide explanations to make the user or system designer aware of why such items are recommended. In this way, it helps to improve the effectiveness, efficiency, persuasiveness, and user satisfaction of recommendation systems. In recent years, a large number of explainable recommendation approaches – especially model-based explainable recommendation algorithms – have been proposed and adopted in real-world systems. In this survey, we review the work on explainable recommendation that has been published in or before the year of 2018. We first highlight the position of explainable recommendation in recommender system research by categorizing recommendation problems into the 5W, i.e., what, when, who, where, and why. We then conduct a comprehensive survey of explainable recommendation itself in terms of three aspects: 1) We provide a chronological research line of explanations in recommender systems, including the user study approaches in the early years, as well as the more recent model-based approaches. 2) We provide a taxonomy for explainable recommendation algorithms, including user-based, item-based, model-based, and post-model explanations. 3) We summarize the application of explainable recommendation in different recommendation tasks, including product recommendation, social recommendation, POI recommendation, etc. We devote a section to discuss the explanation perspectives in the broader IR and machine learning settings, as well as their relationship with explainable recommendation research. We end the survey by discussing potential future research directions to promote the explainable recommendation research area. now Publishers Inc.. Explainable Recommendation: A Survey and New Perspectives. Foundations and Trends