nils-herrmann/clean_scirepeval huggingface dataset:
Thermal therapy, part 1: an introduction to thermal therapy.

Thermal therapy is widely known and electromagnetic (EM) energy, ultrasonic waves, and other thermal-conduction-based devices have been used as heating sources. In particular, advances in EM technology have paved the way for promising trends in thermotherapeutical applications such as oncology, physiotherapy, urology, cardiology, ophthalmology, and in other areas of medicine as well. This series of articles is generally written for oncologists, cancer researchers, medical students, biomedical researchers, clinicians, and others who have an interest in this topic. This article reviews key processes and developments in thermal therapy with emphasis on two techniques, namely, hyperthermia [including long-term low-temperature hyperthermia (40-41 degrees C for 6-72 hr), moderate-temperature hyperthermia (42-45 degrees C for 15-60 min), and thermal ablation, or high-temperature hyperthermia (> 50 degrees C for > 4-6 min)]. The article will also provide an overview of a wide range of possible mechanisms and biological effects of heat. This information will be discussed in light of what is known about the degree of temperature rise that is expected from various sources of energy. The review concludes with an evaluation of human exposure risk to EM energy or the corresponding heat, trends in equipment development, and future research directions.
Grounded theory , ethnography and phenomenology A comparative analysis of three qualitative strategies for marketing research

The paper aims to look at some of the problems commonly associated with qualitative methodologies, suggesting that there is a need for a more rigorous application in order to develop theory and aid effective decision making.  The paper examines three qualitative methodologies: grounded theory, ethnography, and phenomenology. It compares and contrasts their approaches to data collection and interpretation and highlights some of the strengths and weaknesses associated with each one.  The paper suggests that, while qualitative methodologies, as opposed to qualitative methods, are now an accepted feature of consumer research, their application in the truest sense is still in its infancy within the broader field of marketing. It proposes a number of possible contexts that may benefit from in-depth qualitative enquiry.  The paper should be of interest to marketers considering adopting a qualitative perspective, possibly for the first time, as it offers a snap-shot of three widely-used methodologies, their associated procedures and potential pitfalls.
Measuring universal intelligence: Towards an anytime intelligence test

In this paper, we develop the idea of a universal anytime intelligence test. The meaning of the terms “universal” and “anytime” is manifold here: the test should be able to measure the intelligence of any biological or artificial system that exists at this time or in the future. It should also be able to evaluate both inept and brilliant systems (any intelligence level) as well as very slow to very fast systems (any time scale). Also, the test may be interrupted at any time, producing an approximation to the intelligence score, in such a way that the more time is left for the test, the better the assessment will be. In order to do this, our test proposal is based on previous works on the measurement of machine intelligence based on Kolmogorov Complexity and universal distributions, which were developed in the late 1990s (C-tests and compression-enhanced Turing tests). It is also based on the more recent idea of measuring intelligence through dynamic/interactive tests held against a universal distribution of environments. We discuss some of these tests and highlight their limitations since we want to construct a test that is both general and practical. Consequently, we introduce many new ideas that develop early “compression tests” and the more recent definition of “universal intelligence” in order to design new “universal intelligence tests”, where a feasible implementation has been a design requirement. One of these tests is the “anytime intelligence test”, which adapts to the examinee’s level of intelligence in order to obtain an intelligence score within a limited time.
Fast Fine-grained Image Classification via Weakly Supervised Discriminative Localization

Fine-grained image classification is to recognize hundreds of subcategories in each basic-level category. Existing methods employ discriminative localization to find the key distinctions among similar subcategories. However, existing methods generally have two limitations: (1) Discriminative localization relies on region proposal methods to hypothesize the locations of discriminative regions, which are time-consuming and the bottleneck of classification speed. (2) The training of discriminative localization depends on object or part annotations, which are heavily labor-consuming and the obstacle of marching towards practical application. It is highly challenging to address the two key limitations simultaneously, and existing methods only focus on one of them. Therefore, we propose a weakly supervised discriminative localization approach (WSDL) for fast fine-grained image classification to address the two limitations at the same time, and its main advantages are: (1) n-pathway end-to-end discriminative localization network is designed to improve classification speed, which simultaneously localizes multiple different discriminative regions for one image to boost classification accuracy, and shares full-image convolutional features generated by region proposal network to accelerate the process of generating region proposals as well as reduce the computation of convolutional operation. (2) Multi-level attention guided localization learning is proposed to localize discriminative regions with different focuses automatically, without using object and part annotations, avoiding the labor consumption. Different level attentions focus on different characteristics of the image, which are complementary and boost the classification accuracy. Both are jointly employed to simultaneously improve classification speed and eliminate dependence on object and part annotations. Compared with state-of-theart methods on 2 widely-used fine-grained image classification datasets, our WSDL approach achieves both the best accuracy and efficiency of classification.
Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation

This paper proposes a new hybrid architecture that consists of a deep Convolutional Network and a Markov Random Field. We show how this architecture is successfully applied to the challenging problem of articulated human pose estimation in monocular images. The architecture can exploit structural domain constraints such as geometric relationships between body joint locations. We show that joint training of these two model paradigms improves performance and allows us to significantly outperform existing state-of-the-art techniques.
Preemptively Scheduling Hard-Real-Time Sporadic Tasks on One Processor

In this paper, we consider the preemptive scheduling of hard-real-time sporadic task systems on one processor. We rst give necessary and su cient conditions for a sporadic task system to be feasible (i.e., schedulable). The conditions cannot, in general, be tested e ciently (unless P =NP). They do, however, lead to a feasibility test that runs in e cient pseudo-polynomial time for a very large percentage of sporadic task systems.
Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text

The term sentiment analysis can be used to refer to many different, but related, problems. Most commonly, it is used to refer to the task of automatically determining the valence or polarity of a piece of text, whether it is positive, negative, or neutral. However, more generally, it refers to determining one’s attitude towards a particular target or topic. Here, attitude can mean an evaluative judgment, such as positive or negative, or an emotional or affectual attitude such as frustration, joy, anger, sadness, excitement, and so on. Note that some authors consider feelings to be the general category that includes attitude, emotions, moods, and other affectual states. In this chapter, we use ‘sentiment analysis’ to refer to the task of automatically determining feelings from text, in other words, automatically determining valence, emotions, and other affectual states from text. Osgood, Suci, and Tannenbaum (1957) showed that the three most prominent dimensions of meaning are evaluation (good–bad), potency (strong–weak), and activity (active– passive). Evaluativeness is roughly the same dimension as valence (positive–negative). Russell (1980) developed a circumplex model of affect characterized by two primary dimensions: valence and arousal (degree of reactivity to stimulus). Thus, it is not surprising that large amounts of work in sentiment analysis are focused on determining valence.  However, there is some work on automatically detecting arousal (Thelwall, Buckley, Paltoglou, Cai, & Kappas, 2010; Kiritchenko, Zhu, & Mohammad, 2014b; Mohammad, Kiritchenko, & Zhu, 2013a) and growing interest in detecting emotions such as anger, frustration, sadness, and optimism in text (Mohammad, 2012; Bellegarda, 2010; Tokuhisa, Inui, & Matsumoto, 2008; Strapparava & Mihalcea, 2007; John, Boucouvalas, & Xu, 2006; Mihalcea & Liu, 2006; Genereux & Evans, 2006; Ma, Prendinger, & Ishizuka, 2005; Holzman & Pottenger, 2003; Boucouvalas, 2002; Zhe & Boucouvalas, 2002). Further, massive amounts of data emanating from social media have led to significant interest in analyzing blog posts, tweets, instant messages, customer reviews, and Facebook posts for both valence (Kiritchenko et al., 2014b; Kiritchenko, Zhu, Cherry, & Mohammad, 2014a; Mohammad et al., 2013a; Aisopos, Papadakis, Tserpes, & Varvarigou, 2012; Bakliwal, Arora, Madhappan, Kapre, Singh, & Varma, 2012; Agarwal, Xie, Vovsha, Rambow, & Passonneau, 2011; Thelwall, Buckley, & Paltoglou, 2011; Brody & Diakopoulos, 2011; Pak & Paroubek, 2010) and emotions (Hasan, Rundensteiner, & Agu, 2014; Mohammad & Kiritchenko, 2014; Mohammad, Zhu, Kiritchenko, & Martin, 2014; Choudhury, Counts, & Gamon, 2012; Mohammad, 2012a; Wang, Chen, Thirunarayan, & Sheth, 2012; Tumasjan, Sprenger, Sandner, & Welpe, 2010b; Kim, Gilbert, Edwards, &
Association of gene-linked SSR markers to seed glucosinolate content in oilseed rape (Brassica napus ssp. napus)

Breeding of oilseed rape (Brassica napus ssp. napus) has evoked a strong bottleneck selection towards double-low (00) seed quality with zero erucic acid and low seed glucosinolate content. The resulting reduction of genetic variability in elite 00-quality oilseed rape is particularly relevant with regard to the development of genetically diverse heterotic pools for hybrid breeding. In contrast, B. napus genotypes containing high levels of erucic acid and seed glucosinolates (++ quality) represent a comparatively genetically divergent source of germplasm. Seed glucosinolate content is a complex quantitative trait, however, meaning that the introgression of novel germplasm from this gene pool requires recurrent backcrossing to avoid linkage drag for high glucosinolate content. Molecular markers for key low-glucosinolate alleles could potentially improve the selection process. The aim of this study was to identify potentially gene-linked markers for important seed glucosinolate loci via structure-based allele-trait association studies in genetically diverse B. napus genotypes. The analyses included a set of new simple-sequence repeat (SSR) markers whose orthologs in Arabidopsis thaliana are physically closely linked to promising candidate genes for glucosinolate biosynthesis. We found evidence that four genes involved in the biosynthesis of indole, aliphatic and aromatic glucosinolates might be associated with known quantitative trait loci for total seed glucosinolate content in B. napus. Markers linked to homoeologous loci of these genes in the paleopolyploid B. napus genome were found to be associated with a significant effect on the seed glucosinolate content. This example shows the potential of Arabidopsis-Brassica comparative genome analysis for synteny-based identification of gene-linked SSR markers that can potentially be used in marker-assisted selection for an important trait in oilseed rape.
Music therapy for depression.

Depression is a highly prevalent disorder associated with reduced social functioning, impaired quality of life, and increased mortality. Music therapy has been used in the treatment of a variety of mental disorders, but its impact on those with depression is unclear.



To examine the efficacy of music therapy with standard care compared to standard care alone among people with depression and to compare the effects of music therapy for people with depression against other psychological or pharmacological therapies.


SEARCH STRATEGY
CCDANCTR-Studies and CCDANCTR-References were searched on 7/11/2007, MEDLINE, PsycINFO, EMBASE, PsycLit, PSYindex, and other relevant sites were searched in November 2006. Reference lists of retrieved articles were hand searched, as well as specialist music and arts therapies journals.



All randomised controlled trials comparing music therapy with standard care or other interventions for depression.



Data on participants, interventions and outcomes were extracted and entered onto a database independently by two review authors. The methodological quality of each study was also assessed independently by two review authors. The primary outcome was reduction in symptoms of depression, based on a continuous scale.



Five studies met the inclusion criteria of the review. Marked variations in the interventions offered and the populations studied meant that meta-analysis was not appropriate. Four of the five studies individually reported greater reduction in symptoms of depression among those randomised to music therapy than to those in standard care conditions. The fifth study, in which music therapy was used as an active control treatment, reported no significant change in mental state for music therapy compared with standard care. Dropout rates from music therapy conditions appeared to be low in all studies.



Findings from individual randomised trials suggest that music therapy is accepted by people with depression and is associated with improvements in mood. However, the small number and low methodological quality of studies mean that it is not possible to be confident about its effectiveness. High quality trials evaluating the effects of music therapy on depression are required.
Recommender Systems, Missing Data and Statistical Model Estimation

The goal of rating-based recommender systems is to make personalized predictions and recommendations for individual users by leveraging the preferences of a community of users with respect to a collection of items like songs or movies. Recommender systems are often based on intricate statistical models that are estimated from data sets containing a very high proportion of missing ratings. This work describes evidence of a basic incompatibility between the properties of recommender system data sets and the assumptions required for valid estimation and evaluation of statistical models in the presence of missing data. We discuss the implications of this problem and describe extended modelling and evaluation frameworks that attempt to circumvent it. We present prediction and ranking results showing that models developed and tested under these extended frameworks can significantly outperform standard models.
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision

Extending the success of deep neural networks to natural language understanding and symbolic reasoning requires complex operations and external memory. Recent neural program induction approaches have attempted to address this problem, but are typically limited to differentiable memory, and consequently cannot scale beyond small synthetic tasks. In this work, we propose the Manager-ProgrammerComputer framework, which integrates neural networks with non-differentiable memory to support abstract, scalable and precise operations through a friendly neural computer interface. Specifically, we introduce a Neural Symbolic Machine, which contains a sequence-to-sequence neural "programmer", and a nondifferentiable "computer" that is a Lisp interpreter with code assist. To successfully apply REINFORCE for training, we augment it with approximate gold programs found by an iterative maximum likelihood training process. NSM is able to learn a semantic parser from weak supervision over a large knowledge base. It achieves new state-of-the-art performance on WEBQUESTIONSSP, a challenging semantic parsing dataset, with weak supervision. Compared to previous approaches, NSM is end-to-end, therefore does not rely on feature engineering or domain specific knowledge.
Predicting a Business Star in Yelp from Its Reviews Text Alone

Yelp online reviews are invaluable source of information for users to choose where to visit or what to eat among numerous available options. But due to overwhelming number of reviews, it is almost impossible for users to go through all reviews and find the information they are looking for. To provide a business’ overview, one solution is to give the business a 1-5 star(s). This rating can be subjective and biased toward users’ personality. In this paper, we predict a business’ rating based on user-generated reviews’ texts alone. This not only provides an overview of plentiful long review texts but also cancels out subjectivity. Selecting the restaurant category from Yelp Dataset Challenge , we use a combination of three feature generation methods as well as four machine learning models to find the best prediction result. Our approach is to create bag of words from the top frequent words in all raw text reviews, or top frequent words/adjectives from results of Part-of-Speech analysis. Our results show Root Mean Square Error (RMSE) of 0.6 for the combination of Linear Regression with either of the top frequent words from raw data or top frequent adjectives after Part-of-Speech (POS). Yelp; predicting star; linear regression; review
Mean Shift: A Robust Approach Toward Feature Space Analysis

A general nonparametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure, the mean shift. We prove for discrete data the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and thus its utility in detecting the modes of the density. The equivalence of the mean shift procedure to the Nadaraya–Watson estimator from kernel regression and the robust M-estimators of location is also established. Algorithms for two low-level vision tasks, discontinuity preserving smoothing and image segmentation are described as applications. In these algorithms the only user set parameter is the resolution of the analysis, and either gray level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.
DFS: A File System for Virtualized Flash Storage

We present the design, implementation, and evaluation of Direct File System (DFS) for virtualized flash storage. Instead of using traditional layers of abstraction, our layers of abstraction are designed for directly accessing flash memory devices. DFS has two main novel features. First, it lays out its files directly in a very large virtual storage address space provided by FusionIO's virtual flash storage layer. Second, it leverages the virtual flash storage layer to perform block allocations and atomic updates. As a result, DFS performs better and is much simpler than a traditional Unix file system with similar functionalities. Our microbenchmark results show that DFS can deliver 94,000 I/O operations per second (IOPS) for direct reads and 71,000 IOPS for direct writes with the virtualized flash storage layer on FusionIO's ioDrive. For direct access performance, DFS is consistently better than ext3 on the same platform, sometimes by 20%. For buffered access performance, DFS is also consistently better than ext3, and sometimes by over 149%. Our application benchmarks show that DFS outperforms ext3 by 7% to 250% while requiring less CPU power.
Contextual Action Recognition with R*CNN

There are multiple cues in an image which reveal what action a person is performing. For example, a jogger has a pose that is characteristic for jogging, but the scene (e.g. road, trail) and the presence of other joggers can be an additional source of information. In this work, we exploit the simple observation that actions are accompanied by contextual cues to build a strong action recognition system. We adapt RCNN to use more than one region for classification while still maintaining the ability to localize the action. We call our system R*CNN. The action-specific models and the feature maps are trained jointly, allowing for action specific representations to emerge. R*CNN achieves 90.2% mean AP on the PASAL VOC Action dataset, outperforming all other approaches in the field by a significant margin. Last, we show that R*CNN is not limited to action recognition. In particular, R*CNN can also be used to tackle fine-grained tasks such as attribute classification. We validate this claim by reporting state-of-the-art performance on the Berkeley Attributes of People dataset.
Quality Assessment for Linked Data : A Survey A Systematic Literature Review and Conceptual Framework

The development and standardization of semantic web technologies has resulted in an unprecedented volume of data being published on the Web as Linked Data (LD). However, we observe widely varying data quality ranging from extensively curated datasets to crowdsourced and extracted data of relatively low quality. In this article, we present the results of a systematic review of approaches for assessing the quality of LD. We gather existing approaches and analyze them qualitatively. In particular, we unify and formalize commonly used terminologies across papers related to data quality and provide a comprehensive list of 18 quality dimensions and 69 metrics. Additionally, we qualitatively analyze the 30 core approaches and 12 tools using a set of attributes. The aim of this article is to provide researchers and data curators a comprehensive understanding of existing work, thereby encouraging further experimentation and development of new approaches focused towards data quality, specifically for LD.
Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500

In recent years, machine learning research has gained momentum: New developments in the field of deep learning allow for multiple levels of abstraction and are starting to supersede wellknown and powerful tree-based techniques mainly operating on the original feature space. All these methods can be applied to various fields, including finance. This article implements and analyses the effectiveness of deep neural networks (DNN), gradient-boosted-trees (GBT), random forests (RAF), and several ensembles of these methods in the context of statistical arbitrage. Each model is trained on lagged returns of all stocks in the S&P 500, after elimination of survivor bias. From 1992 to 2015, daily one-day-ahead trading signals are generated based on the probability forecast of a stock to outperform the general market. The highest k probabilities are converted into long and the lowest k probabilities into short positions, thus censoring the less certain middle part of the ranking. Empirical findings are promising. A simple, equal-weighted ensemble (ENS1) consisting of one deep neural network, one gradient-boosted tree, and one random forest produces out-of-sample returns exceeding 0.45 percent per day for k = 10, prior to transaction costs. Irrespective of the fact that profits are declining in recent years, our findings pose a severe challenge to the semi-strong form of market efficiency.