nils-herrmann/clean_scirepeval huggingface dataset:
Evaluating the Predictive Accuracy of Association Football Forecasting Systems

Despite the increasing importance and popularity of association football forecasting systems there is no agreed method of evaluating their accuracy. We have classified the evaluators used into two broad categories: those which consider only the prediction for the observed outcome; and those which consider the predictions for the unobserved as well as observed outcome. We highlight fundamental inconsistencies between them and demonstrate that they produce wildly different conclusions about the accuracy of four different forecasting systems (Fink Tank/Castrol Predictor, Bet365, Odds Wizard, and pi-football) based on recent Premier league data. None of the existing evaluators satisfy a set of simple theoretical benchmark criteria. Hence, it is dangerous to assume that any existing evaluator can adequately assess the performance of football forecasting systems and, until evaluators are developed that address all the benchmark criteria, it is best to use multiple types of predictive evaluators (preferably based on posterior validation).
Chaotic Cryptography Using Augmented Lorenz Equations Aided by Quantum Key Distribution

We have recently developed a chaotic gas turbine whose rotational motion might simulate turbulent Rayleigh-Bénard convection. The nondimensionalized equations of motion of our turbine are expressed as a star network of N Lorenz subsystems, referred to as augmented Lorenz equations. Here, we propose an application of the augmented Lorenz equations to chaotic cryptography, as a type of symmetric secret-key cryptographic method, wherein message encryption is performed by superimposing the chaotic signal generated from the equations on a plaintext in much the same way as in one-time pad cryptography. The ciphertext is decrypted by unmasking the chaotic signal precisely reproduced with a secret key consisting of 2N-1 (e.g., N=101) real numbers that specify the augmented Lorenz equations. The transmitter and receiver are assumed to be connected via both a quantum communication channel on which the secret key is distributed using a quantum key distribution protocol and a classical data communication channel on which the ciphertext is transmitted. We discuss the security and feasibility of our cryptographic method.
Poly(ethylene glycol) and Cyclodextrin-Grafted Chitosan: From Methodologies to Preparation and Potential Biotechnological Applications

Chitosan, a polyaminosaccharide obtained by alkaline deacetylation of chitin, possesses useful properties including biodegradability, biocompatibility, low toxicity, and good miscibility with other polymers. It is extensively used in many applications in biology, medicine, agriculture, environmental protection, and the food and pharmaceutical industries. The amino and hydroxyl groups present in the chitosan backbone provide positions for modifications that are influenced by factors such as the molecular weight, viscosity, and type of chitosan, as well as the reaction conditions. The modification of chitosan by chemical methods is of interest because the basic chitosan skeleton is not modified and the process results in new or improved properties of the material. Among the chitosan derivatives, cyclodextrin-grafted chitosan and poly(ethylene glycol)-grafted chitosan are excellent candidates for a range of biomedical, environmental decontamination, and industrial purposes. This work discusses modifications including chitosan with attached cyclodextrin and poly(ethylene glycol), and the main applications of these chitosan derivatives in the biomedical field.
Analyzing Inverse Problems with Invertible Neural Networks

For many applications, in particular in natural science, the task is to determine hidden system parameters from a set of measurements. Often, the forward process from parameterto measurement-space is well-defined, whereas the inverse problem is ambiguous: multiple parameter sets can result in the same measurement. To fully characterize this ambiguity, the full posterior parameter distribution, conditioned on an observed measurement, has to be determined. We argue that a particular class of neural networks is well suited for this task – so-called Invertible Neural Networks (INNs). Unlike classical neural networks, which attempt to solve the ambiguous inverse problem directly, INNs focus on learning the forward process, using additional latent output variables to capture the information otherwise lost. Due to invertibility, a model of the corresponding inverse process is learned implicitly. Given a specific measurement and the distribution of the latent variables, the inverse pass of the INN provides the full posterior over parameter space. We prove theoretically and verify experimentally, on artificial data and real-world problems from medicine and astrophysics, that INNs are a powerful analysis tool to find multi-modalities in parameter space, uncover parameter correlations, and identify unrecoverable parameters.
Quantum computation in brain microtubules: decoherence and biological feasibility.

The Penrose-Hameroff orchestrated objective reduction (orch. OR) model assigns a cognitive role to quantum computations in microtubules within the neurons of the brain. Despite an apparently "warm, wet, and noisy" intracellular milieu, the proposal suggests that microtubules avoid environmental decoherence long enough to reach threshold for "self-collapse" (objective reduction) by a quantum gravity mechanism put forth by Penrose. The model has been criticized as regards the issue of environmental decoherence, and a recent report by Tegmark finds that microtubules can maintain quantum coherence for only 10(-13) s, far too short to be neurophysiologically relevant. Here, we critically examine the decoherence mechanisms likely to dominate in a biological setting and find that (1) Tegmark's commentary is not aimed at an existing model in the literature but rather at a hybrid that replaces the superposed protein conformations of the orch. OR theory with a soliton in superposition along the microtubule; (2) recalculation after correcting for differences between the model on which Tegmark bases his calculations and the orch. OR model (superposition separation, charge vs dipole, dielectric constant) lengthens the decoherence time to 10(-5)-10(-4) s; (3) decoherence times on this order invalidate the assumptions of the derivation and determine the approximation regime considered by Tegmark to be inappropriate to the orch. OR superposition; (4) Tegmark's formulation yields decoherence times that increase with temperature contrary to well-established physical intuitions and the observed behavior of quantum coherent states; (5) incoherent metabolic energy supplied to the collective dynamics ordering water in the vicinity of microtubules at a rate exceeding that of decoherence can counter decoherence effects (in the same way that lasers avoid decoherence at room temperature); (6) microtubules are surrounded by a Debye layer of counterions, which can screen thermal fluctuations, and by an actin gel that might enhance the ordering of water in bundles of microtubules, further increasing the decoherence-free zone by an order of magnitude and, if the dependence on the distance between environmental ion and superposed state is accurately reflected in Tegmark's calculation, extending decoherence times by three orders of magnitude; (7) topological quantum computation in microtubules may be error correcting, resistant to decoherence; and (8) the decohering effect of radiative scatterers on microtubule quantum states is negligible. These considerations bring microtubule decoherence into a regime in which quantum gravity could interact with neurophysiology.
Growth , Adoption , and Use of Mobile

As of May 2013, 56 percent of American adults had a smartphone, and most of them used it to access the Internet. One-third of smartphone users report that their phone is the primary way they go online. Just as the Internet changed retailing in the late 1990s, many argue that the transition to mobile, sometimes referred to as “Web 3.0,” will have a similarly disruptive effect (Brynjolfsson et al. 2013). In this paper, we aim to document some early effects of how mobile devices might change Internet and retail commerce. We present three main findings based on an analysis of eBay’s mobile shopping application and core Internet platform. First, and not surprisingly, the early adopters of mobile e-commerce applications appear
An Index and Test of Linear Moderated Mediation.

I describe a test of linear moderated mediation in path analysis based on an interval estimate of the parameter of a function linking the indirect effect to values of a moderator-a parameter that I call the index of moderated mediation. This test can be used for models that integrate moderation and mediation in which the relationship between the indirect effect and the moderator is estimated as linear, including many of the models described by Edwards and Lambert ( 2007 ) and Preacher, Rucker, and Hayes ( 2007 ) as well as extensions of these models to processes involving multiple mediators operating in parallel or in serial. Generalization of the method to latent variable models is straightforward. Three empirical examples describe the computation of the index and the test, and its implementation is illustrated using Mplus and the PROCESS macro for SPSS and SAS.
Zero-Shot Object Detection

We introduce and tackle the problem of zero-shot object detection (ZSD), which aims to detect object classes which are not observed during training. We work with a challenging set of object classes, not restricting ourselves to similar and/or fine-grained categories as in prior works on zero-shot classification. We present a principled approach by first adapting visual-semantic embeddings for ZSD. We then discuss the problems associated with selecting a background class and motivate two background-aware approaches for learning robust detectors. One of these models uses a fixed background class and the other is based on iterative latent assignments. We also outline the challenge associated with using a limited number of training classes and propose a solution based on dense sampling of the semantic label space using auxiliary data with a large number of categories. We propose novel splits of two standard detection datasets – MSCOCO and VisualGenome, and present extensive empirical results in both the traditional and generalized zero-shot settings to highlight the benefits of the proposed methods. We provide useful insights into the algorithm and conclude by posing some open questions to encourage further research.
Customizing an Adversarial Example Generator with Class-Conditional GANs

Adversarial examples are intentionally crafted data with the purpose of deceiving neural networks into misclassification. When we talk about strategies to create such examples, we usually refer to perturbation-based methods that fabricate adversarial examples by applying invisible perturbations onto normal data. The resulting data reserve their visual appearance to human observers, yet can be totally unrecognizable to DNN models, which in turn leads to completely misleading predictions. In this paper, however, we consider crafting adversarial examples from existing data as a limitation to example diversity. We propose a non-perturbationbased framework that generates native adversarial examples from class-conditional generative adversarial networks. As such, the generated data will not resemble any existing data and thus expand example diversity, raising the difficulty in adversarial defense. We then extend this framework to pre-trained conditional GANs, in which we turn an existing generator into an "adversarial-example generator". We conduct experiments on our approach for MNIST and CIFAR10 datasets and have satisfactory results, showing that this approach can be a potential alternative to previous attack strategies.
Functional Equivalence Revisited : Legal Translation in Persian and English Through Parallel Corpus

The translation of legal texts cannot be done without regarding legal-cultural concepts and differences between legal systems. The level of equivalence of the terms depends on the extent of relatedness of the legal systems and not on that of the languages involved. This article aims at analyzing the aspects of translation equivalence (TE) in legal translation. First, it provides a theoretical framework focusing on legal translation from the existing perspectives. Then, different types of equivalence, especially functional along with its subcategories, namely, near-equivalence, partial equivalence and nonequivalence based on Šarčević (2000) categories are elucidated, and finally some desiderata for legal translators will be suggested.
Effects of an injectable platelet-rich fibrin on osteoblast behavior and bone tissue formation in comparison to platelet-rich plasma.

Platelet-rich plasma (PRP) has been utilized for many years as a regenerative agent capable of inducing vascularization of various tissues using blood-derived growth factors. Despite this, drawbacks mostly related to the additional use of anti-coagulants found in PRP have been shown to inhibit the wound healing process. For these reasons, a novel platelet concentrate has recently been developed with no additives by utilizing lower centrifugation speeds. The purpose of this study was therefore to investigate osteoblast behavior of this novel therapy (injectable-platelet-rich fibrin; i-PRF, 100% natural with no additives) when compared to traditional PRP. Human primary osteoblasts were cultured with either i-PRF or PRP and compared to control tissue culture plastic. A live/dead assay, migration assay as well as a cell adhesion/proliferation assay were investigated. Furthermore, osteoblast differentiation was assessed by alkaline phosphatase (ALP), alizarin red and osteocalcin staining, as well as real-time PCR for genes encoding Runx2, ALP, collagen1 and osteocalcin. The results showed that all cells had high survival rates throughout the entire study period irrespective of culture-conditions. While PRP induced a significant 2-fold increase in osteoblast migration, i-PRF demonstrated a 3-fold increase in migration when compared to control tissue-culture plastic and PRP. While no differences were observed for cell attachment, i-PRF induced a significantly higher proliferation rate at three and five days when compared to PRP. Furthermore, i-PRF induced significantly greater ALP staining at 7 days and alizarin red staining at 14 days. A significant increase in mRNA levels of ALP, Runx2 and osteocalcin, as well as immunofluorescent staining of osteocalcin was also observed in the i-PRF group when compared to PRP. In conclusion, the results from the present study favored the use of the naturally-formulated i-PRF when compared to traditional PRP with anti-coagulants. Further investigation into the direct role of fibrin and leukocytes contained within i-PRF are therefore warranted to better elucidate their positive role in i-PRF on tissue wound healing.
Learning to Draw Dynamic Agent Goals with Generative Adversarial Networks

We address the problem of designing artificial agents capable of reproducing human behavior in a competitive game involving dynamic control. Given data consisting of multiple realizations of inputs generated by pairs of interacting players, we model each agent’s actions as governed by a time-varying latent goal state coupled to a control model. These goals, in turn, are described as stochastic processes evolving according to player-specific value functions depending on the current state of the game. We model these value functions using generative adversarial networks (GANs) and show that our GAN-based approach succeeds in producing sample gameplay that captures the rich dynamics of human agents. The latent goal dynamics inferred and generated by our model has applications to fields like neuroscience and animal behavior, where the underlying value functions themselves are of theoretical interest.
Transrapid Motor Winding with Optimized Grounding System ( * )

ThyssenKrupp Transrapid developed a new motor winding for synchronous long stator propulsion with optimized grounding system. The motor winding using a cable without metallic screen is presented. The function as well as the mechanical and electrical design of the grounding system is illustrated. The new design guarantees a much lower electrical stress than the load capacity of the system. The main design parameters, simulation and testing results as well as calculations of the electrical stress of the grounding system are described.
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation

Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research. This is especially important in the task of molecular graph generation, whose goal is to discover novel molecules with desired properties such as drug-likeness and synthetic accessibility, while obeying physical laws such as chemical valency. However, designing models to find molecules that optimize desired properties while incorporating highly complex and non-differentiable rules remains to be a challenging task. Here we propose Graph Convolutional Policy Network (GCPN), a general graph convolutional network based model for goaldirected graph generation through reinforcement learning. The model is trained to optimize domain-specific rewards and adversarial loss through policy gradient, and acts in an environment that incorporates domain-specific rules. Experimental results show that GCPN can achieve 61% improvement on chemical property optimization over state-of-the-art baselines while resembling known molecules, and achieve 184% improvement on the constrained property optimization task.
Low Resolution Face Recognition Across Variations in Pose and Illumination

We propose a completely automatic approach for recognizing low resolution face images captured in uncontrolled environment. The approach uses multidimensional scaling to learn a common transformation matrix for the entire face which simultaneously transforms the facial features of the low resolution and the high resolution training images such that the distance between them approximates the distance had both the images been captured under the same controlled imaging conditions. Stereo matching cost is used to obtain the similarity of two images in the transformed space. Though this gives very good recognition performance, the time taken for computing the stereo matching cost is significant. To overcome this limitation, we propose a reference-based approach in which each face image is represented by its stereo matching cost from a few reference images. Experimental evaluation on the real world challenging databases and comparison with the state-of-the-art super-resolution, classifier based and cross modal synthesis techniques show the effectiveness of the proposed algorithm.
A survey on context-aware systems

Context-aware systems offer entirely new opportunities for application developers and for end users by gathering context data and adapting systems’ behavior accordingly. Especially in combination with mobile devices such mechanisms are of great value and claim to increase usability tremendously. In this paper, we present a layered architectural framework for context-aware systems. Based on our suggested framework for analysis, we introduce various existing context-aware systems focusing on context-aware middleware and frameworks, which ease the development of context-aware applications. We discuss various approaches and analyze important aspects in context-aware computing on the basis of the presented systems.
Deep Convolutional Neural Networks for efficient vision based tunnel inspection

The inspection, assessment, maintenance and safe operation of the existing civil infrastructure consists one of the major challenges facing engineers today. Such work requires either manual approaches, which are slow and yield subjective results, or automated approaches, which depend upon complex handcrafted features. Yet, for the latter case, it is rarely known in advance which features are important for the problem at hand. In this paper, we propose a fully automated tunnel assessment approach; using the raw input from a single monocular camera we hierarchically construct complex features, exploiting the advantages of deep learning architectures. Obtained features are used to train an appropriate defect detector. In particular, we exploit a Convolutional Neural Network to construct high-level features and as a detector we choose to use a Multi-Layer Perceptron due to its global function approximation properties. Such an approach achieves very fast predictions due to the feedforward nature of Convolutional Neural Networks and Multi-Layer Perceptrons.
Networks, Netwar, and Information- Age Terrorism a New Terrorism (with Old Roots)

The rise of network forms of organization is a key consequence of the ongoing information revolution. Business organizations are being newly energized by networking, and many professional militaries are experimenting with flatter forms of organization. In this chapter, we explore the impact of networks on terrorist capabilities, and consider how this development may be associated with a move away from emphasis on traditional, episodic efforts at coercion to a new view of terror as a form of protracted warfare. Seen in this light, the recent bombings of U.S. embassies in East Africa, along with the retaliatory American missile strikes, may prove to be the opening shots of a war between a leading state and a terror network. We consider both the likely context and the conduct of such a war, and offer some insights that might inform policies aimed at defending against and countering terrorism.
Dual-Primal Graph Convolutional Networks

In recent years, there has been a surge of interest in developing deep learning methods for non-Euclidean structured data such as graphs. In this paper, we propose Dual-Primal Graph CNN, a graph convolutional architecture that alternates convolution-like operations on the graph and its dual. Our approach allows to learn both vertexand edge features and generalizes the previous graph attention (GAT) model. We provide extensive experimental validation showing state-of-the-art results on a variety of tasks tested on established graph benchmarks, including CORA and Citeseer citation networks as well as MovieLens, Flixter, Douban and Yahoo Music graph-guided recommender systems.
Linking the Thoughts: Analysis of Argumentation Structures in Scientific Publications

This paper presents the results of an annotation study focused on the fine-grained analysis of argumentation structures in scientific publications. Our new annotation scheme specifies four types of binary argumentative relations between sentences, resulting in the representation of arguments as small graph structures. We developed an annotation tool that supports the annotation of such graphs and carried out an annotation study with four annotators on 24 scientific articles from the domain of educational research. For calculating the inter-annotator agreement, we adapted existing measures and developed a novel graphbased agreement measure which reflects the semantic similarity of different annotation graphs.