nils-herrmann/clean_scirepeval huggingface dataset:
Buschke-Ollendorff syndrome: absence of LEMD3 mutation in an affected family.

Buschke-Ollendorff syndrome (BOS), an autosomal dominant disorder, features small, acquired, asymptomatic, symmetrical foci of osteosclerosis detected radiographically in epimetaphyseal bone (osteopoikilosis) (OPK) together with connective tissue nevi or juvenile elastomas. Heterozygous, loss-of-function, germline mutation in the LEMD3 gene (which encodes an inner nuclear membrane protein called LEMD3, or MAN1) has been repeatedly documented in patients with BOS or OPK.



We describe a father and son with multiple yellowish papules and nodules coalescing into cobblestone nevoid plaques consistent with nevus elasticus. Radiographs of the father show multiple, small, bone islands within the hands, wrists, distal femurs, proximal tibias, and left distal fibula consistent with OPK. Although the clinical findings are diagnostic of Buschke-Ollendorf syndrome, analysis of the LEMD3 gene showed no exonic mutations.



Absence of LEMD3 mutation in the exons and splice sites of a family with BOS suggests that there is genetic heterogeneity for this disorder.
Hyperglycemia and adverse pregnancy outcomes.

It is controversial whether maternal hyperglycemia less severe than that in diabetes mellitus is associated with increased risks of adverse pregnancy outcomes.



A total of 25,505 pregnant women at 15 centers in nine countries underwent 75-g oral glucose-tolerance testing at 24 to 32 weeks of gestation. Data remained blinded if the fasting plasma glucose level was 105 mg per deciliter (5.8 mmol per liter) or less and the 2-hour plasma glucose level was 200 mg per deciliter (11.1 mmol per liter) or less. Primary outcomes were birth weight above the 90th percentile for gestational age, primary cesarean delivery, clinically diagnosed neonatal hypoglycemia, and cord-blood serum C-peptide level above the 90th percentile. Secondary outcomes were delivery before 37 weeks of gestation, shoulder dystocia or birth injury, need for intensive neonatal care, hyperbilirubinemia, and preeclampsia.



For the 23,316 participants with blinded data, we calculated adjusted odds ratios for adverse pregnancy outcomes associated with an increase in the fasting plasma glucose level of 1 SD (6.9 mg per deciliter [0.4 mmol per liter]), an increase in the 1-hour plasma glucose level of 1 SD (30.9 mg per deciliter [1.7 mmol per liter]), and an increase in the 2-hour plasma glucose level of 1 SD (23.5 mg per deciliter [1.3 mmol per liter]). For birth weight above the 90th percentile, the odds ratios were 1.38 (95% confidence interval [CI], 1.32 to 1.44), 1.46 (1.39 to 1.53), and 1.38 (1.32 to 1.44), respectively; for cord-blood serum C-peptide level above the 90th percentile, 1.55 (95% CI, 1.47 to 1.64), 1.46 (1.38 to 1.54), and 1.37 (1.30 to 1.44); for primary cesarean delivery, 1.11 (95% CI, 1.06 to 1.15), 1.10 (1.06 to 1.15), and 1.08 (1.03 to 1.12); and for neonatal hypoglycemia, 1.08 (95% CI, 0.98 to 1.19), 1.13 (1.03 to 1.26), and 1.10 (1.00 to 1.12). There were no obvious thresholds at which risks increased. Significant associations were also observed for secondary outcomes, although these tended to be weaker.



Our results indicate strong, continuous associations of maternal glucose levels below those diagnostic of diabetes with increased birth weight and increased cord-blood serum C-peptide levels.
Design of a Magnetic-Geared Outer-Rotor Permanent-Magnet Brushless Motor for Electric Vehicles

This paper proposes a novel in-wheel motor, which artfully integrates a magnetic gear into a permanent-magnet brushless (PMBL) DC motor so that they can share a common PM rotor, hence offering both high efficiency and high power density. Moreover, the low-speed requirement for direct driving and the high-speed requirement for compact motor design can be achieved simultaneously. A 2-kW 600/4400-rpm magnetic-geared outer-rotor PMBL DC motor is designed and analyzed, which is particularly suitable for battery-powered electric motorcycles
What makes an image popular?

Hundreds of thousands of photographs are uploaded to the internet every minute through various social networking and photo sharing platforms. While some images get millions of views, others are completely ignored. Even from the same users, different photographs receive different number of views. This begs the question: What makes a photograph popular? Can we predict the number of views a photograph will receive even before it is uploaded? These are some of the questions we address in this work. We investigate two key components of an image that affect its popularity, namely the image content and social context. Using a dataset of about 2.3 million images from Flickr, we demonstrate that we can reliably predict the normalized view count of images with a rank correlation of 0.81 using both image content and social cues. In this paper, we show the importance of image cues such as color, gradients, deep learning features and the set of objects present, as well as the importance of various social cues such as number of friends or number of photos uploaded that lead to high or low popularity of images.
Treatment of dairy and swine manure effluents using freshwater algae: fatty acid content and composition of algal biomass at different manure loading rates

An alternative to land spreading of manure effluents is to mass-culture algae on the N and P present in the manure and convert manure N and P into algal biomass. The objective of this study was to determine how the fatty acid (FA) content and composition of algae respond to changes in the type of manure, manure loading rate, and to whether the algae was grown with supplemental carbon dioxide. Algal biomass was harvested weekly from indoor laboratory-scale algal turf scrubber (ATS) units using different loading rates of raw and anaerobically digested dairy manure effluents and raw swine manure effluent. Manure loading rates corresponded to N loading rates of 0.2 to 1.3 g TN m−2 day−1 for raw swine manure effluent and 0.3 to 2.3 g TN m−2 day−1 for dairy manure effluents. In addition, algal biomass was harvested from outdoor pilot-scale ATS units using different loading rates of raw and anaerobically digested dairy manure effluents. Both indoor and outdoor units were dominated by Rhizoclonium sp. FA content values of the algal biomass ranged from 0.6 to 1.5% of dry weight and showed no consistent relationship to loading rate, type of manure, or to whether supplemental carbon dioxide was added to the systems. FA composition was remarkably consistent among samples and >90% of the FA content consisted of 14:0, 16:0, 16:1ω7, 16:1ω9, 18:0, 18:1ω9, 18:2 ω6, and 18:3ω3.
Understanding the spatial distribution of crime based on its related variables using geospatial discriminative patterns

Crime tends to clust er geographi cally. This has led to the wide usage of hotspot analysis to identify and visualize crime. Accurately identified crime hotspots can greatly benefit the public by creating accurate threat visualizations, more efficiently allocating police resources, and predicting crime. Yet existing mapping methods usually identify hotspots without considering the underlying correlates of crime. In this study, we introduce a spatial data mining framework to study crime hotspots through their related variables. We use Geospatial Discriminative Patterns (GDPatterns) to capture the significant difference between two classes (hotspots and normal areas) in a geo-spatial dataset. Utilizing GDPatterns, we develop a novel model—Hotspot Optimization Tool (HOT)—to improve the identification of crime hotspots. Finally, based on a similarity measure, we group GDPattern clusters and visualize the distribution and characteristics of crime related variables. We evaluate our approach using a real world dataset collected from a northeast city in the United States.
Efficient architecture of variable size HEVC 2D-DCT for FPGA platforms

This study presents a design of two-dimensional (2D) discrete cosine transform (DCT) hardware architecture dedicated for High Efficiency Video Coding (HEVC) in field programmable gate array (FPGA) platforms. The proposed methodology efficiently proceeds 2D-DCT computation to fit internal components and characteristics of FPGA resources. A four-stage circuit architecture is developed to implement the proposed methodology. This architecture supports variable size of DCT computation, including 4×4, 8×8, 16×16, and 32×32. The proposed architecture has been implemented in System Verilog and synthesized in various FPGA platforms. Compared with existing related works in literature, this proposed architecture demonstrates significant advantages in hardware cost and performance improvement. The proposed architecture is able to sustain 4K@30fps ultra high definition (UHD) TV real-time encoding applications with a reduction of 31-64% in hardware cost. H.265/HEVC, two-dimensional discrete cosine transform (2D-DCT), FPGA platform, hardware architecture
Development of Quantum Device Simulator, NEMO-VN2

We develop some versions of quantum devices simulators such as NEMO-VN, NEMO-VN1 and NEMO-VN2. The quantum device simulator – NEMO-VN2 focuses on carbon nanotube FET (CNTFET). CNTFETs have been studied in recent years as potential alternatives to CMOS devices because of their compelling properties. Studies of phonon scattering in CNTs and its influence in CNTFET have focused on metallic tubes or on long semiconducting tubes. Phonon scattering in short channel CNTFETs, which is important for nanoelectronic applications, remains unexplored. In this work the non-equilibrium Green function (NEGF) is used to perform a comprehensive study of CNT transistors. The program has been written by using graphic user interface (GUI) of Matlab. We find that the effect of scattering on current-voltage characteristics of CNTFET is significant. The degradation of drain current due to scattering has been observed. Some typical simulation results have been presented for illustration.
KID ALGEBRA: RADIOHEAD’S EUCLIDEAN

HE BRITISH ROCK GROUP Radiohead has carved out a unique place in the post-millennial rock milieu by tempering their highly experimental idiolect with structures more commonly heard in Top Forty rock styles. 1 In what I describe as a Goldilocks principle, much of their music after OK Computer (1997) inhabits a space between banal convention and sheer experimentation—a dichotomy which I have elsewhere dubbed the 'Spears–Stockhausen Continuum.' 2 In the timbral domain, the band often introduces sounds rather foreign to rock music such as the ondes Martenot and highly processed lead vocals within textures otherwise dominated by guitar, bass, and drums (e.g., 'The National Anthem,' 2000), and song forms that begin with paradigmatic verse–chorus structures often end with new material instead of a recapitulated chorus (e.g., 'All I Need,' 2007). In this T
Sentence Modeling with Gated Recursive Neural Network

Recently, neural network based sentence modeling methods have achieved great progress. Among these methods, the recursive neural networks (RecNNs) can effectively model the combination of the words in sentence. However, RecNNs need a given external topological structure, like syntactic tree. In this paper, we propose a gated recursive neural network (GRNN) to model sentences, which employs a full binary tree (FBT) structure to control the combinations in recursive structure. By introducing two kinds of gates, our model can better model the complicated combinations of features. Experiments on three text classification datasets show the effectiveness of our model.
Rapid prototyping of low loss 3D printed waveguides for millimeter-wave applications

Traditional hollow metallic waveguide manufacturing techniques are readily capable of producing components with high-precision geometric tolerances, yet generally lack the ability to customize individual parts on demand or to deliver finished components with low lead times. This paper proposes a Rapid-Prototyping (RP) method for relatively low-loss millimeter-wave hollow waveguides produced using consumer-grade stere-olithographic (SLA) Additive Manufacturing (AM) technology, in conjunction with an electroless metallization process optimized for acrylate-based photopolymer substrates. To demonstrate the capabilities of this particular AM process, waveguide prototypes are fabricated for the W- and D-bands. The measured insertion loss at W-band is between 0.12 dB/in to 0.25 dB/in, corresponding to a mean value of 0.16 dB/in. To our knowledge, this is the lowest insertion loss figure presented to date, when compared to other W-Band AM waveguide designs reported in the literature. Printed D-band waveguide prototypes exhibit a transducer loss of 0.26 dB/in to 1.01 dB/in, with a corresponding mean value of 0.65 dB/in, which is similar performance to a commercial metal waveguide.
Privacy, Discovery, and Authentication for the Internet of Things

Automatic service discovery is essential to realizing the full potential of the Internet of Things (IoT). While discovery protocols like Multicast DNS, Apple AirDrop, and Bluetooth Low Energy have gained widespread adoption across both IoT and mobile devices, most of these protocols do not offer any form of privacy control for the service, and often leak sensitive information such as service type, device hostname, device owner’s identity, and more in the clear. To address the need for better privacy in both the IoT and the mobile landscape, we develop two protocols for private service discovery and private mutual authentication. Our protocols provide private and authentic service advertisements, zero round-trip (0-RTT) mutual authentication, and are provably secure in the Canetti-Krawczyk key-exchange model. In contrast to alternatives, our protocols are lightweight and require minimal modification to existing key-exchange protocols. We integrate our protocols into an existing open-source distributed applications framework, and provide benchmarks on multiple hardware platforms: Intel Edisons, Raspberry Pis, smartphones, laptops, and desktops. Finally, we discuss some privacy limitations of the Apple AirDrop protocol (a peer-to-peer file sharing mechanism) and show how to improve the privacy of Apple AirDrop using our private mutual authentication protocol.
Using Belief Theory to Diagnose Control Knowledge Quality: Application to Cartographic Generalisation

Both humans and artificial systems frequently use trial and error methods to problem solving. In order to be effective, this type of strategy implies having high quality control knowledge to guide the quest for the optimal solution. Unfortunately, this control knowledge is rarely perfect. Moreover, in artificial systems--as in humans--self-evaluation of one’s own knowledge is often difficult. Yet, this self-evaluation can be very useful to manage knowledge and to determine when to revise it. The objective of our work is to propose an automated approach to evaluate the quality of control knowledge in artificial systems based on a specific trial and error strategy, namely the informed tree search strategy. Our revision approach consists in analysing the system’s execution logs, and in using the belief theory to evaluate the global quality of the knowledge. We present a real-world industrial application in the form of an experiment using this approach in the domain of cartographic generalisation. Thus far, the results of using our approach have been encouraging. Knowledge Quality Diagnosis; Belief Theory; Problem Solving; Informed Tree Search Strategy; Cartographic Generalisation
Computational Representation of Practical Argument

In this paper we consider persuasion in the context of practical reasoning, and discuss the problems associated with construing reasoning about actions in a manner similar to reasoning about beliefs. We propose a perspective on practical reasoning as presumptive justification of a course of action, along with critical questions of this justification, building on the account of Walton. From this perspective, we articulate an interaction protocol, which we call PARMA, for dialogues over proposed actions based on this theory. We outline an axiomatic semantics for the PARMA Protocol, and discuss two implementations which use this protocol to mediate a discussion between humans. We then show how our proposal can be made computational within the framework of agents based on the Belief-Desire-Intention model, and illustrate this proposal with an example debate within a multi agent system.
Polaritons in van der Waals materials

Light trappedat thenanoscale, deep below the optical wavelength, exhibits an increase in the associated electric field strength, which results in enhanced light-matter interaction. This leads to strong nonlinearities, large photonic forces, and enhanced emission and absorption probabilities. A practical approach toward nanoscale light trapping andmanipulation is offered by interfaces separating media with permittivities of opposite signs. Such interfaces sustainhybrid light-mattermodes involving collective oscillations of polarization charges in matter, hence the termpolaritons. Surface plasmon polaritons, supported by electrons in metals, constitute amost-studiedprominent example. Yet there are many other varieties of polaritons, including those formed by atomic vibrations in polar insulators, excitons in semiconductors, Cooper pairs in superconductors, and spin resonances in (anti) ferromagnets. Together, they span a broad region of the electromagnetic spectrum, ranging frommicrowave to ultraviolet wavelengths. We discuss polaritons in van der Waals (vdW) materials: layeredsystems inwhich individualatomic planes are bonded by weak vdW attraction (see the figure). This class of quantum materials includes graphene and other two-dimensional crystals. In artificial structures assembled from dissimilar vdW atomic layers, polaritons associated with different constituents can interact to produce unique optical effects by design.
Deep Reinforcement Learning for Unsupervised Video Summarization With Diversity-Representativeness Reward

Video summarization aims to facilitate large-scale video browsing by producing short, concise summaries that are diverse and representative of original videos. In this paper, we formulate video summarization as a sequential decisionmaking process and develop a deep summarization network (DSN) to summarize videos. DSN predicts for each video frame a probability, which indicates how likely a frame is selected, and then takes actions based on the probability distributions to select frames, forming video summaries. To train our DSN, we propose an end-to-end, reinforcement learningbased framework, where we design a novel reward function that jointly accounts for diversity and representativeness of generated summaries and does not rely on labels or user interactions at all. During training, the reward function judges how diverse and representative the generated summaries are, while DSN strives for earning higher rewards by learning to produce more diverse and more representative summaries. Since labels are not required, our method can be fully unsupervised. Extensive experiments on two benchmark datasets show that our unsupervised method not only outperforms other stateof-the-art unsupervised methods, but also is comparable to or even superior than most of published supervised approaches.
Sexual and reproductive health in cancer survivors.

As patients live longer after cancer diagnosis and treatment, attention to symptoms and quality of life (QoL) are of increasing importance both during treatment and throughout survivorship. Two complications of multi-modal cancer treatment that can profoundly affect both men and women are sexual dysfunction and infertility. Survivors at highest risk for treatment-related sexual dysfunction are those with tumors that involve the sexual or pelvic organs and those whose treatment affects the hormonal systems mediating sexual function. Sexual dysfunction may not abate without appropriate intervention. Therefore, early identification and treatment strategies are essential. Likewise, multiple factors contribute to the risk of infertility from cancer treatment and many cancer patients of reproductive age would prefer to maintain their fertility, if possible. Fortunately, advances in reproductive technology have created options for young newly diagnosed patients to preserve their ability to have a biologic child. This paper will focus on the sexual and reproductive problems encountered by cancer survivors and discuss some treatment options.
Music classification using extreme learning machines

Over the last years, automatic music classification has become a standard benchmark problem in the machine learning community. This is partly due to its inherent difficulty, and also to the impact that a fully automated classification system can have in a commercial application. In this paper we test the efficiency of a relatively new learning tool, Extreme Learning Machines (ELM), for several classification tasks on publicly available song datasets. ELM is gaining increasing attention, due to its versatility and speed in adapting its internal parameters. Since both of these attributes are fundamental in music classification, ELM provides a good alternative to standard learning models. Our results support this claim, showing a sustained gain of ELM over a feedforward neural network architecture. In particular, ELM provides a great decrease in computational training time, and has always higher or comparable results in terms of efficiency.
Diprosopus conjoined twins: radiologic, autoptic, and histologic study of a case.

Conjoined twins are a rare and intriguing nature's phenomena; diprosopus or craniofacial duplication is the rarest with a reported incidence of 1 case in 180,000-15 million births. We present a radiologic, autoptic, and histologic study of a 37-week-old male diprosopus twin in a dichorionic pregnancy of a 26-old-year woman. Diprosopus malformation is part of duplication involving face and cranium like janiceps and dicephalus. Our case also shows partial duplication of the stomach with ectopic pancreas. Most studies are required to understand the exact mechanism of this malformation.
Foster Human Users ’ Creativity ? Theory and Praxis of Mixed-Initiative Co-Creativity

This article discusses the impact of artificially intelligent computers to the process of design, play and educational activities. A computational process which has the necessary intelligence and creativity to take a proactive role in such activities can not only support human creativity but also foster it and prompt lateral thinking. The argument is made both from the perspective of human creativity, where the computational input is treated as an external stimulus which triggers re-framing of humans’ routines and mental associations, but also from the perspective of computational creativity where human input and initiative constrains the search space of the algorithm, enabling it to focus on specific possible solutions to a problem rather than globally search for the optimal. The article reviews four mixed-initiative tools (for design and educational play) based on how they contribute to human-machine co-creativity. These paradigms serve different purposes, afford different human interaction methods and incorporate different computationally creative processes. Assessing how co-creativity is facilitated on a per-paradigm basis strengthens the theoretical argument and provides an initial seed for future work in the burgeoning domain of mixed-initiative interaction.
Face Recognition: From Traditional to Deep Learning Methods

Starting in the seventies, face recognition has become one of the most researched topics in computer vision and biometrics. Traditional methods based on hand-crafted features and traditional machine learning techniques have recently been superseded by deep neural networks trained with very large datasets. In this paper we provide a comprehensive and upto-date literature review of popular face recognition methods including both traditional (geometry-based, holistic, featurebased and hybrid methods) and deep learning methods.