nils-herrmann/clean_scirepeval huggingface dataset:
A Novel Biologically Inspired Visual Cognition Model: Automatic Extraction of Semantics, Formation of Integrated Concepts, and Reselection Features for Ambiguity

Techniques that integrate neuroscience and information science benefit both fields. Many related models have been proposed in computer vision; however, in general, the robustness and recognition precision are still key problems in object recognition models. In this paper, inspired by the process by which humans recognize objects and its biological mechanisms, a new integrated and dynamic framework is proposed that mimics the semantic extraction, concept formation and feature reselection found in human visual processing. The main contributions of the proposed model are as follows: 1) semantic feature extraction: local semantic features are learned from episodic features extracted from raw images using a deep neural network; 2) integrated concept formation: concepts are formed using the local semantic information and structural information is learned through a network; and 3) feature reselection: when ambiguity is detected during the recognition process, distinctive features based on the differences between the ambiguous candidates are reselected for recognition. Experimental results on four datasets show that—compared with other methods—the new proposed model is more robust and achieves higher precision for visual recognition, especially when the input samples are semantically ambiguous. Meanwhile, the introduced biological mechanisms further strengthen the interaction between neuroscience and information science.
Women in physics in the United States: recruitment and retention

Initiatives to increase the number, persistence, and success of women in physics in the US reach pre-teen girls through senior women. Programs exist at both the local and national levels. In addition, researchers have investigated issues related to gender equity in physics and physics education. Anecdotal evidence suggests increased media coverage of the underrepresentation of women in science. All of these efforts are both motivated and made more effective by the collection and presentation of data on the presence, persistence, and promise of women in physics.
Quote RTs on Twitter: usage of the new feature for political discourse

Social media platforms provide several social interactional features. Due to the large scale reach of social media, these interactional features help enable various types of political discourse. Constructive and diversified discourse is important for sustaining healthy communities and reducing the impact of echo chambers. In this paper, we empirically examine the role of a newly introduced Twitter feature, 'quote retweets' (or 'quote RTs') in political discourse, specifically whether it has led to improved, civil, and balanced exchange. Quote RTs allow users to quote the tweet they retweet, while adding a short comment. Our analysis using content, network and crowd labeled data indicates that the feature has increased political discourse and its diffusion, compared to existing features. We discuss the implications of our findings in understanding and reducing online polarization.
Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning

Most successful information extraction systems operate with access to a large collection of documents. In this work, we explore the task of acquiring and incorporating external evidence to improve extraction accuracy in domains where the amount of training data is scarce. This process entails issuing search queries, extraction from new sources and reconciliation of extracted values, which are repeated until sufficient evidence is collected. We approach the problem using a reinforcement learning framework where our model learns to select optimal actions based on contextual information. We employ a deep Q-network, trained to optimize a reward function that reflects extraction accuracy while penalizing extra effort. Our experiments on two databases -- of shooting incidents, and food adulteration cases -- demonstrate that our system significantly outperforms traditional extractors and a competitive meta-classifier baseline.
Altmetrics of "altmetrics" using Google Scholar, Twitter, Mendeley, Facebook, Google-plus, CiteULike, Blogs and Wiki

We measure the impact of "altmetrics" field by deploying altmetrics indicators using the data from Google Scholar, Twitter, Mendeley, Facebook, Google-plus, CiteULike, Blogs and Wiki during 2010- 2014. To capture the social impact of scientific publications, we propose an index called alt-index, analogues to h-index. Across the deployed indices, our results have shown high correlation among the indicators that capture social impact. While we observe medium Pearson's correlation (\r{ho}= .247) among the alt-index and h-index, a relatively high correlation is observed between social citations and scholarly citations (\r{ho}= .646). Interestingly, we find high turnover of social citations in the field compared with the traditional scholarly citations, i.e. social citations are 42.2 % more than traditional citations. The social mediums such as Twitter and Mendeley appear to be the most effective channels of social impact followed by Facebook and Google-plus. Overall, altmetrics appears to be working well in the field of "altmetrics".
Classifying Syntactic Regularities for Hundreds of Languages

This paper presents a comparison of classification methods for linguistic typology for the purpose of expanding an extensive, but sparse language resource: the World Atlas of Language Structures (WALS) (Dryer and Haspelmath, 2013). We experimented with a variety of regression and nearest-neighbor methods for use in classification over a set of 325 languages and six syntactic rules drawn from WALS. To classify each rule, we consider the typological features of the other five rules; linguistic features extracted from a word-aligned Bible in each language; and genealogical features (genus and family) of each language. In general, we find that propagating the majority label among all languages of the same genus achieves the best accuracy in label pre- diction. Following this, a logistic regression model that combines typological and linguistic features offers the next best performance. Interestingly, this model actually outperforms the majority labels among all languages of the same family.
Thermally controlled comb generation and soliton modelocking in microresonators.

We report, to the best of our knowledge, the first demonstration of thermally controlled soliton mode-locked frequency comb generation in microresonators. By controlling the electric current through heaters integrated with silicon nitride microresonators, we demonstrate a systematic and repeatable pathway to single- and multi-soliton mode-locked states without adjusting the pump laser wavelength. Such an approach could greatly simplify the generation of mode-locked frequency combs and facilitate applications such as chip-based dual-comb spectroscopy.
The phenomenology of trapped inflation

Trapped inflation is a mechanism in which particle production from the moving inflaton is the main source of friction in the inflaton equation of motion. The produced fields source inflaton perturbations, which dominate over the vacuum ones. We review the computation of the perturbations performed in the original work and in a successive analysis that makes use of the effective field theory (EFT) of inflation. This second paper employs the power spectrum of the inflaton perturbations obtained in the original work, and obtains a bispectrum which parametrically agrees with that of the original work, even if it originates from a different operator. We show that one approximation used in the original work to compute the correlators of the produced quanta is invalid. We replace this approximation with an exact solution of the system of equations developed in the original work and obtain a different amplitude for the power spectrum of the inflaton perturbations, which has a significant consequence for the allowed region of parameter space in this mechanism. We then show that the operator responsible for the bispectrum in the EFT computation does not arise from the computational scheme developed in the original paper and that the agreement between the bispectra of the two works is also caused by the invalid approximation.
How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation

We investigate evaluation metrics for dialogue response generation systems where supervised labels, such as task completion, are not available. Recent works in response generation have adopted metrics from machine translation to compare a model's generated response to a single target response. We show that these metrics correlate very weakly with human judgements in the non-technical Twitter domain, and not at all in the technical Ubuntu domain. We provide quantitative and qualitative results highlighting specific weaknesses in existing metrics, and provide recommendations for future development of better automatic evaluation metrics for dialogue systems.
On the Simultaneous Preservation of Privacy and Community Structure in Anonymized Networks

We consider the problem of performing community detection on a network, while maintaining privacy, assuming that the adversary has access to an auxiliary correlated network. We ask the question "Does there exist a regime where the network cannot be deanonymized perfectly, yet the community structure could be learned?." To answer this question, we derive information theoretic converses for the perfect deanonymization problem using the Stochastic Block Model and edge sub-sampling. We also provide an almost tight achievability result for perfect deanonymization. 
We also evaluate the performance of percolation based deanonymization algorithm on Stochastic Block Model data-sets that satisfy the conditions of our converse. Although our converse applies to exact deanonymization, the algorithm fails drastically when the conditions of the converse are met. Additionally, we study the effect of edge sub-sampling on the community structure of a real world dataset. Results show that the dataset falls under the purview of the idea of this paper. There results suggest that it may be possible to prove stronger partial deanonymizability converses, which would enable better privacy guarantees.
Resnet in Resnet: Generalizing Residual Architectures

Residual networks (ResNets) have recently achieved state-of-the-art on challenging computer vision tasks. We introduce Resnet in Resnet (RiR): a deep dual-stream architecture that generalizes ResNets and standard CNNs and is easily implemented with no computational overhead. RiR consistently improves performance over ResNets, outperforms architectures with similar amounts of augmentation on CIFAR-10, and establishes a new state-of-the-art on CIFAR-100.
On kernel methods for covariates that are rankings

Permutation-valued features arise in a variety of applications, either in a direct way when preferences are elicited over a collection of items, or an indirect way in which numerical ratings are converted to a ranking. To date, there has been relatively limited study of regression, classification, and testing problems based on permutation-valued features, as opposed to permutation-valued responses. This paper studies the use of reproducing kernel Hilbert space methods for learning from permutation-valued features. These methods embed the rankings into an implicitly defined function space, and allow for efficient estimation of regression and test functions in this richer space. Our first contribution is to characterize both the feature spaces and spectral properties associated with two kernels for rankings, the Kendall and Mallows kernels. Using tools from representation theory, we explain the limited expressive power of the Kendall kernel by characterizing its degenerate spectrum, and in sharp contrast, we prove that Mallows' kernel is universal and characteristic. We also introduce families of polynomial kernels that interpolate between the Kendall (degree one) and Mallows' (infinite degree) kernels. We show the practical effectiveness of our methods via applications to Eurobarometer survey data as well as a Movielens ratings dataset.
The Haber Process Made Efficient by Hydroxylated Graphene

The Haber-Bosch process is the main industrial method for producing ammonia from diatomic nitrogen and hydrogen. Very demanding energetically, it uses an iron catalyst, and requires high temperature and pressure. Any improvement of the Haber process will have an extreme scientific and economic impact. We report a significant increase of ammonia production using hydroxylated graphene. Exploiting the polarity difference between N2/H2 and NH3, as well as the universal proton acceptor behavior of NH3, we demonstrate a strong shift of the equilibrium of the Haber-Bosch process towards ammonia. Hydroxylated graphene provides the polar environment favoring the forward reaction, and remain stable under the investigated thermodynamic conditions. Ca. 50 kJ mol-1 enthalpy gain and ca. 60-70 kJ mol-1 free energy gain are achieved at 298-1300 K and 1-1000 bar, strongly shifting the reaction equilibrium towards the product. A clear microscopic interpretation of the observed phenomenon is given using electronic structure calculations and real-time reactive simulations. The demonstrated principle can be applied with many polar groups functionalizing a substrate with a high surface area, provided that the system is chemically inert to H2, N2 and NH3. The modified Haber-Bosch process is of significant importance to the chemical industry, since it provides a substantial increase of the reaction yield while decreasing the temperature and pressure, thereby, reducing the cost.
"Did I Say Something Wrong?" A Word-Level Analysis of Wikipedia Articles for Deletion Discussions

This thesis focuses on gaining linguistic insights into textual discussions on a word level. It was of special interest to distinguish messages that constructively contribute to a discussion from those that are detrimental to them. Thereby, we wanted to determine whether “I”- and “You”-messages are indicators for either of the two discussion styles. These messages are nowadays often used in guidelines for successful communication. Although their effects have been successfully evaluated multiple times, a large-scale analysis has never been conducted. Thus, we used Wikipedia Articles for Deletion (short: AfD) discussions together with the records of blocked users and developed a fully automated creation of an annotated data set. In this data set, messages were labelled either constructive or disruptive. We applied binary classifiers to the data to determine characteristic words for both discussion styles. Thereby, we also investigated whether function words like pronouns and conjunctions play an important role in distinguishing the two. We found that “You”-messages were a strong indicator for disruptive messages which matches their attributed effects on communication. However, we found “I”-messages to be indicative for disruptive messages as well which is contrary to their attributed effects. The importance of function words could neither be confirmed nor refuted. Other characteristic words for either communication style were not found. Yet, the results suggest that a different model might represent disruptive and constructive messages in textual discussions better.
Do You See What I Mean? Visual Resolution of Linguistic Ambiguities

Understanding language goes hand in hand with the ability to integrate complex contextual information obtained via perception. In this work, we present a novel task for grounded language understanding: disambiguating a sentence given a visual scene which depicts one of the possible interpretations of that sentence. To this end, we introduce a new multimodal corpus containing ambiguous sentences, representing a wide range of syntactic, semantic and discourse ambiguities, coupled with videos that visualize the different interpretations for each sentence. We address this task by extending a vision model which determines if a sentence is depicted by a video. We demonstrate how such a model can be adjusted to recognize different interpretations of the same underlying sentence, allowing to disambiguate sentences in a unified fashion across the different ambiguity types.
GPU Computing in Bayesian Inference of Realized Stochastic Volatility Model

The realized stochastic volatility (RSV) model that utilizes the realized volatility as additional information has been proposed to infer volatility of financial time series. We consider the Bayesian inference of the RSV model by the Hybrid Monte Carlo (HMC) algorithm. The HMC algorithm can be parallelized and thus performed on the GPU for speedup. The GPU code is developed with CUDA Fortran. We compare the computational time in performing the HMC algorithm on GPU (GTX 760) and CPU (Intel i7-4770 3.4GHz) and find that the GPU can be up to 17 times faster than the CPU. We also code the program with OpenACC and find that appropriate coding can achieve the similar speedup with CUDA Fortran.
Input-output relationship in social communications characterized by spike train analysis

We study the dynamical properties of human communication through different channels, i.e., short messages, phone calls, and emails, adopting techniques from neuronal spike train analysis in order to characterize the temporal fluctuations of successive interevent times. We first measure the so-called local variation (LV) of incoming and outgoing event sequences of users and find that these in- and out-LV values are positively correlated for short messages and uncorrelated for phone calls and emails. Second, we analyze the response-time distribution after receiving a message to focus on the input-output relationship in each of these channels. We find that the time scales and amplitudes of response differ between the three channels. To understand the effects of the response-time distribution on the correlations between the LV values, we develop a point process model whose activity rate is modulated by incoming and outgoing events. Numerical simulations of the model indicate that a quick response to incoming events and a refractory effect after outgoing events are key factors to reproduce the positive LV correlations.
Pointing the Unknown Words

The problem of rare and unknown words is an important issue that can potentially influence the performance of many NLP systems, including both the traditional count-based and the deep learning models. We propose a novel way to deal with the rare and unseen words for the neural network models using attention. Our model uses two softmax layers in order to predict the next word in conditional language models: one predicts the location of a word in the source sentence, and the other predicts a word in the shortlist vocabulary. At each time-step, the decision of which softmax layer to use choose adaptively made by an MLP which is conditioned on the context.~We motivate our work from a psychological evidence that humans naturally have a tendency to point towards objects in the context or the environment when the name of an object is not known.~We observe improvements on two tasks, neural machine translation on the Europarl English to French parallel corpora and text summarization on the Gigaword dataset using our proposed model.
Data-driven dynamic decision models

This article outlines a method for automatically generating models of dynamic decision-making that both have strong predictive power and are interpretable in human terms. This is useful for designing empirically grounded agent-based simulations and for gaining direct insight into observed dynamic processes. We use an efficient model representation and a genetic algorithm-based estimation process to generate simple approximations that explain most of the structure of complex stochastic processes. This method, implemented in C++ and R, scales well to large data sets. We apply our methods to empirical data from human subjects game experiments and international relations. We also demonstrate the method's ability to recover known data-generating processes by simulating data with agent-based models and correctly deriving the underlying decision models for multiple agent models and degrees of stochasticity.
Robust Optimization of Credit Portfolios

We introduce a dynamic credit portfolio framework where optimal investment strategies are robust against misspecifications of the reference credit model. The risk-averse investor models his fear of credit risk misspecification by considering a set of plausible alternatives whose expected log likelihood ratios are penalized. We provide an explicit characterization of the optimal robust bond investment strategy, in terms of default state dependent value functions associated with the max-min robust optimization criterion. The value functions can be obtained as the solutions of a recursive system of Hamilton-Jacobi-Bellman (HJB) equations. We show that each HJB equation is equivalent to a suitably truncated equation admitting a unique bounded regular solution. The truncation technique relies on estimates for the solution of the master HJB equation that we establish.
To Follow or Not to Follow: Analyzing the Growth Patterns of the Trumpists on Twitter

In this paper, we analyze the growth patterns of Donald Trump's followers (Trumpists, henceforth) on Twitter. We first construct a random walk model with a time trend to study the growth trend and the effects of public debates. We then analyze the relationship between Trump's activity on Twitter and the growth of his followers. Thirdly, we analyze the effects of such controversial events as calling for Muslim ban and his 'schlonged' remark.
A Flexible Galerkin Scheme for Option Pricing in Lévy Models

One popular approach to option pricing in L\'evy models is through solving the related partial integro differential equation (PIDE). For the numerical solution of such equations powerful Galerkin methods have been put forward e.g. by Hilber et al. (2013). As in practice large classes of models are maintained simultaneously, flexibility in the driving L\'evy model is crucial for the implementation of these powerful tools. In this article we provide such a flexible finite element Galerkin method. To this end we exploit the Fourier representation of the infinitesimal generator, i.e. the related symbol, which is explicitly available for the most relevant L\'evy models. Empirical studies for the Merton, NIG and CGMY model confirm the numerical feasibility of the method.