nils-herrmann/clean_scirepeval huggingface dataset:
Clustering performance comparison using K-means and expectation maximization algorithms

Clustering is an important means of data mining based on separating data categories by similar features. Unlike the classification algorithm, clustering belongs to the unsupervised type of algorithms. Two representatives of the clustering algorithms are the K-means and the expectation maximization (EM) algorithm. Linear regression analysis was extended to the category-type dependent variable, while logistic regression was achieved using a linear combination of independent variables. To predict the possibility of occurrence of an event, a statistical approach is used. However, the classification of all data by means of logistic regression analysis cannot guarantee the accuracy of the results. In this paper, the logistic regression analysis is applied to EM clusters and the K-means clustering method for quality assessment of red wine, and a method is proposed for ensuring the accuracy of the classification results.
Line–Line Fault Analysis and Protection Challenges in Solar Photovoltaic Arrays

Fault analysis in solar photovoltaic (PV) arrays is a fundamental task to protect PV modules from damage and to eliminate risks of safety hazards. This paper focuses on line–line faults in PV arrays that may be caused by short-circuit faults or double ground faults. The effect on fault current from a maximum-power-point tracking of a PV inverter is discussed and shown to, at times, prevent overcurrent protection devices (OCPDs) to operate properly. Furthermore, fault behavior of PV arrays is highly related to the fault location, fault impedance, irradiance level, and use of blocking diodes. Particularly, this paper examines the challenges to OCPD in a PV array brought by unique faults: One is a fault that occurs under low-irradiance conditions, and the other is a fault that occurs at night and evolves during “night-to-day” transition. In both circumstances, the faults might remain hidden in the PV system, no matter how irradiance changes afterward. These unique faults may subsequently lead to unexpected safety hazards, reduced system efficiency, and reduced reliability. A small-scale experimental PV system has been developed to further validate the conclusions.
Baseline Detection in Historical Documents Using Convolutional U-Nets

Baseline detection is still a challenging task for heterogeneous collections of historical documents. We present a novel approach to baseline extraction in such settings, turning out the winning entry to the ICDAR 2017 Competition on Baseline detection (cBAD). It utilizes deep convolutional nets (CNNs) for both, the actual extraction of baselines, as well as for a simple form of layout analysis in a pre-processing step. To the best of our knowledge it is the first CNN-based system for baseline extraction applying a U-net architecture and sliding window detection, profiting from a high local accuracy of the candidate lines extracted. Final baseline post-processing complements our approach, compensating for inaccuracies mainly due to missing context information during sliding window detection. We experimentally evaluate the components of our system individually on the cBAD dataset. Moreover, we investigate how it generalizes to different data by means of the dataset used for the baseline extraction task of the ICDAR 2017 Competition on Layout Analysis for Challenging Medieval Manuscripts (HisDoc). A comparison with the results reported for HisDoc shows that it also outperforms the contestants of the latter.
A survey on defect detection in bare PCB and assembled PCB using image processing techniques

In recent years, the development of low cost consumer electronic products such as television, mobile, washing machine etc. make high demands on manufacturing of defect free Printed Circuit Board and Printed Circuit Board Assembly. Therefore, it is necessary to know the different types of defects and defect detection techniques of Printed Circuit Board in order to produce high quality zero defect printed circuit boards. This paper discusses various methods which have been used in detecting defects so as to understand the gaps which would be helpful for the researchers to do the future work related to defect detection.
Collaborative Recommendation with Multi-Criteria Ratings

Recommendation systems utilize data analysis techniques to the problem of helping users find the items they would like. Example applications include the recommendation systems for movies, books, CDs and many others. As recommendation systems emerge as an independent research area, the rating structure plays a critical role in recent studies. Among many alternatives, the collaborative filtering algorithms are generally accepted to be successful to estimate user ratings of unseen items and then to derive proper recommendations. In this paper, we extend the concept of single criterion ratings to multi-criteria ones, i.e., an item can be evaluated in many different aspects. For example, the goodness of a restaurant can be evaluated in terms of its food, decor, service and cost. Since there are usually conflicts among different criteria, the recommendation problem cannot be formulated as an optimization problem any more. Instead, we propose in this paper to use data query techniques to solve this multi-criteria recommendation problem. Empirical studies show that our approach is of both theoretical and practical values.
Use of ultrasound-activated resorbable poly-D-L-lactide pins (SonicPins) and foil panels (Resorb-X) for horizontal bone augmentation of the maxillary and mandibular alveolar ridges.

Horizontal bone augmentation of the maxillary and mandibular alveolar ridges has been conventionally performed using mini titanium alloy screws. The titanium alloy screws are used to fixate corticocancellous block grafts to the recipient site or for tenting the mucoperiosteum to retain particulate bone grafts. Nonresorbable guided tissue regenerative membranes reinforced with titanium have also been developed to use with particulate bone grafts to augment alveolar ridge defects. This report demonstrates the use of resorbable ultrasound-activated pins and resorbable foil panels developed by KLS Martin for augmenting the alveolar ridges with particulate bone grafts.
Acute care inpatients with long-term delayed-discharge: evidence from a Canadian health region

Acute hospital discharge delays are a pressing concern for many health care administrators. In Canada, a delayed discharge is defined by the alternate level of care (ALC) construct and has been the target of many provincial health care strategies. Little is known on the patient characteristics that influence acute ALC length of stay. This study examines which characteristics drive acute ALC length of stay for those awaiting nursing home admission.



Population-level administrative and assessment data were used to examine 17,111 acute hospital admissions designated as alternate level of care (ALC) from a large Canadian health region. Case level hospital records were linked to home care administrative and assessment records to identify and characterize those ALC patients that account for the greatest proportion of acute hospital ALC days.



ALC patients waiting for nursing home admission accounted for 41.5% of acute hospital ALC bed days while only accounting for 8.8% of acute hospital ALC patients. Characteristics that were significantly associated with greater ALC lengths of stay were morbid obesity (27 day mean deviation, 99% CI = ±14.6), psychiatric diagnosis (13 day mean deviation, 99% CI = ±6.2), abusive behaviours (12 day mean deviation, 99% CI = ±10.7), and stroke (7 day mean deviation, 99% CI = ±5.0). Overall, persons with morbid obesity, a psychiatric diagnosis, abusive behaviours, or stroke accounted for 4.3% of all ALC patients and 23% of all acute hospital ALC days between April 1st 2009 and April 1st, 2011. ALC patients with the identified characteristics had unique clinical profiles.



A small number of patients with non-medical days waiting for nursing home admission contribute to a substantial proportion of total non-medical days in acute hospitals. Increases in nursing home capacity or changes to existing funding arrangements should target the sub-populations identified in this investigation to maximize effectiveness. Specifically, incentives should be introduced to encourage nursing homes to accept acute patients with the least prospect for community-based living, while acute patients with the greatest prospect for community-based living are discharged to transitional care or directly to community-based care.
Oral leukoplakia associated with amalgam restorations.

The World Health Organization has defined oral leukoplakia (OL) as "a white patch or plaque of the oral mucosa that cannot be characterized clinically or pathologically as any other disease". A 21-year-old male with OL presented with a bilateral burning sensation in the buccal mucosa. The patient had amalgam restorations, and an epicutaneous patch test indicated a positive response to amalgam. The amalgam restorations were therefore removed and the cavities were refilled with a composite resin restorative material. During 5 years of follow-up, there was no recurrence of the oral lesions. This case illustrates that amalgam fillings may cause OL lesions.
A new design of wilkinson power divider using radial stubs featuring size reduction and bandwidth enhancement with physical isolation

In this paper, we present a new design of a compact power divider based on the modification of the conventional Wilkinson power divider. In this new configuration, length reduction of the high-impedance arms is achieved through capacitive loading using open stubs. Radial configuration was adopted for bandwidth enhancement. Additionally, by insertion of the complex isolation network between the high-impedance transmission lines at an arbitrary phase angle other than 90 degrees, both electrical and physical isolation were achieved. Design equations as well as the synthesis procedure of the isolation network are demonstrated using an example centred at 1 GHz. The measurement results revealed a reduction of 60% in electrical length compared to the conventional Wilkinson power divider with a total length of only 30 degrees at the centre frequency of operation.
Multi-lingual Geoparsing based on Machine Translation

Our method for multi-lingual geoparsing uses monolingual tools and resources along with machine translation and alignment to return location words in many languages. Not only does our method save the time and cost of developing geoparsers for each language separately, but also it allows the possibility of a wide range of language capabilities within a single interface. We evaluated our method in our LanguageBridge prototype on location named entities using newswire, broadcast news and telephone conversations in English, Arabic and Chinese data from the Linguistic Data Consortium (LDC). Our results for geoparsing Chinese and Arabic text using our multi-lingual geoparsing method are comparable to our results for geoparsing English text with our English tools. Furthermore, experiments using our machine translation approach results in accuracy comparable to results from the same data that was translated manually.
Final Year Project Final Report SimNav : Simulation Training Framework for Collision Avoidance in Robot Navigation

The development of robotics has been rapid recently in different areas, for instance, industrial, social and medical. Robot navigation is a fundamental part among robotic applications in all these domains. The task of navigating from one point to another with collision-free trajectories is referred as collision avoidance. Conventional methods exploited geometric rules or required excessive amount of real-world data. Without the need of expensive real-world data, this project aims to show that a robot can be trained with synthetic data only in simulation environment and navigate safely. Deliverable includes a simulation environment learning framework, two high-quality simulation environments and two trained navigation policies, which serves as a decision maker to navigate the robot and avoid collision.
A Tutorial on Stochastic Programming

This tutorial is aimed at introducing some basic ideas of stochastic programming. The intended audience of the tutorial is optimization practitioners and researchers who wish to acquaint themselves with the fundamental issues that arise when modeling optimization problems as stochastic programs. The emphasis of the paper is on motivation and intuition rather than technical completeness (although we could not avoid giving some technical details). Since it is not intended to be a historical overview of the subject, relevant references are given in the “Notes” section at the end of the paper, rather than in the text. Stochastic programming is an approach for modeling optimization problems that involve uncertainty. Whereas deterministic optimization problems are formulated with known parameters, real world problems almost invariably include parameters which are unknown at the time a decision should be made. When the parameters are uncertain, but assumed to lie in some given set of possible values, one might seek a solution that is feasible for all possible parameter choices and optimizes a given objective function. Such an approach might make sense for example when designing a least-weight bridge with steel having a tensile strength that is known only to within some tolerance. Stochastic programming models are similar in style but try to take advantage of the fact that probability distributions governing the data are known or can be estimated. Often these models apply to settings in which decisions are made repeatedly in essentially the same circumstances, and the objective is to come up with a decision that will perform well on average. An example would be designing truck routes for daily milk delivery to customers with random demand. Here probability distributions (e.g., of demand) could be estimated from data that have been collected over time. The goal is to find some policy that is feasible for all (or almost all) the possible parameter realizations and optimizes the expectation of some function of the decisions and the random variables.
Stochastic lattice-based modelling of malaria dynamics

The transmission of malaria is highly variable and depends on a range of climatic and anthropogenic factors. In addition, the dispersal of Anopheles mosquitoes is a key determinant that affects the persistence and dynamics of malaria. Simple, lumped-population models of malaria prevalence have been insufficient for predicting the complex responses of malaria to environmental changes. A stochastic lattice-based model that couples a mosquito dispersal and a susceptible-exposed-infected-recovered epidemics model was developed for predicting the dynamics of malaria in heterogeneous environments. The It$$\hat{o}$$ o^ approximation of stochastic integrals with respect to Brownian motion was used to derive a model of stochastic differential equations. The results show that stochastic equations that capture uncertainties in the life cycle of mosquitoes and interactions among vectors, parasites, and hosts provide a mechanism for the disruptions of malaria. Finally, model simulations for a case study in the rural area of Kilifi county, Kenya are presented. A stochastic lattice-based integrated malaria model has been developed. The applicability of the model for capturing the climate-driven hydrologic factors and demographic variability on malaria transmission has been demonstrated.
Diabetes mellitus, fasting glucose, and risk of cause-specific death.

The extent to which diabetes mellitus or hyperglycemia is related to risk of death from cancer or other nonvascular conditions is uncertain.



We calculated hazard ratios for cause-specific death, according to baseline diabetes status or fasting glucose level, from individual-participant data on 123,205 deaths among 820,900 people in 97 prospective studies.



After adjustment for age, sex, smoking status, and body-mass index, hazard ratios among persons with diabetes as compared with persons without diabetes were as follows: 1.80 (95% confidence interval [CI], 1.71 to 1.90) for death from any cause, 1.25 (95% CI, 1.19 to 1.31) for death from cancer, 2.32 (95% CI, 2.11 to 2.56) for death from vascular causes, and 1.73 (95% CI, 1.62 to 1.85) for death from other causes. Diabetes (vs. no diabetes) was moderately associated with death from cancers of the liver, pancreas, ovary, colorectum, lung, bladder, and breast. Aside from cancer and vascular disease, diabetes (vs. no diabetes) was also associated with death from renal disease, liver disease, pneumonia and other infectious diseases, mental disorders, nonhepatic digestive diseases, external causes, intentional self-harm, nervous-system disorders, and chronic obstructive pulmonary disease. Hazard ratios were appreciably reduced after further adjustment for glycemia measures, but not after adjustment for systolic blood pressure, lipid levels, inflammation or renal markers. Fasting glucose levels exceeding 100 mg per deciliter (5.6 mmol per liter), but not levels of 70 to 100 mg per deciliter (3.9 to 5.6 mmol per liter), were associated with death. A 50-year-old with diabetes died, on average, 6 years earlier than a counterpart without diabetes, with about 40% of the difference in survival attributable to excess nonvascular deaths.



In addition to vascular disease, diabetes is associated with substantial premature death from several cancers, infectious diseases, external causes, intentional self-harm, and degenerative disorders, independent of several major risk factors. (Funded by the British Heart Foundation and others.).
User-Steered Image Segmentation Paradigms: Live Wire and Live Lane

In multidimensional image analysis, there are, and will continue to be, situations wherein automatic image segmentation methods fail, calling for considerable user assistance in the process. The main goals of segmentation research for such situations ought to be (i) to provide ffective controlto the user on the segmentation process while it is being executed, and (ii) to minimize the total user’s time required in the process. With these goals in mind, we present in this paper two paradigms, referred to aslive wireandlive lane, for practical image segmentation in large applications. For both approaches, we think of the pixel vertices and oriented edges as forming a graph, assign a set of features to each oriented edge to characterize its “boundariness,” and transform feature values to costs. We provide training facilities and automatic optimal feature and transform selection methods so that these assignments can be made with consistent effectiveness in any application. In live wire, the user first selects an initial point on the boundary. For any subsequent point indicated by the cursor, an optimal path from the initial point to the current point is found and displayed in real time. The user thus has a live wire on hand which is moved by moving the cursor. If the cursor goes close to the boundary, the live wire snaps onto the boundary. At this point, if the live wire describes the boundary appropriately, the user deposits the cursor which now becomes the new starting point and the process continues. A few points (livewire segments) are usually adequate to segment the whole 2D boundary. In live lane, the user selects only the initial point. Subsequent points are selected automatically as the cursor is moved within a lane surrounding the boundary whose width changes
Object Tracking in Video Images based on Image Segmentation and Pattern Matching

(Deemed University) is an authentic work carried out by him under my supervision and guidance. To the best of my knowledge, the matter embodied in the thesis has not been submitted to any other University / Institute for the award of any Degree or Diploma. ii ACKNOWLEDGEMENTS This project is by far the most significant accomplishment in my life and it would be impossible without people who supported me and believed in me. I would like to extend my gratitude and my sincere thanks to my honorable, esteemed supervisor Prof. Dipti Patra, Department of Electrical Engineering. She is not only a great lecturer with deep vision but also most importantly a kind person. I sincerely thank for her exemplary guidance and encouragement. Her trust and support inspired me in the most important moments of making right decisions and I am glad to work under her supervision. I am very much thankful to our Head of the Department, Prof. Bidyadhar Subudhi, for providing us with best facilities in the department and his timely suggestions and all my teachers Dr. for providing a solid background for my studies. They have been great sources of inspiration to me and I thank them from the bottom of my heart. I would like to thank all my friends and especially my classmates for all the thoughtful and mind stimulating discussions we had, which prompted us to think beyond the obvious. I've enjoyed their companionship so much during my stay at NIT, Rourkela. I would like to thank all those who made my stay in Rourkela an unforgettable and rewarding experience. Last but not least I would like to thank my parents, who taught me the value of hard work by their own example. They rendered me enormous support being apart during the whole tenure of my stay in NIT Rourkela.
Science journalism goes global.

Graphene is a wonder material with many superlatives to its name. It is the thinnest known material in the universe and the strongest ever measured. Its charge carriers exhibit giant intrinsic mobility, have zero effective mass, and can travel for micrometers without scattering at room temperature. Graphene can sustain current densities six orders of magnitude higher than that of copper, shows record thermal conductivity and stiffness, is impermeable to gases, and reconciles such conflicting qualities as brittleness and ductility. Electron transport in graphene is described by a Dirac-like equation, which allows the investigation of relativistic quantum phenomena in a benchtop experiment. This review analyzes recent trends in graphene research and applications, and attempts to identify future directions in which the field is likely to develop.
The EGF receptor family: spearheading a merger of signaling and therapeutics.

The ErbB receptor tyrosine kinases evolved as key regulatory entities enabling the extracellular milieu to communicate with the intracellular machinery to bring forth the appropriate biological response in an ever-changing environment. Since its discovery, many aspects of the ErbB family have been deciphered, with emphasis on aberration of signaling in human diseases. However, only now, with the availability of the atomic coordinates of these receptors, can we construct a comprehensive model of the mechanisms underlying ligand-induced receptor dimerization and subsequent tyrosine kinase activation. Furthermore, the recent introduction of new high-throughput screening methodologies, combined with the materialization of a systems biology perspective, reveals an overwhelming network complexity, enabling robust signaling and evolvability. This knowledge is likely to impact our view of diseases as system perturbations and resistance to ErbB-targeted therapeutics as manifestations of robustness.