nils-herrmann/clean_scirepeval huggingface dataset:
The New York Times , and the Propaganda Model

An assessment of Herman and Chomsky’s 1988 five-filter propaganda model suggests it is mainly valuable for identifying areas in which researchers should look for evidence of collaboration (whether intentional or otherwise) between mainstream media and the propaganda aims of the ruling establishment. The model does not identify methodologies for determining the relative weight of independent filters in different contexts, something that would be useful in its future development. There is a lack of precision in the characterization of some of the filters. The model privileges the structural factors that determine propagandized news selection, and therefore eschews or marginalizes intentionality. This paper extends the model to include the “buying out” of journalists or their publications by intelligence and related special interest organizations. It applies the extended six-filter model to controversies over reporting by The New York Times of the build-up towards the US invasion of Iraq in 2003, the issue of weapons of mass destruction in general, and the reporting of The New York Times correspondent Judith Miller in particular, in the context of broader critiques of US mainstream media war coverage. The controversies helped elicit evidence of the operation of some filters of the propaganda model, including dependence on official sources, fear of flak, and ideological convergence. The paper finds that the filter of routine news operations needs to be counterbalanced by its opposite, namely non-routine abuses of standard operating procedures. While evidence of the operation of other filters was weaker, this is likely due to difficulties of observability, as there are powerful deductive reasons for maintaining all six filters within the framework of media propaganda analysis.
A survey and extension of high efficiency grid connected transformerless solar inverters with focus on leakage current characteristics

In the utility grid interconnection of photovoltaic (PV) energy sources, inverters determine the overall system performance, which result in the demand to route the grid connected transformerless PV inverters (GCTIs) for residential and commercial applications, especially due to their high efficiency, light weight, and low cost benefits. In spite of these benefits of GCTIs, leakage currents due to distributed PV module parasitic capacitances are a major issue in the interconnection, as they are undesired because of safety, reliability, protective coordination, electromagnetic compatibility, and PV module lifetime issues. This paper classifies the kW and above range power rating GCTI topologies based on their leakage current attributes and investigates and/illustrates their leakage current characteristics by making use of detailed microscopic waveforms of a representative topology of each class. The cause and quantity of leakage current for each class are identified, not only providing a good understanding, but also aiding the performance comparison and inverter design. With the leakage current characteristic investigation, the study places most topologies under small number of classes with similar leakage current attributes facilitating understanding, evaluating, and the design of GCTIs. Establishing a clear relation between the topology type and leakage current characteristic, the topology families are extended with new members, providing the design engineers a variety of GCTI topology configurations with different characteristics.
Colour Me In--an empirical study on consumer responses to the traffic light signposting system in nutrition labelling.

As a means of empowering consumers, nutrition labelling has become a widely discussed topic. Simplicity and uniformity of labelling systems are regarded as the prevailing demands from the consumer side. In the present study, we analyse the effects of the traffic light signposting scheme on consumers' food choices.



In an online survey, respondents first rated the understandability of the traffic light signposting scheme. In a following conjoint experiment, they indicated which products they would select as the healthiest of the presented products, based on the nutritive information provided by the traffic light signposting scheme.



A major German university.



In total 2002 undergraduate students participated in the survey. Two-thirds (69 %) of the respondents were female and the majority of the respondents (70 %) were between 18 and 24 years old. Seventy-seven per cent of the participants indicated that they had a higher level of education.



Overall, the participants rated the understandability of the traffic light nutrition signposting scheme fairly high (5.9 out of 7). Sugar and fat were found to be the most important attributes of the scheme. Participants placed greater emphases on a change in a product's nutrient characteristic from 'amber to 'red' compared with a change from 'green' to 'amber'.



Our results confirm the signalling effect of colour coding as it helps reduce the complexity of decision making. Our findings shed new light on the ongoing discussion concerning appropriate and efficient nutrition labelling and provide interesting insights for further research as well as implications for public policy making.
The Semantics of Comparatives and Other Degree Constructions

(1) is an example of an adjectival comparative. In it, the adjective important is flanked by more and a comparative clause headed by than. This article is a survey of recent ideas about the interpretation of comparatives, including (i) the underlying semantics based on the idea of a threshold; (ii) the interpretation of comparative clauses that include quantifiers (brighter than on many other days); (iii) remarks on differentials such as much in (1) above: what they do in the comparative and what they do elsewhere in the language; (iv) the relationship between comparatives and other Degree constructions (e.g. as important, too important); and (v) the types of phrases in which comparatives are found (adjective: tighter versus noun: more water). Given the nature and purpose of this essay, I have tried not to presuppose background in formal semantics and I have departed from standard practice in journal articles by, as much as possible, not interrupting the flow with footnotes and references. There are two appendices. The first provides more analytical detail and there I do rely on formal techniques of natural language semantics. The second covers the sources for the ideas surveyed here.
End-to-end Neural Coreference Resolution

We introduce the first end-to-end coreference resolution model and show that it significantly outperforms all previous work without using a syntactic parser or handengineered mention detector. The key idea is to directly consider all spans in a document as potential mentions and learn distributions over possible antecedents for each. The model computes span embeddings that combine context-dependent boundary representations with a headfinding attention mechanism. It is trained to maximize the marginal likelihood of gold antecedent spans from coreference clusters and is factored to enable aggressive pruning of potential mentions. Experiments demonstrate state-of-the-art performance, with a gain of 1.5 F1 on the OntoNotes benchmark and by 3.1 F1 using a 5-model ensemble, despite the fact that this is the first approach to be successfully trained with no external resources.
The Influences of Advertising Endorser , Brand Image , Brand Equity , Price Promotion , on Purchase Intention-The Mediating Effect of Advertising Endorser

Advertising endorser play a key role on information transmission between manufacturers and consumers. Its purpose is to draw consumers’ attention and interest in order to achieve the object of communication with consumers. This research is mainly in the discussion of the influences of endorser, brand image, brand equity, price promotion on purchase intention, and the results of the study are as follows:(1) brand equity has a significant influence on endorser, (2) brand image has a significant influence on endorser, (3) endorser have a significant influence on purchase intention, (4) price promotion has a significant influence on brand equity, (5) price promotion has a significant influence on purchase intention, (6) advertising endorser mediates the relationship between brand image and purchase intention, and (7) advertising endorser mediates the relationship between brand equity and the purchase intention.
Electrical Transport in Single-Wall Carbon Nanotubes

We review recent progress in the measurement and understanding of the electrical properties of individual metal and semiconducting single-wall carbon nanotubes. The fundamental scattering mechanisms governing the electrical transport in nanotubes are discussed, along with the properties of p–n and Schottkybarrier junctions in semiconductor tubes. The use of advanced nanotube devices for electronic, high-frequency, and electromechanical applications is discussed. We then examine quantum transport in carbon nanotubes, including the observation of quantized conductance, proximity-induced supercurrents, and spin-dependent ballistic transport. We move on to explore the properties of single and coupled carbon-nanotube quantum dots. Spin and orbital (isospin) magnetic moments lead to fourfold shell structure and unusual Kondo phenomena. We conclude with a discussion of unanswered questions and a look to future research directions.
Nonlinear neural networks: Principles, mechanisms, and architectures

An historical discussion is provided of the intellectual trends that caused nineteenth century interdisciplinary studies of physics and psychobiology by leading scientists such as Helmholtz, Maxwell, and Mach to splinter into separate twentieth-century scientific movements. The nonlinear, nonstationary, and nonlocal nature of behavioral and brain data are emphasized. Three sources of contemporary neural network researchâ€”the binary, linear, and continuous-nonlinear modelsâ€”are noted. The remainder of the article describes results about continuous-nonlinear models: Many models of contentaddressable memory are shown to be special cases of the Cohen-Grossberg model and global Liapunov function, including the additive, brain-state-in-a-box, McCulloch-Pitts, Boltzmann machine, Hartline-Ratliff-Miller, shunting, masking field, bidirectional associative memory, Volterra-Lotka, Gilpin-Ayala, and Eigen-Schuster models. A Liapunov functional method is described for proving global limit or oscillation theorems Purchase Export
Semantic Image Inpainting with Perceptual and Contextual Losses

In this paper, we propose a novel method for image inpainting based on a Deep Convolutional Generative Adversarial Network (DCGAN). We define a loss function consisting of two parts: (1) a contextual loss that preserves similarity between the input corrupted image and the recovered image, and (2) a perceptual loss that ensures a perceptually realistic output image. Given a corrupted image with missing values, we use back-propagation on this loss to map the corrupted image to a smaller latent space. The mapped vector is then passed through the generative model to predict the missing content. The proposed framework is evaluated on the CelebA and SVHN datasets for two challenging inpainting tasks with random 80% corruption and large blocky corruption. Experiments show that our method can successfully predict semantic information in the missing region and achieve pixel-level photorealism, which is impossible by almost all existing methods.
Antenna diversity in mobile communications

The conditions for antenna diversity action are investigated. In terms of the fields, a condition is shown to be that the incident field and the far field of the diversity antenna should obey (or nearly obey) an orthogonality relationship. The role of mutual coupling is central, and it is different from that in a conventional array antenna. In terms of antenna parameters, a sufficient condition for diversity action for a certain class of high gain antennas at the mobile, which approximates most practical mobile antennas, is shown to be zero (or low) mutual resistance between elements. This is not the case at the base station, where the condition is necessary only. The mutual resistance condition offers a powerful design tool, and examples of new mobile diversity antennas are discussed along with some existing designs.
Estimating a Kernel Fisher Discriminant in the Presence of Label Noise

Data noise is present in many machine learning problems domains, some of these are well studied but others have received less attention. In this paper we propose an algorithm for constructing a kernel Fisher discriminant (KFD) from training examples withnoisy labels. The approach allows to associate with each example a probability of the label being flipped. We utilise an expectation maximization (EM) algorithm for updating the probabilities. The E-step uses class conditional probabilities estimated as a by-product of the KFD algorithm. The M-step updates the flip probabilities and determines the parameters of the discriminant. We demonstrate the feasibility of the approach on two real-world data-sets.
EvoDAG: A semantic Genetic Programming Python library

Genetic Programming (GP) is an evolutionary algorithm that has received a lot of attention lately due to its success in solving hard real-world problems. Lately, there has been considerable interest in GP's community to develop semantic genetic operators, i.e., operators that work on the phenotype. In this contribution, we describe EvoDAG (Evolving Directed Acyclic Graph) which is a Python library that implements a steady-state semantic Genetic Programming with tournament selection using an extension of our previous crossover operators based on orthogonal projections in the phenotype space. To show the effectiveness of EvoDAG, it is compared against state-of-the-art classifiers on different benchmark problems, experimental results indicate that EvoDAG is very competitive.
Comparison of Neutral-Point-Clamped, Symmetrical, and Hybrid Asymmetrical Multilevel Inverters

This paper presents a comparison of three topologies of multilevel inverters applied to drive an induction motor of 500 kVA/4.16 kV rating. The multilevel inverters analyzed are the following: a neutral-point-clamped inverter, a symmetrical cascaded multilevel inverter, and a hybrid asymmetrical cascaded multilevel inverter. The performance indexes used in the comparison are total harmonic distortion, first-order distortion factor, second-order distortion factor, common-mode voltage, semiconductor power loss distribution, and heat-sink volume. The multilevel inverters are designed to present 99% efficiency at the nominal operating point, and the aforementioned performance indexes are compared for distinct values of amplitude modulation depth.
Rational Choice and the Framing of Decisions

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Wideband unbalanced fed 180-degree phase shifter using phase inverter

In this paper, the design of unbalanced fed 180° phase shifter with a wideband performances is proposed. This phase shifter uses a single dielectric substrate, and consists of multi-section Wilkinson divider, reference line, and phase inverter containing balanced-unbalanced transition in the input and output port. The simulated and measured results show that this device provides 180° phase shift with low insertion loss in the frequency band from 1 to 10 GHz.
Classification-based RNN machine translation using GRUs

We report the results of our classification-based machine translation model, built upon the framework of a recurrent neural network using gated recurrent units. Unlike other RNN models that attempt to maximize the overall conditional log probability of sentences against sentences, our model focuses a classification approach of estimating the conditional probability of the next word given the input sequence. This simpler approach using GRUs was hoped to be comparable with more complicated RNN models, but achievements in this implementation were modest and there remains a lot of room for improving this classification approach.
Integrating motivational, social, and contextual work design features: a meta-analytic summary and theoretical extension of the work design literature.

The authors developed and meta-analytically examined hypotheses designed to test and extend work design theory by integrating motivational, social, and work context characteristics. Results from a summary of 259 studies and 219,625 participants showed that 14 work characteristics explained, on average, 43% of the variance in the 19 worker attitudes and behaviors examined. For example, motivational characteristics explained 25% of the variance in subjective performance, 2% in turnover perceptions, 34% in job satisfaction, 24% in organizational commitment, and 26% in role perception outcomes. Beyond motivational characteristics, social characteristics explained incremental variances of 9% of the variance in subjective performance, 24% in turnover intentions, 17% in job satisfaction, 40% in organizational commitment, and 18% in role perception outcomes. Finally, beyond both motivational and social characteristics, work context characteristics explained incremental variances of 4% in job satisfaction and 16% in stress. The results of this study suggest numerous opportunities for the continued development of work design theory and practice.
DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients

We propose DoReFa-Net, a method to train convolutional neural networks that have low bitwidth weights and activations using low bitwidth parameter gradients. In particular, during backward pass, parameter gradients are stochastically quantized to low bitwidth numbers before being propagated to convolutional layers. As convolutions during forward/backward passes can now operate on low bitwidth weights and activations/gradients respectively, DoReFa-Net can use bit convolution kernels to accelerate both training and inference. Moreover, as bit convolutions can be efficiently implemented on CPU, FPGA, ASIC and GPU, DoReFatNet opens the way to accelerate training of low bitwidth neural network on these hardware. Our experiments on SVHN and ImageNet datasets prove that DoReFa-Net can achieve comparable prediction accuracy as 32-bit counterparts. For example, a DoReFa-Net derived from AlexNet that has 1-bit weights, 2-bit activations, can be trained from scratch using 4-bit gradients to get 47% top-1 accuracy on ImageNet validation set.1 The DoReFa-Net AlexNet model is released publicly.
Do Bedside Visual Tools Improve Patient and Caregiver Satisfaction? A Systematic Review of the Literature.

Although common, the impact of low-cost bedside visual tools, such as whiteboards, on patient care is unclear.



To systematically review the literature and assess the influence of bedside visual tools on patient satisfaction.



Medline, Embase, SCOPUS, Web of Science, CINAHL, and CENTRAL.



Studies of adult or pediatric hospitalized patients reporting physician identification, understanding of provider roles, patient-provider communication, and satisfaction with care from the use of visual tools were included. Outcomes were categorized as positive, negative, or neutral based on survey responses for identification, communication, and satisfaction. Two reviewers screened studies, extracted data, and assessed the risk of study bias.



Sixteen studies met the inclusion criteria. Visual tools included whiteboards (n = 4), physician pictures (n = 7), whiteboard and picture (n = 1), electronic medical record-based patient portals (n = 3), and formatted notepads (n = 1). Tools improved patients' identification of providers (13/13 studies). The impact on understanding the providers' roles was largely positive (8/10 studies). Visual tools improved patient-provider communication (4/5 studies) and satisfaction (6/8 studies). In adults, satisfaction varied between positive with the use of whiteboards (2/5 studies) and neutral with pictures (1/5 studies). Satisfaction related to pictures in pediatric patients was either positive (1/3 studies) or neutral (1/3 studies). Differences in tool format (individual pictures vs handouts with pictures of all providers) and study design (randomized vs cohort) may explain variable outcomes.



The use of bedside visual tools appears to improve patient recognition of providers and patient-provider communication. Future studies that include better design and outcome assessment are necessary before widespread use can be recommended.
A Mixed Hierarchical Attention based Encoder-Decoder Approach for Standard Table Summarization

Structured data summarization involves generation of natural language summaries from structured input data. In this work, we consider summarizing structured data occurring in the form of tables as they are prevalent across a wide variety of domains. We formulate the standard table summarization problem, which deals with tables conforming to a single predefined schema. To this end, we propose a mixed hierarchical attention based encoderdecoder model which is able to leverage the structure in addition to the content of the tables. Our experiments on the publicly available WEATHERGOV dataset show around 18 BLEU (∼ 30%) improvement over the current state-of-the-art.
BinPro: A Tool for Binary Source Code Provenance

Enforcing open source licenses such as the GNU General Public License (GPL), analyzing a binary for possible vulnerabilities, and code maintenance are all situations where it is useful to be able to determine the source code provenance of a binary. While previous work has either focused on computing binary-to-binary similarity or source-to-source similarity, BinPro is the first work we are aware of to tackle the problem of source-to-binary similarity. BinPro can match binaries with their source code even without knowing which compiler was used to produce the binary, or what optimization level was used with the compiler. To do this, BinPro utilizes machine learning to compute optimal code features for determining binaryto-source similarity and a static analysis pipeline to extract and compute similarity based on those features. Our experiments show that on average BinPro computes a similarity of 81% for matching binaries and source code of the same applications, and an average similarity of 25% for binaries and source code of similar but different applications. This shows that BinPro’s similarity score is useful for determining if a binary was derived from a particular source code.