nils-herrmann/clean_scirepeval huggingface dataset:
Recurrent macromastia after subcutaneous mastectomy.

Management of sudden unrelenting breast growth in a young woman included use of antiestrogen hormone therapy and subcutaneous mastectomy. Later, massive breast growth again occurred during pregnancy, requiring a repeat postpartum subcutaneous mastectomy. The dramatic response to a specific antiestrogen agent and the subsequent massive regrowth of breast tissue after subcutaneous mastectomy suggests that breast tissue is extremely sensitive to circulating hormones in certain patients with macromastia. The unusual nature of this patient's recurrent macromastia warrants this review of reports of similarly affected patients and discussion of general concepts in the medical and surgical management of the disorder.
Multi-horizon ternary time series forecasting

Time series forecasting techniques have been widely applied in domains such as weather forecasting, electric power demand forecasting, earthquake forecasting, and financial market forecasting. Because of the fact that these time series are affected by a multitude of interrelating macroscopic and microscopic variables, the underlying models that generate these time series are nonlinear and extremely complex. Therefore, it is computationally infeasible to develop full-scale models with the present computing technology. Therefore, researchers have resorted to smaller-scale models that require frequent recalibration. Despite advances in forecasting technology over the past few decades, there have not been algorithms that can consistently produce accurate forecasts with statistical significance. This is mainly because state-of-the-art forecasting algorithms essentially perform single-horizon forecasts and produce continuous numbers as outputs. This paper proposes a novel multi-horizon ternary forecasting algorithm that forecasts whether a time series is heading for an uptrend or downtrend, or going sideways. The proposed system utilizes a cascade of support vector machines, each of which is trained to forecast a specific horizon. Individual forecasts of these support vector machines are combined to form an extrapolated time series. A higher level forecasting system then forward-runs the extrapolated time series and then forecasts the future trend of the input time series in accordance with some volatility measure. Experiments have been carried out on some datasets. Over these datasets, this system achieves accuracy rates well above the baseline accuracy rate, implying statistical significance. The experimental results demonstrate the efficacy of our framework.
Adaptive Combination of Volterra Kernels and Its Application to Nonlinear Acoustic Echo Cancellation

The combination of filters concept is a simple and flexible method to circumvent various compromises hampering the operation of adaptive linear filters. Recently, applications which require the identification of not only linear, but also nonlinear systems are widely studied. In this paper, we propose a combination of adaptive Volterra filters as the most versatile nonlinear models with memory. Moreover, we develop a novel approach that shows a similar behavior but significantly reduces the computational load by combining Volterra kernels rather than complete Volterra filters. Following an outline of the basic principles, the second part of the paper focuses on the application to nonlinear acoustic echo cancellation scenarios. As the ratio of the linear to nonlinear echo signal power is, in general, a priori unknown and time-variant, the performance of nonlinear echo cancellers may be inferior to a linear echo canceller if the nonlinear distortion is very low. Therefore, a modified version of the combination of kernels is developed obtaining a robust behavior regardless of the level of nonlinear distortion. Experiments with noise and speech signals demonstrate the desired behavior and the robustness of both the combination of Volterra filters and the combination of kernels approaches in different application scenarios.
LPV Identification of Wind Turbine Rotor Vibrational Dynamics Using Periodic Disturbance Basis Functions

This brief presents an identification experiment performed on the coupled dynamics of the edgewise bending vibrations of the rotor blades and the in-plane motion of the drivetrain of three-bladed wind turbines. These dynamics vary with rotor speed, and are subject to periodic wind flow disturbances. This brief demonstrates that this time-varying behavior can be captured in a linear parameter-varying (LPV) model with the rotor speed as the scheduling signal, and with additional sinusoidal inputs that are used as basis functions for the periodic wind flow disturbances. By including these inputs, the predictor-based LPV subspace identification approach (LPV PBSIDopt) was tailored for wind turbine applications. Using this tailor-made approach, the LPV model is identified from data measured with the three-bladed Controls Advanced Research Turbine (CART3) at the National Renewable Energy Laboratory's National Wind Technology Center.
Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17

Drug molecules consist of a few tens of atoms connected by covalent bonds. How many such molecules are possible in total and what is their structure? This question is of pressing interest in medicinal chemistry to help solve the problems of drug potency, selectivity, and toxicity and reduce attrition rates by pointing to new molecular series. To better define the unknown chemical space, we have enumerated 166.4 billion molecules of up to 17 atoms of C, N, O, S, and halogens forming the chemical universe database GDB-17, covering a size range containing many drugs and typical for lead compounds. GDB-17 contains millions of isomers of known drugs, including analogs with high shape similarity to the parent drug. Compared to known molecules in PubChem, GDB-17 molecules are much richer in nonaromatic heterocycles, quaternary centers, and stereoisomers, densely populate the third dimension in shape space, and represent many more scaffold types.
A Distributed FMCW Radar System Based on Fiber-Optic Links for Small Drone Detection

This paper discusses a distributed frequency modulation continuous wave radar system. This K-band radar system has high sensitivity, linearity, and flatness to detect low-radar cross section targets and measure their range and velocity. To reduce the leakage between a transmitter and a receiver, the system uses not RF cables but fiber-optic links that have low distortion characteristics and low propagation loss. The transmitter and the receiver are each mounted on a designed fixture to reduce the ground reflections. In addition, they are located on different platforms to reduce the leakage signal flowing directly from the transmitter to the receiver. Measurements in terms of the range and the velocity of a small drone have been carried out to evaluate the proposed distributed radar system. The results show that we can clearly detect the small drone within a 500 m range, which demonstrates the high sensitivity of the system and high isolation between the transmitter and the receiver.
Assisting IoT Projects and Developers in Designing Interoperable Semantic Web of Things Applications

Internet of Things (IoT) is becoming more and more popular. Increasingly, European projects (CityPulse, IoT.est, IoT-i and IERC), standard development organizations (ETSI M2M, oneM2M and W3C) and developers are involved in integrating Semantic Web technologies to Internet of Things. All of them design IoT application uses cases which are not necessarily interoperable with each other. The main innovative research challenge is providing a unified system to build interoperable semantic-based IoT applications. In this paper, to overcome this challenge, we design the Semantic Web of Things (SWoT) generator to assist IoT projects and developers in: (1) building interoperable Semantic Web of Things (SWoT) applications by providing interoperable semantic-based IoT application templates, (2) easily inferring high-level abstractions from sensor measurements thanks to the rules provided by the template, (3) designing domain-specific or inter-domain IoT applications thanks to the interoperable domain knowledge provided by the template, and (4) encouraging to reuse as much as possible the background knowledge already designed. We demonstrate the usefulness of our contribution though three use cases: (1) cloud-based IoT developers, (2) mobile application developers, and (3) assisting IoT projects. A proof-of concept for providing Semantic Web of Things application templates is available at http://www.sensormeasurement.appspot.com/?p=m3api.
Helping Users Bootstrap Ontologies: An Empirical Investigation

An ontology is a machine processable artifact that captures knowledge about some domain of interest. Ontologies are used in various domains including healthcare, science, and commerce. In this paper we examine the ontology bootstrapping problem. Specifically, we look at an approach that uses both competency questions and knowledge source reuse via recommendations to address the "cold start problem" that is, the task of creating an ontology from scratch. We describe this approach, an implementation of it, and we present an evaluation in the form of a controlled user study. We find that the approach leads users into creating significantly more detailed initial ontologies that have a greater domain coverage than ontologies produced without this support. Furthermore, in spite of a more involved workflow, the usability and user satisfaction of the bootstrapping approach is as good as a state-of-the-art ontology editor with no additional support.
Computational design of walking automata

Creating mechanical automata that can walk in stable and pleasing manners is a challenging task that requires both skill and expertise. We propose to use computational design to offset the technical difficulties of this process. A simple drag-and-drop interface allows casual users to create personalized walking toys from a library of pre-defined template mechanisms. Provided with this input, our method leverages physical simulation and evolutionary optimization to refine the mechanical designs such that the resulting toys are able to walk. The optimization process is guided by an intuitive set of objectives that measure the quality of the walking motions. We demonstrate our approach on a set of simulated mechanical toys with different numbers of legs and various distinct gaits. Two fabricated prototypes showcase the feasibility of our designs.
Comparison of Processing Algorithms for a Delay/Doppler Altimeter

The use of a radar altimeter is to measure the height of the reflecting surface when the instrument passes overhead. A delay/Doppler altimeter (DDA) reduces the along-track resolution cell using correlation of pulses within burst, as in a synthetic aperture radar, with the net effect of synthesizing an array with a narrower lobe. The processing of DDA data therefore implies the correction of the phase delay of pulses, which is said to be Doppler beam sharpening since it can be reduced to a DFT in the along-track direction. The noise term on Doppler echoes is decreased by incoherent summing of contributions of different bursts. Summation of these contributions requires that pointing of each beam is directed toward a given output grid of samples on ground, and this can be a nontrivial task, depending on the topography profile. In this letter, a comparison between different beam sharpening algorithms is presented: Two of them are similar in their basic idea even if with different implementations; the third one can be considered as reference since no approximations are needed in principle, but is computationally inefficient. What results is that the first couple can be considered basically equivalent in terms of precision and computational efficiency, with little differences at the change of the environmental conditions. Results based on simulated data of simple and complex scenarios are presented as support of the reasoning.
A Low Overhead DPA Countermeasure Circuit Based on Ring Oscillators

Side-channel attacks, particularly differential power analysis (DPA) attacks, are efficient ways to extract secret keys of the attacked devices by leaked physical information. To resist DPA attacks, hiding and masking methods are commonly used, but it usually resulted in high area overhead and performance degradation. In this brief, a DPA countermeasure circuit based on digital controlled ring oscillators is presented to efficiently resist the first-order DPA attack. The implementation of the critical S-box of the advanced encryption standard (AES) algorithm shows that the area overhead of a single S-box is about 19% without any extra delay in the critical path. Moreover, the countermeasure circuit can be mounted onto different S-box implementations based on composite field or look-up table (LUT). Based on our approach, a DPA-resistant AES chip can be proposed to maintain the same throughput with less than 2K extra gates.
Association of Sleep and Academic Performance

Poor school performance by adolescent students has been attributed in part to insufficient sleep. It is recognized that a number of factors lead to diminished total sleep time and chief among these are early school start times and sleep phase delay in adolescence. Political initiatives are gaining momentum across the United States to require later school start times with the intent of increasing total sleep time and consequently improving school performance. Later school start times come with significant costs and impact other activities of families and communities. The decision to implement later school start times cannot be made lightly and deserves support of well-performed research on the impact of these changes. A study evaluating the association of academic performance and total sleep time was performed in middle school and high school students in a suburban Maryland school system. Preliminary results of this study show no correlation of total sleep time with academic performance. Before mandating costly changes in school schedules, it would be useful to perform further research to determine the effects of increasing sleep time on the behaviors of adolescent students.
A Novel Surgical Technique to Correct Intrareolar Polythelia.

Polythelia is a rare congenital malformation that occurs in 1-2% of the population. Intra-areolar polythelia is the presence of one or more supernumerary nipples located within the areola. This is extremely rare. This article presents 3 cases of intra-areolar polythelia treated at our Department. These cases did not present other associated malformation. Surgical correction was performed for psychological and cosmetic reasons using advancement flaps. The aesthetic and functional results were satisfactory.
Ku-band waveguide band-pass filter with iris radius

This paper presents the design, simulation, and implementation of band pass filters in rectangular waveguides with radius, having 0.1 dB pass band ripple and 6.3% ripple at the center frequency of 14.2 GHz. A Mician microwave wizard software based on the Mode Matching Method (MMM) was used to simulate the structure of the filter. Simulation results are in good agreement with the measured one which improve the validity of the waveguide band pass filter design method.
Unusual presentation of carcinoma of penis.

A case of carcinoma of the penis in a 55-year-old landlord is described. He presented with a fungating growth on the shaft of his penis with an unusual history. The lesion started as a nodule in the coronal sulcus leading to thinning of urinary stream and ultimately retention of urine, which was diagnosed and treated as a case of urethral stricture. Wedge biopsy of the growth revealed the case of squamous cell carcinoma of penis. Ultrasonography and CT scan of pelvis and abdomen proved the disease to be localized to penis and total penectomy was carried out.
KASR: A Keyword-Aware Service Recommendation Method on MapReduce for Big Data Applications

Service recommender systems have been shown as valuable tools for providing appropriate recommendations to users. In the last decade, the amount of customers, services and online information has grown rapidly, yielding the big data analysis problem for service recommender systems. Consequently, traditional service recommender systems often suffer from scalability and inefficiency problems when processing or analysing such large-scale data. Moreover, most of existing service recommender systems present the same ratings and rankings of services to different users without considering diverse users' preferences, and therefore fails to meet users' personalized requirements. In this paper, we propose a Keyword-Aware Service Recommendation method, named KASR, to address the above challenges. It aims at presenting a personalized service recommendation list and recommending the most appropriate services to the users effectively. Specifically, keywords are used to indicate users' preferences, and a user-based Collaborative Filtering algorithm is adopted to generate appropriate recommendations. To improve its scalability and efficiency in big data environment, KASR is implemented on Hadoop, a widely-adopted distributed computing platform using the MapReduce parallel processing paradigm. Finally, extensive experiments are conducted on real-world data sets, and results demonstrate that KASR significantly improves the accuracy and scalability of service recommender systems over existing approaches.
Development of high frequency circuit model for oil-immersed power transformers and its application for lightning surge analysis

The lightning impulse withstand voltage for an oil-immersed power transformer is determined by the value of the lightning surge overvoltage generated at the transformer terminal. This overvoltage value has been conventionally obtained through lightning surge analysis using the electromagnetic transients program (EMTP), where the transformer is often simulated by a single lumped capacitance. However, since high frequency surge overvoltages ranging from several kHz to several MHz are generated in an actual system, a transformer circuit model capable of simulating the range up to this high frequency must be developed for further accurate analysis. In this paper, a high frequency circuit model for an oil-immersed transformer was developed and its validity was verified through comparison with the measurement results on the model winding actually produced. Consequently, it emerged that a high frequency model with three serially connected LC parallel circuits could adequately simulate the impedance characteristics of the winding up to a high frequency range of several MHz. Following lightning surge analysis for a 500 kV substation using this high frequency model, the peak value of the waveform was evaluated as lower than that simulated by conventional lumped capacitance even though the front rising was steeper. This phenomenon can be explained by the charging process of the capacitance circuit inside the transformer. Furthermore, the waveform analyzed by each model was converted into an equivalent standard lightning impulse waveform and the respective peak values were compared. As a result, the peak value obtained by the lumped capacitance simulation was evaluated as relatively higher under the present analysis conditions.
Haptic feedback in mixed-reality environment

The training process in industries is assisted with computer solutions to reduce costs. Normally, computer systems created to simulate assembly or machine manipulation are implemented with traditional Human-Computer interfaces (keyboard, mouse, etc). But, this usually leads to systems that are far from the real procedures, and thus not efficient in term of training. Two techniques could improve this procedure: mixed-reality and haptic feedback. We propose in this paper to investigate the integration of both of them inside a single framework. We present the hardware used to design our training system. A feasibility study allows one to establish testing protocol. The results of these tests convince us that such system should not try to simulate realistically the interaction between real and virtual objects as if it was only real objects.
of Crop Yield Forecasting Methods and Early Warning Systems

The following review paper presents an overview of the current crop yield forecasting methods and early warning systems for the global strategy to improve agricultural and rural statistics across the globe. Different sections describing simulation models, remote sensing, yield gap analysis, and methods to yield forecasting compose the manuscript. 1. Rationale Sustainable land management for crop production is a hierarchy of systems operating in— and interacting with—economic, ecological, social, and political components of the Earth. This hierarchy ranges from a field managed by a single farmer to regional, national, and global scales where policies and decisions influence crop production, resource use, economics, and ecosystems at other levels. Because sustainability concepts must integrate these diverse issues, agricultural researchers who wish to develop sustainable productive systems and policy makers who attempt to influence agricultural production are confronted with many challenges. A multiplicity of problems can prevent production systems from being sustainable; on the other hand, with sufficient attention to indicators of sustainability, a number of practices and policies could be implemented to accelerate progress. Indicators to quantify changes in crop production systems over time at different hierarchical levels are needed for evaluating the sustainability of different land management strategies. To develop and test sustainability concepts and yield forecast methods globally, it requires the implementation of long-term crop and soil management experiments that include measurements of crop yields, soil properties, biogeochemical fluxes, and relevant socioeconomic indicators. Long-term field experiments cannot be conducted with sufficient detail in space and time to find the best land management practices suitable for sustainable crop production. Crop and soil simulation models, when suitably tested in reasonably diverse space and time, provide a critical tool for finding combinations of management strategies to reach multiple goals required for sustainable crop production. The models can help provide land managers and policy makers with a tool to extrapolate experimental results from one location to others where there is a lack of response information. Agricultural production is significantly affected by environmental factors. Weather influences crop growth and development, causing large intra-seasonal yield variability. In addition, spatial variability of soil properties, interacting with the weather, cause spatial yield variability. Crop agronomic management (e.g. planting, fertilizer application, irrigation, tillage, and so on) can be used to offset the loss in yield due to effects of weather. As a result, yield forecasting represents an important tool for optimizing crop yield and to evaluate the crop-area insurance …
Efficient Near-optimal Algorithms for Barter Exchange

We study polynomial-time clearing algorithms for the barter exchange problem. We put forward a family of carefully designed approximation algorithms with desirable worst-case guarantees. We further apply a series of novel heuristics to implement these algorithms. We demonstrate via kidney exchange data sets that these algorithms achieve near-optimal performances while outperforming the state-of-the-art ILP based algorithms in running time by orders of magnitude.