nils-herrmann/clean_scirepeval huggingface dataset:
Translating resistance in art activism: Hip Hop and 100 Thousand Poets for Change

This article examines the role of translational and polylingual practices in global forms of art activism. It is through translation, both narrowly and broadly speaking, that local issues with a universal resonance overcome cultural and political borders and are addressed by art activists sharing common social and political strategies, such as Hiphoppas, and artists belonging to resistance movements, like the 100 Thousand Poets for Change (100TPC) Translation represents both an object of enquiry, as a shaping force in the construction of these artists' identities, and a methodological tool for examining the interplay of central issues, such as language, culture and identity. Narrative theory and the notion of prefigurative politics are also applied to provide an interdisciplinary approach that is paramount in the analysis of all global processes.
Subjectivity and the Obliteration of Meaning: Contemporary Art, Activism, Social Movement Politics

In this article I analyse the notion that social movement politics and contemporary art interventions increasingly traverse a porous boundary, be it in terms of practices, relations, or institutions. Premised on Nicolas Bourriaud's seminal reading of 1990s art, I contend that the theory of "relational aesthetics" (2002) offers a synthetic platform from which we can understand how artistic interventions with activist connotations are increasingly moving away from the utopian and prescriptive, and thus echoing the "subjective turn" of social movement politics more widely. Based on fieldwork with contemporary artists and social movement actors in Brazil, the chapter mobilises relational aesthetics as a criteria to differentiate various forms of contemporary art intervention. Through conversation with ethnographies of radical politics, I argue that an analysis that foregrounds ephemerality, the "absolute centrality of diversity" and different forms of dissonance, allows us to productively theorise how subjectivity is elaborated and meaning created. If art really is the locus of "imminence" then understanding how these processes are contested is to grasp how prefigurative politics can have consequences for the immediate future.
SoRS: Social recommendation using global rating reputation and local rating similarity

Recommendation is an important and also challenging problem in online social networks. It needs to consider not only users' personalized interests, but also social relations between users. Indeed, in practice, users are often inclined to accept recommendations from friends or opinion leaders (users with high reputations) In this paper, we present a novel recommendation framework, social recommendation using global rating reputation and local rating similarity, which combine user reputation and social similarity based on ratings. User reputation can be obtained by iteratively calculating the correlation of historical ratings of user and intrinsic qualities of items. We view the user reputation as the user's global influence and the similarity based on rating of social relation as the user's local influence, introduce it in the basic social recommender model. Thus users with high reputation have a strong influence on the others, and on the other hand, the effect of a user with low reputation has been weakened. The recommendation accuracy of proposed framework can be improved by effectively removing nature noise because of less rigorous user ratings and strengthening the effect of user influence with high reputation. We also improve the similarity based on ratings by avoiding the high similarity with the less common ratings between friends. We evaluate our approach on three datasets including Movielens, Epinions and Douban. Empirical results demonstrate that proposed framework achieves significant improvements on recommendation accuracy. User reputation and local similarity which are both based on ratings have a lot of helpful in improvement of prediction accuracy. The reputation also can help to improve the recommendation precision with the small training sets.
Accurate and scalable social recommendation using mixed membership stochastic block models
Venue: Proceedings of the National Academy of Sciences

Recommendation systems are designed to predict users' preferences and provide them with recommendations for items such as books or movies that suit their needs. Recent developments show that some probabilistic models for user preferences yield better predictions than latent feature models such as matrix factorization. However, it has not been possible to use them in real world datasets because they are not computationally efficient. We have developed a rigorous probabilistic model that outperforms leading approaches for recommendation and whose parameters can be fitted efficiently with an algorithm whose running time scales linearly with the size of the dataset. This model and inference algorithm open the door to more approaches to recommendation and to other problems where matrix factorization is currently used. With increasing amounts of information available, modeling and predicting user preferences for books or articles, for example are becoming more important. We present a collaborative filtering model, with an associated scalable algorithm, that makes accurate predictions of users' ratings. Like previous approaches, we assume that there are groups of users and of items and that the rating a user gives an item is determined by their respective group memberships. However, we allow each user and each item to belong simultaneously to mixtures of different groups and, unlike many popular approaches such as matrix factorization, we do not assume that users in each group prefer a single group of items. In particular, we do not assume that ratings depend linearly on a measure of similarity, but allow probability distributions of ratings to depend freely on the user's and item's groups. The resulting overlapping groups and predicted ratings can be inferred with an expectation maximization algorithm whose running time scales linearly with the number of observed ratings. Our approach enables us to predict user preferences in large datasets and is considerably more accurate than the current algorithms for such large datasets.
Comparative Study on the Structures of Chinese and Korean Compound Words

The goal of the research was to compare the compound words in Chinese, an isolated language, and Korean, an agglutinative language. This research used library research. The researchers found that the main characteristics of the formation of Korean compound words were that the latter element was the central word. The method of word formation decided its lexical category. Moreover, most of the internal relationships of the compound words were connection and modification. While in Chinese, the endocentric compound noun decided the part of speech of the compound word, and could be the proceeding element or the latter element. Furthermore, Chinese contained no complicated morphological changes. It is concluded that Korean is a Subject Object Verb (SOV) language, where verb elements demonstrate a central feature of the compound verb are always a trailing part. Thus, there is no exocentric compound verb in Korean. By contrast, Chinese is a typical SVO language. When constituting the compound verbs, nouns or adjectives can function as the structural elements. Therefore, there is no permanent position for head words.
A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation

Deep learning techniques have been widely applied, achieving state of the art results in various fields of study. This survey focuses on deep learning solutions that target learning control policies for robotics applications. We carry out our discussions on the two main paradigms for learning control with deep networks: deep reinforcement learning and imitation learning. For deep reinforcement learning (DRL) we begin from traditional reinforcement learning algorithms, showing how they are extended to the deep context and effective mechanisms that could be added on top of the DRL algorithms. We then introduce representative works that utilize DRL to solve navigation and manipulation tasks in robotics. We continue our discussion on methods addressing the challenge of the reality gap for transferring DRL policies trained in simulation to real world scenarios, and summarize robotics simulation platforms for conducting DRL research. For imitation leaning, we go through its three main categories, behavior cloning, inverse reinforcement learning and generative adversarial imitation learning, by introducing their formulations and their corresponding robotics applications. Finally, we discuss the open challenges and research frontiers.
The Impact of Music Activities in a Multi Sensory Room for Children with Multiple Disabilities on Developing Positive Emotions: a Case Study

The idea of a multi sensory environment (MSE) is not only to provide stimulation for users with disabilities, but also to be calming. Through many studies by the research team, the results had proven the effectiveness of using music activities in multisensory environments for children with various types of disabilities. The purpose of the study was to duplicate the use of the multisensory environment teaching approach with music activities on developing positive emotions and other areas of development for a child with multiple disabilities. A 3 year old boy with multiple disabilities was selected by purposive sampling to participate in the study. This was a 12 week study, with 50 minute sessions once per week in a group setting. Prior to the formal teaching, 4 observations were taken to determine the participant's current condition. Both qualitative and quantitative methods were used to assess the results. The results showed the positive impact of using music activities on increasing positive emotions for the participant in the multisensory environment. Furthermore, the finding for the participant's communication skills, social interaction with peers and level of intelligence were improved when the research sessions were assessed.
Testing the Factor Structure and Measurement Invariance Across Gender of the Big Five Inventory Through Exploratory Structural Equation Modeling
Venue: Journal of personality assessment

Confirmatory factor analyses (CFAs) typically fail to support the a priori 5 factor structure of Big Five self report instruments, due in part to the overly restrictive CFA assumptions. We show that exploratory structural equation modeling (ESEM) an integration of CFA and exploratory factor analysis, overcomes these problems in relation to responses to the 44 item Big Five Inventory (BFI) administered to a large Italian community sample. ESEM fitted the data better and resulted in less correlated factors than CFA, although ESEM and CFA factor scores correlated at near unity with observed raw scores. Tests of gender invariance with a 13 model taxonomy of full measurement invariance showed that the factor structure of the BFI is gender invariant and that women score higher on Neuroticism, Agreeableness, Extraversion, and Conscientiousness. Through ESEM one could address substantively important issues about BFI psychometric properties that could not be appropriately addressed through traditional approaches.
Spectral Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach
Venue: IEEE Transactions on Geoscience and Remote Sensing

In this paper, we propose a spectral spatial feature based classification (SSFC) framework that jointly uses dimension reduction and deep learning techniques for spectral and spatial feature extraction, respectively. In this framework, a balanced local discriminant embedding algorithm is proposed for spectral feature extraction from high dimensional hyperspectral data sets. In the meantime, convolutional neural network is utilized to automatically find spatial related features at high levels. Then, the fusion feature is extracted by stacking spectral and spatial features together. Finally, the multiple feature based classifier is trained for image classification. Experimental results on well known hyperspectral data sets show that the proposed SSFC method outperforms other commonly used methods for hyperspectral image classification.
Towards high potential and ultra long life cathodes for sodium ion batteries: freestanding 3D hybrid foams of Na7V4(P2O7)4(PO4) and Na7V3(P2O7)4@biomass derived porous carbon

High potential sodium hosts have attracted enormous attention recently in view of the requirement for improving energy density for sodium ion batteries. Na7V4(P2O7)4(PO4) and Na7V3(P2O7)4 with operating potentials near 4.0 V versus Na+/Na are promising cathode candidates. But the low conductivity, limited ion intercalation kinetics and inferior stability remain critical drawbacks for their practical applications. In this paper, the design of freestanding three dimensional (3D) hybrid foams of high potential sodium hosts@biomass derived porous carbon is reported. The biological fungus realizes the formation of highly porous graphene like carbon, which constructs the 3D framework for the high potential polyanions. The polyanion nanocrystals are closely enwrapped by the biomass derived framework and build the hybrid architecture. Both the highly conductive skeleton and the hierarchically porous architecture of hybrid foams are favourable for highly efficient electron and ion transport. Furthermore, the depressed structural deterioration rate and improved contact between the active material and conductive substrate are achieved for the 3D hybrid foams in comparison with the conventional electrodes on the basis of the dynamic studies. Without additional additive or binders, the freestanding hybrid foams achieve desirable characteristics of high operating potentials, fast sodium intercalation and excellent cycling stability. The Na7V4(P2O7)4(PO4) and Na7V3(P2O7)4 based hybrid foams retain 94% and 91% respectively, of the capacities after 800 cycles at alternative 20C and 3C, demonstrating their superior high rate ultra long term cycling capability. Therefore, this research provides a low cost, highly efficient and widely applicable architecture to construct high potential and ultra long life cathodes for sodium batteries.
Unraveling the Determinants of Cancer Patients' Intention to Express Concerns
Venue: Journal of health communication

Little is known about the behavioral determinants that underlie cancer patients' intention to express concerns during a consultation. This information can be relevant to developing effective interventions for cancer patients. In this study, the integrative model of behavioral prediction (IMBP) is used as a framework to unravel the determinants of patients' intention to express concerns. The objectives of this study are to examine which of the IMBP determinants (attitude, perceived social norm, and/or self efficacy) are significantly related to intention and what content of these determinants can be targeted to effect a change in patients' intention. An online survey based on the IMBP determinants was distributed. A total of 236 cancer patients and cancer survivors participated. The results of the survey showed that patients' attitudes and perceived social norm were the most important determinants of their intention to express concerns. The largest change in patients' intention might be achieved by targeting the affective attitude, referring to the extent to which patients believe that expressing concerns is (un)pleasant, and the social norm, referring to the extent to which patients feel (un)supported by significant others in expressing concerns.
Knot Theory
Venue: Arch. Formal Proofs

In this project, developed in the course of "Projecto em Matematica" we present some basic concepts and results of knot theory. First of all, we introduce the subject, including important definitions and basic concepts. In the following section, we discuss the Reidemeister moves and their relation to equivalence between knots. Then, we develop some combinatorial techniques, including tricolorability and the linking number. In the following section, we discuss how to tabulate knots, i.e. how to describe projections and diagrams. The final section focuses on knot polynomials, one of the most important ways of telling knots apart.
Knot Theory and Its Applications

Knot theory is the embedding of one topological space into another topological space. Knot theory was first proposed as the flawed vortex model of atoms by Lord Kelvin, but was later proved to be incorrect. Some fundamental theorems of involved in knot theory include one involving Reidemeister moves (transforming knots into unknots) and knot invariants such as the Alexander Polynomial, Conway Polynomial and Jones Polynomial that originated in math and physics. It has applications in cell replication, where DNA must be unknotted and undergo a recombination process. Knots can also be used to improve encryption processes and increase security. Ellie O'Brien Knot Theory and its Applications April 26, 2016 2 4  Two diagrams represent equivalent knots if and only if one can be transformed into the other by a finite sequence of Reidemeister moves. 2 The knot invariant polynomials are skein related which means they are identical at every point except at one crossing. Ellie O'Brien Knot Theory and its Applications April 26, 2016 3 4
Knot theory in modern chemistry.
Venue: Chemical Society reviews

Knot theory is a branch of pure mathematics, but it is increasingly being applied in a variety of sciences. Knots appear in chemistry, not only in synthetic molecular design, but also in an array of materials and media, including some not traditionally associated with knots. Mathematics and chemistry can now be used synergistically to identify, characterise and create knots, as well as to understand and predict their physical properties. This tutorial review provides a brief introduction to the mathematics of knots and related topological concepts in the context of the chemical sciences. We then survey the broad range of applications of the theory to contemporary research in the field.
CITRIC ACID PRODUCTION FROM ASPERGILLUS NIGER USING Mango (Mangifera indica L. and Sweet orange (Citrus sinensis) Peels as Substrate

The demand, production and trade of citric acid have increased as it has become a commercially valuable acid due to its application in cosmetics, pharmaceutical and food industries. In the present study, mango peels as novel substrate and sweet orange were utilized for citric acid production by Aspergillus niger and the fermentation parameters were optimized. Maximum yield of citric acid was obtained using mango peel 11% sucrose concentration 5% inoculum 2% potassium dihydrogen phosphate and ammonium nitrate at pH 5 and 32deg C after 8 days. In sweet orange peel fermentation, maximum yield was obtained using 11% sweet orange peel, disodium hydrogen phosphate, inoculum 2% sucrose concentration 25% at 32deg C and pH 4 after 6 days.
Novel Applications of Potatoes

The exceptional physicochemical and compositional characteristics of potatoes and their starches make them useful for a wide variety of novel and nonfood applications. The potential of potatoes for industrial scale use is being explored in areas such as biomedicine, pharmaceuticals, paper, biodegradable packaging, and automobiles. Many studies suggest the use of potato industry waste for the production of valuable products such as biodegradable cutlery, packaging materials, xanthan gum, biosurfactants, polylactic acid (PLA) etc. Fiber reinforced PLA composite materials have been reportedly used in making interior components for automobiles. Because of the higher cost of production of these plastics compared to those of petroleum origin, development of an efficient production technology is required. Potato starch based bioplastics are increasingly becoming popular owing to their biodegradability and good physical properties. The characteristics of potato starch such as swelling power, solubility, gelatinization, rheological characteristics, and mechanical behavior are of utmost importance when selecting a starch source for a distinctive application. Worldwide industrial players are making vast investments in bio based technologies and processing plants, with an expectation of establishing a multibillion dollar industry.
A pillar[5]arene based cyanide sensor bearing on a novel cyanide induced self assemble mechanism

We have designed and synthesized a novel copillar[5]arene based chemosensor, which employ 8 hydroxyquinoline group as a binding site and signal group. By a novel cyanide induced self assemble mechanism, the pillar[5]arene based chemosensor shown high sensitivity and selectivity for cyanide in aqueous media. When cyanide was added to the solution of the chemosensor, a strong chartreuse fluorescence appeared. The detection limit of the fluorescent spectrum is 1.08 x 10 8 mol L 1 for cyanide. Moreover, test strips based on the sensor were fabricated, the chemosensor is a good cyanide test kits. In addition, we made life applications of cyanide detected, the chemosensor is also could detect cyanide in aqueous extracts from sprouting potatoes. It is worth mentioning that cyanide induced self assemble mechanism is a novel strategy for the design of pillararene based chemosensor. Moreover, to the best of our knowledge, it's the first example of pillararene based cyanide chemosensors.
A salient reduction of energy consumption and drying time in a novel PV solar collector assisted intermittent infrared dryer

Drier systems, as one of the high energy consumption systems, undertake a considerable amount of the energy consumption due to its wide applications in different industries such as agricultural process industries. In the present study, a solar assisted intermittent infrared dryer powered with a photovoltaic system has been built to investigate the effective parameters of dryer systems. Hence, the drying kinetic and temperature history of potato slices are studied in the thicknesses of 3, 5 and 7 mm at temperatures of 50, 60 and 70 degC. The obtained results are compared with the similar experiments of an infrared intermittent dryer. The results reveal that there is a reduction in the electrical energy consumption which is provided by a photovoltaic system in the range of minimum 40% to maximum 69% In addition, a minimum 31% to maximum 52% salient reduction of the drying time has been observed with respect to infrared intermittent dryer. Finally, it is suggested using an assisted dryer for drying potatoes at the temperature of 50 degC.
3D semantic labeling of ALS point clouds by exploiting multi scale, multi type neighborhoods for feature extraction

The semantic labeling of 3D point clouds acquired via airborne laser scanning typically relies on the use of geometric features. In this paper, we present a framework considering complementary types of geometric features extracted from multi scale, multi type neighborhoods to describe (i) the local 3D structure for neighborhoods of different scale and type and (ii) how the local 3D structure behaves across different scales and across different neighborhood types. The derived features are provided as input for several classifiers with different learning principles in order to show the potential and limitations of the proposed geometric features with respect to the classification task. To allow a comparison of the performance of our framework to the performance of existing and future approaches, we evaluate our framework on the publicly available dataset provided for the ISPRS benchmark on 3D semantic labeling.