nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive Synthetic-Nominal (ADASYN-N) and Adaptive Synthetic-KNN (ADASYN-KNN) for Multiclass Imbalance Learning on Laboratory Test Data

Annually about 1,500 cases of cervical cancer are found in Indonesia, which made Indonesia as the country with the highest number of cervical cancer cases in the world. Cervical cancer screening and HPV testing are done with a Pap smear test. However, this examination requires a lot of time, costly and highly susceptible bias of the observer during the process of investigation and analysis. To overcome these problems, several studies have modeled the machine learning with a variety of approaches have been made. However, these studies are constrained by the limitation of the data amounts and the imbalanced data that caused by the different ratio of each case. This can lead to errors in the classification of the minority due to the tendency of the classification results that focus on the majority class. This study addressed the handling imbalance data on classification of cases Pap test results using the method of over-sampling. ADASYN-N and ADASYN-KNN algorithms were proposed as a development of ADASYN algorithm to handle datasets with nominal data types. This study included SMOTE-N algorithm to deal with the problem as comparison algorithm. As the results, ADASYN-KNN with the preference “0” gave the highest accuracy, precision, recall, and f-score of 95.38%; 95.583%; 95.383%; and 95.283%. The highest ROC area value was obtained with the ADASYN-KNN with preference “1” of 99.183%.
Universal Haptic Drive: A Robot for Arm and Wrist Rehabilitation

In this paper we present a universal haptic drive (UHD), a device that enables rehabilitation of either arm (“ARM” mode) or wrist (“WRIST” mode) movement in two degrees-of-freedom. The mode of training depends on the selected mechanical configuration, which depends on locking/unlocking of a passive universal joint. Actuation of the device is accomplished by utilizing a series elastic actuation principle, which enables use of off-the-shelf mechanical and actuation components. A proportional force control scheme, needed for implementation of impedance control based movement training, was implemented. The device performance in terms of achievable lower and upper bound of viable impedance range was evaluated through adequately chosen sinusoidal movement in eight directions of a planar movement for the “ARM” mode and in eight directions of a combined wrist flexion/extension and forearm pronation/supination movement for the “WRIST” mode. Additionally, suitability of the universal haptic drive for movement training was tested in a series of training sessions conducted with a chronic stroke subject. The results have shown that reliable and repeatable performance can be achieved in both modes of operation for all tested directions.
Oral diagnosis and treatment planning: part 6. Preventive and treatment planning for periodontal disease

A high level of sustained personal plaque control is fundamental for successful treatment outcomes in patients with active periodontal disease and, hence, oral hygiene instructions are the cornerstone of periodontal treatment planning. Other risk factors for periodontal disease also should be identified and modified where possible. Many restorative dental treatments in particular require the establishment of healthy periodontal tissues for their clinical success. Failure by patients to control dental plaque because of inappropriate designs and materials for restorations and prostheses will result in the long-term failure of the restorations and the loss of supporting tissues. Periodontal treatment planning considerations are also very relevant to endodontic, orthodontic and osseointegrated dental implant conditions and proposed therapies.
A Mode 5 signal detection method based on phase and amplitude correlation

The new western Mode 5 IFF (Identification Foe or Friend) system is introduced. Based on analysis of signal features and format characteristics of Mode 5, a new signal detection method using phase and Amplitude correlation is put forward. This method utilizes odd and even channels to separate the signal, and then the separated signals are performed correlation with predefined mask. Through detecting preamble, the detection of Mode 5 signal is implemented. Finally, simulation results show the validity of the proposed method.
14nm FinFET technology for analog and RF applications

This paper highlights a 14nm Analog and RF technology based on a logic FinFET platform for the first time. An optimized RF device layout shows excellent Ft/Fmax of (314GHz/180GHz) and (285GHz/140GHz) for NFET and PFET respectively. A higher PFET RF performance compared to 28nm technology is due to a source/drain stressor mobility improvement. A benefit of better FinFET channel electrostatics can be seen in the self-gain (Gm/Gds), which shows a significant increase to 40 and 34 for NFET and PFET respectively. Superior 1/f noise of 17/35 f(V∗μm)2/Hz @ 1KHz for N/PFET respectively is also achieved. To extend further low voltage operation and power saving, ultra-low Vt devices are also developed. Furthermore, a deep N-well (triple well) process is introduced to improve the ultra-low signal immunity from substrate noise, while offering useful devices like VNPN and high breakdown voltage deep N-well diodes. A superior Ft/Fmax, high self-gain, low 1/f noise and substrate isolation characteristics truly extend the capability of the 14nm FinFETs for analog and RF applications.
Nonsyndromic craniosynostosis: the Rady Children's Hospital approach.

Surgical correction for nonsyndromic craniosynostosis has continued to evolve over the last century. The criterion standard has remained open correction of the cranial deformities, and many techniques have been described that yield satisfactory results. However, technology has allowed for minimally invasive techniques to be developed with the aid of endoscopic visualization. With proper patient selection and the aid of postoperative helmet therapy, there is increasing evidence that supports these techniques' safety and efficacy. In this article, our purpose was to describe our algorithm for treating nonsyndromic craniosynostosis at Rady Children's Hospital.
Reliability and validity of the Advanced Basal Cell Carcinoma Index (aBCCdex).

Patient-reported outcome (PRO) questionnaires were recently developed specifically for use with patients with advanced basal cell carcinoma (aBCC) and basal cell carcinoma naevus syndrome (BCCNS).



To evaluate the measurement properties of PRO questionnaires for use in patients with aBCC or BCCNS.



In total 129 patients from 10 clinical sites in the U.S.A. and the BCCNS Support Network completed the two newly developed questionnaires multiple times over 3 months. Patients also completed the Skindex-16 and the 12-Item Short-Form Health Survey as collateral measures. Psychometric properties of the questionnaires were evaluated, including internal consistency and test-retest reliability, construct and known-groups validity, and responsiveness.



Based on the results of exploratory factor analysis and clinical input, the two newly developed questionnaires were combined into a single questionnaire, called the aBCCdex, which is relevant for patients with both aBCC and BCCNS. The internal consistency reliability was acceptable, and all aBCCdex scale scores correlated significantly with conceptually similar scales. When divided into groups that differed based on scores from collateral measures, aBCCdex scale scores differentiated between groups (known-groups validity) and were responsive to change.



The aBCCdex is a brief and comprehensive questionnaire appropriate for use with patients with aBCC and BCCNS. Its reliability and validity have been confirmed. Further research is necessary to estimate the minimally important difference in a larger patient population.
Habitat fragmentation and landscape change

Broad-scale destruction and fragmentation of native vegetation is a highly visible result of human land-use throughout the world (Chapter 4). From the Atlantic Forests of South America to the tropical forests of Southeast Asia, and in many other regions on Earth, much of the original vegetation now remains only as fragments amidst expanses of land committed to feeding and housing human beings. Destruction and fragmentation of habitats are major factors in the global decline of populations and species (Chapter 10), the modification of native plant and animal communities and the alteration of ecosystem processes (Chapter 3). Dealing with these changes is among the greatest challenges facing the “mission-orientated crisis discipline” of conservation biology (Soulé 1986; see Chapter 1). Habitat fragmentation, by definition, is the “breaking apart” of continuous habitat, such as tropical forest or semi-arid shrubland, into distinct pieces. When this occurs, three interrelated processes take place: a reduction in the total amount of the original vegetation (i.e. habitat loss); subdivision of the remaining vegetation into fragments, remnants or patches (i.e. habitat fragmentation); and introduction of new forms of land-use to replace vegetation that is lost. These three processes are closely intertwined such that it is often difficult to separate the relative effect of each on the species or community of concern. Indeed, many studies have not distinguished between these components, leading to concerns that “habitat fragmentation” is an ambiguous, or even meaningless, concept (Lindenmayer and Fischer 2006). Consequently, we use “landscape change” to refer to these combined processes and “habitat fragmentation” for issues directly associated with the subdivision of vegetation and its ecological consequences. This chapter begins by summarizing the conceptual approaches used to understand conservation in fragmented landscapes. We then examine the biophysical aspects of landscape change, and how such change affects species and communities, posing two main questions: (i) what are the implications for the patterns of occurrence of species and communities?; and (ii) how does landscape change affect processes that influence the distribution and viability of species and communities? The chapter concludes by identifying the kinds of actions that will enhance the conservation of biota in fragmented landscapes.
CT imaging of trunk muscles in chronic low back pain patients and healthy control subjects

Increasing documentation on the size and appearance of muscles in the lumbar spine of low back pain (LBP) patients is available in the literature. However, a comparative study between unoperated chronic low back pain (CLBP) patients and matched (age, gender, physical activity, height and weight) healthy controls with regard to muscle cross-sectional area (CSA) and the amount of fat deposits at different levels has never been undertaken. Moreover, since a recent focus in the physiotherapy management of patients with LBP has been the specific training of the stabilizing muscles, there is a need for quantifying and qualifying the multifidus. A comparative study between unoperated CLBP patients and matched control subjects was conducted. Twenty-three healthy volunteers and 32 patients were studied. The muscle and fat CSAs were derived from standard computed tomography (CT) images at three different levels, using computerized image analysis techniques. The muscles studied were: the total paraspinal muscle mass, the isolated multifidus and the psoas. The results showed that only the CSA of the multifidus and only at the lowest level (lower end-plate of L4) was found to be statistically smaller in LBP patients. As regards amount of fat, in none of the three studied muscles was a significant difference found between the two groups. An aetiological relationship between atrophy of the multifidus and the occurrence of LBP can not be ruled out as a possible explanation. Alternatively, atrophy may be the consequence of LBP: after the onset of pain and possible long-loop inhibition of the multifidus a combination of reflex inhibition and substitution patterns of the trunk muscles may work together and could cause a selective atrophy of the multifidus. Since this muscle is considered important for lumbar segmental stability, the phenomenon of atrophy may be a reason for the high recurrence rate of LBP.
Diagonal recurrent neural networks for dynamic systems control

A new neural paradigm called diagonal recurrent neural network (DRNN) is presented. The architecture of DRNN is a modified model of the fully connected recurrent neural network with one hidden layer, and the hidden layer comprises self-recurrent neurons. Two DRNN's are utilized in a control system, one as an identifier called diagonal recurrent neuroidentifier (DRNI) and the other as a controller called diagonal recurrent neurocontroller (DRNC). A controlled plant is identified by the DRNI, which then provides the sensitivity information of the plant to the DRNC. A generalized dynamic backpropagation algorithm (DBP) is developed and used to train both DRNC and DRNI. Due to the recurrence, the DRNN can capture the dynamic behavior of a system. To guarantee convergence and for faster learning, an approach that uses adaptive learning rates is developed by introducing a Lyapunov function. Convergence theorems for the adaptive backpropagation algorithms are developed for both DRNI and DRNC. The proposed DRNN paradigm is applied to numerical problems and the simulation results are included.
Biofilm Disrupting Technology for Orthopedic Implants: What’s on the Horizon?

The use of orthopedic implants in joints has revolutionized the treatment of patients with many debilitating chronic musculoskeletal diseases such as osteoarthritis. However, the introduction of foreign material into the human body predisposes the body to infection. The treatment of these infections has become very complicated since the orthopedic implants serve as a surface for multiple species of bacteria to grow at a time into a resistant biofilm layer. This biofilm layer serves as a protectant for the bacterial colonies on the implant making them more resistant and difficult to eradicate when using standard antibiotic treatment. In some cases, the use of antibiotics alone has even made the bacteria more resistant to treatment. Thus, there has been surge in the creation of non-antibiotic anti-biofilm agents to help disrupt the biofilms on the orthopedic implants to help eliminate the infections. In this study, we discuss infections of orthopedic implants in the shoulder then we review the main categories of anti-biofilm agents that have been used for the treatment of infections on orthopedic implants. Then, we introduce some of the newer biofilm disrupting technology that has been studied in the past few years that may advance the treatment options for orthopedic implants in the future.
Formal Distributional Semantics: Introduction to the Special Issue

Formal Semantics and Distributional Semantics are two very influential semantic frameworks in Computational Linguistics. Formal Semantics is based on a symbolic tradition and centered around the inferential properties of language. Distributional Semantics is statistical and data-driven, and focuses on aspects of meaning related to descriptive content. The two frameworks are complementary in their strengths, and this has motivated interest in combining them into an overarching semantic framework: a “Formal Distributional Semantics.” Given the fundamentally different natures of the two paradigms, however, building an integrative framework poses significant theoretical and engineering challenges. The present issue of Computational Linguistics advances the state of the art in Formal Distributional Semantics; this introductory article explains the motivation behind it and summarizes the contributions of previous work on the topic, providing the necessary background for the articles that follow.
Active Learning Methods for Remote Sensing Image Classification

In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst labels them manually and the process is iterated. Starting with a small and nonoptimal training set, the model itself builds the optimal set of samples which minimizes the classification error. We have applied the proposed algorithms to a variety of remote sensing data, including very high resolution and hyperspectral images, using support vector machines. Experimental results confirm the consistency of the methods. The required number of training samples can be reduced to 10% using the methods proposed, reaching the same level of accuracy as larger data sets. A comparison with a state-of-the-art active learning method, margin sampling, is provided, highlighting advantages of the methods proposed. The effect of spatial resolution and separability of the classes on the quality of the selection of pixels is also discussed.
Ohtaekwangia koreensis gen. nov., sp. nov. and Ohtaekwangia kribbensis sp. nov., isolated from marine sand, deep-branching members of the phylum Bacteroidetes.

Two Gram-stain-negative, non-motile, non-spore-forming, rod-shaped bacterial strains, designated 3B-2(T) and 10AO(T), were isolated from a sand sample collected from the west coast of the Korean peninsula by using low-nutrient media, and their taxonomic positions were investigated in a polyphasic study. The strains did not grow on marine agar. They grew optimally at 30 °C and pH 6.5-7.5. Strains 3B-2(T) and 10AO(T) shared 97.5 % 16S rRNA gene sequence similarity and mean level of DNA-DNA relatedness of 12 %. In phylogenetic trees based on 16S rRNA gene sequences, strains 3B-2(T) and 10AO(T), together with several uncultured bacterial clones, formed independent lineages within the evolutionary radiation encompassed by the phylum Bacteroidetes. Strains 3B-2(T) and 10AO(T) contained MK-7 as the predominant menaquinone and iso-C(15 : 0) and C(16 : 1)ω5c as the major fatty acids. The DNA G+C contents of strains 3B-2(T) and 10AO(T) were 42.8 and 44.6 mol%, respectively. Strains 3B-2(T) and 10AO(T) exhibited very low levels of 16S rRNA gene sequence similarity (<85.0 %) to the type strains of recognized bacterial species. These data were sufficient to support the proposal that the novel strains should be differentiated from previously known genera of the phylum Bacteroidetes. On the basis of the data presented, we suggest that strains 3B-2(T) and 10AO(T) represent two distinct novel species of a new genus, for which the names Ohtaekwangia koreensis gen. nov., sp. nov. (the type species; type strain 3B-2(T)  = KCTC 23018(T)  = CCUG 58939(T)) and Ohtaekwangia kribbensis sp. nov. (type strain 10AO(T)  = KCTC 23019(T)  = CCUG 58938(T)) are proposed.
Interactive swept surface modeling in virtual reality with motion-tracked controllers

In this work we describe an interactive technique which enables gestural curve and surface design in an immersive virtual environment. We use a pair of motion tracked controllers to allow the user to intuitively control a Hermite spline curve which can be swept through space to create surfaces, or to define the location and orientation of cloned meshes. A head mounted display and tracked controllers replace the traditional keyboard and mouse for view selection and object interaction. Natural and expressive body motions allow the user to specify the parameters which define the curves and surfaces. Results are demonstrated using the HTC Vive VR system.
An empirical study of machine learning techniques for affect recognition in human–robot interaction

Given the importance of implicit communication in human interactions, it would be valuable to have this capability in robotic systems wherein a robot can detect the motivations and emotions of the person it is working with. Recognizing affective states from physiological cues is an effective way of implementing implicit human–robot interaction. Several machine learning techniques have been successfully employed in affect-recognition to predict the affective state of an individual given a set of physiological features. However, a systematic comparison of the strengths and weaknesses of these methods has not yet been done. In this paper, we present a comparative study of four machine learning methods—K-Nearest Neighbor,  Tree (RT), Bayesian Network and Support Vector Machine (SVM) as applied to the domain of affect recognition using physiological signals. The results showed that SVM gave the best classification accuracy even though all the methods performed competitively. RT gave the next best classification accuracy and was the most space and time efficient.
Research Note - How Much Should You Invest in Each Customer Relationship? A Competitive Strategic Approach

W analyze firms’ decisions to invest in customer relationship management (CRM) initiatives such as acquisition and retention in a competitive context, a topic largely ignored in past CRM research. We characterize each customer by her intrinsic preference towards each firm, the contribution margin she generates for each firm, and her responsiveness to each firm’s retention and acquisition efforts. We show that a firm should invest most heavily in retaining those customers that exhibit moderate responsiveness to its CRM efforts. Further, a firm should most aggressively seek to attract those customers that exhibit moderate responsiveness to their provider’s CRM efforts and those that are moderately profitable for their current provider. Investing more in customers that are more responsive does not always lead to higher firm profits, because stronger competition for such customers tends to erode the effects of higher CRM efforts of an individual firm. When firms develop a customer relationship over time to generate higher contribution margin or customer responsiveness, we show that such developments may not always be desirable, because sometimes these future benefits may lead to more intense competition and hence lower profits for both firms.
An ultra-low noise MEMS accelerometer for seismic imaging

A new MEMS capacitive accelerometer has been developed to meet the requirements for oil and gas exploration, specifically for imaging deep and complex subterranean features. The sensor has been optimized to have a very low noise floor in a frequency range of 1–200 Hz. Several design and process parameters were modified from our previous sensors to reduce noise. Testing of the sensor has demonstrated a noise floor of 10ng/√Hz, in agreement with our predictive noise models. The sensor has a dynamic range of 120db with a maximum acceleration of +/− 80mg. In addition to the performance specifications, automated calibration routines have been implemented, allowing bias and sensitivity calibrations to be done in the field to ensure valid and accurate data. The sensor frequency and quality factor can also be measured in the field for an automated sensor health check.