nils-herrmann/clean_scirepeval huggingface dataset:
Covert retrieval practice benefits retention as much as overt retrieval practice.

Many experiments provide evidence that practicing retrieval benefits retention relative to conditions of no retrieval practice. Nearly all prior research has employed retrieval practice requiring overt responses, but a few experiments have shown that covert retrieval also produces retention advantages relative to control conditions. However, direct comparisons between overt and covert retrieval are scarce: Does covert retrieval-thinking of but not producing responses-on a first test produce the same benefit as overt retrieval on a criterial test given later? We report 4 experiments that address this issue by comparing retention on a second test following overt or covert retrieval on a first test. In Experiment 1 we used a procedure designed to ensure that subjects would retrieve on covert as well as overt test trials and found equivalent testing effects in the 2 cases. In Experiment 2 we replicated these effects using a procedure that more closely mirrored natural retrieval processes. In Experiment 3 we showed that overt and covert retrieval produced equivalent testing effects after a 2-day delay. Finally, in Experiment 4 we showed that covert retrieval benefits retention more than restudying. We conclude that covert retrieval practice is as effective as overt retrieval practice, a conclusion that contravenes hypotheses in the literature proposing that overt responding is better. This outcome has an important educational implication: Students can learn as much from covert self-testing as they would from overt responding.
Adversarial Autoencoders for Generating 3D Point Clouds

Deep generative architectures provide a way to model not only images, but also complex, 3-dimensional objects, such as point clouds. In this work, we present a novel method to obtain meaningful representations of 3D shapes that can be used for clustering and reconstruction. Contrary to existing methods for 3D point cloud generation that train separate decoupled models for representation learning and generation, our approach is the first end-to-end solution that allows to simultaneously learn a latent space of representation and generate 3D shape out of it. To achieve this goal, we extend a deep Adversarial Autoencoder model (AAE) to accept 3D input and create 3D output. Thanks to our end-to-end training regime, the resulting method called 3D Adversarial Autoencoder (3dAAE) obtains either binary or continuous latent space that covers much wider portion of training data distribution, hence allowing smooth interpolation between the shapes. Finally, our extensive quantitative evaluation shows that 3dAAE provides state-of-theart results on a set of benchmark tasks.
Epidemiology of major depressive disorder: results from the National Epidemiologic Survey on Alcoholism and Related Conditions.

To present nationally representative data on 12-month and lifetime prevalence, correlates, and comorbidity of DSM-IV major depressive disorder (MDD) among adults in the United States.  Face-to-face survey of more than 43 000 adults aged 18 years and older residing in households and group quarters in the United States.



Prevalence and associations of MDD with sociodemographic correlates and Axis I and II disorders.



The prevalence of 12-month and lifetime DSM-IV MDD was 5.28% (95% confidence interval, 4.98-5.57) and 13.23% (95% confidence interval, 12.64-13.81), respectively. Being female; Native American; middle-aged; widowed, separated, or divorced; and low income increased risk, and being Asian, Hispanic, or black decreased risk (P<.05). Women were significantly more likely to receive treatment than men. Both current and lifetime MDD were significantly associated with other specific psychiatric disorders, notably substance dependence, panic and generalized anxiety disorder, and several personality disorders.



This large survey suggests a higher prevalence of MDD in the US population than large-sample estimates from the 1980s and 1990s. The shift in highest lifetime risk from young to middle-aged adults is an important transformation in the distribution of MDD in the United States and specificity in risk for an age-period cohort. Associations between MDD and Axis I and II disorders were strong and significant, with variation within broad categories by specific diagnoses signaling the need for attention to the genetic and environmental reasons for such variation, as well as the implications for treatment response.
High-speed railway catenary components detection using the cascaded convolutional neural networks

With the massive construction of the China high-speed railway, it is of a great significance to propose an automatic approach to inspect the defects of the catenary support devices. Based on the obtained high resolution images, the detection and extraction of the components on the catenary support devices are the vital steps prior to their defect report. Inspired by the existing object detection Faster R-CNN framework, a cascaded convolutional neural network (CNN) architecture is built to successively detect the various components and the tiny fasteners in the complex catenary support device structures. Meanwhile, some missing states of the fasteners on the cantilever joints are directly reported via our proposed architecture. Experiments on the Wuhan-Guangzhou high-speed railway dataset demonstrate a practical performance of the component detection with good adaptation and robustness in complex environments, feasible to accurately inspect the extremely tiny defects on the various catenary components.
Video Propagation Networks

We propose a technique that propagates information forward through video data. The method is conceptually simple and can be applied to tasks that require the propagation of structured information, such as semantic labels, based on video content. We propose a Video Propagation Network that processes video frames in an adaptive manner. The model is applied online: it propagates information forward without the need to access future frames. In particular we combine two components, a temporal bilateral network for dense and video adaptive filtering, followed by a spatial network to refine features and increased flexibility. We present experiments on video object segmentation and semantic video segmentation and show increased performance comparing to the best previous task-specific methods, while having favorable runtime. Additionally we demonstrate our approach on an example regression task of color propagation in a grayscale video.
On the difficulty of training recurrent neural networks

There are two widely known issues with properly training Recurrent Neural Networks, the vanishing and the exploding gradient problems detailed in Bengio et al. (1994). In this paper we attempt to improve the understanding of the underlying issues by exploring these problems from an analytical, a geometric and a dynamical systems perspective. Our analysis is used to justify a simple yet effective solution. We propose a gradient norm clipping strategy to deal with exploding gradients and a soft constraint for the vanishing gradients problem. We validate empirically our hypothesis and proposed solutions in the experimental section.
Ontology-based information extraction for juridical events with case studies in Brazilian legal realm

The number of available legal documents has presented an enormous growth in recent years, and the digital processing of such materials is prompting the necessity of systems that support the automatic relevant information extraction. This work presents a system for ontology-based information extraction from natural language texts, able to identify a set of legal events. The system is based on an innovative methodology based on domain ontology of legal events and a set of linguistic rules, integrated through inference mechanism, resulting in a flexible approach and scalable approach. A case study with the use of documents from the Superior Court in Brazil is related, with satisfactory results in precision and recall.
Leadership and Neuroscience : Can We Revolutionize the Way That Inspirational Leaders Are Identified and Developed ?

Executive Overview Recent advances in the field of neuroscience can significantly add to our understanding of leadership and its development. Specifically, we are interested in what neuroscience can tell us about inspirational leadership. Based on our findings, we discuss how future research in leadership can be combined with neuroscience, as well as potential neurofeedback interventions for the purpose of leadership development. We also consider ethical implications and applications to management-related areas beyond leadership.
The evolutionary neuroscience of musical beat perception: the Action Simulation for Auditory Prediction (ASAP) hypothesis

EVERY HUMAN CULTURE HAS SOME FORM OF MUSIC WITH A BEAT
a perceived periodic pulse that structures the perception of musical rhythm and which serves as a framework for synchronized movement to music. What are the neural mechanisms of musical beat perception, and how did they evolve? One view, which dates back to Darwin and implicitly informs some current models of beat perception, is that the relevant neural mechanisms are relatively general and are widespread among animal species. On the basis of recent neural and cross-species data on musical beat processing, this paper argues for a different view. Here we argue that beat perception is a complex brain function involving temporally-precise communication between auditory regions and motor planning regions of the cortex (even in the absence of overt movement). More specifically, we propose that simulation of periodic movement in motor planning regions provides a neural signal that helps the auditory system predict the timing of upcoming beats. This "action simulation for auditory prediction" (ASAP) hypothesis leads to testable predictions. We further suggest that ASAP relies on dorsal auditory pathway connections between auditory regions and motor planning regions via the parietal cortex, and suggest that these connections may be stronger in humans than in non-human primates due to the evolution of vocal learning in our lineage. This suggestion motivates cross-species research to determine which species are capable of human-like beat perception, i.e., beat perception that involves accurate temporal prediction of beat times across a fairly broad range of tempi.
Sentiment Analysis of Twitter Data : A Survey of Techniques

With the advancement of web technology and its growth, there is a huge volume of data present in the web for internet users and a lot of data is generated too. Internet has become a platform for online learning, exchanging ideas and sharing opinions. Social networking sites like Twitter, Facebook, Google+ are rapidly gaining popularity as they allow people to share and express their views about topics, have discussion with different communities, or post messages across the world. There has been lot of work in the field of sentiment analysis of twitter data. This survey focuses mainly on sentiment analysis of twitter data which is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous and are either positive or negative, or neutral in some cases. In this paper, we provide a survey and a comparative analyses of existing techniques for opinion mining like machine learning and lexicon-based approaches, together with evaluation metrics. Using various machine learning algorithms like Naive Bayes, Max Entropy, and Support Vector Machine, we provide research on twitter data streams.We have also discussed general challenges and applications of Sentiment Analysis on Twitter.
A fuzzy-logic-based approach to qualitative modeling

This paper discusses a general approach to qualitative modeling based on fuzzy logic. The method of qualitative modeling is divided into two parts: fuzzy modeling and linguistic approximation. It proposes to use a fuzzy clustering method (fuzzy c-means method) to identify the structure of a fuzzy model. To clarify the advantages of the proposed method, it also shows some examples of modeling, among them a model of a dynamical process and a model of a human operator’s control action.
EFFECTS OF HABITAT FRAGMENTATION ON BIODIVERSITY

■ Abstract The literature on effects of habitat fragmentation on biodiversity is huge. It is also very diverse, with different authors measuring fragmentation in different ways and, as a consequence, drawing different conclusions regarding both the magnitude and direction of its effects. Habitat fragmentation is usually defined as a landscape-scale process involving both habitat loss and the breaking apart of habitat. Results of empirical studies of habitat fragmentation are often difficult to interpret because ( a) many researchers measure fragmentation at the patch scale, not the landscape scale and ( b) most researchers measure fragmentation in ways that do not distinguish between habitat loss and habitat fragmentation per se, i.e., the breaking apart of habitat after controlling for habitat loss. Empirical studies to date suggest that habitat loss has large, consistently negative effects on biodiversity. Habitat fragmentation per se has much weaker effects on biodiversity that are at least as likely to be positive as negative. Therefore, to correctly interpret the influence of habitat fragmentation on biodiversity, the effects of these two components of fragmentation must be measured independently. More studies of the independent effects of habitat loss and fragmentation per se are needed to determine the factors that lead to positive versus negative effects of fragmentation per se. I suggest that the term “fragmentation” should be reserved for the breaking apart of habitat, independent of habitat loss.
Investigation of Flotation Parameters for Copper Recovery from Enargite and Chalcopyrite Mixed Ore

A flotation pre-treatment study for the separation of enargite (Cu3AsS4) from chalcopyrite (CuFeS2) ores of different origins was investigated in this work. The copper ore bearing enargite mineral contained 5.87mass% As and 16.50mass% Cu while the chalcopyrite bearing ore contained 0.32mass% As and 21.63mass% Cu. The two ore samples were mixed at 7 : 3 (enargite : chalcopyrite) by weight ratio to prepare a mixed ore sample with As content at 3.16 and 18.25mass% Cu for the flotation study. Effect of particle size, slurry pH, flotation time, collector type, collector addition or dosage and depressants were investigated to evaluate efficiency of enargite separation from chalcopyrite and recovery of both minerals as separate concentrates. For enargite single ore flotation, the 38­75μm size fraction showed that over 98% of enargite was selectively recovered within 5min at slurry pH of 4 and As content in the final tailings was reduced to 0.22mass%. In mix ore (enargite + chalcopyrite) flotation, 97% of enargite was first removed at pH 4 followed by chalcopyrite flotation at pH 8, and over 95% recovery was achieved in 15min flotation time. The As content in the final tailings was reduced to 0.1mass%.
Transfer Learning Based Evolutionary Algorithm for Composite Face Sketch Recognition

Matching facial sketches to digital face images has widespread application in law enforcement scenarios. Recent advancements in technology have led to the availability of sketch generation tools, minimizing the requirement of a sketch artist. While these sketches have helped in manual authentication, matching composite sketches with digital mugshot photos automatically show high modality gap. This research aims to address the task of matching a composite face sketch image to digital images by proposing a transfer learning based evolutionary algorithm. A new feature descriptor, Histogram of Image Moments, has also been presented for encoding features across modalities. Moreover, IIITD Composite Face Sketch Database of 150 subjects is presented to fill the gap due to limited availability of databases in this problem domain. Experimental evaluation and analysis on the proposed dataset show the effectiveness of the transfer learning approach for performing cross-modality recognition.
Extreme Learning Machine for Regression and Multiclass Classification

Due to the simplicity of their implementations, least square support vector machine (LS-SVM) and proximal support vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM and PSVM cannot be used in regression and multiclass classification applications directly, although variants of LS-SVM and PSVM have been proposed to handle such cases. This paper shows that both LS-SVM and PSVM can be simplified further and a unified learning framework of LS-SVM, PSVM, and other regularization algorithms referred to extreme learning machine (ELM) can be built. ELM works for the “generalized” single-hidden-layer feedforward networks (SLFNs), but the hidden layer (or called feature mapping) in ELM need not be tuned. Such SLFNs include but are not limited to SVM, polynomial network, and the conventional feedforward neural networks. This paper shows the following: 1) ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly; 2) from the optimization method point of view, ELM has milder optimization constraints compared to LS-SVM and PSVM; 3) in theory, compared to ELM, LS-SVM and PSVM achieve suboptimal solutions and require higher computational complexity; and 4) in theory, ELM can approximate any target continuous function and classify any disjoint regions. As verified by the simulation results, ELM tends to have better scalability and achieve similar (for regression and binary class cases) or much better (for multiclass cases) generalization performance at much faster learning speed (up to thousands times) than traditional SVM and LS-SVM.
Malignant Narcissism: from fairy tales to harsh reality.

Malignant Narcissism has been recognized as a serious condition but it has been largely ignored in psychiatric literature and research. In order to bring this subject to the attention of mental health professionals, this paper presents a contemporary synthesis of the biopsychosocial dynamics and recommendations for treatment of Malignant Narcissism.



We reviewed the literature on Malignant Narcissism which was sparse. It was first described in psychiatry by Otto Kernberg in 1984. There have been few contributions to the literature since that time. We discovered that the syndrome of Malignant Narcissism was expressed in fairy tales as a part of the collective unconscious long before it was recognized by psychiatry. We searched for prominent malignant narcissists in recent history. We reviewed the literature on treatment and developed categories for family assessment.



Malignant Narcissism is described as a core Narcissistic personality disorder, antisocial behavior, ego-syntonic sadism, and a paranoid orientation. There is no structured interview or self-report measure that identifies Malignant Narcissism and this interferes with research, clinical diagnosis and treatment. This paper presents a synthesis of current knowledge about Malignant Narcissism and proposes a foundation for treatment.



Malignant Narcissism is a severe personality disorder that has devastating consequences for the family and society. It requires attention within the discipline of psychiatry and the social science community. We recommend treatment in a therapeutic community and a program of prevention that is focused on psychoeducation, not only in mental health professionals, but in the wider social community.
Swing-Up Control of the Pendubot: An Impulse–Momentum Approach

The standard control problem of the pendubot refers to the task of stabilizing its equilibrium configuration with the highest potential energy. Linearization of the dynamics of the pendubot about this equilibrium results in a completely controllable system and allows a linear controller to be designed for local asymptotic stability. For the underactuated pendubot, the important task is, therefore, to design a controller that will swing up both links and bring the configuration variables of the system within the region of attraction of the desired equilibrium. This paper provides a new method for swing-up control based on a series of rest-to-rest maneuvers of the first link about its vertically upright configuration. The rest-to-rest maneuvers are designed such that each maneuver results in a net gain in energy of the second link. This results in swing-up of the second link and the pendubot configuration reaching the region of attraction of the desired equilibrium. A four-step algorithm is provided for swing-up control followed by stabilization. Simulation results are presented to demonstrate the efficacy of the approach.
Entity Embeddings with Conceptual Subspaces as a Basis for Plausible Reasoning

Conceptual spaces are geometric representations of conceptual knowledge in which entities correspond to points, natural properties correspond to convex regions, and the dimensions of the space correspond to salient features. While conceptual spaces enable elegant models of various cognitive phenomena, the lack of automated methods for constructing such representations have so far limited their application in artificial intelligence. To address this issue, we propose a method which learns a vector-space embedding of entities from Wikipedia and constrains this embedding such that entities of the same semantic type are located in some lower-dimensional subspace. We experimentally demonstrate the usefulness of these subspaces as approximate conceptual space representations by showing, among others, that important features can be modelled as directions and that natural properties tend to correspond to convex regions.
Deep Learning Methods for Improved Decoding of Linear Codes

The problem of low complexity, close to optimal, channel decoding of linear codes with short to moderate block length is considered. It is shown that deep learning methods can be used to improve a standard belief propagation decoder, despite the large example space. Similar improvements are obtained for the min-sum algorithm. It is also shown that tying the parameters of the decoders across iterations, so as to form a recurrent neural network architecture, can be implemented with comparable results. The advantage is that significantly less parameters are required. We also introduce a recurrent neural decoder architecture based on the method of successive relaxation. Improvements over standard belief propagation are also observed on sparser Tanner graph representations of the codes. Furthermore, we demonstrate that the neural belief propagation decoder can be used to improve the performance, or alternatively reduce the computational complexity, of a close to optimal decoder of short BCH codes.
The Combination of HMAX and HOGs in an Attention Guided Framework for Object Localization

Object detection and localization is a challenging task. Among several approaches, more recently hierarchical methods of feature-based object recognition have been developed and demonstrated high-end performance measures. Inspired by the knowledge about the architecture and function of the primate visual system, the computational HMAX model has been proposed. At the same time robust visual object recognition was proposed using feature distributions, e.g. histograms of oriented gradients (HOGs). Since both models build upon an edge representation of the input image, the question arises, whether one kind of approach might be superior to the other. Introducing a new biologically inspired attention steered processing framework, we demonstrate that the combination of both approaches gains the best results.