nils-herrmann/clean_scirepeval huggingface dataset:
Approximate Inference via Weighted Rademacher Complexity

Rademacher complexity is often used to characterize the learnability of a hypothesis class and is known to be related to the class size. We leverage this observation and introduce a new technique for estimating the size of an arbitrary weighted set, defined as the sum of weights of all elements in the set. Our technique provides upper and lower bounds on a novel generalization of Rademacher complexity to the weighted setting in terms of the weighted set size. This generalizes Massart’s Lemma, a known upper bound on the Rademacher complexity in terms of the unweighted set size. We show that the weighted Rademacher complexity can be estimated by solving a randomly perturbed optimization problem, allowing us to derive high-probability bounds on the size of any weighted set. We apply our method to the problems of calculating the partition function of an Ising model and computing propositional model counts (#SAT). Our experiments demonstrate that we can produce tighter bounds than competing methods in both the weighted and unweighted settings.
Ubiquitous access to cultural tourism portals

Web-based tourism information systems are more and more required to provide besides traditional tourism information about hotel facilities and infrastructure also cultural content comprising material heritage, performing art, folk tradition, handicraft or simply habits of everyday life. These cultural Web applications are required not to offer online brochures only, but rather to provide both, value and service. This paper focuses on two crucial aspects of cultural Web applications comprising quality of content and quality of access. As an example for achieving quality of content in terms of comprehensiveness and cross-national nature, the MEDINA portal is presented, allowing one-stop access to cultural information of fourteen Mediterranean countries. In order to provide quality of access, the notion of ubiquity is introduced, allowing to customize Web applications towards different kinds of contexts, thus supporting the cultural tourist with device-independent, time-aware, location-aware, and personalized services.
Toward a Theory of Knowledge Reuse: Types of Knowledge Reuse Situations and Factors in Reuse Success

This paper represents a step toward a theory of knowledge reusability, with emphasis on knowledge management systems and repositories, often called organizational memory systems. Synthesis of evidence from a wide variety of sources suggests four distinct types of knowledge reuse situations according to the knowledge reuser and the purpose of knowledge reuse. The types involve shared work producers, who produce knowledge they later reuse; shared work practitioners, who reuse each other’s knowledge contributions; expertise-seeking novices; and secondary knowledge miners. Each type of knowledge reuser has different requirements for knowledge repositories. Owing to how repositories are created, reusers’ requirements often remain unmet. Repositories often require considerable rework to be useful for new reusers, but knowledge producers rarely have the resources and incentives to do a good job of repurposing knowledge. Solutions include careful use of incentives and human and technical intermediaries. 04 markus.p65 05/22/2001, 4:32 PM 57
Theory of protein folding: the energy landscape perspective.

The energy landscape theory of protein folding is a statistical description of a protein's potential surface. It assumes that folding occurs through organizing an ensemble of structures rather than through only a few uniquely defined structural intermediates. It suggests that the most realistic model of a protein is a minimally frustrated heteropolymer with a rugged funnel-like landscape biased toward the native structure. This statistical description has been developed using tools from the statistical mechanics of disordered systems, polymers, and phase transitions of finite systems. We review here its analytical background and contrast the phenomena in homopolymers, random heteropolymers, and protein-like heteropolymers that are kinetically and thermodynamically capable of folding. The connection between these statistical concepts and the results of minimalist models used in computer simulations is discussed. The review concludes with a brief discussion of how the theory helps in the interpretation of results from fast folding experiments and in the practical task of protein structure prediction.
Model-Based Language Engineering with EMFText

Model-based techniques are in wide-spread use for the design and implementation of domain specific languages (DSLs) and their tooling. The Eclipse Modeling Framework (EMF) is a frequently used environment for model-based language engineering. With its underlying modelling language Ecore, its XML serialisation support and its versatile extensibility it provides a solid grounding for many task-specific language development tools. In this tutorial, we give an introduction to model-based language engineering using EMFText, which allows users to develop powerful textual editors for Ecore-based DSLs that are tightly integrated with the EMF.
Calibration of p Values for Testing Precise Null Hypotheses

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A Correlation Clustering Framework for Community Detection

Graph clustering, or community detection, is the task of identifying groups of closely related objects in a large network. In this paper we introduce a new community-detection framework called LambdaCC that is based on a specially weighted version of correlation clustering. A key component in our methodology is a clustering resolution parameter, λ, which implicitly controls the size and structure of clusters formed by our framework. We show that, by increasing this parameter, our objective effectively interpolates between two different strategies in graph clustering: finding a sparse cut and forming dense subgraphs. Our methodology unifies and generalizes a number of other important clustering quality functions including modularity, sparsest cut, and cluster deletion, and places them all within the context of an optimization problem that has been well studied from the perspective of approximation algorithms. Our approach is particularly relevant in the regime of finding dense clusters, as it leads to a 2-approximation for the cluster deletion problem. We use our approach to cluster several graphs, including large collaboration networks and social networks.
Forecasting agriculturally driven global environmental change.

During the next 50 years, which is likely to be the final period of rapid agricultural expansion, demand for food by a wealthier and 50% larger global population will be a major driver of global environmental change. Should past dependences of the global environmental impacts of agriculture on human population and consumption continue, 10(9) hectares of natural ecosystems would be converted to agriculture by 2050. This would be accompanied by 2.4- to 2.7-fold increases in nitrogen- and phosphorus-driven eutrophication of terrestrial, freshwater, and near-shore marine ecosystems, and comparable increases in pesticide use. This eutrophication and habitat destruction would cause unprecedented ecosystem simplification, loss of ecosystem services, and species extinctions. Significant scientific advances and regulatory, technological, and policy changes are needed to control the environmental impacts of agricultural expansion.
Magnetic resonance imaging (MRI) of sexual intercourse: second experience in missionary position and initial experience in posterior position.

Our objective was to confirm that it is feasible to take images of the male and female genitals during coitus and to compare this present study with previous theories and recent radiological studies of the anatomy during sexual intercourse. Magnetic resonance imaging was used to study the anatomy of the male and female genitals during coitus. Three experiments were performed with one couple in two positions and after male ejaculation. The images obtained confirmed that during intercourse in the missionary position, the penis reaches the anterior fornix with preferential contact of the anterior vaginal wall. The posterior bladder wall was pushed forward and upward and the uterus was pushed upward and backward. The images obtained from the rear-entry position showed for the first time that the penis seems to reach the posterior fornix with preferential contact of the posterior vaginal wall. In this position, the bladder and uterus were pushed forward. A different preferential contact of the penis with the female genitals was observed with each position. These images could contribute to a better understanding of the anatomy of sexual intercourse.
A New Recurrent Neural CRF for Learning Non-linear Edge Features

Conditional Random Field (CRF) and recurrent neural models have achieved success in structured prediction. More re cently, there is a marriage of CRF and recurrent neural models, so that we can gain from both non-linear dense features and globally normalized CRF objective. These recurrent neu ral CRF models mainly focus on encode node features in CRF undirected graphs. However, edge features prove important to CRF in structured prediction. In this work, we introduce a new recurrent neural CRF model, which learns non-linear edge features, and thus makes non-linear featur es encoded completely. We compare our model with different neural models in well-known structured prediction tasks. E xperiments show that our model outperforms state-of-the-ar t methods in NP chunking, shallow parsing, Chinese word segmentation and POS tagging.
A Structured Self-attentive Sentence Embedding

This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention. Instead of using a vector, we use a 2-D matrix to represent the embedding, with each row of the matrix attending on a different part of the sentence. We also propose a self-attention mechanism and a special regularization term for the model. As a side effect, the embedding comes with an easy way of visualizing what specific parts of the sentence are encoded into the embedding. We evaluate our model on 3 different tasks: author profiling, sentiment classification and textual entailment. Results show that our model yields a significant performance gain compared to other sentence embedding methods in all of the 3 tasks.
A Theory on Urban Resilience to Floods — A Basis for Alternative Planning Practices

River cities require a management approach based on resilience to floods rather than on resistance. Resisting floods by means of levees, dams, and channelization neglects inherent uncertainties arising from human–nature couplings and fails to address the extreme events that are expected to increase with climate change, and is thereby not a reliable approach to long-term flood safety. By applying resilience theory to address system persistence through changes, I develop a theory on “urban resilience to floods” as an alternative framework for urban flood hazard management. Urban resilience to floods is defined as a city’s capacity to tolerate flooding and to reorganize should physical damage and socioeconomic disruption occur, so as to prevent deaths and injuries and maintain current socioeconomic identity. It derives from living with periodic floods as learning opportunities to prepare the city for extreme ones. The theory of urban resilience to floods challenges the conventional wisdom that cities cannot live without flood control, which in effect erodes resilience. To operationalize the theory for planning practice, a surrogate measure—the percent floodable area—is developed for assessing urban resilience to floods. To enable natural floodplain functions to build urban resilience to floods, flood adaptation is advocated in order to replace flood control for mitigating flood hazards.
A Practical Guide to CNNs and Fisher Vectors for Image Instance Retrieval

With deep learning becoming the dominant approach in computer vision, the use of representations extracted from Convolutional Neural Nets (CNNs) is quickly gaining ground on Fisher Vectors (FVs) as favoured state-of-the-art global image descriptors for image instance retrieval. While the good performance of CNNs for image classification are unambiguously recognised, which of the two has the upper hand in the image retrieval context is not entirely clear yet. In this work, we propose a comprehensive study that systematically evaluates FVs and CNNs for image retrieval. The first part compares the performances of FVs and CNNs on multiple publicly available data sets. We investigate a number of details specific to each method. For FVs, we compare sparse descriptors based on interest point detectors with dense single-scale and multi-scale variants. For CNNs, we focus on understanding the impact of depth, architecture and training data on retrieval results. Our study shows that no descriptor is systematically better than the other and that performance gains can usually be obtained by using both types together. The second part of the study focuses on the impact of geometrical transformations such as rotations and scale changes. FVs based on interest point detectors are intrinsically resilient to such transformations while CNNs do not have a built-in mechanism to ensure such invariance. We show that performance of CNNs can quickly degrade in presence of rotations while they are far less affected by changes in scale. We then propose a number of ways to incorporate the required invariances in the CNN pipeline. Overall, our work is intended as a reference guide offering practically useful and simply implementable guidelines to anyone looking for state-of-the-art global descriptors best suited to their specific image instance retrieval problem.
ALCOHOL ADVERTISING AND ADVERTISING BANS : A SURVEY OF RESEARCH METHODS , RESULTS , AND POLICY IMPLICATIONS

This chapter surveys the literatures on advertising bans and alcohol consumption or abuse, and advertising expenditures and alcohol consumption. Studies of state-level bans of billboards are examined as well as studies of international bans that cover broadcasting media. For expenditures, the survey concentrates on econometric methods and the existence of an industry advertising-sales response function. Selected results from survey-research studies of advertising and youth alcohol behaviors also are discussed. The chapter concludes that advertising bans do not reduce alcohol consumption or abuse; advertising expenditures do not have a marketwide expansion effect; and survey-research studies of youth behaviors are seriously incomplete as a basis for public policy. Results of the survey are applied to the Supreme Court’s Central Hudson test for constitutionality of restrictions on commercial speech.
BUSINESS CYCLE ANALYSIS WITH MULTIVARIATE MARKOV SWITCHING MODELS

The paper aims to describe the cyclical phases of the economy by using multivariate Markov switching models. The class of Markov switching models can be extended in two main directions in a multivariate framework. In the first approach, the switching dynamics are introduced by way of one common latent factor (Diebold and Rudebusch, 1996). In the second approach, introduced by Krolzig (1997), a VAR model with parameters depending on one common Markov chain is considered (MS VAR). We will extend the MS VAR approach allowing for the presence of specific Markov chain in each equation of the VAR (Multiple Markov Switching VAR models, MMS VAR). Dynamic factor models with regime switches, MS VAR and MMS VAR models allow for a multi-country or a multi-sector simultaneous analysis in the search of common phases which are represented by the states of the switching latent factor. Moreover, in the MMS VAR approach we explore the introduction of correlated Markov chains which allow us to evaluate the relationships among phases in different economies or sectors and introduce causality relationships, which can allow more parsimonious representations. We apply the MMS model in order to study the relationship between cyclical phases in the U.S. and Euro zone industrial production. Moreover, we construct a MMS model in order to explore the cyclical relationship between the Euro zone industrial production and the industrial component of the European Sentiment Index (ESI).  “Dating, detecting and forecasting turning points”
An Artificial Neural Network Architecture Based on Context Transformations in Cortical Minicolumns

Cortical minicolumns are considered a model of cortical organization. Their function is still a source of research and not reflected properly in modern architecture of nets in algorithms of Artificial Intelligence. We assume its function and describe it in this article. Furthermore, we show how this proposal allows to construct a new architecture, that is not based on convolutional neural networks, test it on MNIST data and receive close to Convolutional Neural Network accuracy. We also show that the proposed architecture possesses an ability to train on a small quantity of samples. To achieve these results, we enable the minicolumns to remember context transformations.
Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing

Classifying semantic relations between entity pairs in sentences is an important task in Natural Language Processing (NLP). Most previous models for relation classification rely on the high-level lexical and syntatic features obtained by NLP tools such as WordNet, dependency parser, part-ofspeech (POS) tagger, and named entity recognizers (NER). In addition, state-of-the-art neural models based on attention mechanisms do not fully utilize information of entity that may be the most crucial features for relation classification. To address these issues, we propose a novel end-to-end recurrent neural model which incorporates an entity-aware attention mechanism with a latent entity typing (LET) method. Our model not only utilizes entities and their latent types as features effectively but also is more interpretable by visualizing attention mechanisms applied to our model and results of LET. Experimental results on the SemEval-2010 Task 8, one of the most popular relation classification task, demonstrate that our model outperforms existing state-ofthe-art models without any high-level features.
StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing

Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures. Annotating NL utterances with their corresponding MRs is expensive and timeconsuming, and thus the limited availability of labeled data often becomes the bottleneck of data-driven, supervised models. We introduce STRUCTVAE, a variational auto-encoding model for semisupervised semantic parsing, which learns both from limited amounts of parallel data, and readily-available unlabeled NL utterances. STRUCTVAE models latent MRs not observed in the unlabeled data as treestructured latent variables. Experiments on semantic parsing on the ATIS domain and Python code generation show that with extra unlabeled data, STRUCTVAE outperforms strong supervised
Fuzzy Logic

Fuzzy Logic was initiated in 1965 , , , by Lotfi A. Zadeh , professor for computer science at the University of California in Berkeley. Basically, Fuzzy Logic (FL) is a multivalued logic, that allows intermediate values to be defined between conventional evaluations like true/false, yes/no, high/low, etc. Notions like rather tall or very fast can be formulated mathematically and processed by computers, in order to apply a more human-like way of thinking in the programming of computers . Fuzzy systems is an alternative to traditional notions of set membership and logic that has its origins in ancient Greek philosophy. The precision of mathematics owes its success in large part to the efforts of Aristotle and the philosophers who preceded him. In their efforts to devise a concise theory of logic, and later mathematics, the so-called ”Laws of Thought” were posited [5]. One of these, the ”Law of the Excluded Middle,” states that every proposition must either be True or False. Even when Parminedes proposed the first version of this law (around 400 B.C.) there were strong and immediate objections: for example, Heraclitus proposed that things could be simultaneously True and not True. It was Plato who laid the foundation for what would become fuzzy logic, indicating that there was a third region (beyond True and False) where these opposites ”tumbled about.” Other, more modern philosophers echoed his sentiments, notably Hegel, Marx, and Engels. But it was Lukasiewicz who first proposed a systematic alternative to the bi–valued logic of Aristotle . Even in the present time some Greeks are still outstanding examples for fussiness and fuzziness, (note: the connection to logic got lost somewhere during the last 2 mileniums [7]). Fuzzy Logic has emerged as a a profitable tool for the controlling and steering of of systems and complex industrial processes, as well as for household and entertainment electronics, as well as for other expert systems and applications like the classification of SAR data.
CS 224 n : Natural Language Processing with Deep Learning

Global Vectors for Word Representation (GloVe). Intrinsic and extrinsic evaluations. Effect of hyperparameters on analogy evaluation tasks. Correlation of human judgment with word vector distances. Dealing with ambiguity in word using contexts. Window classification. This set of notes first introduces the GloVe model for training word vectors. Then it extends our discussion of word vectors (interchangeably called word embeddings) by seeing how they can be evaluated intrinsically and extrinsically. As we proceed, we discuss the example of word analogies as an intrinsic evaluation technique and how it can be used to tune word embedding techniques. We then discuss training model weights/parameters and word vectors for extrinsic tasks. Lastly we motivate artificial neural networks as a class of models for natural language processing tasks.
Representation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval

Methods of deep neural networks (DNNs) have recently demonstrated superior performance on a number of natural language processing tasks. However, in most previous work, the models are learned based on either unsupervised objectives, which does not directly optimize the desired task, or singletask supervised objectives, which often suffer from insufficient training data. We develop a multi-task DNN for learning representations across multiple tasks, not only leveraging large amounts of cross-task data, but also benefiting from a regularization effect that leads to more general representations to help tasks in new domains. Our multi-task DNN approach combines tasks of multiple-domain classification (for query classification) and information retrieval (ranking for web search), and demonstrates significant gains over strong baselines in a comprehensive set of domain adaptation.