nils-herrmann/clean_scirepeval huggingface dataset:
Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios

The ocean moderates anthropogenic climate change at the cost of profound alterations of its physics, chemistry, ecology, and services. Here, we evaluate and compare the risks of impacts on marine and coastal ecosystems—and the goods and services they provide—for growing cumulative carbon emissions under two contrasting emissions scenarios. The current emissions trajectory would rapidly and significantly alter many ecosystems and the associated services on which humans heavily depend. A reduced emissions scenario—consistent with the Copenhagen Accord’s goal of a global temperature increase of less than 2°C—is much more favorable to the ocean but still substantially alters important marine ecosystems and associated goods and services. The management options to address ocean impacts narrow as the ocean warms and acidifies. Consequently, any new climate regime that fails to minimize ocean impacts would be incomplete and inadequate.
How We Transmit Memories to Other Brains: Constructing Shared Neural Representations Via Communication

Humans are able to mentally construct an episode when listening to another person's recollection, even though they themselves did not experience the events. However, it is unknown how strongly the neural patterns elicited by mental construction resemble those found in the brain of the individual who experienced the original events. Using fMRI and a verbal communication task, we traced how neural patterns associated with viewing specific scenes in a movie are encoded, recalled, and then transferred to a group of naïve listeners. By comparing neural patterns across the 3 conditions, we report, for the first time, that event-specific neural patterns observed in the default mode network are shared across the encoding, recall, and construction of the same real-life episode. This study uncovers the intimate correspondences between memory encoding and event construction, and highlights the essential role our common language plays in the process of transmitting one's memories to other brains.
Scenarios for 5G mobile and wireless communications: the vision of the METIS project

METIS is the EU flagship 5G project with the objective of laying the foundation for 5G systems and building consensus prior to standardization. The METIS overall approach toward 5G builds on the evolution of existing technologies complemented by new radio concepts that are designed to meet the new and challenging requirements of use cases today's radio access networks cannot support. The integration of these new radio concepts, such as massive MIMO, ultra dense networks, moving networks, and device-to-device, ultra reliable, and massive machine communications, will allow 5G to support the expected increase in mobile data volume while broadening the range of application domains that mobile communications can support beyond 2020. In this article, we describe the scenarios identified for the purpose of driving the 5G research direction. Furthermore, we give initial directions for the technology components (e.g., link level components, multinode/multiantenna, multi-RAT, and multi-layer networks and spectrum handling) that will allow the fulfillment of the requirements of the identified 5G scenarios.
Regulation of OsSPL14 by OsmiR156 defines ideal plant architecture in rice

Increasing crop yield is a major challenge for modern agriculture. The development of new plant types, which is known as ideal plant architecture (IPA), has been proposed as a means to enhance rice yield potential over that of existing high-yield varieties. Here, we report the cloning and characterization of a semidominant quantitative trait locus, IPA1 (Ideal Plant Architecture 1), which profoundly changes rice plant architecture and substantially enhances rice grain yield. The IPA1 quantitative trait locus encodes OsSPL14 (SOUAMOSA PROMOTER BINDING PROTEIN-LIKE 14) and is regulated by microRNA (miRNA) OsmiR156 in vivo. We demonstrate that a point mutation in OsSPL14 perturbs OsmiR156-directed regulation of OsSPL14, generating an 'ideal' rice plant with a reduced tiller number, increased lodging resistance and enhanced grain yield. Our study suggests that OsSPL14 may help improve rice grain yield by facilitating the breeding of new elite rice varieties.
Light load efficiency enhancement of a LLC resonant converter

Zero Voltage Switching (ZVS) technique minimizes the turn-on loss of the MOSFETs in LLC resonant converter structure. However, as switching frequency increases, the converter efficiency will suffer due to the increased turn-off loss and gate charge loss, and these effects are especially problematic at light load conditions, where the switching frequency has to be further increased in order to regulate the output. A new hysteretic control scheme is proposed in this paper. By fixing the switching frequency equal to the resonant frequency all the time and skipping pulses proportional to the load condition, the switching losses are reduced and the light load efficiency is improved. The experimental results prove the functionality of the proposed hysteretic control scheme.
Plasma Fetuin-A Levels and the Risk of Type 2 Diabetes

The liver-secreted protein fetuin-A induces insulin resistance in animals, and circulating fetuin-A is elevated in insulin resistance and fatty liver in humans. We investigated whether plasma fetuin-A levels predict the incidence of type 2 diabetes in a large prospective, population-based study.



A case-cohort study within the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study comprising 27,548 subjects was designed. We randomly selected a subcohort of 2,500 individuals of whom 2,164 were diabetes free at baseline and had anamnestic, anthropometrical, and metabolic data for analysis. Of the 849 incident diabetic case subjects identified in the full cohort during 7 years of follow-up, 703 remained for analyses after similar exclusions.



Plasma fetuin-A levels were positively associated with diabetes risk after adjustment for age (relative risk [RR] for extreme quintiles 1.75 [95% CI 1.32-2.31]; RR for 10 mug/ml 1.04 [1.03-1.06]). The association remained significant after adjustment for sex, BMI, waist circumference, and lifestyle risk factors (RR for 10 mug/ml 1.03 [1.01-1.06]). Adjustment for glucose, triglycerides, HDL cholesterol, A1C, gamma-glutamyltransferase, or high-sensitivity C-reactive protein or mutual adjustment for these biomarkers did not appreciably change this result (RR for 10 mug/ml full adjusted model 1.05 [1.02-1.07]). Furthermore, fetuin-A was associated with increased diabetes risk particularly in individuals with elevated plasma glucose.



Our data suggest that fetuin-A is an independent risk factor of type 2 diabetes.
Answering Opinion Questions on Products by Exploiting Hierarchical Organization of Consumer Reviews

This paper proposes to generate appropriate answers for opinion questions about products by exploiting the hierarchical organization of consumer reviews. The hierarchy organizes product aspects as nodes following their parent-child relations. For each aspect, the reviews and corresponding opinions on this aspect are stored. We develop a new framework for opinion Questions Answering, which enables accurate question analysis and effective answer generation by making use the hierarchy. In particular, we first identify the (explicit/implicit) product aspects asked in the questions and their sub-aspects by referring to the hierarchy. We then retrieve the corresponding review fragments relevant to the aspects from the hierarchy. In order to generate appropriate answers from the review fragments, we develop a multi-criteria optimization approach for answer generation by simultaneously taking into account review salience, coherence, diversity, and parent-child relations among the aspects. We conduct evaluations on 11 popular products in four domains. The evaluated corpus contains 70,359 consumer reviews and 220 questions on these products. Experimental results demonstrate the effectiveness of our approach.
An 800-MHz–6-GHz Software-Defined Wireless Receiver in 90-nm CMOS

A software-defined radio receiver is designed from a low-power ADC perspective, exploiting programmability of windowed integration sampler and clock-programmable discrete-time analog filters. To cover the major frequency bands in use today, a wideband RF front-end, including the low-noise amplifier (LNA) and a wide tuning-range synthesizer, spanning over 800 MHz to 6 GHz is designed. The wideband LNA provides 18-20 dB of maximum gain and 3-3.5 dB of noise figure over 800 MHz to 6 GHz. A low 1/f noise and high-linearity mixer is designed which utilizes the passive mixer core properties and provides around +70 dBm IIP2 over the bandwidth of operation. The entire receiver circuits are implemented in 90-nm CMOS technology. Programmability of the receiver is tested for GSM and 802.11g standards
CCFinder: A Multilinguistic Token-Based Code Clone Detection System for Large Scale Source Code

ÐA code clone is a code portion in source files that is identical or similar to another. Since code clones are believed to reduce the maintainability of software, several code clone detection techniques and tools have been proposed. This paper proposes a new clone detection technique, which consists of the transformation of input source text and a token-by-token comparison. For its implementation with several useful optimization techniques, we have developed a tool, named CCFinder, which extracts code clones in C, C++, Java, COBOL, and other source files. As well, metrics for the code clones have been developed. In order to evaluate the usefulness of CCFinder and metrics, we conducted several case studies where we applied the new tool to the source code of JDK, FreeBSD, NetBSD, Linux, and many other systems. As a result, CCFinder has effectively found clones and the metrics have been able to effectively identify the characteristics of the systems. In addition, we have compared the proposed technique with other clone detection techniques. Index TermsÐCode clone, duplicated code, CASE tool, metrics, maintenance.
Thermal characterisation of a copper-clip-bonded IGBT module with double-sided cooling

In pursuing higher power density power electronic equipment, the copper clip bonding concept is one of the double-sided cooling approaches under investigation for power semiconductors. This paper presents a comprehensive thermal assessment of the copper clip concept by undertaking comparative studies on one bespoke copper-clip-bonded IGBT power module and a conventional wire-bonded counterpart. A series of single-sided cooling tests and double-sided cooling tests have been undertaken on these two modules. Reductions are reported in the junction to case thermal resistance of up to 23% for one individual die and around 18% for the parallel operation of two dies in the single-sided cooling tests. Whilst in the double-sided cooling experiments, an additional average 18% thermal improvement is achieved due to the addition of a top fan-cooled heatsink mounted onto the copper clip. The results show clearly that the copper clip technology could provide significant benefits in the heat removal for power semiconductors and enable more power dense equipment.
Doubly Attentive Transformer Machine Translation

In this paper a doubly attentive transformer machine translation model (DATNMT) is presented in which a doubly-attentive transformer decoder normally joins spatial visual features obtained via pretrained convolutional neural networks, conquering any gap between image captioning and translation. In this framework, the transformer decoder figures out how to take care of source-language words and parts of an image freely by methods for two separate attention components in an Enhanced Multi-Head Attention Layer of doubly attentive transformer, as it generates words in the target language. We find that the proposed model can effectively exploit not just the scarce multimodal machine translation data, but also large general-domain textonly machine translation corpora, or imagetext image captioning corpora. The experimental results show that the proposed doublyattentive transformer-decoder performs better than a single-decoder transformer model, and gives the state-of-the-art results in the EnglishGerman multimodal machine translation task.
A Task-based Approach for Ontology Evaluation

The need for the establishment of evaluation methods that can measure respective improvements or degradations of ontological models e.g. yielded by a precursory ontology population stage is undisputed. We propose an evaluation scheme that allows to employ a number of different ontologies and to measure their performance on specific tasks. In this paper we present the resulting task-based approach for quantitative ontology evaluation, which also allows for a bootstrapping approach to ontology population. Benchmark tasks commonly feature a so-called gold-standard defining perfect performance. By selecting ontology-based approaches for the respective tasks, the ontology-dependent part of the performance can be measured. Following this scheme, we present the results of an experiment for testing and incrementally augmenting ontologies using a well-defined benchmark problem based on a evaluation goldstandard.
Introduction to the Special Issue: The Literature Review in Information Systems

There has been a flowering of scholarly interest in the literature review as a research method in the information systems discipline. We feel privileged to contribute to this conversation and introduce the work of the authors represented in this special issue. Some of the highlights include three new methods for conducting literature analysis and guidelines, tutorials, and approaches for coping with some of the challenges involved in carrying out a literature review. Of the three “new method” papers, one (ontological meta-analysis and synthesis) is entirely new, and two (stylized facts and critical discourse analysis) are novel in the information systems context. The other four paper address more general issues: the challenges of effective search strategies when confronted with the burgeoning volume of research available, a detailed tool-supported approach for conducting a rigorous review, a detailed tutorial for conducting a qualitative literature review, and a discussion of quality issues. Collectively, the papers place emphasis beyond the traditional “narrative synthesis” on the importance of selecting the appropriate approach for the research context and the importance of attention to quality and transparency at all stages of the process, regardless of which approach is adopted.
Bootstrapping Unsupervised Bilingual Lexicon Induction

The task of unsupervised lexicon induction is to find translation pairs across monolingual corpora. We develop a novel method that creates seed lexicons by identifying cognates in the vocabularies of related languages on the basis of their frequency and lexical similarity. We apply bidirectional bootstrapping to a method which learns a linear mapping between context-based vector spaces. Experimental results on three language pairs show consistent improvement over prior work.
Review Article: An Overview of Template Matching Technique in Image Processing

Template matching is one of the areas of profound interests in recent times. It has turned out to be a revolution in the field of computer vision. Template matching provides a new dimension into the image-processing capabilities, although there have been many attempts to resolve different issues in this field there have always been newer concepts emerging in this ever challenging field. In this study, various techniques are discussed and a new FPGA based spectral architecture is proposed to achieve a fast similarity measure between template and input image.
A Structured Vector Space Model for Word Meaning in Context

We address the task of computing vector space representations for the meaning of word occurrences, which can vary widely according to context. This task is a crucial step towards a robust, vector-based compositional account of sentence meaning. We argue that existing models for this task do not take syntactic structure sufficiently into account. We present a novel structured vector space model that addresses these issues by incorporating the selectional preferences for words’ argument positions. This makes it possible to integrate syntax into the computation of word meaning in context. In addition, the model performs at and above the state of the art for modeling the contextual adequacy of paraphrases.
YASENN: Explaining Neural Networks via Partitioning Activation Sequences

We introduce a novel approach to feed-forward neural network interpretation based on partitioning the space of sequences of neuron activations. In line with this approach, we propose a model-specific interpretation method, called YASENN. Our method inherits many advantages of modelagnostic distillation, such as an ability to focus on the particular input region and to express an explanation in terms of features different from those observed by a neural network. Moreover, examination of distillation error makes the method applicable to the problems with low tolerance to interpretation mistakes. Technically, YASENN distills the network with an ensemble of layer-wise gradient boosting decision trees and encodes the sequences of neuron activations with leaf indices. The finite number of unique codes induces a partitioning of the input space. Each partition may be described in a variety of ways, including examination of an interpretable model (e.g. a logistic regression or a decision tree) trained to discriminate between objects of those partitions. Our experiments provide an intuition behind the method and demonstrate revealed artifacts in neural network decision making.
SNAP and SPAN: Towards Dynamic Spatial Ontology

We propose a modular ontology of the dynamic features of reality. This amounts, on the one hand, to a purely spatial ontology supporting snapshot views of the world at successive instants of time and, on the other hand, to a purely spatiotemporal ontology of change and process. We argue that dynamic spatial ontology must combine these two distinct types of inventory of the entities and relationships in reality, and we provide characterizations of spatiotemporal reasoning in the light of the interconnections between them.
Fully Spectral Partial Shape Matching

We propose an efficient procedure for calculating partial dense intrinsic correspondence between deformable shapes performed entirely in the spectral domain. Our technique relies on the recently introduced partial functional maps formalism and on the joint approximate diagonalization (JAD) of the Laplace-Beltrami operators previously introduced for matching non-isometric shapes. We show that a variant of the JAD problem with an appropriately modified coupling term (surprisingly) allows to construct quasi-harmonic bases localized on the latent corresponding parts. This circumvents the need to explicitly compute the unknown parts by means of the cumbersome alternating minimization used in the previous approaches, and allows performing all the calculations in the spectral domain with constant complexity independent of the number of shape vertices. We provide an extensive evaluation of the proposed technique on standard non-rigid correspondence benchmarks and show state-of-the-art performance in various settings, including partiality and the presence of topological noise.
PixelVAE: A Latent Variable Model for Natural Images

Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent representation and model global structure well but have difficulty capturing small details. PixelCNN models details very well, but lacks a latent code and is difficult to scale for capturing large structures. We present PixelVAE, a VAE model with an autoregressive decoder based on PixelCNN. Our model requires very few expensive autoregressive layers compared to PixelCNN and learns latent codes that are more compressed than a standard VAE while still capturing most non-trivial structure. Finally, we extend our model to a hierarchy of latent variables at different scales. Our model achieves state-of-the-art performance on binarized MNIST, competitive performance on 64 × 64 ImageNet, and high-quality samples on the LSUN bedrooms dataset.
Soft gelatin capsules (softgels).

It is estimated that more than 40% of new chemical entities (NCEs) coming out of the current drug discovery process have poor biopharmaceutical properties, such as low aqueous solubility and/or permeability. These suboptimal properties pose significant challenges for the oral absorption of the compounds and for the development of orally bioavailable dosage forms. Development of soft gelatin capsule (softgel) dosage form is of growing interest for the oral delivery of poorly water soluble compounds (BCS class II or class IV). The softgel dosage form offers several advantages over other oral dosage forms, such as delivering a liquid matrix designed to solubilize and improve the oral bioavailability of a poorly soluble compound as a unit dose solid dosage form, delivering low and ultra-low doses of a compound, delivering a low melting compound, and minimizing potential generation of dust during manufacturing and thereby improving the safety of production personnel. However, due to the very dynamic nature of the softgel dosage form, its development and stability during its shelf-life are fraught with several challenges. The goal of the current review is to provide an in-depth discussion on the softgel dosage form to formulation scientists who are considering developing softgels for therapeutic compounds.
Association of depression and anxiety alone and in combination with chronic musculoskeletal pain in primary care patients.

To assess the relationship between depression and anxiety comorbidity on pain intensity, pain-related disability, and health-related quality of life (HRQL).



Analysis of baseline data from the Stepped Care for Affective Disorders and Musculoskeletal Pain (SCAMP) study. All patients (n = 500) had chronic pain (>or=3-month duration) of the low back, hip, or knee. Patients with depression were oversampled for the clinical trial component of SCAMP and thus represented 50% of the study population. Patients were categorized according to pain comorbid with depression, anxiety, or both. We used analysis of variance and multivariate analysis of variance models to assess the relationships between independent and dependent variables.



Participants had a mean age of 59 years; they were 55% women, 56% White, and 40% Black. Fifty-four percent (n = 271) reported pain only, 20% (n = 98) had pain and depression, 3% (n = 15) had pain and anxiety, and 23% (n = 116) had pain, depression, and anxiety. Patients with pain and both depression and anxiety experienced the greatest pain severity (p < .0001) and pain-related disability (p < .0001). Psychiatric comorbidity was strongly associated with disability days in the past 3 months (p < .0001), with 18.1 days reported by patients with pain only, 32.2 days by those with pain and anxiety, 38.0 days by those with pain and depression, and 42.6 days in those with all three conditions. We found a similar pattern of poorer HRQL (p < .0001) in those with pain, depression, and anxiety.



The added morbidity of depression and anxiety with chronic pain is strongly associated with more severe pain, greater disability, and poorer HRQL.