nils-herrmann/clean_scirepeval huggingface dataset:
Caregiver burden and coping strategies in caregivers of patients with Alzheimer’s disease

Alzheimer's disease (AD) causes considerable distress in caregivers who are continuously required to deal with requests from patients. Coping strategies play a fundamental role in modulating the psychologic impact of the disease, although their role is still debated. The present study aims to evaluate the burden and anxiety experienced by caregivers, the effectiveness of adopted coping strategies, and their relationships with burden and anxiety.



Eighty-six caregivers received the Caregiver Burden Inventory (CBI) and the State-Trait Anxiety Inventory (STAI Y-1 and Y-2). The coping strategies were assessed by means of the Coping Inventory for Stressful Situations (CISS), according to the model proposed by Endler and Parker in 1990.



The CBI scores (overall and single sections) were extremely high and correlated with dementia severity. Women, as well as older caregivers, showed higher scores. The trait anxiety (STAI-Y-2) correlated with the CBI overall score. The CISS showed that caregivers mainly adopted task-focused strategies. Women mainly adopted emotion-focused strategies and this style was related to a higher level of distress.



AD is associated with high distress among caregivers. The burden strongly correlates with dementia severity and is higher in women and in elderly subjects. Chronic anxiety affects caregivers who mainly rely on emotion-oriented coping strategies. The findings suggest providing support to families of patients with AD through tailored strategies aimed to reshape the dysfunctional coping styles.
OPTIMAL CONTROL VIA NEURAL NETWORKS: A CONVEX APPROACH

Control of complex systems involves both system identification and controller design. Deep neural networks have proven to be successful in many identification tasks, however, from model-based control perspective, these networks are difficult to work with because they are typically nonlinear and nonconvex. Therefore many systems are still identified and controlled based on simple linear models despite their poor representation capability. In this paper we bridge the gap between model accuracy and control tractability faced by neural networks, by explicitly constructing networks that are convex with respect to their inputs. We show that these input convex networks can be trained to obtain accurate models of complex physical systems. In particular, we design input convex recurrent neural networks to capture temporal behavior of dynamical systems. Then optimal controllers can be achieved via solving a convex model predictive control problem. Experiment results demonstrate the good potential of the proposed input convex neural network based approach in a variety of control applications. In particular we show that in the MuJoCo locomotion tasks, we could achieve over 10% higher performance using 5× less time compared with state-of-the-art model-based reinforcement learning method; and in the building HVAC control example, our method achieved up to 20% energy reduction compared with classic linear models.
MTNet : A Neural Approach for Cross-Domain Recommendation with Unstructured Text

Collaborative filtering (CF) is the key technique for recommender systems (RSs). CF exploits user-item behavior interactions (e.g., clicks) only and suffers from the data sparsity issue. One solution is to integrate the content information such as product reviews and news titles, leading to hybrid filtering methods. Another solution is to transfer knowledge from a related source domain such as improving the movie recommendation with the knowledge of the book domain, leading to cross-domain methods where transfer learning is a key technique. In real life, no single service can satisfy a user’s all information needs. Thus it motivates us to exploit information from both the content and across domains for RSs in this paper. We achieve this by developing approaches to capture the text content and to transfer cross-domain knowledge. We propose a novel neural model, MTNet (“M” for memory and “T” for transfer), for cross-domain recommendation with unstructured text in an end-to-end manner. MTNet can attentively extract useful content via a memory network (MNet) and can selectively transfer knowledge from across domains by a transfer network (TNet), a novel network. The principle underlying these two components is the neural attention mechanism. A shared layer of feature interactions is stacked on the top to couple the high-level representations learned from individual networks. On two real-world datasets, MTNet shows better performance in terms of three ranking metrics by comparing with various baselines, including single/cross domain, shallow/deep, and hybrid methods. We conduct thorough analyses to understand how the text content and transferred knowledge help the proposed model.
The effects of amino acid supplementation on muscular performance during resistance training overreaching.

The purpose of this study was to examine the effects of amino acid supplementation on muscular strength, power, and high-intensity endurance during short-term resistance training overreaching. Seventeen resistance-trained men were randomly assigned to either an amino acid (AA) or placebo (P) group and underwent 4 weeks of total-body resistance training consisting of two 2-week phases of overreaching (phase 1: 3 x 8-12 repetitions maximum [RM], 8 exercises; phase 2: 5 x 3-5 RM, 5 exercises). Muscle strength, power, and high-intensity endurance were determined before (T1) and at the end of each training week (T2-T5). One repetition maximum squat and bench press decreased at T2 in P (5.2 and 3.4 kg, respectively) but not in AA, and significant increases in 1 RM squat and bench press were observed at T3-T5 in both groups. A decrease in the ballistic bench press peak power was observed at T3 in P but not AA. The fatigue index during the 20-repetition jump squat assessment did not change in the P group at T3 and T5 (fatigue index = 18.6 and 18.3%, respectively) whereas a trend for reduction was observed in the AA group (p = 0.06) at T3 (12.8%) but not T5 (15.2%; p = 0.12). These results indicate that the initial impact of high-volume resistance training overreaching reduces muscle strength and power, and it appears that these reductions are attenuated with amino acid supplementation. In addition, an initial high-volume, moderate-intensity phase of overreaching followed by a higher intensity, moderate-volume phase appears to be very effective for enhancing muscle strength in resistance-trained men.
Bayesian Classification With Gaussian Processes

We consider the problem of assigning an input vector to one of m classes by predicting P(c|x) for c = 1, o, m. For a twoclass problem, the probability of class one given x is estimated by s(y(x)), where s(y) = 1/(1 + ey ). A Gaussian process prior is placed on y(x), and is combined with the training data to obtain predictions for new x points. We provide a Bayesian treatment, integrating over uncertainty in y and in the parameters that control the Gaussian process prior; the necessary integration over y is carried out using Laplace’s approximation. The method is generalized to multiclass problems (m > 2) using the softmax function. We demonstrate the effectiveness of the method on a number of datasets.
Deeper Image Quality Transfer: Training Low-Memory Neural Networks for 3D Images

In this paper we address the memory demands that come with the processing of 3-dimensional, high-resolution, multi-channeled medical images in deep learning. We exploit memory-efficient backpropagation techniques, to reduce the memory complexity of network training from being linear in the network’s depth, to being roughly constant – permitting us to elongate deep architectures with negligible memory increase. We evaluate our methodology in the paradigm of Image Quality Transfer, whilst noting its potential application to various tasks that use deep learning. We study the impact of depth on accuracy and show that deeper models have more predictive power, which may exploit larger training sets. We obtain substantially better results than the previous state-of-the-art model with a slight memory increase, reducing the rootmean-squared-error by 13%. Our code is publicly available.
Decentralization of Governance and Development

A ll around the world in matters of governance, decentralization is the rage. Even apart from the widely debated issues of subsidiarity and devolution in the European Union and states’ rights in the United States, decentralization has been at the center stage of policy experiments in the last two decades in a large number of developing and transition economies in Latin America, Africa and Asia. The World Bank, for example, has embraced it as one of the major governance reforms on its agenda (for example, World Bank, 2000; Burki, Perry and Dillinger, 1999). Take also the examples of the two largest countries of the world, China and India. Decentralization has been regarded as the major institutional framework for the phenomenal industrial growth in the last two decades in China, taking place largely in the nonstate nonprivate sector. India ushered in a landmark constitutional reform in favor of decentralization around the same time it launched a major program of economic reform in the early 1990s. On account of its many failures, the centralized state everywhere has lost a great deal of legitimacy, and decentralization is widely believed to promise a range of bene ts. It is often suggested as a way of reducing the role of the state in general, by fragmenting central authority and introducing more intergovernmental competition and checks and balances. It is viewed as a way to make government more responsive and efcient. Technological changes have also made it somewhat easier than before to provide public services (like electricity and water supply) relatively ef ciently in smaller market areas, and the lower levels of government have now a greater ability to handle certain tasks. In a world of rampant ethnic con icts and separatist movements, decentralization is also regarded as a way of diffusing social and political tensions and ensuring local cultural and political autonomy. These potential bene ts of decentralization have attracted a very diverse range
Framing and Deliberation : How Citizens ' Conversations Limit Elite Influence

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Adaptive Learning of Local Semantic and Global Structure Representations for Text Classification

Representation learning is a key issue for most Natural Language Processing (NLP) tasks. Most existing representation models either learn little structure information or just rely on pre-defined structures, leading to degradation of performance and generalization capability. This paper focuses on learning both local semantic and global structure representations for text classification. In detail, we propose a novel Sandwich Neural Network (SNN) to learn semantic and structure representations automatically without relying on parsers. More importantly, semantic and structure information contribute unequally to the text representation at corpus and instance level. To solve the fusion problem, we propose two strategies: Adaptive Learning Sandwich Neural Network (AL-SNN) and Self-Attention Sandwich Neural Network (SA-SNN). The former learns the weights at corpus level, and the latter further combines attention mechanism to assign the weights at instance level. Experimental results demonstrate that our approach achieves competitive performance on several text classification tasks, including sentiment analysis, question type classification and subjectivity classification. Specifically, the accuracies are MR (82.1%), SST-5 (50.4%), TREC (96%) and SUBJ (93.9%).
Deep Learning Based vs. Markov Chain Based Text Generation for Cross Domain Adaptation for Sentiment Classification

Cross-domain adaptation for sentiment classification is the process of adapting a classifier that uses knowledge from one or multiple source domains and little data from the target domain to function with acceptable accuracy, precision, and recall on the target domain. One of the challenges facing cross-domain adaptation for sentiment classification is the limited availability of labeled samples in the target domain. In this paper, we introduce text generation in the target domain as a solution to provide a set of labeled data in the target domain then compare deep learning based text generators such as LSTM RNN and GRU RNN against Markov chain based text generators. We first use a rule-based classifier that utilizes knowledge from different source domains in labeling the unlabeled samples in the target domain (Kitchen Product reviews) then we have selected high confidence labeled samples for training LSTM RNN, GRU RNN and Markov chain based text generators. We have evaluated the deep learning based and Markov chain based text generators by measuring the fscores and accuracies of the end classifier when trained on the data generated from each of these models when tested on the kitchen benchmark test set.
Closing the gap towards end-to-end autonomous vehicle system

Designing a driving policy for autonomous vehicles is a difficult task. Recent studies suggested an end-toend (E2E) training of a policy to predict car actuators directly from raw sensory inputs. It is appealing due to the ease of labeled data collection and since handcrafted features are avoided. Explicit drawbacks such as interpretability, safety enforcement and learning efficiency limit the practical application of the approach. In this paper, we amend the basic E2E architecture to address these shortcomings, while retaining the power of end-to-end learning. A key element in our proposed architecture is formulation of the learning problem as learning of trajectory. We also apply a Gaussian mixture model loss to contend with multi-modal data, and adopt a finance risk measure, conditional value at risk, to emphasize rare events. We analyze the effect of each concept and present driving performance in a highway scenario in the TORCS simulator. Video is available in this link.
*SEM 2013 shared task: Semantic Textual Similarity

In Semantic Textual Similarity (STS), systems rate the degree of semantic equivalence, on a graded scale from 0 to 5, with 5 being the most similar. This year we set up two tasks: (i) a core task (CORE), and (ii) a typed-similarity task (TYPED). CORE is similar in set up to SemEval STS 2012 task with pairs of sentences from sources related to those of 2012, yet different in genre from the 2012 set, namely, this year we included newswire headlines, machine translation evaluation datasets and multiple lexical resource glossed sets. TYPED, on the other hand, is novel and tries to characterize why two items are deemed similar, using cultural heritage items which are described with metadata such as title, author or description. Several types of similarity have been defined, including similar author, similar time period or similar location. The annotation for both tasks leverages crowdsourcing, with relative high interannotator correlation, ranging from 62% to 87%. The CORE task attracted 34 participants with 89 runs, and the TYPED task attracted 6 teams with 14 runs.
Exploiting Multiple Sources for Open-Domain Hypernym Discovery

Hypernym discovery aims to extract such noun pairs that one noun is a hypernym of the other. Most previous methods are based on lexical patterns but perform badly on opendomain data. Other work extracts hypernym relations from encyclopedias but has limited coverage. This paper proposes a simple yet effective distant supervision framework for Chinese open-domain hypernym discovery. Given an entity name, we try to discover its hypernyms by leveraging knowledge from multiple sources, i.e., search engine results, encyclopedias, and morphology of the entity name. First, we extract candidate hypernyms from the above sources. Then, we apply a statistical ranking model to select correct hypernyms. A set of novel features is proposed for the ranking model. We also present a heuristic strategy to build a large-scale noisy training data for the model without human annotation. Experimental results demonstrate that our approach outperforms the state-of-the-art methods on a manually labeled test dataset.
Trilobites and the origin of arthropods.

While the question of whether the Arthropoda represent more than one phylum of animals is debatable, the jointed exoskeleton, a fundamental feature of arthropods, evolved independently in two groups that shared a worm-like common ancestor. The two major branches of Arthropoda, the primitively marine TCC and the primitively terrestrial (with one exception) Uniramia, independently arrived at arthropodization as the solution to the same problems of adaptation of the body mechanical system. New discoveries on trilobite anatomy show the unity of TCC as a group that shared a trilobite-like ancestor near the beginning of the Cambrian. With change in the constituency of Arthropoda through geologic time, the ways in which it would be categorized as a taxonomic group have also changed. The seeming isolation of the major modern arthropod groups is in large part an artifact of extinction of primitive intermediate forms such as trilobites which, in the Early Paleozoic, made the Arthropoda more diverse in basic modes of body organization than the group is at present. The appearance of fossilizable hard parts in arthropods resulted from shift in supporting function from the body cavity, primitively a hydrostatic skeleton, to the cuticle, which came to be strengthened in becoming an exoskeleton. Energetic efficiency, more than protection from predators or evolutionary size increase in itself, was probably the impetus behind the transition. On the scale provided by the general evolutionary trend toward progressive specialization of segments, TCC became arthropodized at earlier stages than did Uniramia. Among TCC, the shift may have been driven by the evolution of locomotory and feeding mechanisms that were exclusively geared to an aqueous medium.
Anthropometry in body composition. An overview.

Anthropometry is a simple reliable method for quantifying body size and proportions by measuring body length, width, circumference (C), and skinfold thickness (SF). More than 19 sites for SF, 17 for C, 11 for width, and 9 for length have been included in equations to predict body fat percent with a standard error of estimate (SEE) range of +/- 3% to +/- 11% of the mean of the criterion measurement. Recent studies indicate that not only total body fat, but also regional fat and skeletal muscle, can be predicted from anthropometrics. Our Rosetta database supports the thesis that sex, age, ethnicity, and site influence anthropometric predictions; the prediction reliabilities are consistently higher for Whites than for other ethnic groups, and also by axial than by peripheral sites (biceps and calf). The reliability of anthropometrics depends on standardizing the caliper and site of measurement, and upon the measuring skill of the anthropometrist. A reproducibility of +/- 2% for C and +/- 10% for SF measurements usually is required to certify the anthropometrist.
A Typed Lambda Calculus with Categorical Type Constructors

A typed lambda calculus with categorical type constructors is introduced. It has a uniform category theoretic mechanism to declare new types. Its type structure includes categorical objects like products and coproducts as well as recursive types like natural numbers and lists. It also allows duals of recursive types, i.e. lazy types, like infinite lists. It has generalized iterators for recursive types and duals of iterators for lazy types. We will give reduction rules for this simply typed lambda calculus and show that they are strongly normalizing even though it has infinite things like infinite lists.
Pose-based human action recognition with Extreme Gradient Boosting

This Paper investigate action recognition by using Extreme Gradient Boosting (XGBoost). XGBoost is a supervised classification technique using an ensemble of decision trees. In this study, we also compare the performance of Xboost using another machine learning techniques Support Vector Machine (SVM) and Naive Bayes (NB). The experimental study on the human action dataset shows that XGBoost better as compared to SVM and NB in classification accuracy. Although takes more computational time the XGBoost performs good classification on action recognition.
Election Forecasts With Twitter: How 140 Characters Reflect the Political Landscape

This study investigates whether microblogging messages on Twitter validly mirror the political landscape off-line and can be used to predict election results. In the context of the 2009 German federal election, we conducted a sentiment analysis of over 100,000 messages containing a reference to either a political party or a politician. Our results show that Twitter is used extensively for political deliberation and that the mere number of party mentions accurately reflects the election result. The tweets’ sentiment (e.g., positive and negative emotions associated with a politician) corresponds closely to voters’ political preferences. In addition, party sentiment profiles reflect the similarity of political positions between parties. We derive suggestions for further research and discuss the use of microblogging services to aggregate dispersed information.
Mode Regularized Generative Adversarial Networks

Although Generative Adversarial Networks achieve state-of-the-art results on a variety of generative tasks, they are regarded as highly unstable and prone to miss modes. We argue that these bad behaviors of GANs are due to the very particular functional shape of the trained discriminators in high dimensional spaces, which can easily make training stuck or push probability mass in the wrong direction, towards that of higher concentration than that of the data generating distribution. We introduce several ways of regularizing the objective, which can dramatically stabilize the training of GAN models. We also show that our regularizers can help the fair distribution of probability mass across the modes of the data generating distribution, during the early phases of training and thus providing a unified solution to the missing modes problem.
Multi-Agent Generative Adversarial Imitation Learning

Imitation learning algorithms can be used to learn a policy from expert demonstrations without access to a reward signal. However, most existing approaches are not applicable in multi-agent settings due to the existence of multiple (Nash) equilibria and non-stationary environments. We propose a new framework for multi-agent imitation learning for general Markov games, where we build upon a generalized notion of inverse reinforcement learning. We further introduce a practical multi-agent actor-critic algorithm with good empirical performance. Our method can be used to imitate complex behaviors in high-dimensional environments with multiple cooperative or competing agents.