nils-herrmann/clean_scirepeval huggingface dataset:
The Architecture of a Churn Prediction System Based on Stream Mining

Churning is the movement of customers from a company to another. For any company, being able to predict with some time which of their customers will churn is essential to take actions in order to retain them, and for this reason most sectors invest substantial effort in techniques for (semi)automatically predicting churning, and data mining and machine learning are among the techniques successfully used to this effect. In this paper we describe a prototype for churn prediction using stream mining methods, which offer the additional promise of detecting new patterns of churn in real-time streams of high-speed data, and adapting quickly to a changing reality. The prototype is implemented on top of the MOA (Massive Online Analysis) framework for stream mining. The application implicit in the prototype is the telecommunication operator (mobile phone) sector. A shorter version of this paper, omitting Section 5, was presented at CCIA’13 (http://mon.uvic.cat/ccia2013/en/).
Cultivation of Pleurotus ostreatus and other edible mushrooms

Pleurotus ostreatus is the second most cultivated edible mushroom worldwide after Agaricus bisporus. It has economic and ecological values and medicinal properties. Mushroom culture has moved toward diversification with the production of other mushrooms. Edible mushrooms are able to colonize and degrade a large variety of lignocellulosic substrates and other wastes which are produced primarily through the activities of the agricultural, forest, and food-processing industries. Particularly, P. ostreatus requires a shorter growth time in comparison to other edible mushrooms. The substrate used for their cultivation does not require sterilization, only pasteurization, which is less expensive. Growing oyster mushrooms convert a high percentage of the substrate to fruiting bodies, increasing profitability. P. ostreatus demands few environmental controls, and their fruiting bodies are not often attacked by diseases and pests, and they can be cultivated in a simple and cheap way. All this makes P. ostreatus cultivation an excellent alternative for production of mushrooms when compared to other mushrooms.
Active Polarization Descattering

Vision in scattering media is important but challenging. Images suffer from poor visibility due to backscattering and attenuation. Most prior methods for scene recovery use active illumination scanners (structured and gated), which can be slow and cumbersome, while natural illumination is inapplicable to dark environments. The current paper addresses the need for a non-scanning recovery method, that uses active scene irradiance. We study the formation of images under widefield artificial illumination. Based on the formation model, the paper presents an approach for recovering the object signal. It also yields rough information about the 3D scene structure. The approach can work with compact, simple hardware, having active widefield, polychromatic polarized illumination. The camera is fitted with a polarization analyzer. Two frames of the scene are taken, with different states of the analyzer or polarizer. A recovery algorithm follows the acquisition. It allows both the backscatter and the object reflection to be partially polarized. It thus unifies and generalizes prior polarization-based methods, which had assumed exclusive polarization of either of these components. The approach is limited to an effective range, due to image noise and illumination falloff. Thus, the limits and noise sensitivity are analyzed. We demonstrate the approach in underwater field experiments.
Research Paper: Integrating SNOMED CT into the UMLS: An Exploration of Different Views of Synonymy and Quality of Editing

The integration of SNOMED CT into the Unified Medical Language System (UMLS) involved the alignment of two views of synonymy that were different because the two vocabulary systems have different intended purposes and editing principles. The UMLS is organized according to one view of synonymy, but its structure also represents all the individual views of synonymy present in its source vocabularies. Despite progress in knowledge-based automation of development and maintenance of vocabularies, manual curation is still the main method of determining synonymy. The aim of this study was to investigate the quality of human judgment of synonymy.



Sixty pairs of potentially controversial SNOMED CT synonyms were reviewed by 11 domain vocabulary experts (six UMLS editors and five noneditors), and scores were assigned according to the degree of synonymy.



The synonymy scores of each subject were compared to the gold standard (the overall mean synonymy score of all subjects) to assess accuracy. Agreement between UMLS editors and noneditors was measured by comparing the mean synonymy scores of editors to noneditors.



Average accuracy was 71% for UMLS editors and 75% for noneditors (difference not statistically significant). Mean scores of editors and noneditors showed significant positive correlation (Spearman's rank correlation coefficient 0.654, two-tailed p < 0.01) with a concurrence rate of 75% and an interrater agreement kappa of 0.43.



The accuracy in the judgment of synonymy was comparable for UMLS editors and nonediting domain experts. There was reasonable agreement between the two groups.
Modeling and predictive capacity adjustment for job shop systems with RMTs

Demand fluctuations along with the requirements of cost-effectiveness, high customization, high product quality lead to a significant increase in manufacturing dynamics and complexity. These fluctuations disturb the work plan and lead to performance deterioration. Capacity adjustment is one of the major approaches to react on the fluctuations in demand. In this paper, we focus on improving the potential of current processes to react flexibly on these fluctuations on short term level by means of reconfigurable machine tools (RMTs). Furthermore, we propose a closed-loop feedback control system based on model predictive control (MPC) to align uncertainties and dynamics in terms of logistic objectives. The proposed method provides a new solution for manufacturers to optimize resources and improve the production performance in a dynamically changing environment. We demonstrate the effectiveness of the proposed method by simulation of a three-product four-workstation job shop system.
Design of a Compact Actuation and Control System for Flexible Medical Robots

Flexible medical robots can improve surgical procedures by decreasing invasiveness and increasing accessibility within the body. Using preoperative images, these robots can be designed to optimize a procedure for a particular patient. To minimize invasiveness and maximize biocompatibility, the actuation units of flexible medical robots should be placed fully outside the patient's body. In this letter, we present a novel, compact, lightweight, modular actuation, and control system for driving a class of these flexible robots, known as concentric tube robots. A key feature of the design is the use of three-dimensional printed waffle gears to enable compact control of two degrees of freedom within each module. We measure the precision and accuracy of a single actuation module and demonstrate the ability of an integrated set of three actuation modules to control six degrees of freedom. The integrated system drives a three-tube concentric tube robot to reach a final tip position that is on average less than 2 mm from a given target. In addition, we show a handheld manifestation of the device and present its potential applications.
Robust solving of optical motion capture data by denoising

Raw optical motion capture data often includes errors such as occluded markers, mislabeled markers, and high frequency noise or jitter. Typically these errors must be fixed by hand - an extremely time-consuming and tedious task. Due to this, there is a large demand for tools or techniques which can alleviate this burden. In this research we present a tool that sidesteps this problem, and produces joint transforms directly from raw marker data (a task commonly called "solving") in a way that is extremely robust to errors in the input data using the machine learning technique of denoising. Starting with a set of marker configurations, and a large database of skeletal motion data such as the CMU motion capture database [CMU 2013b], we synthetically reconstruct marker locations using linear blend skinning and apply a unique noise function for corrupting this marker data - randomly removing and shifting markers to dynamically produce billions of examples of poses with errors similar to those found in real motion capture data. We then train a deep denoising feed-forward neural network to learn a mapping from this corrupted marker data to the corresponding transforms of the joints. Once trained, our neural network can be used as a replacement for the solving part of the motion capture pipeline, and, as it is very robust to errors, it completely removes the need for any manual clean-up of data. Our system is accurate enough to be used in production, generally achieving precision to within a few millimeters, while additionally being extremely fast to compute with low memory requirements.
Growth factor-induced MAPK network topology shapes Erk response determining PC-12 cell fate

The mitogen-activated protein kinase (MAPK) network is a conserved signalling module that regulates cell fate by transducing a myriad of growth-factor signals. The ability of this network to coordinate and process a variety of inputs from different growth-factor receptors into specific biological responses is, however, still not understood. We investigated how the MAPK network brings about signal specificity in PC-12 cells, a model for neuronal differentiation. Reverse engineering by modular-response analysis uncovered topological differences in the MAPK core network dependent on whether cells were activated with epidermal or neuronal growth factor (EGF or NGF). On EGF stimulation, the network exhibited negative feedback only, whereas a positive feedback was apparent on NGF stimulation. The latter allows for bi-stable Erk activation dynamics, which were indeed observed. By rewiring these regulatory feedbacks, we were able to reverse the specific cell responses to EGF and NGF. These results show that growth factor context determines the topology of the MAPK signalling network and that the resulting dynamics govern cell fate.
A Co-training Approach for Multi-view Spectral Clustering

We propose a spectral clustering algorithm for the multi-view setting where we have access to multiple views of the data, each of which can be independently used for clustering. Our spectral clustering algorithm has a flavor of co-training, which is already a widely used idea in semi-supervised learning. We work on the assumption that the true underlying clustering would assign a point to the same cluster irrespective of the view. Hence, we constrain our approach to only search for the clusterings that agree across the views. Our algorithm does not have any hyperparameters to set, which is a major advantage in unsupervised learning. We empirically compare with a number of baseline methods on synthetic and real-world datasets to show the efficacy of the proposed algorithm.
The Business Strategy of Mcdonald ’ s

As one of the most successful fast food chain in the world, throughout the development of McDonald’s, we could easily identify many successful business strategy implementations. In this paper, I will discuss some critical business strategies, which linked to the company’s structure and external environment. This paper is organized as follows: In the first section, I will give brief introduction to the success of McDonald’s. In the second section, I will analyze some particular strategies used by McDonald’s and how these strategies are suitable to their business structure. I will then analyze why McDonald’s choose these strategies in response to the changing external environment. Finally, I will summarize the approaches used by McDonald’s to achieve their strategic goals.
[Poor school performance: an updated review].

This study aims to develop a comprehensive review on the issue of poor school performance for professionals in both health and education areas. It discusses current aspects of education, learning and the main conditions involved in underachievement. It also presents updated data on key aspects of neurobiology, epidemiology, etiology, clinical presentation, comorbidities and diagnosis, early intervention and treatment of the major pathologies comprised. It is a comprehensive, non-systematic literature review on learning, school performance, learning disorders (dyslexia, dyscalculia and dysgraphia), attention deficit / hyperactivity disorder (ADHD) and developmental coordination disorder (DCD). Poor school performance is a frequent problem faced by our children, causing serious emotional, social and economic issues. An updated view of the subject facilitates clinical reasoning, accurate diagnosis and appropriate treatment.
A practical analysis of low-density parity-check erasure codes for wide-area storage applications

As peer-to-peer and widely distributed storage systems proliferate, the need to perform efficient erasure coding, instead of replication, is crucial to performance and efficiency. Low-density parity-check (LDPC) codes have arisen as alternatives to standard erasure codes, such as Reed-Solomon codes, trading off vastly improved decoding performance for inefficiencies in the amount of data that must be acquired to perform decoding. The scores of papers written on LDPC codes typically analyze their collective and asymptotic behavior. Unfortunately, their practical application requires the generation and analysis of individual codes for finite systems. This paper attempts to illuminate the practical considerations of LDPC codes for peer-to-peer and distributed storage systems. The three main types of LDPC codes are detailed, and a huge variety of codes are generated, then analyzed using simulation. This analysis focuses on the performance of individual codes for finite systems, and addresses several important heretofore unanswered questions about employing LDPC codes in real-world systems.
Recent Developments in Graphene-Based Membranes: Structure, Mass-Transport Mechanism and Potential Applications.

Significant achievements have been made on the development of next-generation filtration and separation membranes using graphene materials, as graphene-based membranes can afford numerous novel mass-transport properties that are not possible in state-of-art commercial membranes, making them promising in areas such as membrane separation, water desalination, proton conductors, energy storage and conversion, etc. The latest developments on understanding mass transport through graphene-based membranes, including perfect graphene lattice, nanoporous graphene and graphene oxide membranes are reviewed here in relation to their potential applications. A summary and outlook is further provided on the opportunities and challenges in this arising field. The aspects discussed may enable researchers to better understand the mass-transport mechanism and to optimize the synthesis of graphene-based membranes toward large-scale production for a wide range of applications.
Toward Information Diffusion Model for Viral Marketing in Business

Current obstacles in the study of social media marketing include dealing with massive data and real-time updates have motivated to contribute solutions that can be adopted for viral marketing. Since information diffusion and social networks are the core of viral marketing, this article aims to investigate the constellation of diffusion methods for viral marketing. Studies on diffusion methods for viral marketing have applied different computational methods, but a systematic investigation of these methods has limited. Most of the literature have focused on achieving objectives such as influence maximization or community detection. Therefore, this article aims to conduct an in-depth review of works related to diffusion for viral marketing. Viral marketing has applied to business-to-consumer transactions but has seen limited adoption in business-to-business transactions. The literature review reveals a lack of new diffusion methods, especially in dynamic and large-scale networks. It also offers insights into applying various mining methods for viral marketing. It discusses some of the challenges, limitations, and future research directions of information diffusion for viral marketing. The article also introduces a viral marketing information diffusion model. The proposed model attempts to solve the dynamicity and large-scale data of social networks by adopting incremental clustering and a stochastic differential equation for business-to-business transactions. information diffusion; viral marketing; social media marketing; social networks
Parasites of alien aquatic animals in the upper Volga basin

Climate warming has created favorable conditions for the range expansion of many southern Ponto-Caspian freshwater fish and mollusks through the Caspian-Volga-Baltic “invasion corridor.” Some parasites can be used as “biological tags” of migration activity and generic similarity of new host populations in the Middle and Upper Volga. The study demonstrates a low biodiversity of parasites even of the most common estuarial invaders sampled from the northern reservoir such as the Ponto-Caspian kilka Clupeonella cultriventris (16 species), tubenose goby Proterorhinus semilunaris (19 species), and round goby Neogobius (=Appollonia) malanostomus (14 species). In 2000–2010, only a few cases of a significant increase in occurrence (up to 80–100%) and abundance indexes were recorded for some nonspecific parasites such as peritricha ciliates Epistilys lwoffi, Trichodina acuta, and Ambiphrya ameiuri on the gills of the tubenose goby; the nematode Contracoecum microcephalum and the acanthocephalan Pomphorhynchus laevis from the round goby; and metacercariae of trematodes Bucaphalus polymorphus and Apophallus muehlingi from the muscles of kilka. In some water bodies, the occurrence of the trematode Bucephalus polymorphus tended to decrease after a partial replacement of its intermediate host zebra mussel Dreissena polymorpha by D. bugensi (quagga mussel). High occurrence of parthenites of Apophallus muehlingi in the mollusk Lithoglyphus naticoides was recorded in the Upper Volga (up to 70%) as compared to the Middle Volga (34%). Fry of fish with a considerable degree of muscle injury caused by the both trematode species have lower mobility and become more available food objects for birds and carnivorous fish.
When is multitask learning effective? Semantic sequence prediction under varying data conditions

Multitask learning has been applied successfully to a range of tasks, mostly morphosyntactic. However, little is known on when MTL works and whether there are data characteristics that help to determine its success. In this paper we evaluate a range of semantic sequence labeling tasks in a MTL setup. We examine different auxiliary tasks, amongst which a novel setup, and correlate their impact to datadependent conditions. Our results show that MTL is not always effective, significant improvements are obtained only for 1 out of 5 tasks. When successful, auxiliary tasks with compact and more uniform label distributions are preferable.
Unexplained Lymphadenopathy: Evaluation and Differential Diagnosis.

Lymphadenopathy is benign and self-limited in most patients. Etiologies include malignancy, infection, and autoimmune disorders, as well as medications and iatrogenic causes. The history and physical examination alone usually identify the cause of lymphadenopathy. When the cause is unknown, lymphadenopathy should be classified as localized or generalized. Patients with localized lymphadenopathy should be evaluated for etiologies typically associated with the region involved according to lymphatic drainage patterns. Generalized lymphadenopathy, defined as two or more involved regions, often indicates underlying systemic disease. Risk factors for malignancy include age older than 40 years, male sex, white race, supraclavicular location of the nodes, and presence of systemic symptoms such as fever, night sweats, and unexplained weight loss. Palpable supraclavicular, popliteal, and iliac nodes are abnormal, as are epitrochlear nodes greater than 5 mm in diameter. The workup may include blood tests, imaging, and biopsy depending on clinical presentation, location of the lymphadenopathy, and underlying risk factors. Biopsy options include fine-needle aspiration, core needle biopsy, or open excisional biopsy. Antibiotics may be used to treat acute unilateral cervical lymphadenitis, especially in children with systemic symptoms. Corticosteroids have limited usefulness in the management of unexplained lymphadenopathy and should not be used without an appropriate diagnosis.
WPP, No. 103: Speech Perception in Dyslexic Children With and Without Language Impairments

Developmental dyslexia refers to a group of children who fail to learn to read at the normal rate despite apparently normal vision and neurological functioning. Dyslexic children typically manifest problems in printed word recognition and spelling, and difficulties in phonological processing are quite common (Lyon, 1995; Rack, Snowling, & Olson, 1992; Stanovich, 1988; Wagner & Torgesen, 1987). The phonological processing problems include, but are not limited to difficulties in pronouncing nonsense words, poor phonemic awareness, problems in representing phonological information in short-term memory and difficulty in rapidly retrieving the names of familiar objects, digits and letters (Stanovich, 1988; Wagner & Torgesen, 1987; Wolf & Bowers, 1999). The underlying cause of phonological deficits in dyslexic children is not yet clear. One possible source is developmentally deviant perception of speech at the phoneme level. A number of studies have shown that dyslexics' categorizations of speech sounds are less sharp than normal readers (Chiappe, Chiappe, & Siegel, 2001; Godfrey, Syrdal-Lasky, Millay, & Knox, 1981; Maassen, Groenen, Crul, Assman-Hulsmans, & Gabreels, 2001;  1989; Serniclaes, Sprenger-Charolles, Carré, & Demonet, 2001;Werker & Tees, 1987). These group differences have appeared in tasks requiring the labeling of stimuli varying along a perceptual continuum (such as voicing or place of articulation), as well as on speech discrimination tasks. In two studies, there was evidence that dyslexics showed better discrimination of sounds differing phonetically within a category boundary (Serniclaes et al, 2001; Werker & Tees, 1987), whereas in one study, dyslexics were poorer at both within-phoneme and between phoneme discrimination (Maassen et al, 2001). There is evidence that newborns and 6-month olds with a familial risk for dyslexia have reduced sensitivity to speech and non-speech sounds (Molfese, 2000; Pihko, Leppanen, Eklund, Cheour, Guttorm & Lyytinen, 1999). If dyslexics are impaired from birth in auditory processing, or more specifically in speech perception, this would affect the development and use of phonological representations on a wide variety of tasks, most intensively in phonological awareness and decoding. Although differences in speech perception have been observed, it has also been noted that the effects are often weak, small in size or shown by only some of the dyslexic subjects (Adlard & Hazan, 1998; Brady, Shankweiler, & Mann, 1983; Elliot, Scholl, Grant, & Hammer, 1990; Manis, McBride-Chang, Seidenberg, Keating, Doi, Munson, & Petersen (1997); Nittrouer, 1999; Snowling, Goulandris, Bowlby, & Howell, 1986). One reason for small, or variable effects, might be that the dyslexic population is heterogeneous, and that speech perception problems are more common among particular subgroups of dyslexics. A specific hypothesis is that speech perception problems are more concentrated among dyslexic children showing greater