nils-herrmann/clean_scirepeval huggingface dataset:
Investigation of Control Strategies for Variable-Speed Pump-Turbine Units by Using a Simplified Model of the Converters

This paper presents the modeling, simulation, and analysis of the dynamic behavior of a fictitious 2 × 320 MW variable-speed pump-turbine power plant, including a hydraulic system, electrical equipment, rotating inertias, and control systems. The modeling of the hydraulic and electrical components of the power plant is presented. The dynamic performances of a control strategy in generating mode and one in pumping mode are investigated by the simulation of the complete models in the case of change of active power set points. Then, a pseudocontinuous model of the converters feeding the rotor circuits is described. Due to this simplification, the simulation time can be reduced drastically (approximately factor 60). A first validation of the simplified model of the converters is obtained by comparison of the simulated results coming from the simplified and complete models for different modes of operation of the power plant. Experimental results performed on a 2.2-kW low-power test bench are also compared with the simulated results coming from both complete and simplified models related to this case and confirm the validity of the proposed simplified approach for the converters.
Soft and Declarative Fishing of Information in Big Data Lake

In recent years, many fields that experience a sudden proliferation of data, which increases the volume of data that must be processed and the variety of formats the data is stored in have been identified. This causes pressure on existing compute infrastructures and data analysis methods, as more and more data are considered as a useful source of information for making critical decisions in particular fields. Among these fields exist several areas related to human life, e.g., various branches of medicine, where the uncertainty of data complicates the data analysis, and where the inclusion of fuzzy expert knowledge in data processing brings many advantages. In this paper, we show how fuzzy techniques can be incorporated in big data analytics carried out with the declarative U-SQL language over a big data lake located on the cloud. We define the concept of big data lake together with the Extract, Process, and Store process performed while schematizing and processing data from the Data Lake, and while storing results of the processing. Our solution, developed as a Fuzzy Search Library for Data Lake, introduces the possibility of massively parallel, declarative querying of big data lake with simple and complex fuzzy search criteria, using fuzzy linguistic terms in various data transformations, and fuzzy grouping. Presented ideas are exemplified by a distributed analysis of large volumes of biomedical data on Microsoft Azure cloud. Results of performed tests confirm that the presented solution is highly scalable on the Cloud and is a successful step toward soft and declarative processing of data on a large scale. The solution presented in this paper directly addresses three characteristics of big data, i.e.,  volume, variety, and velocity, and indirectly addresses, veracity  and value.
Low-Noise Amplifier Protection Switch Using p-i-n Diodes With Tunable Open Stubs for Solid-State Pulsed Radar

In this letter, a tunable protection switch device using open stubs for  $X$ -band low-noise amplifiers (LNAs) is proposed. The protection switch is implemented using p-i-n diodes. As the parasitic inductance in the p-i-n diodes may degrade the protection performance, tunable open stubs are attached to these diodes to obtain a grounding effect. The performance is optimized for the desired frequency band by adjusting the lengths of the microstrip line open stubs. The designed LNA protection switch is fabricated and measured, and sufficient isolation is obtained for a 200 MHz operating band. The proposed protection switch is suitable for solid-state power amplifier radars in which the LNAs need to be protected from relatively long pulses.
Multiple event analysis for large-scale power systems through cluster-based sparse coding

Accurate event analysis in real time is of paramount importance for high-fidelity situational awareness such that proper actions can take place before any isolated faults escalate to cascading blackouts. For large-scale power systems, due to the large intra-class variance and inter-class similarity, the nonlinear nature of the system, and the large dynamic range of the event scale, multi-event analysis presents an intriguing problem. Existing approaches are limited to detecting only single or double events or a specified event type. Although some previous works can well distinguish multiple events in small-scale power systems, the performance tends to degrade dramatically in large-scale systems. In this paper, we focus on multiple event detection, recognition, and temporal localization in large-scale power systems. We discover that there always exist groups of buses whose reaction to each event shows high degree similarity, and the group membership generally remains the same regardless of the type of event(s). We further verify that this reaction to multiple events can be approximated as a linear combination of reactions to each constituent event. Based on these findings, we propose a novel method, referred to as cluster-based sparse coding (CSC), to extract all the underlying single events involved in a multi-event scenario. Experimental results based on simulated large-scale system model (i.e., NPCC) show that the proposed CSC algorithm presents high detection and recognition rate with low false alarms.
Diagnostic accuracy of instrumented and manual talar tilt tests in chronic ankle instability populations.

Diagnostic accuracy of the talar tilt test is not well established in a chronic ankle instability (CAI) population. Our purpose was to determine the diagnostic accuracy of instrumented and manual talar tilt tests in a group with varied ankle injury history compared with a reference standard of self-report questionnaire. Ninety-three individuals participated, with analysis occurring on 88 (39 CAI, 17 ankle sprain copers, and 32 healthy controls). Participants completed the Cumberland Ankle Instability Tool, arthrometer inversion talar tilt tests (LTT), and manual medial talar tilt stress tests (MTT). The ability to determine CAI status using the LTT and MTT compared with a reference standard was performed. The sensitivity (95% confidence intervals) of LTT and MTT was low [LTT = 0.36 (0.23-0.52), MTT = 0.49 (0.34-0.64)]. Specificity was good to excellent (LTT: 0.72-0.94; MTT: 0.78-0.88). Positive likelihood ratio (+ LR) values for LTT were 1.26-6.10 and for MTT were 2.23-4.14. Negative LR for LTT were 0.68-0.89 and for MTT were 0.58-0.66. Diagnostic odds ratios ranged from 1.43 to 8.96. Both clinical and arthrometer laxity testing appear to have poor overall diagnostic value for evaluating CAI as stand-alone measures. Laxity testing to assess CAI may only be useful to rule in the condition.
Spatial compactness meets topical consistency: jointly modeling links and content for community detection

In this paper, we address the problem of discovering topically meaningful, yet compact (densely connected) communities in a social network. Assuming the social network to be an integer-weighted graph (where the weights can be intuitively defined as the number of common friends, followers, documents exchanged, etc.), we transform the social network to a more efficient representation. In this new representation, each user is a bag of her one-hop neighbors. We propose a mixed-membership model to identify compact communities using this transformation. Next, we augment the representation and the model to incorporate user-content information imposing topical consistency in the communities. In our model a user can belong to multiple communities and a community can participate in multiple topics. This allows us to discover community memberships as well as community and user interests. Our method outperforms other well known baselines on two real-world social networks. Finally, we also provide a fast, parallel approximation of the same.
Assessing and addressing the re-eutrophication of Lake Erie: Central basin hypoxia

Donald Scavia, J. David Allan, Kristin K. Arend, Steven Bartell, Dmitry Beletsky, Nate S. Bosch, Stephen B. Brandt, Ruth D. Briland, Irem Daloğlu, Joseph V. DePinto, David M. Dolan, Mary Anne Evans, Troy M. Farmer, Daisuke Goto, Haejin Han, Tomas O. Höök, Roger Knight, Stuart A. Ludsin, Doran Mason, Anna M. Michalak, R. Peter Richards, James J. Roberts, Daniel K. Rucinski, Edward Rutherford, David J. Schwab, Timothy M. Sesterhenn, Hongyan Zhang, Yuntao Zhou
Implications of Circadian Rhythm in Dopamine and Mood Regulation

Mammalian physiology and behavior are regulated by an internal time-keeping system, referred to as circadian rhythm. The circadian timing system has a hierarchical organization composed of the master clock in the suprachiasmatic nucleus (SCN) and local clocks in extra-SCN brain regions and peripheral organs. The circadian clock molecular mechanism involves a network of transcription-translation feedback loops. In addition to the clinical association between circadian rhythm disruption and mood disorders, recent studies have suggested a molecular link between mood regulation and circadian rhythm. Specifically, genetic deletion of the circadian nuclear receptor Rev-erbα induces mania-like behavior caused by increased midbrain dopaminergic (DAergic) tone at dusk. The association between circadian rhythm and emotion-related behaviors can be applied to pathological conditions, including neurodegenerative diseases. In Parkinson's disease (PD), DAergic neurons in the substantia nigra pars compacta progressively degenerate leading to motor dysfunction. Patients with PD also exhibit non-motor symptoms, including sleep disorder and neuropsychiatric disorders. Thus, it is important to understand the mechanisms that link the molecular circadian clock and brain machinery in the regulation of emotional behaviors and related midbrain DAergic neuronal circuits in healthy and pathological states. This review summarizes the current literature regarding the association between circadian rhythm and mood regulation from a chronobiological perspective, and may provide insight into therapeutic approaches to target psychiatric symptoms in neurodegenerative diseases involving circadian rhythm dysfunction.
The relaxed min-max heap

A data structure that implements a mergeable double-ended priority queue, namely therelaxed min-max heap, is presented. A relaxed min-max heap ofn items can be constructed inO(n) time. In the worst case, operationsfind_min() andfind_max() can be performed in constant time, while each of the operationsmerge(),insert(),delete_min(),delete_max(),decrease_key(), anddelete_key() can be performed inO(logn) time. Moreover,insert() hasO(1) amortized running time. If lazy merging is used,merge() will also haveO(1) worst-case and amortized time. The relaxed min-max heap is the first data structure that achieves these bounds using only two pointers (puls one bit) per item.
A randomized comparison of coronary-stent placement and balloon angioplasty in the treatment of coronary artery disease. Stent Restenosis Study Investigators.

Coronary-stent placement is a new technique in which a balloon-expandable, stainless-steel, slotted tube is implanted at the site of a coronary stenosis. The purpose of this study was to compare the effects of stent placement and standard balloon angioplasty on angiographically detected restenosis and clinical outcomes.



We randomly assigned 410 patients with symptomatic coronary disease to elective placement of a Palmaz-Schatz stent or to standard balloon angioplasty. Coronary angiography was performed at base line, immediately after the procedure, and six months later.



The patients who underwent stenting had a higher rate of procedural success than those who underwent standard balloon angioplasty (96.1 percent vs. 89.6 percent, P = 0.011), a larger immediate increase in the diameter of the lumen (1.72 +/- 0.46 vs. 1.23 +/- 0.48 mm, P < 0.001), and a larger luminal diameter immediately after the procedure (2.49 +/- 0.43 vs. 1.99 +/- 0.47 mm, P < 0.001). At six months, the patients with stented lesions continued to have a larger luminal diameter (1.74 +/- 0.60 vs. 1.56 +/- 0.65 mm, P = 0.007) and a lower rate of restenosis (31.6 percent vs. 42.1 percent, P = 0.046) than those treated with balloon angioplasty. There were no coronary events (death; myocardial infarction; coronary-artery bypass surgery; vessel closure, including stent thrombosis; or repeated angioplasty) in 80.5 percent of the patients in the stent group and 76.2 percent of those in the angioplasty group (P = 0.16). Revascularization of the original target lesion because of recurrent myocardial ischemia was performed less frequently in the stent group than in the angioplasty group (10.2 percent vs. 15.4 percent, P = 0.06).



In selected patients, placement of an intracoronary stent, as compared with balloon angioplasty, results in an improved rate of procedural success, a lower rate of angiographically detected restenosis, a similar rate of clinical events after six months, and a less frequent need for revascularization of the original coronary lesion.
Engineered in vitro disease models.

The ultimate goal of most biomedical research is to gain greater insight into mechanisms of human disease or to develop new and improved therapies or diagnostics. Although great advances have been made in terms of developing disease models in animals, such as transgenic mice, many of these models fail to faithfully recapitulate the human condition. In addition, it is difficult to identify critical cellular and molecular contributors to disease or to vary them independently in whole-animal models. This challenge has attracted the interest of engineers, who have begun to collaborate with biologists to leverage recent advances in tissue engineering and microfabrication to develop novel in vitro models of disease. As these models are synthetic systems, specific molecular factors and individual cell types, including parenchymal cells, vascular cells, and immune cells, can be varied independently while simultaneously measuring system-level responses in real time. In this article, we provide some examples of these efforts, including engineered models of diseases of the heart, lung, intestine, liver, kidney, cartilage, skin and vascular, endocrine, musculoskeletal, and nervous systems, as well as models of infectious diseases and cancer. We also describe how engineered in vitro models can be combined with human inducible pluripotent stem cells to enable new insights into a broad variety of disease mechanisms, as well as provide a test bed for screening new therapies.
Modelling the dynamics of dengue real epidemics.

In this work, we use a mathematical model for dengue transmission with the aim of analysing and comparing two dengue epidemics that occurred in Salvador, Brazil, in 1995-1996 and 2002. Using real data, we obtain the force of infection, Λ, and the basic reproductive number, R(0), for both epidemics. We also obtain the time evolution of the effective reproduction number, R(t), which results in a very suitable measure to compare the patterns of both epidemics. Based on the analysis of the behaviour of R(0) and R(t) in relation to the adult mosquito control parameter of the model, we show that the control applied only to the adult stage of the mosquito population is not sufficient to stop dengue transmission, emphasizing the importance of applying the control to the aquatic phase of the mosquito.
The Sampling Oscilloscope as a Microwave Instrument

Most of the equipment required is readily available in most microwave labs: a vector network analyzer, a microwave signal generator, and, of course, a sampling oscilloscope. In this paper, the authors summarize many of the corrections discussed in " Terminology for high-speed sampling-oscilloscope calibration"  2006] and "Magnitude and phase calibrations for RF, microwave, and high-speed digital signal measurements" [Remley and Hale, 2007] that are necessary for metrology-grade measurements and Illustrate the application of these oscilloscopes to the characterization of microwave signals.
Cross-program design space exploration by ensemble transfer learning

Due to the increasing complexity of the processor architecture and the time-consuming software simulation, efficient design space exploration (DSE) has become a critical challenge in processor design. To address this challenge, recently machine learning techniques have been widely explored for predicting the performance of various configurations through conducting only a small number of simulations as the training samples. However, most existing methods randomly select some samples for simulation from the entire configuration space as training samples to build program-specific predictors. When a new program is considered, a large number of new program-specific simulations are needed for building a new predictor. Thus considerable simulation cost is required for each program. In this paper, we propose an efficient cross-program DSE framework TrEE by combining a flexible statistical sampling strategy and ensemble transfer learning technique. Specifically, TrEE includes the following two phases which also form our major contributions: 1) proposing an orthogonal array based foldover design for flexibly sampling the representative configurations for simulation, and 2) proposing an ensemble transfer learning algorithm that can effectively transfer knowledge among different types of programs for improving the prediction performance for the new program. We evaluate the proposed TrEE on the benchmarks of SPEC CPU 2006 suite. The results demonstrate that TrEE is much more efficient and robust than state-of-art DSE techniques.
Compact SIW bandpass filter for wideband millimeter-wave applications

A compact substrate intergrated waveguide (SIW) bandpass filter (BPF) is proposed for millimeter-wave (mmW) application. Four vias are located inside the SIW cavity in the form of rotationally symmetrical structure to provide transmission zeros at upper-stopband. As design examples, one substrate integrated waveguide bandpass filter prototype using Low Temperature Co-fired Ceramic (LTCC) on single layer substrate is designed and experimentally examined. The measured results show that the filter achieves a passband insertion loss of 0.8 dB at 50 GHz, return loss below -13dB and 3-dB fractional bandwidth of 30%.
Architecture Classification for Indian Monuments

In this project, the task of architecture classification for monuments and buildings from the Indian subcontinent was explored. Five major classes of architecture were taken and various supervised learning methods, both probabilistic and nonprobabilistic, were experimented with in order to classify the monuments into one of the five categories. The categories were: ’Ancient’, ’British’, ’IndoIslamic’, ’Maratha’ and ’Sikh’. Local ORB feature descriptors were used to represent each image and clustering was applied to quantize the obtained features to a smaller size. Other than the typical method of using features to do an image-wise classification, another method where descriptor wise classification is done was also explored. In this method, image label was provided as the mode of the labels of the descriptors of that image. It was found that among the different classifiers, k nearest neighbors for the case of descriptor-wise classification performed the best.
Metric Learning from Probabilistic Labels

Metric learning aims to learn a good distance metric that can capture the relationships among instances, and its importance has long been recognized in many fields. In the traditional settings of metric learning, an implicit assumption is that the associated labels of the instances are deterministic. However, in many real-world applications, the associated labels come naturally with probabilities instead of deterministic values. Thus, the existing metric learning methods cannot work well in these applications. To tackle this challenge, in this paper, we study how to effectively learn the distance metric from datasets that contain probabilistic information, and then propose two novel metric learning mechanisms for two types of probabilistic labels, i.e., the instance-wise probabilistic label and the group-wise probabilistic label. Compared with the existing metric learning methods, our proposed mechanisms are capable of learning distance metrics directly from the probabilistic labels with high accuracy. We also theoretically analyze the two proposed mechanisms and provide theoretical bounds on the sample complexity for both of them. Additionally, extensive experiments based on real-world datasets are conducted to verify the desirable properties of the proposed mechanisms.
Lyophilized Drug Product Cake Appearance: What Is Acceptable?

Cake appearance is an important attribute of freeze-dried products, which may or may not be critical with respect to product quality (i.e., safety and efficacy). Striving for "uniform and elegant" cake appearance may continue to remain an important goal during the design and development of a lyophilized drug product. However, "sometimes" a non-ideal cake appearance has no impact on product quality and is an inherent characteristic of the product (due to formulation, drug product presentation, and freeze-drying process). This commentary provides a summary of challenges related to visual appearance testing of freeze-dried products, particularly on how to judge the criticality of cake appearance. Furthermore, a harmonized nomenclature and description for variations in cake appearance from the ideal expectation of uniform and elegant is provided, including representative images. Finally, a science and risk-based approach is discussed on establishing acceptance criteria for cake appearance.
Semi-supervised forecasting of fraudulent financial statements

Prediction of potential fraudulent activities may prevent both the stakeholders and the appropriate regulatory authorities of national or international level from being deceived. The objective difficulties on collecting adequate data that are obsessed by completeness affects the reliability of the most supervised Machine Learning methods. This work examines the effectiveness of forecasting fraudulent financial statements using semi-supervised classification techniques (SSC) that require just a few labeled examples for achieving robust learning behaviors mining useful data patterns from a larger pool of unlabeled examples. Based on data extracted from Greek firms, a number of comparisons between supervised and semi-supervised algorithms has been conducted. According to the produced results, the later algorithms are favored being examined over several scenarios of different Labeled Ratio (R) values.