nils-herrmann/clean_scirepeval huggingface dataset:
ATRIX CAPSULES WITH EM ROUTING

A capsule is a group of neurons whose outputs represent different properties of the same entity. Each layer in a capsule network contains many capsules. We describe a version of capsules in which each capsule has a logistic unit to represent the presence of an entity and a 4x4 matrix which could learn to represent the relationship between that entity and the viewer (the pose). A capsule in one layer votes for the pose matrix of many different capsules in the layer above by multiplying its own pose matrix by trainable viewpoint-invariant transformation matrices that could learn to represent part-whole relationships. Each of these votes is weighted by an assignment coefficient. These coefficients are iteratively updated for each image using the Expectation-Maximization algorithm such that the output of each capsule is routed to a capsule in the layer above that receives a cluster of similar votes. The transformation matrices are trained discriminatively by backpropagating through the unrolled iterations of EM between each pair of adjacent capsule layers. On the smallNORB benchmark, capsules reduce the number of test errors by 45% compared to the state-of-the-art. Capsules also show far more resistance to white box adversarial attacks than our baseline convolutional neural network.
Democratic Institutions and Regime Survival : Parliamentary and Presidential Democracies Reconsidered

We review arguments and empirical evidence found in the comparative literature that bear on the differences in the survival rates of parliamentary and presidential democracies. Most of these arguments focus on the fact that presidential democracies are based on the separation of executive and legislative powers, while parliamentary democracies are based on the fusion of these powers. From this basic distinction several implications are derived which would lead to radically different behavior and outcomes under each regime. We argue that this perspective is misguided and that we cannot deduce the functioning of the political system from the way governments are formed. There are other provisions, constitutional or otherwise, that also affect the way parliamentary and presidential democracies operate and that may counteract some of the tendencies that we would expect to observe if we were to derive the regime’s performance from its basic constitutional principle.
A Rare Case of Gestational Gigantomastia with Hypercalcemia: The Challenges of Management and Follow up

Gigantomastia is a breast disorder marked by exaggerated rapid growth of the breasts, generally bilaterally. Since this disorder is very rare and has been reported only in sparse case reports its etiology has yet to be fully established. Treatment is aimed at improving the clinical and psychological symptoms and reducing the treatment side effects; however, the best therapeutic option varies from case to case.



The present report described a case of gestational gigantomastia in a 30-year-old woman, gravida 2, parity 1, 17 week pregnant admitted to Pars Hospital, Tehran, Iran, on May 2014. The patient was admitted to hospital at week 17 of pregnancy, although her breasts initially had begun to enlarge from the first trimester. The patient developed hypercalcemia in her 32nd week of pregnancy. The present report followed this patient from diagnosis until the completion of treatment.



Although gestational gigantomastia is a rare condition, its timely prognosis and careful examination of some conditions like hyperprolactinemia and hypercalcemia is essential in successful management of this condition.
Donation Behavior in Online Micro Charities: An Investigation of Charitable Crowdfunding Projects

Charitable crowdfunding is a burgeoning online micro charity paradigm where fund seekers request micro donations from a large group of potential funders. Despite micro charities have gone digital for more than a decade, our knowledge on individuals’ donation behavior in online micro charities (e.g., charitable crowdfunding) remains limited. To fill this gap, this study develops a model that explains individuals’ donation behavior in charitable crowdfunding. Our model was tested using data collected from 205 individuals who have read charitable crowdfunding projects. The results reveal that empathy and perceived credibility of charitable crowdfunding jointly determine a funder’s intention to donate money. Furthermore, website quality and project content quality positively influence both empathy and perceived credibility. Also noteworthy is that initiator reputation is positively related to perceived credibility while project popularity is positively associated with empathy. The findings contribute to a more nuanced understanding of individuals’ donation behavior in online micro charities.
INSTITUTIONAL INVESTORS AND STOCK MARKET VOLATILITY *

We present a theory of excess stock market volatility, in which market movements are due to trades by very large institutional investors in relatively illiquid markets. Such trades generate significant spikes in returns and volume, even in the absence of important news about fundamentals. We derive the optimal trading behavior of these investors, which allows us to provide a unified explanation for apparently disconnected empirical regularities in returns, trading volume and investor size.
Public-Private Collaboration in the Emergence of a National Electronic Identification Policy: The Case of NemID in Denmark

Governments envisioning large-scale national egovernment policies increasingly draw on collaboration with private actors, yet the relationship between dynamics and outcomes of public-private partnership (PPP) is still unclear. The involvement of the banking sector in the emergence of a national electronic identification (e-ID) in Denmark is a case in point. Drawing on an analysis of primary and secondary data, we adopt the theoretical lens of collective action to investigate how transformations over time in the convergence of interests, the interdependence of resources, and the alignment of governance models between government and the banking sector shaped the emergence of the Danish national e-ID. We propose a process model to conceptualize paths towards the emergence of public-private collaboration for digital information infrastructure – a common good.
A Virtual Reality Visualization Tool for Neuron Tracing

Tracing neurons in large-scale microscopy data is crucial to establishing a wiring diagram of the brain, which is needed to understand how neural circuits in the brain process information and generate behavior. Automatic techniques often fail for large and complex datasets, and connectomics researchers may spend weeks or months manually tracing neurons using 2D image stacks. We present a design study of a new virtual reality (VR) system, developed in collaboration with trained neuroanatomists, to trace neurons in microscope scans of the visual cortex of primates. We hypothesize that using consumer-grade VR technology to interact with neurons directly in 3D will help neuroscientists better resolve complex cases and enable them to trace neurons faster and with less physical and mental strain. We discuss both the design process and technical challenges in developing an interactive system to navigate and manipulate terabyte-sized image volumes in VR. Using a number of different datasets, we demonstrate that, compared to widely used commercial software, consumer-grade VR presents a promising alternative for scientists.
Sparse Coding of Neural Word Embeddings for Multilingual Sequence Labeling

In this paper we propose and carefully evaluate a sequence labeling framework which solely utilizes sparse indicator features derived from dense distributed word representations. The proposed model obtains (near) state-of-the art performance for both part-of-speech tagging and named entity recognition for a variety of languages. Our model relies only on a few thousand sparse coding-derived features, without applying any modification of the word representations employed for the different tasks. The proposed model has favorable generalization properties as it retains over 89.8% of its average POS tagging accuracy when trained at 1.2% of the total available training data, i.e. 150 sentences per language.
What Makes a Leader ? The Impact of Cognitive and

We estimated the effect of human capital on leadership. Our human capital measures included not only the traditional measures of education and on-the-job learning but also measures of cognitive and noncognitive abilities. The measures of cognitive abilities included numeracy, literacy, and problem solving, and the noncognitive abilities measures included perseverance, openness to learning, and social trust. Our data came from the Programme for the International Assessment of Adult Competencies (PIAAC) survey for the United States. The results indicated that, in addition to education and on-the-job learning, both cognitive and noncognitive abilities were significant and substantial determinants of leadership. More specifically, out of the cognitive abilities, the most important factor was problem-solving ability; and among noncognitive abilities included, perseverance was most important.
Incorporating Prior Domain Knowledge into Deep Neural Networks

In recent years, the large amount of labeled data available has also helped tend research toward using minimal domain knowledge, e.g., in deep neural network research. However, in many situations, data is limited and of poor quality. Can domain knowledge be useful in such a setting? In this paper, we propose domain adapted neural networks (DANN) to explore how domain knowledge can be integrated into model training for deep networks. In particular, we incorporate loss terms for knowledge available as monotonicity constraints and approximation constraints. We evaluate our model on both synthetic data generated using the popular Bohachevsky function and a real-world dataset for predicting oxygen solubility in water. In both situations, we find that our DANN model outperforms its domain-agnostic counterpart yielding an overall mean performance improvement of 19.5% with a worst- and best-case performance improvement of 4% and 42.7%, respectively.
A machine learning approach to predict movie box-office success

Predicting society's reaction to a new product in the sense of popularity and adaption rate has become an emerging field of data analysis. The motion picture industry is a multi-billion-dollar business, and there is a massive amount of data related to movies is available over the internet. This study proposes a decision support system for movie investment sector using machine learning techniques. This research helps investors associated with this business for avoiding investment risks. The system predicts an approximate success rate of a movie based on its profitability by analyzing historical data from different sources like IMDb, Rotten Tomatoes, Box Office Mojo and Metacritic. Using Support Vector Machine (SVM), Neural Network and Natural Language Processing the system predicts a movie box office profit based on some pre-released features and post-released features. This paper shows Neural Network gives an accuracy of 84.1% for pre-released features and 89.27% for all features while SVM has 83.44% and 88.87% accuracy for pre-released features and all features respectively when one away prediction is considered. Moreover, we figure out that budget, IMDb votes and no. of screens are the most important features which play a vital role while predicting a movie's box-office success.
Interaction between the native and second language phonetic subsystems

The underlying premise of this study was that the two phonetic subsystems of a bilingual interact. The study tested the hypothesis that the vowels a bilingual produces in a second language (L2) may differ from vowels produced by monolingual native speakers of the L2 as the result of either of two mechanisms: phonetic category assimilation or phonetic category dissimilation. Earlier work revealed that native speakers of Italian identify English /e/ tokens as instances of the Italian /e/ category even though English /e/ is produced with more tongue movement than Italian /e/ is. Acoustic analyses in the present study examined /e/s produced by four groups of Italian–English bilinguals who differed according to their age of arrival in Canada from Italy (early versus late) and frequency of continued Italian use (low-L1use versus high-L1-use). Early bilinguals who seldom used Italian (Early-low) were found to produce English /e/ with significantly more movement than native English speakers. However, both groups of late bilinguals (Late-low, Latehigh) tended to produced /e/ with lessmovement than NE speakers. The exaggerated movement in /e/s produced by the Early-low group participants was attributed to the dissimilation of a phonetic category they formed for English /e/ from Italian /e/. The undershoot of movement in /e/s produced by late bilinguals, on the other hand, was attributed to their failure to establish a new category for English /e/, which led to the merger of the phonetic properties of English /e/ and Italian /e/. 2002 Elsevier Science B.V. All rights reserved.
Semantic Segmentation of Colon Glands with Deep Convolutional Neural Networks and Total Variation Segmentation

Segmentation of histopathology sections is an ubiquitous requirement in digital pathology and due to the large variability of biological tissue, machine learning techniques have shown superior performance over standard image processing methods. As part of the GlaS@MICCAI2015 colon gland segmentation challenge, we present a learning-based algorithm to segment glands in tissue of benign and malignant colorectal cancer. Images are preprocessed according to the HematoxylinEosin staining protocol and two deep convolutional neural networks (CNN) are trained as pixel classifiers. The CNN predictions are then regularized using a figure-ground segmentation based on weighted total variation to produce the final segmentation result. On two test sets, our approach achieves a tissue classification accuracy of 98% and 94%, making use of the inherent capability of our system to distinguish between benign and malignant tissue.
On Mixing Fingerprints

This work explores the possibility of mixing two different fingerprints, pertaining to two different fingers, at the image level in order to generate a new fingerprint. To mix two fingerprints, each fingerprint pattern is decomposed into two different components, viz., the continuous and spiral components. After prealigning the components of each fingerprint, the continuous component of one fingerprint is combined with the spiral component of the other fingerprint. Experiments on the West Virginia University (WVU) and FVC2002 datasets show that mixing fingerprints has several benefits: (a) it can be used to generate virtual identities from two different fingers; (b) it can be used to obscure the information present in an individual's fingerprint image prior to storing it in a central database; and (c) it can be used to generate a cancelable fingerprint template, i.e., the template can be reset if the mixed fingerprint is compromised.
DEFENSIVE QUANTIZATION: WHEN EFFICIENCY MEETS ROBUSTNESS

Neural network quantization is becoming an industry standard to efficiently deploy deep learning models on hardware platforms, such as CPU, GPU, TPU, and FPGAs. However, we observe that the conventional quantization approaches are vulnerable to adversarial attacks. This paper aims to raise people’s awareness about the security of the quantized models, and we designed a novel quantization methodology to jointly optimize the efficiency and robustness of deep learning models. We first conduct an empirical study to show that vanilla quantization suffers more from adversarial attacks. We observe that the inferior robustness comes from the error amplification effect, where the quantization operation further enlarges the distance caused by amplified noise. Then we propose a novel Defensive Quantization (DQ) method by controlling the Lipschitz constant of the network during quantization, such that the magnitude of the adversarial noise remains non-expansive during inference. Extensive experiments on CIFAR-10 and SVHN datasets demonstrate that our new quantization method can defend neural networks against adversarial examples, and even achieves superior robustness than their fullprecision counterparts, while maintaining the same hardware efficiency as vanilla quantization approaches. As a by-product, DQ can also improve the accuracy of quantized models without adversarial attack.
Cannabis Roots: A Traditional Therapy with Future Potential for Treating Inflammation and Pain

The roots of the cannabis plant have a long history of medical use stretching back millennia. However, the therapeutic potential of cannabis roots has been largely ignored in modern times.  In the first century, Pliny the Elder described in Natural Histories that a decoction of the root in water could be used to relieve stiffness in the joints, gout, and related conditions. By the 17th century, various herbalists were recommending cannabis root to treat inflammation, joint pain, gout, and other conditions. There has been a subsequent paucity of research in this area, with only a few studies examining the composition of cannabis root and its medical potential. Active compounds identified and measured in cannabis roots include triterpenoids, friedelin (12.8 mg/kg) and epifriedelanol (21.3 mg/kg); alkaloids, cannabisativine (2.5 mg/kg) and anhydrocannabisativine (0.3 mg/kg); carvone and dihydrocarvone; N-(p-hydroxy-β-phenylethyl)-p-hydroxy-trans-cinnamamide (1.6 mg/kg); various sterols such as sitosterol (1.5%), campesterol (0.78%), and stigmasterol (0.56%); and other minor compounds, including choline. Of note, cannabis roots are not a significant source of Δ9-tetrahydrocannabinol (THC), cannabidiol, or other known phytocannabinoids.  The current available data on the pharmacology of cannabis root components provide significant support to the historical and ethnobotanical claims of clinical efficacy. Certainly, this suggests the need for reexamination of whole root preparations on inflammatory and malignant conditions employing modern scientific techniques.
Fundamental frequency estimation by least-squares harmonic model fitting

This paper proposes a pitch estimation algorithm that is based on optimal harmonic model fitting. The algorithm operates directly on the time-domain signal and has a relatively simple mathematical background. To increase its efficiency and accuracy, the algorithm is applied in combination with an autocorrelation-based initialization phase. For testing purposes we compare its performance on pitch-annotated corpora with several conventional time-domain pitch estimation algorithms, and also with a recently proposed one. The results show that even the autocorrelation-based first phase significantly outperforms the traditional methods, and also slightly the recently proposed yin algorithm. After applying the second phase – the harmonic approximation step – the amount of errors can be further reduced by about 20% relative to the error obtained in the first phase.
Probabilistic programming in Python using PyMC3

Probabilistic programming allows for automatic Bayesian inference on user-defined probabilistic models. Recent advances in Markov chain Monte Carlo (MCMC) sampling allow inference on increasingly complexmodels. This class ofMCMC, known as Hamiltonian Monte Carlo, requires gradient information which is often not readily available. PyMC3 is a new open source probabilistic programming framework written in Python that uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs on-the-fly to C for increased speed. Contrary to other probabilistic programming languages, PyMC3 allows model specification directly in Python code. The lack of a domain specific language allows for great flexibility and direct interaction with the model. This paper is a tutorial-style introduction to this software package. Subjects Data Mining and Machine Learning, Data Science, Scientific Computing and Simulation
The Emotional GAN : Priming Adversarial Generation of Art with Emotion

We propose a neural network method for turning emotion into art. Our approach relies on a class-conditioned generative adversarial network trained on a dataset of modern artworks labeled with emotions. We generate this dataset through a large-scale user study of art perception with human subjects. Preliminary results show our framework generates images which, apart from aesthetically appealing, exhibit various features associated with the emotions they are conditioned on.
Learning designs and learning analytics

Government and institutionally-driven reforms focused on quality teaching and learning in universities emphasize the importance of developing replicable, scalable teaching approaches that can be evaluated. In this context, learning design and learning analytics are two fields of research that may help university teachers design quality learning experiences for their students, evaluate how students are learning within that intended learning context and support personalized learning experiences for students. Learning Designs are ways of describing an educational experience such that it can be applied across a range of disciplinary contexts. Learning analytics offers new approaches to investigating the data associated with a learner's experience. This paper explores the relationship between learning designs and learning analytics.
Data Mining Application for Cyber Credit-Card Fraud Detection System

Since the evolution of the internet, many small and large companies have moved their businesses to the internet to provide services to customers worldwide. Cyber credit‐card fraud or no card present fraud is increasingly rampant in the recent years for the reason that the credit‐card i s majorly used to request payments by these companies on the internet. Therefore the need to ensure secured transactions for credit-card owners when consuming their credit cards to make electronic payments for goods and services provided on the internet is a criterion. Data mining has popularly gained recognition in combating cyber credit-card fraud because of its effective artificial intelligence (AI) techniques and algorithms that can be implemented to detect or predict fraud through Knowledge Discovery from unusual patterns derived from gathered data. In this study, a system’s model for cyber credit card fraud detection is discussed and designed. This system implements the supervised anomaly detection algorithm of Data mining to detect fraud in a real time transaction on the internet, and thereby classifying the transaction as legitimate, suspicious fraud and illegitimate transaction. The anomaly detection algorithm is designed on the Neural Networks which implements the working principal of the human brain (as we humans learns from past experience and then make our present day decisions on what we have learned from our past experience). To understand how cyber credit card fraud are being committed, in this study the different types of cyber fraudsters that commit cyber credit card fraud and the techniques used by these cyber fraudsters to commit fraud on the internet is discussed.