nils-herrmann/clean_scirepeval huggingface dataset:
Identifying Significance of Discrepancies in Radiology Reports

At many teaching hospitals, it is common practice for on-call radiology residents to interpret radiology examinations; such reports are later reviewed and revised by an attending physician before being used for any decision making. In case there are substantial problems in the resident’s initial report, the resident is called and the problems are reviewed to prevent similar future reporting errors. However, due to the large volume of reports produced, attending physicians rarely discuss the problems side by side with residents, thus missing an educational opportunity. In this work, we introduce a pipeline to discriminate between reports with significant discrepancies and those with non-significant discrepancies. The former contain severe errors or mis-interpretations, thus representing a great learning opportunity for the resident; the latter presents only minor differences (often stylistic) and have a minor role in the education of a resident. By discriminating between the two, the proposed system could flag those reports that an attending radiology should definitely review with residents under their supervision. We evaluated our approach on 350 manually annotated radiology reports sampled from a collection of tens of thousands. The proposed classifier achieves an Area Under the Curve (AUC) of 0.837, which represent a 14% improvement over the baselines. Furthermore, the classifier reduces the False Negative Rate (FNR) by 52%, a desirable performance metric for any recall-oriented task such as the one studied
Controllable Generative Adversarial Network

Recently introduced generative adversarial networks (GANs) have been shown numerous promising results to generate realistic samples. In the last couple of years, it has been studied to control features in synthetic samples generated by the GAN. Auxiliary classifier GAN (ACGAN), a conventional method to generate conditional samples, employs a classification layer in discriminator to solve the problem. However, in this paper, we demonstrate that the auxiliary classifier can hardly provide good guidance for training of the generator, where the classifier suffers from overfitting. Since the generator learns from classification loss, such a problem has a chance to hinder the training. To overcome this limitation, here, we propose a controllable GAN (ControlGAN) structure. By separating a feature classifier from the discriminator, the classifier can be trained with data augmentation technique, which can support to make a fine classifier. Evaluated with the CIFAR-10 dataset, ControlGAN outperforms AC-WGAN-GP which is an improved version of the ACGAN, where Inception score of the ControlGAN is 8.61 ± 0.10. Furthermore, we demonstrate that the ControlGAN can generate intermediate features and opposite features for interpolated input and extrapolated input labels that are not used in the training process. It implies that the ControlGAN can significantly contribute to the variety of generated samples.
WEIGHTING: WHY, WHEN, AND HOW?*

1.  Fundamental questions about weighting  seem to be ~ most common during the analysis of survey data and I encounter them almost every week. Yet we "lack a single, reasonably comprehensive, introductory explanation of the process of weighting" , readily available to and usable by survey practitioners, who are looking for simple guidance, and this paper aims to meet some of that need. Some partial treatments have appeared in the survey literature [e.g., Kish 1965], but the topic seldom appears even in the indexes. However, we can expect growing interest, as witnessed by six publications since 1987 listed in the references.
Putting the face in context: Body expressions impact facial emotion processing in human infants

Body expressions exert strong contextual effects on facial emotion perception in adults. Specifically, conflicting body cues hamper the recognition of emotion from faces, as evident on both the behavioral and neural level. We examined the developmental origins of the neural processes involved in emotion perception across body and face in 8-month-old infants by measuring event-related brain potentials (ERPs). We primed infants with body postures (fearful, happy) that were followed by either congruent or incongruent facial expressions. Our results revealed that body expressions impact facial emotion processing and that incongruent body cues impair the neural discrimination of emotional facial expressions. Priming effects were associated with attentional and recognition memory processes, as reflected in a modulation of the Nc and Pc evoked at anterior electrodes. These findings demonstrate that 8-month-old infants possess neural mechanisms that allow for the integration of emotion across body and face, providing evidence for the early developmental emergence of context-sensitive facial emotion perception.
High-Efficiency Cellular Power Amplifiers Based on a Modified LDMOS Process on Bulk Silicon and Silicon-On-Insulator Substrates With Integrated Power Management Circuitry

An RF high-voltage CMOS technology is presented for cost-effective monolithic integration of cellular RF transmit functions. The technology integrates a modified LDMOS RF power transistor capable of nearly comparable linear and saturated RF power characteristics to GaAs solutions at cellular frequency bands. Measured results for multistage cellular power amplifier (PA) designs processed on bulk-Si and silicon-on-insulator on high-resistivity Si substrates (1 kΩ·cm ) are presented. The low-band multistage PA achieves greater than 60% power-added efficiency (PAE) with more than 35.5-dBm output power. The high-band PA achieves 45%-53% PAE across the band with greater than 33.4-dBm output power. Measured linearity performance is presented using an EDGE modulation source. A dc/dc buck converter was also integrated in the PA die as the power management circuitry. Measured results for the output power, PAE, and spurious emissions in the receive band while the dc/dc converter is biasing the PA and running at different modes are reported.
Indirect Effects of Pesticides on Natural Enemies

Pesticides including insecticides and miticides are primarily used to regulate arthropod (insect and mite) pest populations in agricultural and horticultural crop production systems. However, continual reliance on pesticides may eventually result in a number of potential ecological problems including resistance, secondary pest outbreaks, and/or target pest resurgence . Therefore, implementation of alternative management strategies is justified in order to preserve existing pesticides and produce crops with minimal damage from arthropod pests. One option that has gained interest by producers is integrating pesticides with biological control agents or natural enemies including parasitoids and predators . This is often referred to as ‘compatibility,’ which is the ability to integrate or combine natural enemies with pesticides so as to regulate arthropod pest populations without directly or indirectly affecting the life history parameters or population dynamics of natural enemies [2,4]. This may also refer to pesticides being effective against targeted arthropod pests but relatively non-harmful to natural enemies [5,6].
Detox diets for toxin elimination and weight management: a critical review of the evidence.

Detox diets are popular dieting strategies that claim to facilitate toxin elimination and weight loss, thereby promoting health and well-being. The present review examines whether detox diets are necessary, what they involve, whether they are effective and whether they present any dangers. Although the detox industry is booming, there is very little clinical evidence to support the use of these diets. A handful of clinical studies have shown that commercial detox diets enhance liver detoxification and eliminate persistent organic pollutants from the body, although these studies are hampered by flawed methodologies and small sample sizes. There is preliminary evidence to suggest that certain foods such as coriander, nori and olestra have detoxification properties, although the majority of these studies have been performed in animals. To the best of our knowledge, no randomised controlled trials have been conducted to assess the effectiveness of commercial detox diets in humans. This is an area that deserves attention so that consumers can be informed of the potential benefits and risks of detox programmes.
Human gut microbes impact host serum metabolome and insulin sensitivity

Insulin resistance is a forerunner state of ischaemic cardiovascular disease and type 2 diabetes. Here we show how the human gut microbiome impacts the serum metabolome and associates with insulin resistance in 277 non-diabetic Danish individuals. The serum metabolome of insulin-resistant individuals is characterized by increased levels of branched-chain amino acids (BCAAs), which correlate with a gut microbiome that has an enriched biosynthetic potential for BCAAs and is deprived of genes encoding bacterial inward transporters for these amino acids. Prevotella copri and Bacteroides vulgatus are identified as the main species driving the association between biosynthesis of BCAAs and insulin resistance, and in mice we demonstrate that P. copri can induce insulin resistance, aggravate glucose intolerance and augment circulating levels of BCAAs. Our findings suggest that microbial targets may have the potential to diminish insulin resistance and reduce the incidence of common metabolic and cardiovascular disorders.
Learning Hybrid Representations to Retrieve Semantically Equivalent Questions

Retrieving similar questions in online Q&A community sites is a difficult task because different users may formulate the same question in a variety of ways, using different vocabulary and structure. In this work, we propose a new neural network architecture to perform the task of semantically equivalent question retrieval. The proposed architecture, which we call BOW-CNN, combines a bag-ofwords (BOW) representation with a distributed vector representation created by a convolutional neural network (CNN). We perform experiments using data collected from two Stack Exchange communities. Our experimental results evidence that: (1) BOW-CNN is more effective than BOW based information retrieval methods such as TFIDF; (2) BOW-CNN is more robust than the pure CNN for long texts.
Retrieving Real Time Data Through IOT Devices and Storing Securely on Cloud Using Li-Fi

Internet of things is an accumulation of physical objects which are able to send information over the network. Nowaday, an association utilizes IoT devices to gather real time and continuous data from sensors. This information can be utilized to enhance the customer satisfaction and to make better business decisions. The cloud has various advantages over on-premises storage for storing IoT information. But there are apprehensions with using the cloud for IoT data storage. The real one is security. To exchange the information over the cloud IoT devices utilizes WiFi technology. However, it has a few limits that confine the potential outcomes of the Internet of Things. On the off chance that more devices or clients that will be associated with the internet utilizing WiFi, the transfer speed gets separated among the clients hence the outcome will be slower network. Consequently, there is a necessity of a speedier and a solid internet administration to the Internet of Things to be completely operational. This paper shows the strategy which permits exchanging gathered loT information over the cloud safely utilizing LiFi innovation by applying role based access control approaches and the cryptography techniques.
3D Reconstruction of Interreflection-affected Surface Concavities using Photometric Stereo

Image-based reconstruction of 3D shapes is inherently biased under the occurrence of interreflections, since the observed intensity at surface concavities consists of direct and global illumination components. This issue is commonly not considered in a Photometric Stereo (PS) framework. Under the usual assumption of only direct reflections, this corrupts the normal estimation process in concave regions and thus leads to inaccurate results. For this reason, global illumination effects need to be considered for the correct reconstruction of surfaces affected by interreflections. While there is ongoing research in the field of inverse lighting (i.e. separation of global and direct illumination components), the interreflection aspect remains oftentimes neglected in the field of 3D shape reconstruction. In this study, we present a computationally driven approach for iteratively solving that problem. Initially, we introduce a photometric stereo approach that roughly reconstructs a surface with at first unknown reflectance properties. Then, we show that the initial surface reconstruction result can be refined iteratively regarding non-distant light sources and, especially, interreflections. The benefit for the reconstruction accuracy is evaluated on real Lambertian surfaces using laser range scanner data as ground truth.
Hybrid-feature-guided lung nodule type classification on CT images

In this paper, we propose a novel classification method for lung nodules from CT images based on hybrid features. Towards nodules of different types, including well-circumscribed, vascularized, juxta-pleural, pleural-tail, as well as ground glass optical (GGO) and non-nodule from CT scans, our method has achieved promising classification results. The proposed method utilizes hybrid descriptors consisting of statistical features from multi-view multi-scale convolutional neural networks (CNNs) and geometrical features from Fisher vector (FV) encodings based on scaleinvariant feature transform (SIFT). First, we approximate the nodule radii based on icosahedron sampling and intensity analysis. Then, we apply high frequency content measure analysis to obtain sampling views with more abundant information. After that, based on re-sampled views, we train multi-view multi-scale CNNs to extract statistical features and calculate FV encodings as geometrical features. Finally, we achieve hybrid features by merging statistical and geometrical features based on multiple kernel learning (MKL) and classify nodule types through a multi-class support vector machine. The experiments on LIDC-IDRI and ELCAP have shown that our method has achieved promising results and can be of great assistance for radiologists’ diagnosis of lung cancer in clinical practice.
Exosomes from Plasmodium-infected hosts inhibit tumor angiogenesis in a murine Lewis lung cancer model

Previous research to investigate the interaction between malaria infection and tumor progression has revealed that malaria infection can potentiate host immune response against tumor in tumor-bearing mice. Exosomes may play key roles in disseminating pathogenic host-derived molecules during infection because several studies have shown the involvement and roles of extracellular vesicles in cell–cell communication. However, the role of exosomes generated during Plasmodium infection in tumor growth, progression and angiogenesis has not been studied either in animals or in the clinics. To test this hypothesis, we designed an animal model to generate and isolate exosomes from mice which were subsequently used to treat the tumor. Intra-tumor injection of exosomes derived from the plasma of Plasmodium-infected mice provided significantly reduced Lewis lung cancer growth in mice. We further co-cultured the isolated exosomes with endothelial cells and observed significantly reduced expression of VEGFR2 and migration in the endothelial cells. Interestingly, high level of micro-RNA (miRNA) 16/322/497/17 was detected in the exosomes derived from the plasma of mice infected with Plasmodium compared with those from control mice. We observed that overexpression of the miRNA 16/322/497/17 in endothelial cell corresponded with decreased expression of VEGFR2, inhibition of angiogenesis and inhibition of the miRNA 16/322/497/17 significantly alleviated these effects. These data provide novel scientific evidence of the interaction between Plasmodium infection and lung cancer growth and angiogenesis.
REAL-TIME CONSTRAINED TRAJECTORY GENERATION APPLIED TO A FLIGHT CONTROL EXPERIMENT

A computational approach to generate real-time, optimal trajectories for a flight control experiment is presented. Minimum time trajectories are computed for hover-to-hover and forward flight maneuvers. Instantaneous changes in the trajectory constraints that model obstacles and threats are also investigated. Experimental results using the Nonlinear Trajectory Generation software package show good closedloop performance for all maneuvers. Success of the algorithm demonstrates that highconfidence real-time trajectory generation is achievable in spite of the highly nonlinear and non-convex nature of the problem.
Reactively matched 3-stage C-X-Ku band GaN MMIC power amplifier

A 3-stage wideband power amplifier (PA) using a 0.15 pm gallium nitride (GaN) monolithic microwave integrated circuit (MMIC) process from NRC is designed, fabricated, and measured. After characterization of the high electron mobility transistor (HEMT), a non-linear model was created from the measured data for use in the design. The reactively matched 3.8 mm × 1.8 mm PA also uses resistive elements for gain compensation and circuit stability. Measurements at 20 dBm source power show 35–38 dBm output power and 10–18% PAE over a 6 to 17 GHz bandwidth. These results demonstrate the highest output power per die area for a 3-stage GaN MMIC PA of this bandwidth in this power range.
Cultural hybridity in participatory design

In this paper we examine challenges identified with participatory design research in the developing world and develop the postcolonial notion of cultural hybridity as a sensitizing concept. While participatory design intentionally addresses power relationships, its methodology does not to the same degree cover cultural power relationships, which extend beyond structural power and voice. The notion of cultural hybridity challenges the static cultural binary opposition between the self and the other, Western and non-Western, or the designer and the user---offering a more nuanced approach to understanding the malleable nature of culture. Drawing from our analysis of published literature in the participatory design community, we explore the complex relationship of participatory design to international development projects and introduce postcolonial cultural hybridity via postcolonial theory and its application within technology design thus far. Then, we examine how participatory approaches and cultural hybridity may interact in practice and conclude with a set of sensitizing insights and topics for further discussion in the participatory design community.
Circumcision with the Plastibell Device in Hooded Prepuce or Glanular Hypospadias

To retrospectively review our experience in infants with glanular hypospadias or hooded prepuce without meatal anomaly, who underwent circumcision with the plastibell device. Although circumcision with the plastibell device is well described, there are no reported experiences pertaining to hooded prepuce or glanular hypospadias that have been operated on by this technique.



Between September 2002 and September 2008, 21 children with hooded prepuce (age 1 to 11 months, mean 4.6 months) were referred for hypospadias repair. Four of them did not have meatal anomaly. Their parents accepted this small anomaly and requested circumcision without glanuloplasty. In all cases, the circumcision was corrected by a plastibell device.



No complications occurred in the circumcised patients, except delayed falling of bell in one case that was removed by a surgeon, after the tenth day.



Circumcision with the plastibell device is a suitable method for excision of hooded prepuce. It can also be used successfully in infants, who have miniglanular hypospadias, and whose parents accepted this small anomaly.
Correlations among prototypical usability metrics: evidence for the construct of usability

Correlations between prototypical usability metrics from 90 distinct usability tests were strong when measured at the task-level (r between .44 and .60). Using test-level satisfaction ratings instead of task-level ratings attenuated the correlations (r between .16 and .24). The method of aggregating data from a usability test had a significant effect on the magnitude of the resulting correlations. The results of principal components and factor analyses on the prototypical usability metrics provided evidence for an underlying construct of general usability with objective and subjective factors.
BotMosaic: Collaborative Network Watermark for Botnet Detection

Recent research has made great strides in the field of detecting botnets. However, botnets of all kinds continue to plague the Internet, as many ISPs and organizations do not deploy these techniques. We aim to mitigate this state by creating a very low-cost method of detecting infected bot host. Our approach is to leverage the botnet detection work carried out by some organizations to easily locate collaborating bots elsewhere. We created BotMosaic as a countermeasure to IRC-based botnets. BotMosaic relies on captured bot instances controlled by a watermarker, who inserts a particular pattern into their network traffic. This pattern can then be detected at a very low cost by client organizations and the watermark can be tuned to provide acceptable false-positive rates. A novel feature of the watermark is that it is inserted collaboratively into the flows of multiple captured bots at once, in order to ensure the signal is strong enough to be detected. BotMosaic can also be used to detect stepping stones and to help trace back to the botmaster. It is content agnostic and can operate on encrypted traffic. We evaluate BotMosaic using simulations and a testbed deployment.
Replacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data Analysis

An increasing number of sensors on mobile, Internet of things (IoT), and wearable devices generate time-series measurements of physical activities. Though access to the sensory data is critical to the success of many beneficial applications such as health monitoring or activity recognition, a wide range of potentially sensitive information about the individuals can also be discovered through access to sensory data and this cannot easily be protected using traditional privacy approaches. In this paper, we propose a privacy-preserving sensing framework for managing access to time-series data in order to provide utility while protecting individuals' privacy. We introduce Replacement AutoEncoder, a novel feature-learning algorithm which learns how to transform discriminative features of multi-variate time-series that correspond to sensitive inferences, into some features that have been more observed in non-sensitive inferences, to protect users' privacy. This efficiency is achieved by defining a user-customized objective function for deep autoencoders. Replacement will not only eliminate the possibility of recognition sensitive inferences, it also eliminates the possibility of detecting the occurrence of them, that is the main weakness of other approaches such as filtering or randomization. We evaluate the efficacy of the algorithm with an activity recognition task in a multi-sensing environment using extensive experiments on three benchmark datasets. We show that it can retain the recognition accuracy of state-of-the-art techniques while simultaneously preserving the privacy of sensitive information. Finally, we utilize the GANs for detecting the occurrence of replacement, after releasing data, and show that this can be done only if the adversarial network is trained on the users' original data.