nils-herrmann/clean_scirepeval huggingface dataset:
Adolescents' attitudes toward sports, exercise, and fitness predict physical activity 5 and 10 years later.

To determine whether adolescent attitudes towards sports, exercise, and fitness predict moderate-to-vigorous physical activity 5 and 10 years later.



A diverse group of 1902 adolescents participating in Project Eating and Activity in Teens, reported weekly moderate-to-vigorous physical activity and attitudes toward sports, exercise, and fitness in Eating and Activity in Teens-I (1998-99), Eating and Activity in Teens-II (2003-04), and Eating and Activity in Teens-III (2008-09).



Mean moderate-to-vigorous physical activity was 6.4, 5.1, and 4.0 hours/week at baseline, 5-year, and 10-year follow-up, respectively. Attitudes toward sports, exercise, and fitness together predicted moderate-to-vigorous physical activity at 5 and 10 years. Among the predictors of 5- and 10-year moderate-to-vigorous physical activity, attitude's effect size, though modest, was comparable to the effect sizes for sports participation and body mass index. Adolescents with more-favorable attitudes toward sports, exercise, and fitness engaged in approximately 30%-40% more weekly moderate-to-vigorous physical activity at follow-up (2.1 hour/week at 5 years and 1.2 hour/week at 10 years) than those with less-favorable attitudes.



Adolescents' exercise-related attitudes predict subsequent moderate-to-vigorous physical activity independent of baseline behavior suggesting that youth moderate-to-vigorous physical activity promotion efforts may provide long-term benefits by helping youth develop favorable exercise attitudes.
Recollection and familiarity in recognition memory: an event-related functional magnetic resonance imaging study.

The question of whether recognition memory judgments with and without recollection reflect dissociable patterns of brain activity is unresolved. We used event-related, functional magnetic resonance imaging (fMRI) of 12 healthy volunteers to measure hemodynamic responses associated with both studying and recognizing words. Volunteers made one of three judgments to each word during recognition: whether they recollected seeing it during study (R judgments), whether they experienced a feeling of familiarity in the absence of recollection (K judgments), or whether they did not remember seeing it during study (N judgments). Both R and K judgments for studied words were associated with enhanced responses in left prefrontal and left parietal cortices relative to N judgments for unstudied words. The opposite pattern was observed in bilateral temporoccipital regions and amygdalae. R judgments for studied words were associated with enhanced responses in anterior left prefrontal, left parietal, and posterior cingulate regions relative to K judgments. At study, a posterior left prefrontal region exhibited an enhanced response to words subsequently given R versus K judgments, but the response of this region during recognition did not differentiate R and K judgments. K judgments for studied words were associated with enhanced responses in right lateral and medial prefrontal cortex relative to both R judgments for studied words and N judgments for unstudied words, a difference we attribute to greater monitoring demands when memory judgments are less certain. These results suggest that the responses of different brain regions do dissociate according to the phenomenology associated with memory retrieval.
Exact matrix completion via convex optimization

Suppose that one observes an incomplete subset of entries selected from a low-rank matrix. When is it possible to complete the matrix and recover the entries that have not been seen? We demonstrate that in very general settings, one can perfectly recover all of the missing entries from most sufficiently large subsets by solving a convex programming problem that finds the matrix with the minimum nuclear norm agreeing with the observed entries. The techniques used in this analysis draw upon parallels in the field of compressed sensing, demonstrating that objects other than signals and images can be perfectly reconstructed from very limited information.
RENOIR - A Dataset for Real Low-Light Noise Image Reduction

The application of noise reduction or image denoising is a very important topic in the field of computer vision and image processing. Many modern and popular state of the art image denoising algorithms are trained and evaluated using images with added artificial noise. These trained algorithms and their evaluations on synthetic data may lead to incorrect conclusions about their performances on real noise. In this paper we introduce a benchmark dataset of uncompressed color images corrupted by natural noise due to low-light conditions, together with spatially and intensity-aligned low noise images of the same scenes. The dataset contains over 100 scenes and more than 400 images, including both 16-bit RAW formatted images and 8-bit BMP pixel and intensity-aligned images from 2 digital cameras (Canon S90 and Canon T3i) and a mobile phone (Xiaomi Mi3). We also introduce a method for estimating the true noise level in each of our images, since even the low noise images contain a small amount of noise. Finally, we exemplify the use of our dataset by evaluating four denoising algorithms: Active Random Field, BM3D, Bilevel MRF optimization, and Multi-Layer Perceptron. We show that while the Multi-Layer Perceptron algorithm works as well as or even better than BM3D on synthetic noise, it does not do the same on our dataset.
The intrapsychics of gender: a model of self-socialization.

This article outlines a model of the structure and the dynamics of gender cognition in childhood. The model incorporates 3 hypotheses featured in different contemporary theories of childhood gender cognition and unites them under a single theoretical framework. Adapted from Greenwald et al. (2002), the model distinguishes three constructs: gender identity, gender stereotypes, and attribute self-perceptions. The model specifies 3 causal processes among the constructs: Gender identity and stereotypes interactively influence attribute self-perceptions (stereotype emulation hypothesis); gender identity and attribute self-perceptions interactively influence gender stereotypes (stereotype construction hypothesis); and gender stereotypes and attribute self-perceptions interactively influence identity (identity construction hypothesis). The model resolves nagging ambiguities in terminology, organizes diverse hypotheses and empirical findings under a unifying conceptual umbrella, and stimulates many new research directions.
Are Computers Gender-Neutral ? Gender Stereotypic Responses to Computers

This study tested whether computers embedded with the most minimal gender cues will evoke sex-based stereotypic responses. Using an experimental paradigm (N=40) that involved computers with voice output, the study tested three sex-based stereotypes under conditions in which all suggestions of gender were removed, with the sole exception of vocal cues. In all three cases, gender stereotypic responses were obtained. Because the experimental manipulation involved no deception regarding the source of the voices, this study presents evidence that the tendency to gender stereotype is extremely powerful, extending even to inanimate machines. Are computers gender-neutral?
The Frobenius anatomy of word meanings I: subject and object relative pronouns

This paper develops a compositional vector-based semantic s of subject and object relative pronouns within a categorical fram ework. Frobenius algebras are used to formalise the operations required to mode l the semantics of relative pronouns, including passing information between the relative clause and the modified noun phrase, as well as copying, combining, and d iscarding parts of the relative clause. We develop two instantiations of the ab str ct semantics, one based on a truth-theoretic approach and one based on corpus s tatistics.
A Survey on Preprocessing Methods for Web Usage Data

World Wide Web is a huge repository of web pages and links. It provides abundance of information for the Internet users. The growth of web is tremendous as approximately one million pages are added daily. Users’ accesses are recorded in web logs. Because of the tremendous usage of web, the web log files are growing at a faster rate and the size is becoming huge. Web data mining is the application of data mining techniques in web data. Web Usage Mining applies mining techniques in log data to extract the behavior of users which is used in various applications like personalized services, adaptive web sites, customer profiling, prefetching, creating attractive web sites etc., Web usage mining consists of three phases preprocessing, pattern discovery and pattern analysis. Web log data is usually noisy and ambiguous and preprocessing is an important process before mining. For discovering patterns sessions are to be constructed efficiently. This paper reviews existing work done in the preprocessing stage. A brief overview of various data mining techniques for discovering patterns, and pattern analysis are discussed. Finally a glimpse of various applications of web usage mining is also presented.  Cleaning, Path Completion, Session Identification , User Identification, Web Log Mining
When polyhedral transformations meet SIMD code generation

Data locality and parallelism are critical optimization objectives for performance on modern multi-core machines. Both coarse-grain parallelism (e.g., multi-core) and fine-grain parallelism (e.g., vector SIMD) must be effectively exploited, but despite decades of progress at both ends, current compiler optimization schemes that attempt to address data locality and both kinds of parallelism often fail at one of the three objectives.
 We address this problem by proposing a 3-step framework, which aims for integrated data locality, multi-core parallelism and SIMD execution of programs. We define the concept of vectorizable codelets, with properties tailored to achieve effective SIMD code generation for the codelets. We leverage the power of a modern high-level transformation framework to restructure a program to expose good ISA-independent vectorizable codelets, exploiting multi-dimensional data reuse. Then, we generate ISA-specific customized code for the codelets, using a collection of lower-level SIMD-focused optimizations.
 We demonstrate our approach on a collection of numerical kernels that we automatically tile, parallelize and vectorize, exhibiting significant performance improvements over existing compilers.
Power Control in Ad-Hoc Networks: Theory, Architecture, Algorithm and Implementation of the COMPOW Protocol

We present a new protocol for power control in ad hoc networks. We describe the issues in conceptualizing the power control problem, and provide an architecturally simple as well as theoretically well founded solution. The solution is shown to simultaneously satisfy the three objectives of maximizing the traffic carrying capacity of the entire network, extending battery life through providing low power routes, and reducing the contention at the MAC layer. Further, the protocol has the plug and play feature that it can be employed in conjunction with any routing protocol that pro-actively maintains a routing table. The protocol, called COMPOW, has been implemented in the Linux kernel and we describe the software architecture and implementation details.
Smart tag tracking for livestock farming

The problem of the outdoor farming of livestock are losing of the livestock or be stolen. Owing to such problem, we hereby study, research and develop a Smart Tag for livestock farming or STLiF. In this research, there are three processes: system design, implementation of STLiF, and testing. In the first step, we design the Smart Tag by assembling IoT (Internet of Thing) devices (GPS, 3G, and Arduino) within the livestock Tag. The GPS Module receive a value of a position from the nearest satellites. The 3G Module & SIM are used for sending data to Database Server by 3G signal. The Arduino Microcontroller board are used as a controller. Then an applications on smartphone gets the data from Database Server, analyzes and indicates results. Our application can track the latest position of Livestock, show its position with Smart Tag's position, history of its movement in specific period, and then warn us when it gets out of the allowed area. The values mentioned in this research comprise latitude, longitude, date time, and the ID of Smart Tag. Finally we evaluate the Smart Tag by to testing with goat at a farm in order to make sure that Smart Tag work properly.
Distributed proportional-fairness control in microgrids via blockchain smart contracts

Residential microgrids (MGs) may host a large number of Distributed Energy Resources (DERs). The strategy that maximizes the revenue for each individual DER is the one in which the DER operates at capacity, injecting all available power into the grid. However, when the DER penetration is high and the consumption low, this strategy may lead to power surplus that causes voltage increase over recommended limits. In order to create incentives for the DER to operate below capacity, we propose a proportional-fairness control strategy in which (i) a subset of DERs decrease their own power output, sacrificing the individual revenue, and (ii) the DERs in the subset are dynamically selected based on the record of their control history. The trustworthy implementation of the scheme is carried out through a custom-designed blockchain mechanism that maintains a distributed database trusted by all DERs. In particular, the blockchain is used to stipulate and store a smart contract that enforces proportional fairness. The simulation results verify the potential of the proposed framework.
Improving the Fisher Kernel for Large-Scale Image Classification

The Fisher kernel (FK) is a generic framework which combines the benefits of generative and discriminative approaches. In the context of image classification the FK was shown to extend the popular bag-of-visual-words (BOV) by going beyond count statistics. However, in practice, this enriched representation has not yet shown its superiority over the BOV. In the first part we show that with several well-motivated modifications over the original framework we can boost the accuracy of the FK. On PASCAL VOC 2007 we increase the Average Precision (AP) from 47.9% to 58.3%. Similarly, we demonstrate state-of-the-art accuracy on CalTech 256. A major advantage is that these results are obtained using only SIFT descriptors and costless linear classifiers. Equipped with this representation, we can now explore image classification on a larger scale. In the second part, as an application, we compare two abundant resources of labeled images to learn classifiers: ImageNet and Flickr groups. In an evaluation involving hundreds of thousands of training images we show that classifiers learned on Flickr groups perform surprisingly well (although they were not intended for this purpose) and that they can complement classifiers learned on more carefully annotated datasets.
Predictive analytics and data mining

Put Predictive Analytics into Action Learn the basics of Predictive Analysis and Data Mining through an easy to understand conceptual framework and immediately practice the concepts learned using the open source RapidMiner tool. Whether you are brand new to Data Mining or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions.  Mining has become an essential tool for any enterprise that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, business intelligence and data warehousing professionals and for anyone who wants to learn Data Mining. You'll be able to: 1. Gain the necessary knowledge of different data mining techniques, so that you can select the right technique for a given data problem and create a general purpose analytics process. 2. Get up and running fast with more than two dozen commonly used powerful algorithms for predictive analytics using practical use cases. 3. Implement a simple step-by-step process for predicting an outcome or discovering hidden relationships from the data using RapidMiner, an open source GUI based data mining tool Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Naïve Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection. Implementation files can be downloaded from the book companion site
ABMOF: A Novel Optical Flow Algorithm for Dynamic Vision Sensors

Dynamic Vision Sensors (DVS), which output asynchronous log intensity change events, have potential applications in high-speed robotics, autonomous cars and drones. The precise event timing, sparse output, and wide dynamic range of the events are well suited for optical flow, but conventional optical flow (OF) algorithms are not well matched to the event stream data. This paper proposes an event-driven OF algorithm called adaptive block-matching optical flow (ABMOF). ABMOF uses time slices of accumulated DVS events. The time slices are adaptively rotated based on the input events and OF results. Compared with other methods such as gradient-based OF, ABMOF can efficiently be implemented in compact logic circuits. Results show that ABMOF achieves comparable accuracy to conventional standards such as Lucas-Kanade (LK). The main contributions of our paper are new adaptive time-slice rotation methods that ensure the generated slices have sufficient features for matching,including a feedback mechanism that controls the generated slices to have average slice displacement within the block search range. An LK method using our adapted slices is also implemented. The ABMOF accuracy is compared with this LK method on natural scene data including sparse and dense texture, high dynamic range, and fast motion exceeding 30,000 pixels per second. The paper dataset and source code are available from http://sensors.ini.uzh.ch/databases.html.
IOFlow: a software-defined storage architecture

In data centers, the IO path to storage is long and complex. It comprises many layers or "stages" with opaque interfaces between them. This makes it hard to enforce end-to-end policies that dictate a storage IO flow's performance (e.g., guarantee a tenant's IO bandwidth) and routing (e.g., route an untrusted VM's traffic through a sanitization middlebox). These policies require IO differentiation along the flow path and global visibility at the control plane. We design IOFlow, an architecture that uses a logically centralized control plane to enable high-level flow policies. IOFlow adds a queuing abstraction at data-plane stages and exposes this to the controller. The controller can then translate policies into queuing rules at individual stages. It can also choose among multiple stages for policy enforcement.
 We have built the queue and control functionality at two key OS stages-- the storage drivers in the hypervisor and the storage server. IOFlow does not require application or VM changes, a key strength for deployability. We have deployed a prototype across a small testbed with a 40 Gbps network and storage devices. We have built control applications that enable a broad class of multi-point flow policies that are hard to achieve today.
On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task

Deep convolutional neural networks are powerful tools for learning visual representations from images. However, designing e cient deep architectures to analyse volumetric medical images remains challenging. This work investigates e cient and flexible elements of modern convolutional networks such as dilated convolution and residual connection. With these essential building blocks, we propose a high-resolution, compact convolutional network for volumetric image segmentation. To illustrate its e ciency of learning 3D representation from large-scale image data, the proposed network is validated with the challenging task of parcellating 155 neuroanatomical structures from brain MR images. Our experiments show that the proposed network architecture compares favourably with state-of-the-art volumetric segmentation networks while being an order of magnitude more compact. We consider the brain parcellation task as a pretext task for volumetric image segmentation; our trained network potentially provides a good starting point for transfer learning. Additionally, we show the feasibility of voxel-level uncertainty estimation using a sampling approximation through dropout.
Three-dimensional imaging techniques: A literature review

Imaging is one of the most important tools for orthodontists to evaluate and record size and form of craniofacial structures. Orthodontists routinely use 2-dimensional (2D) static imaging techniques, but deepness of structures cannot be obtained and localized with 2D imaging. Three-dimensional (3D) imaging has been developed in the early of 1990's and has gained a precious place in dentistry, especially in orthodontics. The aims of this literature review are to summarize the current state of the 3D imaging techniques and to evaluate the applications in orthodontics.
Theories of lexical semantics

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Design of the Artificial: lessons from the biological roots of general intelligence

Our desire and fascination with intelligent machines dates back to the antiquity’s mythical automaton Talos, Aristotle’s mode of mechanical thought (syllogism) and Heron of Alexandria’s mechanical machines and automata. However, the quest for Artificial General Intelligence (AGI) is troubled with repeated failures of strategies and approaches throughout the history. This decade has seen a shift in interest towards bio-inspired software and hardware, with the assumption that such mimicry entails intelligence. Though these steps are fruitful in certain directions and have advanced automation, their singular design focus renders them highly inefficient in achieving AGI. Which set of requirements have to be met in the design of AGI? What are the limits in the design of the artificial? Here, a careful examination of computation in biological systems hints that evolutionary tinkering of contextual processing of information enabled by a hierarchical architecture is the key to build AGI.