nils-herrmann/clean_scirepeval huggingface dataset:
Code-Carrier Divergence Monitoring for the GPS Local Area Augmentation System

Code-carrier smoothing is a commonly used method in Differential GPS (DGPS) systems to mitigate the effects of receiver noise and multipath. The FAA’s Local Area Augmentation System (LAAS) uses this technique to help provide the navigation performance needed for aircraft precision approach and landing. However, unless the reference and user smoothing filter implementations are carefully matched, divergence between the code and carrier ranging measurements will cause differential ranging errors. The FAA’s LAAS Ground Facility (LGF) reference station will implement a prescribed first-order Linear Time Invariant (LTI) filter. Yet flexibility must be provided to avionics manufacturers in their airborne filter implementations. While the LGF LTI filter is one possible means for airborne use, its relatively slow transient response (acceptable for a ground based receiver) is not ideal at the aircraft because of frequent filter resets following losses of low elevation satellite signals (caused by aircraft attitude motion). However, in the presence of a code-carrier divergence (CCD) anomaly at the GPS satellite, large divergence rates are theoretically possible, and therefore protection must be provided by the LGF through direct monitoring for such events. In response, this paper addresses the impact of the CCD threat to LAAS differential ranging error and defines an LGF monitor to ensure navigation integrity. Differential ranging errors resulting from unmatched filter designs and different ground/air filter start times are analyzed in detail, and the requirements for the LGF CCD monitor are derived. A CCD integrity monitor algorithm is then developed to directly estimate and detect anomalous divergence rates. The monitor algorithm is implemented and successfully tested using archived field data from the LAAS Test Prototype (LTP) at the William J. Hughes FAA Technical Center. Finally, the paper provides recommendations for initial monitor implementation and future work.
FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors

Face Super-Resolution (SR) is a domain-specific superresolution problem. The facial prior knowledge can be leveraged to better super-resolve face images. We present a novel deep end-to-end trainable Face Super-Resolution Network (FSRNet), which makes use of the geometry prior, i.e., facial landmark heatmaps and parsing maps, to super-resolve very low-resolution (LR) face images without well-aligned requirement. Specifically, we first construct a coarse SR network to recover a coarse high-resolution (HR) image. Then, the coarse HR image is sent to two branches: a fine SR encoder and a prior information estimation network, which extracts the image features, and estimates landmark heatmaps/parsing maps respectively. Both image features and prior information are sent to a fine SR decoder to recover the HR image. To generate realistic faces, we also propose the Face Super-Resolution Generative Adversarial Network (FSRGAN) to incorporate the adversarial loss into FSRNet. Further, we introduce two related tasks, face alignment and parsing, as the new evaluation metrics for face SR, which address the inconsistency of classic metrics w.r.t. visual perception. Extensive experiments show that FSRNet and FSRGAN significantly outperforms state of the arts for very LR face SR, both quantitatively and qualitatively.
Scikit-learn: Machine Learning in Python

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.org.
Nutritional genomics: the next frontier in the postgenomic era.

The interface between the nutritional environment and cellular/genetic processes is being referred to as "nutrigenomics." Nutrigenomics seeks to provide a molecular genetic understanding for how common dietary chemicals (i.e., nutrition) affect health by altering the expression and/or structure of an individual's genetic makeup. The fundamental concepts of the field are that the progression from a healthy phenotype to a chronic disease phenotype must occur by changes in gene expression or by differences in activities of proteins and enzymes and that dietary chemicals directly or indirectly regulate the expression of genomic information. We present a conceptual basis and specific examples for this new branch of genomic research that focuses on the tenets of nutritional genomics: 1) common dietary chemicals act on the human genome, either directly or indirectly, to alter gene expression or structure; 2) under certain circumstances and in some individuals, diet can be a serious risk factor for a number of diseases; 3) some diet-regulated genes (and their normal, common variants) are likely to play a role in the onset, incidence, progression, and/or severity of chronic diseases; 4) the degree to which diet influences the balance between healthy and disease states may depend on an individual's genetic makeup; and 5) dietary intervention based on knowledge of nutritional requirement, nutritional status, and genotype (i.e., "individualized nutrition") can be used to prevent, mitigate, or cure chronic disease.
Big data classification: problems and challenges in network intrusion prediction with machine learning

This paper focuses on the specific problem of Big Data classification of network intrusion traffic. It discusses the system challenges presented by the Big Data problems associated with network intrusion prediction. The prediction of a possible intrusion attack in a network requires continuous collection of traffic data and learning of their characteristics on the fly. The continuous collection of traffic data by the network leads to Big Data problems that are caused by the volume, variety and velocity properties of Big Data. The learning of the network characteristics require machine learning techniques that capture global knowledge of the traffic patterns. The Big Data properties will lead to significant system challenges to implement machine learning frameworks. This paper discusses the problems and challenges in handling Big Data classification using geometric representation-learning techniques and the modern Big Data networking technologies. In particular this paper discusses the issues related to combining supervised learning techniques, representation-learning techniques, machine lifelong learning techniques and Big Data technologies (e.g. Hadoop, Hive and Cloud) for solving network traffic classification problems.
Effects of coating roughness and biofouling on ship resistance and powering.

Predictions of full-scale ship resistance and powering are made for antifouling coating systems with a range of roughness and fouling conditions. The estimates are based on results from laboratory-scale drag measurements and boundary layer similarity law analysis. In the present work, predictions are made for a mid-sized naval surface combatant at cruising speed and near maximum speed. The results indicate that slime films can lead to significant increases in resistance and powering, and heavy calcareous fouling results in powering penalties up to 86% at cruising speed. The present estimates show good agreement with results from full-scale ship power trials.
Deep Learning for Classification of Colorectal Polyps on Whole-slide Images

Histopathological characterization of colorectal polyps is critical for determining the risk of colorectal cancer and future rates of surveillance for patients. However, this characterization is a challenging task and suffers from significant inter- and intra-observer variability.



We built an automatic image analysis method that can accurately classify different types of colorectal polyps on whole-slide images to help pathologists with this characterization and diagnosis.



Our method is based on deep-learning techniques, which rely on numerous levels of abstraction for data representation and have shown state-of-the-art results for various image analysis tasks.



Our method covers five common types of polyps (i.e., hyperplastic, sessile serrated, traditional serrated, tubular, and tubulovillous/villous) that are included in the US Multisociety Task Force guidelines for colorectal cancer risk assessment and surveillance. We developed multiple deep-learning approaches by leveraging a dataset of 2074 crop images, which were annotated by multiple domain expert pathologists as reference standards.



We evaluated our method on an independent test set of 239 whole-slide images and measured standard machine-learning evaluation metrics of accuracy, precision, recall, and F1 score and their 95% confidence intervals.



Our evaluation shows that our method with residual network architecture achieves the best performance for classification of colorectal polyps on whole-slide images (overall accuracy: 93.0%, 95% confidence interval: 89.0%-95.9%).



Our method can reduce the cognitive burden on pathologists and improve their efficacy in histopathological characterization of colorectal polyps and in subsequent risk assessment and follow-up recommendations.
Model Predictive Control in Power Electronics: A Hybrid Systems Approach

The field of power electronics poses challenging control problems that cannot be treated in a complete manner using traditional modelling and controller design approaches. The main difficulty arises from the hybrid nature of these systems due to the presence of semiconductor switches that induce different modes of operation and operate with a high switching frequency. Since the control techniques traditionally employed in industry feature a significant potential for improving the performance and the controller design, the field of power electronics invites the application of advanced hybrid systems methodologies. The computational power available today and the recent theoretical advances in the control of hybrid systems allow one to tackle these problems in a novel way that improves the performance of the system, and is systematic and implementable. In this paper, this is illustrated by two examples, namely the Direct Torque Control of three-phase induction motors and the optimal control of switch-mode dc-dc converters.
Sub-Sampled Newton Methods I: Globally Convergent Algorithms

Large scale optimization problems are ubiquitous in machine learning and data analysis and there is a plethora of algorithms for solving such problems. Many of these algorithms employ sub-sampling, as a way to either speed up the computations and/or to implicitly implement a form of statistical regularization. In this paper, we consider second-order iterative optimization algorithms, i.e., those that use Hessian as well as gradient information, and we provide bounds on the convergence of the variants of Newton’s method that incorporate uniform sub-sampling as a means to estimate the gradient and/or Hessian. Our bounds are non-asymptotic, i.e., they hold for finite number of data points in finite dimensions for finite number of iterations. In addition, they are quantitative and depend on the quantities related to the problem, i.e., the condition number. However, our algorithms are global and are guaranteed to converge from any initial iterate. Using random matrix concentration inequalities, one can sub-sample the Hessian in a way that the curvature information is preserved. Our first algorithm incorporates such sub-sampled Hessian while using the full gradient. We also give additional convergence results for when the sub-sampled Hessian is regularized by modifying its spectrum or ridge-type regularization. Next, in addition to Hessian sub-sampling, we also consider sub-sampling the gradient as a way to further reduce the computational complexity per iteration. We use approximate matrix multiplication results from randomized numerical linear algebra (RandNLA) to obtain the proper sampling strategy. In all these algorithms, computing the update boils down to solving a large scale linear system, which can be computationally expensive. As a remedy, for all of our algorithms, we also give global convergence results for the case of inexact updates where such linear system is solved only approximately. This paper has a more advanced companion paper  in which we demonstrate that, by doing a finer-grained analysis, we can get problem-independent bounds for local convergence of these algorithms and explore tradeoffs to improve upon the basic results of the present paper. ∗International Computer Science Institute, Berkeley, CA 94704 and Department of Statistics, University of California at Berkeley, Berkeley, CA 94720. farbod/mmahoney@stat.berkeley.edu.
A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task

Enabling a computer to understand a document so that it can answer comprehension questions is a central, yet unsolved goal of NLP. A key factor impeding its solution by machine learned systems is the limited availability of human-annotated data. Hermann et al. (2015) seek to solve this problem by creating over a million training examples by pairing CNN and Daily Mail news articles with their summarized bullet points, and show that a neural network can then be trained to give good performance on this task. In this paper, we conduct a thorough examination of this new reading comprehension task. Our primary aim is to understand what depth of language understanding is required to do well on this task. We approach this from one side by doing a careful hand-analysis of a small subset of the problems and from the other by showing that simple, carefully designed systems can obtain accuracies of 72.4% and 75.8% on these two datasets, exceeding current state-of-the-art results by over 5% and approaching what we believe is the ceiling for performance on this
Capturing Deep Correlations with 2-Way Nets

We present a noval bi-directional mapping deep neural network architecture for the task of matching vectors from two data-sources. Our approach employs tied neural network channels to project two views into a common, maximally correlated, space using the euclidean loss. To achieve both maximally correlating projection we built an encoder-decoder framework composed of two parallel networks and incorporated batch-normalization layers and dropout adapted to the model at hand. We show state of the art results on a number of computer vision tasks including MNIST image matching and sentence-image matching on the flickr8k and flickr30k datasets.
KG^2: Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddings

The AI2 Reasoning Challenge (ARC), a new benchmark dataset for question answering (QA) has been recently released. ARC only contains natural science questions authored for human exams, which are hard to answer and require advanced logic reasoning. On the ARC Challenge Set, existing state-of-the-art QA systems fail to significantly outperform random baseline, reflecting the difficult nature of this task. In this paper, we propose a novel framework for answering science exam questions, which mimics human solving process in an open-book exam. To address the reasoning challenge, we construct contextual knowledge graphs respectively for the question itself and supporting sentences. Our model learns to reason with neural embeddings of both knowledge graphs. Experiments on the ARC Challenge Set show that our model outperforms the previous state-of-the-art QA systems.
Deep Filter Banks for Texture Recognition, Description, and Segmentation

Visual textures have played a key role in image understanding because they convey important semantics of images, and because texture representations that pool local image descriptors in an orderless manner have had a tremendous impact in diverse applications. In this paper we make several contributions to texture understanding. First, instead of focusing on texture instance and material category recognition, we propose a human-interpretable vocabulary of texture attributes to describe common texture patterns, complemented by a new describable texture dataset for benchmarking. Second, we look at the problem of recognizing materials and texture attributes in realistic imaging conditions, including when textures appear in clutter, developing corresponding benchmarks on top of the recently proposed OpenSurfaces dataset. Third, we revisit classic texture represenations, including bag-of-visual-words and the Fisher vectors, in the context of deep learning and show that these have excellent efficiency and generalization properties if the convolutional layers of a deep model are used as filter banks. We obtain in this manner state-of-the-art performance in numerous datasets well beyond textures, an efficient method to apply deep features to image regions, as well as benefit in transferring features from one domain to another.
Features for Masking-Based Monaural Speech Separation in Reverberant Conditions

Monaural speech separation is a fundamental problem in speech and signal processing. This problem can be approached from a supervised learning perspective by predicting an ideal time-frequency mask from features of noisy speech. In reverberant conditions at low signal-to-noise ratios SNRs, accurate mask prediction is challenging and can benefit from effective features. In this paper, we investigate an extensive set of acoustic-phonetic features extracted in adverse conditions. Deep neural networks are used as the learning machine, and separation performance is evaluated using standard objective speech intelligibility metrics. Separation performance is systematically evaluated in both nonspeech and speech interference, in a variety of SNRs, reverberation times, and direct-to-reverberant energy ratios. Considerable performance improvement is observed by using contextual information, likely due to temporal effects of room reverberation. In addition, we construct feature combination sets using a sequential floating forward selection algorithm, and combined features outperform individual ones. We also find that optimal feature sets in anechoic conditions are different from those in reverberant conditions.
Global RDF Vector Space Embeddings

Vector space embeddings have been shown to perform well when using RDF data in data mining and machine learning tasks. Existing approaches, such as RDF2Vec, use local information, i.e., they rely on local sequences generated for nodes in the RDF graph. For word embeddings, global techniques, such as GloVe, have been proposed as an alternative. In this paper, we show how the idea of global embeddings can be transferred to RDF embeddings, and show that the results are competitive with traditional local techniques like RDF2Vec.
Hierarchical Convolutional Attention Networks for Text Classification

Recent work in machine translation has demonstrated that self-attention mechanisms can be used in place of recurrent neural networks to increase training speed without sacrificing model accuracy. We propose combining this approach with the benefits of convolutional filters and a hierarchical structure to create a document classification model that is both highly accurate and fast to train – we name our method Hierarchical Convolutional Attention Networks. We demonstrate the effectiveness of this architecture by surpassing the accuracy of the current state-of-the-art on several classification tasks while being twice as fast to train.
FREQUENTIST STATISTICS AS A THEORY OF INDUCTIVE INFERENCE

After some general remarks about the interrelation between philosophical and statistical thinking, the discussion centres largely on significance tests. These are defined as the calculation of p-values rather than as formal procedures for ”acceptance” and ”rejection”. A number of types of null hypothesis are described and a principle for evidential interpretation set out governing the implications of pvalues in the specific circumstances of each application, as contrasted with a long-run interpretation. A number of more complicated situations are discussed in which modification of the simple p-value may be essential.
Running crabs, walking crinoids, grazing gastropods: behavioral diversity and evolutionary implications of the Cabeço da Ladeira Lagerstätte (Middle Jurassic, Portugal) Caranguejos que correm, crinóides que caminham, gastrópodes que se alimentam: diversidade comportamental no Lagerstätte do Cabeço d

Ichnology is a powerful tool for understanding the evolutionary paths of animal clades, through the paleobiology of behavior preserved in Lagerstätten such as the Cabeço da Ladeira (Portugal) site. Here, the peritidal carbonates of the Chão das Pias Formation (Middle Jurassic, upper Bajocian) record the development of microbial mats in a tidal flat. Episodically, the flat was expanded during periods of equinoctial spring tides that may have been responsible for the in situ killing of several members of echinoderm clades, and the fine preservation of their endoskeleton, in addition to the behavior of an arthropod-dominated endoand epifauna. Mat-preserved shallow tiers with a moderate ichnodiversity attributed to the Cruziana ichnofacies include trackways of crabs (Laterigradus lusitanica igen. nov., isp. nov.) and shrimps (Diplopodichnus isp.); trails of fishes (?Undichna), gastropods (Archaeonassa fossulata), isocrinid crinoids (Krinodromos bentou igen. nov., isp. nov.) and insects (Haplotichnus indianensis); and burrows of shrimps or lobsters (Thalassinoides suevicus) and other arthropods (Asterosoma ludwigae, Gyrochorte comosa), or polychaetes (Rhizocorallium commune). This is implied already above (igen. nov., etc.) and seems not needed. The crab trackways and gastropod bulldozing trails, extending up to 12.3 m, are among the longest invertebrate cursorial traces ever found in the fossil record. The mortichnial crawling trail of a crinoid is the first evidence of locomotion for this group in the fossil record, and a rare unambiguous example of this ethology. Extensive continuous trackways attributed to amphibious crabs record, with great detail, typical gaits of underwater punting and walking sideways on dry land. The earliest evidences of sidewalking behavior may also be evidence for the development of the crab form during their rise in the Jurassic.
Well, how accurate is it? A Study of Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations

With this study we investigate the accuracy of deep learning models for the inference of Reynolds-Averaged Navier-Stokes solutions. We focus on a modernized Unet architecture, and evaluate a large number of trained neural networks with respect to their accuracy for the calculation of pressure and velocity distributions. In particular, we illustrate how training data size and the number of weights influence the accuracy of the solutions. With our best models we arrive at a mean relative pressure and velocity error of less than 3% across a range of previously unseen airfoil shapes. In addition all source code is publicly available in order to ensure reproducibility and to provide a starting point for researchers interested in deep learning methods for physics problems. While this work focuses on RANS solutions, the neural network architecture and learning setup are very generic, and applicable to a wide range of PDE boundary value problems on Cartesian grids.