nils-herrmann/clean_scirepeval huggingface dataset:
Investigating Active Learning for Concept Prerequisite Learning

Concept prerequisite learning focuses on machine learning methods for measuring the prerequisite relation among concepts. With the importance of prerequisites for education, it has recently become a promising research direction. A major obstacle to extracting prerequisites at scale is the lack of large scale labels which will enable effective data driven solutions. We investigate the applicability of active learning to concept prerequisite learning. We propose a novel set of features tailored for prerequisite classification and compare the effectiveness of four widely used query strategies. Experimental results for domains including data mining, geometry, physics, and precalculus show that active learning can be used to reduce the amount of training data required. Given the proposed features, the query-by-committee strategy outperforms other compared query strategies.
The modified nodal approach to network analysis

A general technique for obtaining intermediate-band crystal filters from prototype low-pass (LP) networks which are neither symmetric nor antimetric is presented. This immediately enables us to now realize the class of low-transient responses. The bandpass (BP) filter appears as a cascade of symmetric lattice sections, obtained by partitioning the LP prototype filter, inserting constant reactances where necessary, and then applying the LP to BP frequency transformation. Manuscript received January 7, 1974; revised October 9, 1974. The author is with the Systems Development Division, Westinghouse Electric Corporation, Baltimore, Md. The cascade is composed of only two fundamental sections. Finally, the method introduced is illustrated with an example.
Forgiveness and conflict resolution in marriage.

Two studies examined whether forgiveness in married couples is associated with better conflict resolution. Study 1 examined couples in their 3rd year of marriage and identified 2 forgiveness dimensions (retaliation and benevolence). Husbands' retaliatory motivation was a significant predictor of poorer wife-reported conflict resolution, whereas wives' benevolence motivation predicted husbands' reports of better conflict resolution. Examining longer term marriages, Study 2 identified three forgiveness dimensions (retaliation, avoidance and benevolence). Whereas wives' benevolence again predicted better conflict resolution, husbands' avoidance predicted wives' reports of poorer conflict resolution. All findings were independent of both spouses' marital satisfaction. The findings are discussed in terms of the importance of forgiveness for marital conflict and its implications for spouse goals. Future research directions on forgiveness are outlined.
A Point-Based Offsetting Method of Polygonal Meshes

We address the delicate problem of offsetting polygonal meshes. Offsetting is important for stereolithography, NC machining, rounding corners, collision avoidance, and Hausdorff error calculation. We introduce a new fast, and very simple method for offsetting (growing and shrinking) a solid model by arbitrary distance r. Our approach is based on a hybrid data structure combining point samples, voxels, and continuous surfaces. Each face, edge, and vertex of the original solid generate a set of offset points spaced along the (pencil of) normals associated with it. The offset points and normals are sufficiently dense to ensure that all voxels between the original and the offset surfaces are properly labeled as either too close to the original solid or possibly containing the offset surface. Then the offset boundary is generated as the isosurface using these voxels and the associated offset points. We provide a tight error bound on the resulting surface and report experimental results on a variety of CAD models.
Memory Warps for Learning Long-Term Online Video Representations

This paper proposes a novel memory-based online video representation that is efficient, accurate and predictive. This is in contrast to prior works that often rely on computationally heavy 3D convolutions, ignore actual motion when aligning features over time, or operate in an off-line mode to utilize future frames. In particular, our memory (i) holds the feature representation, (ii) is spatially warped over time to compensate for observer and scene motions, (iii) can carry long-term information, and (iv) enables predicting feature representations in future frames. By exploring a variant that operates at multiple temporal scales, we efficiently learn across even longer time horizons. We apply our online framework to object detection in videos, obtaining a large 2.3 times speed-up and losing only 0.9% mAP on ImageNet-VID dataset, compared to prior works that even use future frames. Finally, we demonstrate the predictive property of our representation in two novel detection setups, where features are propagated over time to (i) significantly enhance a real-time detector by more than 10% mAP in a multi-threaded online setup and to (ii) anticipate objects in future frames.
MOOC student dropout: pattern and prevention

Massive Open Online Course (MOOC) is a completely new education method which appeared in 2012. MOOC is great for life-long learning and educational resource sharing. Until now, there are more than twenty MOOC platforms online. Some studies pointed out that students, however, can hardly follow a course till the end on MOOCs. For most courses, less than 13% of students could follow. Such a high dropout rate will restrict development of MOOCs in the future. This paper shows an observation of MOOC data. A statistic analysis is applied to students' behavioral data. The result is discussed in the perspective of course design on MOOC platform. A general system for predicting students' dropout is developed. With unsupervised learning, the system can fit on different on-going courses. A discussion of this system with Data Structures and Algorithms course is conducted. We also apply the system to Introduction to Computing course to test scalability. Additionally, based on the research above, some suggestions for instructing students on MOOC are given.
Asynchronous Task-Based Parallelization of Algebraic Multigrid

As processor clock rates become more dynamic and workloads become more adaptive, the vulnerability to global synchronization that already complicates programming for performance in today's petascale environment will be exacerbated. Algebraic multigrid (AMG), the solver of choice in many large-scale PDE-based simulations, scales well in the weak sense, with fixed problem size per node, on tightly coupled systems when loads are well balanced and core performance is reliable. However, its strong scaling to many cores within a node is challenging. Reducing synchronization and increasing concurrency are vital adaptations of AMG to hybrid architectures. Recent communication-reducing improvements to classical additive AMG by Vassilevski and Yang improve concurrency and increase communication-computation overlap, while retaining convergence properties close to those of standard multiplicative AMG, but remain bulk synchronous. We extend the Vassilevski and Yang additive AMG to asynchronous task-based parallelism using a hybrid MPI+OmpSs (from the Barcelona Supercomputer Center) within a node, along with MPI for internode communications. We implement a tiling approach to decompose the grid hierarchy into parallel units within task containers. We compare against the MPI-only BoomerAMG and the Auxiliary-space Maxwell Solver (AMS) in the hypre library for the 3D Laplacian operator and the electromagnetic diffusion, respectively. In time to solution for a full solve an MPI-OmpSs hybrid improves over an all-MPI approach in strong scaling at full core count (32 threads per single Haswell node of the Cray XC40) and maintains this per node advantage as both weak scale to thousands of cores, with MPI between nodes.
ENTREPRENEURIAL ECOSYSTEMS AND GROWTH ORIENTED ENTREPRENEURSHIP By

Increasing the number of high growth firms (HGFs) is now a major focus for industry policy in developed countries. However, existing approaches are proving ineffective. Simply creating supportive framework conditions is insufficient. Creating favourable environments for business start-ups is not leading to the creation of more HGFs. And transactional forms of support for HGFs (e.g. financial assistance) are proving to have limited effectiveness, at least post-start-up. The entrepreneurship ecosystem approach has emerged as a response. It recognises that HGFs flourish in distinctive types of supportive environment. Distinguishing features of entrepreneurial ecosystems include the following: a core of large established businesses, including some that have been entrepreneur-led (entrepreneurial blockbusters); entrepreneurial recycling – whereby successful cashed out entrepreneurs reinvest their time, money and expertise in supporting new entrepreneurial activity; and an information-rich environment in which this information is both accessible and shared. A key player in this context is the deal-maker who is involved in a fiduciary capacity in several entrepreneurial ventures. Other important aspects of an entrepreneurial ecosystem include its culture, the availability of start-up and growth capital, the presence of large firms, universities and service providers. However, studies have tended to take a static approach to the study of entrepreneurial ecosystems, largely ignoring both their origins and stimulus and also the processes by which they become self-sustaining. Creating entrepreneurial ecosystems poses various challenges for policy-makers. There are several general principles that need to be followed. Policy intervention needs to take a holistic approach, focusing on the following: the entrepreneurial actors within the ecosystem; the resource providers within the ecosystem; entrepreneurial connectors within the ecosystem and the entrepreneurial environment of the ecosystem. Finally, it is important that policy-makers develop metrics in order to determine the strengths and weaknesses of individual ecosystems so that their strengths and weaknesses can be assessed, to identify whether and how to intervene, and monitor over time the effectiveness of such interventions. What to measure, approaches to measurement and access to data at the appropriate geographical scales all pose formidable challenges.
Cinnamaldehyde enhances in vitro parameters of immunity and reduces in vivo infection against avian coccidiosis.

The effects of cinnamaldehyde (CINN) on in vitro parameters of immunity and in vivo protection against avian coccidiosis were evaluated. In vitro stimulation of chicken spleen lymphocytes with CINN (25-400 ng/ml) induced greater cell proliferation compared with the medium control (P < 0·001). CINN activated cultured macrophages to produce higher levels of NO at 1·2-5·0 μg/ml (P < 0·001), inhibited the growth of chicken tumour cells at 0·6-2·5 μg/ml (P < 0·001) and reduced the viability of Eimeria tenella parasites at 10 and 100 μg/ml (P < 0·05 and P < 0·001, respectively), compared with media controls. In chickens fed a diet supplemented with CINN at 14·4 mg/kg, the levels of IL-1β, IL-6, IL-15 and interferon-γ transcripts in intestinal lymphocytes were 2- to 47-fold higher (P < 0·001) compared with chickens given a non-supplemented diet. To determine the effect of CINN diets on avian coccidiosis, chickens were fed diets supplemented with CINN at 14·4 mg/kg (E. maxima or E. tenella) or 125 mg/kg (E. acervulina) from hatch for 24 d, and orally infected with 2·0 × 10(4) sporulated oocysts at age 14 d. CINN-fed chickens showed 16·5 and 41·6 % increased body-weight gains between 0-9 d post-infection (DPI) with E. acervulina or E. maxima, reduced E. acervulina oocyst shedding between 5-9 DPI and increased E. tenella-stimulated parasite antibody responses at 9 DPI compared with controls.
Computing and evaluating factor scores.

A variety of methods for computing factor scores can be found in the psychological literature. These methods grew out of a historic debate regarding the indeterminate nature of the common factor model. Unfortunately, most researchers are unaware of the indeterminacy issue and the problems associated with a number of the factor scoring procedures. This article reviews the history and nature of factor score indeterminacy. Novel computer programs for assessing the degree of indeterminacy in a given analysis, as well as for computing and evaluating different types of factor scores, are then presented and demonstrated using data from the Wechsler Intelligence Scale for Children-Third Edition. It is argued that factor score indeterminacy should be routinely assessed and reported as part of any exploratory factor analysis and that factor scores should be thoroughly evaluated before they are reported or used in subsequent statistical analyses.
Multistrategy ensemble learning: reducing error by combining ensemble learning techniques

Ensemble learning strategies, especially boosting and bagging decision trees, have demonstrated impressive capacities to improve the prediction accuracy of base learning algorithms. Further gains have been demonstrated by strategies that combine simple ensemble formation approaches. We investigate the hypothesis that the improvement in accuracy of multistrategy approaches to ensemble learning is due to an increase in the diversity of ensemble members that are formed. In addition, guided by this hypothesis, we develop three new multistrategy ensemble learning techniques. Experimental results in a wide variety of natural domains suggest that these multistrategy ensemble learning techniques are, on average, more accurate than their component ensemble learning techniques.
Cluster labelling using chi-square-based keyword ranking and mutual information score: a hybrid approach

Cluster labelling is a technique which provides useful information about the cluster to the end users. In this paper, we propose a novel approach which is the follow-up of our previous work. Our earlier approach generates clusters of web documents by using a modified apriori approach which is more efficient and faster than the traditional apriori approach. To label the clusters, the propose approach used an effective feature selection technique which selects the top features of a cluster. Rather than labelling the cluster with ‘bag of words’, a concept driven mechanism has been developed which uses the Wikipedia that takes the top features of a cluster as input to generate the possible candidate labels. Mutual information (MI) score technique has been used for ranking the candidate labels and then the topmost candidates are considered as potential labels of a cluster. Experimental results on two benchmark datasets demonstrate the efficiency of our approach.
The physiology, signaling, and pharmacology of dopamine receptors.

G protein-coupled dopamine receptors (D1, D2, D3, D4, and D5) mediate all of the physiological functions of the catecholaminergic neurotransmitter dopamine, ranging from voluntary movement and reward to hormonal regulation and hypertension. Pharmacological agents targeting dopaminergic neurotransmission have been clinically used in the management of several neurological and psychiatric disorders, including Parkinson's disease, schizophrenia, bipolar disorder, Huntington's disease, attention deficit hyperactivity disorder (ADHD(1)), and Tourette's syndrome. Numerous advances have occurred in understanding the general structural, biochemical, and functional properties of dopamine receptors that have led to the development of multiple pharmacologically active compounds that directly target dopamine receptors, such as antiparkinson drugs and antipsychotics. Recent progress in understanding the complex biology of dopamine receptor-related signal transduction mechanisms has revealed that, in addition to their primary action on cAMP-mediated signaling, dopamine receptors can act through diverse signaling mechanisms that involve alternative G protein coupling or through G protein-independent mechanisms via interactions with ion channels or proteins that are characteristically implicated in receptor desensitization, such as β-arrestins. One of the future directions in managing dopamine-related pathologic conditions may involve a transition from the approaches that directly affect receptor function to a precise targeting of postreceptor intracellular signaling modalities either directly or through ligand-biased signaling pharmacology. In this comprehensive review, we discuss dopamine receptor classification, their basic structural and genetic organization, their distribution and functions in the brain and the periphery, and their regulation and signal transduction mechanisms. In addition, we discuss the abnormalities of dopamine receptor expression, function, and signaling that are documented in human disorders and the current pharmacology and emerging trends in the development of novel therapeutic agents that act at dopamine receptors and/or on related signaling events.
A Critical Investigation of Deep Reinforcement Learning for Navigation

The navigation problem is classically approached in two steps: an exploration step, where mapinformation about the environment is gathered; and an exploitation step, where this information is used to navigate efficiently. Deep reinforcement learning (DRL) algorithms, alternatively, approach the problem of navigation in an end-to-end fashion. Inspired by the classical approach, we ask whether DRL algorithms are able to inherently explore, gather and exploit map-information over the course of navigation. We build upon Mirowski et al. [2017]’s work and introduce a systematic suite of experiments that vary three parameters: the agent’s starting location, the agent’s target location, and the maze structure. We choose evaluation metrics that explicitly measure the algorithm’s ability to gather and exploit map-information. Our experiments show that when trained and tested on the same maps, the algorithm successfully gathers and exploits map-information. However, when trained and tested on different sets of maps, the algorithm fails to transfer the ability to gather and exploit map-information to unseen maps. Furthermore, we find that when the goal location is randomized and the map is kept static, the algorithm is able to gather and exploit map-information but the exploitation is far from optimal. We open-source our experimental suite in the hopes that it serves as a framework for the comparison of future algorithms and leads to the discovery of robust alternatives to classical navigation methods.
A Focus+Context Technique Based on Hyperbolic Geometry for Visualizing Large Hierarchies

We present a new focus+Context (fisheye) technique for visualizing and manipulating large hierarchies. Our technique assigns more display space to a portion of the hierarchy while still embedding it in the context of the entire hierarchy. The essence of this scheme is to layout the hierarchy in a uniform way on a hyperbolic plane and map this plane onto a circular display region. This supports a smooth blending between focus and context, as well as continuous redirection of the focus. We have developed effective procedures for manipulating the focus using pointer clicks as well as interactive dragging, and for smoothly animating transitions across such manipulation. A laboratory experiment comparing the hyperbolic browser with a conventional hierarchy browser was conducted.
RUDDER ROLL STABILIZATION WITH NONLINEAR GMV CONTROL

Application of Nonlinear Generalized Minimum Variance control to ship roll stabilization using rudder input command is considered in this paper. The dynamic weightings in the cost criterion are used to reject the wave disturbance at a specified frequency band while maintaining relatively low controller gain in low and high frequencies to account for rudder angle and rate limits. Moreover, the use of a nonlinear weighting function in the NGMV control law formulation ensures the nonlinearities in the rudder are dealt with in a natural and systematic way. The effectiveness of the approach is demonstrated on a simulated ship model.
#Irony or #Sarcasm - A Quantitative and Qualitative Study Based on Twitter

Current study is with the aim to identify similarities and distinctions between irony and sarcasm by adopting quantitative sentiment analysis as well as qualitative content analysis. The result of quantitative sentiment analysis shows that sarcastic tweets are used with more positive tweets than ironic tweets. The result of content analysis corresponds to the result of quantitative sentiment analysis in identifying the aggressiveness of sarcasm. On the other hand, from content analysis it shows that irony owns two senses. The first sense of irony is equal to aggressive sarcasm with speaker awareness. Thus, tweets of first sense of irony may attack a specific target, and the speaker may tag his/her tweet irony because the tweet itself is ironic. These tweets though tagged as irony are in fact sarcastic tweets. Different from this, the tweets of second sense of irony is tagged to classify an event to be ironic. However, from the distribution in sentiment analysis and examples in content analysis, irony seems to be more broadly used in its second sense.
powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions

Power laws are theoretically interesting probability distributions that are also frequently used to describe empirical data. In recent years, effective statistical methods for fitting power laws have been developed, but appropriate use of these techniques requires significant programming and statistical insight. In order to greatly decrease the barriers to using good statistical methods for fitting power law distributions, we developed the powerlaw Python package. This software package provides easy commands for basic fitting and statistical analysis of distributions. Notably, it also seeks to support a variety of user needs by being exhaustive in the options available to the user. The source code is publicly available and easily extensible.
SPNets: Differentiable Fluid Dynamics for Deep Neural Networks

In this paper we introduce Smooth Particle Networks (SPNets), a framework for integrating fluid dynamics with deep networks. SPNets adds two new layers to the neural network toolbox: ConvSP and ConvSDF, which enable computing physical interactions with unordered particle sets. We use these layers in combination with standard neural network layers to directly implement fluid dynamics inside a deep network, where the parameters of the network are the fluid parameters themselves (e.g., viscosity, cohesion, etc.). Because SPNets are implemented as a neural network, the resulting fluid dynamics are fully differentiable. We then show how this can be successfully used to learn fluid parameters from data, perform liquid control tasks, and learn policies to manipulate liquids.