nils-herrmann/clean_scirepeval huggingface dataset:
A functional CFTR assay using primary cystic fibrosis intestinal organoids

We recently established conditions allowing for long-term expansion of epithelial organoids from intestine, recapitulating essential features of the in vivo tissue architecture. Here we apply this technology to study primary intestinal organoids of people suffering from cystic fibrosis, a disease caused by mutations in CFTR, encoding cystic fibrosis transmembrane conductance regulator. Forskolin induces rapid swelling of organoids derived from healthy controls or wild-type mice, but this effect is strongly reduced in organoids of subjects with cystic fibrosis or in mice carrying the Cftr F508del mutation and is absent in Cftr-deficient organoids. This pattern is phenocopied by CFTR-specific inhibitors. Forskolin-induced swelling of in vitro–expanded human control and cystic fibrosis organoids corresponds quantitatively with forskolin-induced anion currents in freshly excised ex vivo rectal biopsies. Function of the CFTR F508del mutant protein is restored by incubation at low temperature, as well as by CFTR-restoring compounds. This relatively simple and robust assay will facilitate diagnosis, functional studies, drug development and personalized medicine approaches in cystic fibrosis.
School leadership effects revisited : a review of empirical studies guided by indirect-effect models

Fourteen leadership effect studies that used indirect-effect models were quantitatively analysed to explore the most promising mediating variables. The results indicate that total effect sizes based on indirect-effect studies appear to be low, quite comparable to the results of some meta-analyses of direct-effect studies. As the earlier indirect-effect studies tended to include a broad range of mainly school organisational conditions as intermediary variables, more recent studies focus more sharply on instructional conditions. The results of the conceptual analysis and the quantitative research synthesis would seem to support conceptualising educational leadership as a detached and ‘lean’ kind of meta-control, which would make maximum use of the available substitutes and self-organisation offered by the school staff and school organisational structural provisions. The coupling of conceptual analysis and systematic review of studies driven by indirect-effect models provides a new perspective on leadership effectiveness.
Design and research on parameter of LCL filter in three-phase grid-connected inverter

Since LCL filter has smaller inductance value comparing to L type filter with the same performance in harmonic suppression. it is gradually used in high-power and low-frequency current-source-controlled grid-connected converters. However design of LCL filter's parameter not only relates switch frequency ripple attenuation, but also impacts on performance of grid-connected current controller. This paper firstly introduced a harmonic model of LCL filter in grid-connected operation, then researched the variable relationship among LCL filter's parameter and resonance frequency and high-frequency ripple attenuation. Based on above analysis a reasonable design method was brought out in order to achieve optimal effect under the precondition of saving inductance magnetic core of LCL filter, at the same time guaranteeing the resonance frequency of LCL filter was not too small lest restrict current controller resign. Finally this design method was verified by the experimental results.
Fully automatic lesion boundary detection in ultrasound breast images

We propose a novel approach to fully automatic lesion boundary detection in ultrasound breast images. The novelty of the proposed work lies in the complete automation of the manual process of initial Region-of-Interest (ROI) labeling and in the procedure adopted for the subsequent lesion boundary detection. Histogram equalization is initially used to preprocess the images followed by hybrid filtering and multifractal analysis stages. Subsequently, a single valued thresholding segmentation stage and a rule-based approach is used for the identification of the lesion ROI and the point of interest that is used as the seed-point. Next, starting from this point an Isotropic Gaussian function is applied on the inverted, original ultrasound image. The lesion area is then separated from the background by a thresholding segmentation stage and the initial boundary is detected via edge detection. Finally to further improve and refine the initial boundary, we make use of a state-of-the-art active contour method (i.e. gradient vector flow (GVF) snake model). We provide results that include judgments from expert radiologists on 360 ultrasound images proving that the final boundary detected by the proposed method is highly accurate. We compare the proposed method with two existing stateof-the-art methods, namely the radial gradient index filtering (RGI) technique of Drukker et. al. and the local mean technique proposed by Yap et. al., in proving the proposed method’s robustness and accuracy.
Data Mining: Concepts and Techniques, 3rd edition

Many books discuss applications of data mining. For financial data analysis and financial modeling, see Benninga and Czaczkes [BC00] and Higgins [Hig03]. For retail data mining and customer relationship management, see books by Berry and Linoff [BL04] and Berson, Smith, and Thearling [BST99], and the article by Kohavi [Koh01]. For telecommunication-related data mining, see the book by Mattison [Mat97]. Chen, Hsu, and Dayal [CHD00] reported their work on scalable telecommunication tandem traffic analysis under a data warehouse/OLAP framework. For bioinformatics and biological data analysis, there are a large number of introductory references and textbooks. An introductory overview of bioinformatics for computer scientists was presented by Cohen [Coh04]. Recent textbooks on bioinformatics include Krane and Raymer [KR03], Jones and Pevzner [JP04], Durbin, Eddy, Krogh and Mitchison [DEKM98], Setubal and Meidanis [SM97], Orengo, Jones, and Thornton [OJT03], and Pevzner [Pev03]. Summaries of biological data analysis methods and algorithms can also be found in many other books, such as Gusfield [Gus97], Waterman [Wat95], Baldi and Brunak [BB01], and Baxevanis and Ouellette [BO04]. There are many books on scientific data analysis, such as Grossman, Kamath, Kegelmeyer, et al. (eds.) [GKK01]. For geographic data mining, see the book edited by Miller and Han [MH01]. Valdes-Perez [VP99] discusses the principles of human-computer collaboration for knowledge discovery in science. For intrusion detection, see Barbará [Bar02] and Northcutt and Novak [NN02].
Integrated affordance detection and humanoid locomotion planning

A humanoid robot navigating in an unstructured environment requires knowledge of the affordances which allow it to make contact with the environment. This knowledge often comes from a perception system, which processes data from 3D sensors such as LIDAR and extracts available areas for the robot to make contact. Because perception systems run independently of the robot's planner, without knowledge of the robot's goal, they must process the entire visible area. In large environments, or those with complex geometry, the perception system may spend significant time processing areas of the environment that the planner will never consider visiting. By integrating the perception process with the planner, we are able to improve the speed with which the robot can compute a motion plan by only processing those areas of the environment which are considered by the planner for navigation. Two experiments with simulated and real-world point cloud data suggest that our framework can produce comparable plans up to seven times faster than the perceive-then-plan approach.
Errors in pathology and laboratory medicine: consequences and prevention.

Reducing errors and improving quality are an integral part of Pathology and Laboratory Medicine. The rate of errors is reviewed for the pre-analytical, analytical, and post-analytical phases for a specimen. The quality systems in place in pathology today are identified and compared with benchmarks for quality. The types and frequency of errors and quality systems are reviewed for surgical pathology, cytopathology, clinical chemistry, hematology, microbiology, molecular biology, and transfusion medicine. Seven recommendations are made to reduce errors in future for Pathology and Laboratory Medicine.
Feature Selection and Parameter Optimization of a Fuzzy-based Stock Selection Model Using Genetic Algorithms

In the areas of investment research and applications, feasible quantitative models include methodologies stemming from soft computing for prediction of financial time series, multi-objective optimization of investment return and risk reduction, as well as selection of investment instruments for portfolio management, etc. Among all these, stock selection has long been identified as a challenging and important task. This line of research is highly contingent upon reliable stock ranking for successful portfolio construction. Recent advances in machine learning and data mining are leading to significant opportunities to solve these problems more effectively. In this study, we aim at developing a methodology for effective stock selection using fuzzy models as well as genetic algorithms (GA). We first devise a stock scoring mechanism using fundamental variables and apply fuzzy membership functions to re-scale the scores properly. The scores are then used to obtain the relative rankings of stocks and top-ranked stocks can be selected to form a portfolio. On top of the stock scoring model, we employ GA for optimization of model parameters and feature selection for input variables simultaneously. We will show that the investment returns provided by our proposed methodology significantly outperform the benchmark. Based upon the promising results obtained, we expect that this hybrid fuzzy-GA methodology can advance the research in soft computing for finance and provide an effective solution to stock selection in practice.
Myofascial force transmission between the latissimus dorsi and gluteus maximus muscles: an in vivo experiment.

There are extensive connections between the latissimus dorsi (LD) and gluteus maximus (GMax) muscles and the thoracolumbar fascia (TLF), which suggests a possible pathway for myofascial force transmission. The present study was designed to provide empirical evidence of myofascial force transmission from LD to contralateral GMax through TFL in vivo. To accomplish this goal, we evaluated whether active or passive tensioning of the LD results in increased passive tension of the contralateral GMax, indexed by changes in the hip resting position (RP) or passive stiffness. The hip RP was defined as the angular position in which the passive joint torque equals zero, and passive hip stiffness was calculated as the change in passive torque per change in joint angle. Thirty-seven subjects underwent an assessment of their passive hip torque against medial rotation by means of an isokinetic dynamometer. These measures were carried out under three test conditions: (1) control, (2) passive LD tensioning and (3) active LD tensioning. Electromyography was used to monitor the activity of the hip muscles and the LD under all conditions. Repeated measures analyses of variance demonstrated that passive LD tensioning shifted the hip RP towards lateral rotation (p=0.009) but did not change the passive hip stiffness (p>0.05). Active LD tensioning shifted the hip RP towards lateral rotation (p<0.001) and increased the passive hip stiffness (p≤0.004). The results demonstrated that manipulation of the LD tension modified the passive hip variables, providing evidence of myofascial force transmission in vivo.
Skeleton-Based Action Recognition with Synchronous Local and Non-local Spatio-temporal Learning and Frequency Attention

Benefiting from its succinctness and robustness, skeletonbased human action recognition has recently attracted much attention. Most existing methods utilize local networks, such as recurrent networks, convolutional neural networks, and graph convolutional networks, to extract spatio-temporal dynamics hierarchically. As a consequence, the local and nonlocal dependencies, which respectively contain more details and semantics, are asynchronously captured in different level of layers. Moreover, limited to the spatio-temporal domain, these methods ignored patterns in the frequency domain. To better extract information from multi-domains, we propose a residual frequency attention (rFA) to focus on discriminative patterns in the frequency domain, and a synchronous local and non-local (SLnL) block to simultaneously capture the details and semantics in the spatio-temporal domain. To optimize the whole process, we also propose a soft-margin focal loss (SMFL), which can automatically conducts adaptive data selection and encourages intrinsic margins in classifiers. Extensive experiments are performed on several large-scale action recognition datasets and our approach significantly outperforms other state-of-the-art methods.
Modeling Friends and Foes

How can one detect friendly and adversarial behavior from raw data? Detecting whether an environment is a friend, a foe, or anything in between, remains a poorly understood yet desirable ability for safe and robust agents. This paper proposes a definition of these environmental “attitudes” based on an characterization of the environment’s ability to react to the agent’s private strategy. We define an objective function for a one-shot game that allows deriving the environment’s probability distribution under friendly and adversarial assumptions alongside the agent’s optimal strategy. Furthermore, we present an algorithm to compute these equilibrium strategies, and show experimentally that both friendly and adversarial environments possess non-trivial optimal strategies.
TOLERANCE FOR FAILURE AND CORPORATE INNOVATION

We examine whether tolerance for failure spurs corporate innovation based on a sample of venture capital (VC) backed IPO firms. We develop a novel measure of VC investors’ failure tolerance by examining their tendency to continue investing in a venture conditional on the venture not meeting milestones. We find that IPO firms backed by more failure-tolerant VC investors are significantly more innovative. A rich set of empirical tests shows that this result is not driven by the endogenous matching between failure-tolerant VCs and startups with high exante innovation potentials. Further, we find that the marginal impact of VC failure tolerance on startup innovation varies significantly in the cross section. Being financed by a failure-tolerant VC is much more important for ventures that are subject to high failure risk. Finally, we examine the determinants of the cross-sectional heterogeneity in VC failure tolerance. We find that both capital constraints and career concerns can negatively distort VC failure tolerance. We also show that younger and less experienced VCs are more exposed to these distortions, making them less failure tolerant than more established VCs.
Secure Scrum: Development of Secure Software with Scrum

Nowadays, the use of agile software development methods like Scrum is common in industry and academia. Considering the current attacking landscape, it is clear that developing secure software should be a main concern in all software development projects. In traditional software projects, security issues require detailed planning in an initial planning phase, typically resulting in a detailed security analysis (e.g., threat and risk analysis), a security architecture, and instructions for security implementation (e.g., specification of key sizes and cryptographic algorithms to use). Agile software development methods like Scrum are known for reducing the initial planning phases (e.g., sprint 0 in Scrum) and for focusing more on producing running code. Scrum is also known for allowing fast adaption of the emerging software to changes of customer wishes. For security, this means that it is likely that there are no detailed security architecture or security implementation instructions from the start of the project. It also means that a lot of design decisions will be made during the runtime of the project. Hence, to address security in Scrum, it is necessary to consider security issues throughout the whole software development process. Secure Scrum is a variation of the Scrum framework with special focus on the development of secure software throughout the whole software development process. It puts emphasis on implementation of security related issues without the need of changing the underlying Scrum process or influencing team dynamics. Secure Scrum allows even non-security experts to spot security issues, to implement security features, and to verify implementations. A field test of Secure Scrum shows that the security level of software developed using Secure Scrum is higher then the security level of software developed using standard Scrum.
Organisational Change Management : A Critical Review

It can be argued that the successful management of change is crucial to any organisation in order to survive and succeed in the present highly competitive and continuously evolving business environment. However, theories and approaches to change management currently available to academics and practitioners are often contradictory, mostly lacking empirical evidence and supported by unchallenged hypotheses concerning the nature of contemporary organisational change management. The purpose of this article is, therefore, to provide a critical review of some of the main theories and approaches to organisational change management as an important first step towards constructing a new framework for managing change. The article concludes with recommendations for further research.
Unseen disadvantage: how American universities' focus on independence undermines the academic performance of first-generation college students.

American universities increasingly admit first-generation college students whose parents do not have 4-year degrees. Once admitted, these students tend to struggle academically, compared with continuing-generation students--students who have at least 1 parent with a 4-year degree. We propose a cultural mismatch theory that identifies 1 important source of this social class achievement gap. Four studies test the hypothesis that first-generation students underperform because interdependent norms from their mostly working-class backgrounds constitute a mismatch with middle-class independent norms prevalent in universities. First, assessing university cultural norms, surveys of university administrators revealed that American universities focus primarily on norms of independence. Second, identifying the hypothesized cultural mismatch, a longitudinal survey revealed that universities' focus on independence does not match first-generation students' relatively interdependent motives for attending college and that this cultural mismatch is associated with lower grades. Finally, 2 experiments at both private and public universities created a match or mismatch for first-generation students and examined the performance consequences. Together these studies revealed that representing the university culture in terms of independence (i.e., paving one's own paths) rendered academic tasks difficult and, thereby, undermined first-generation students' performance. Conversely, representing the university culture in terms of interdependence (i.e., being part of a community) reduced this sense of difficulty and eliminated the performance gap without adverse consequences for continuing-generation students. These studies address the urgent need to recognize cultural obstacles that contribute to the social class achievement gap and to develop interventions to address them.
Algorithms for Micro-mouse

Robotics is not a new word. It has attracted people from all phases of life. The failure of a project in any field mostly occurs due to incomplete analysis of problem or improper planning. Such disappointments can be avoided by adapting a proper approach towards solving a given task. The problem of micro-mouse is 30 years old but its importance in the field of robotics is unparalleled, as it requires a complete analysis & proper planning to solve this problem. This paper covers one of the most important areas of robot, “Decision making Algorithm” or inlay-man’s language, “Intelligence”. The environment around the robot is not known, so it must have decision-making capabilities. For starting in the field of micro-mouse it is very difficult to begin with highly sophisticated algorithms. This paper begins with very basic wall follower logic to solve the maze. And gradually improves the algorithm to accurately solve the maze in shortest time with some more intelligence. The Algorithm is developed up to some sophisticated level as Flood fill algorithm.
Learning Semantics-Guided Visual Attention for Few-Shot Image Classification

We propose a deep learning framework for few-shot image classification, which exploits information across label semantics and image domains, so that regions of interest can be properly attended for improved classification. The proposed semantics-guided attention module is able to focus on most relevant regions in an image, while the attended image samples allow data augmentation and alleviate possible overfitting during FSL training. Promising performances are presented in our experiments, in which we consider both closed and open-world settings. The former considers the test input belong to the categories of few shots only, while the latter requires recognition of all categories of interest.
On bandlimited signals with fractional Fourier transform

We study bandlimited signals with fractional Fourier transform (FRFT). We show that if a nonzero signal f is bandlimited with FRFT F/sub /spl alpha// for a certain real /spl alpha/, then it is not bandlimited with FRFT F/sub /spl beta// for any /spl beta/ with /spl beta//spl ne//spl plusmn//spl alpha/+n/spl pi/ for any integer n. This is a generalization of the fact that a nonzero signal can not be both timelimited and bandlimited. We also provide sampling theorems for bandlimited signals with FRFT that are similar to the Shannon sampling theorem.
A Compact SPICE Model for Carbon-Nanotube Field-Effect Transistors Including Nonidealities and Its Application—Part I: Model of the Intrinsic Channel Region

This paper presents a circuit-compatible compact model for the intrinsic channel region of the MOSFET-like single-walled carbon-nanotube field-effect transistors (CNFETs). This model is valid for CNFET with a wide range of chiralities and diameters and for CNFET with either metallic or semiconducting carbon-nanotube (CNT) conducting channel. The modeled nonidealities include the quantum confinement effects on both circumferential and axial directions, the acoustical/optical phonon scattering in the channel region, and the screening effect by the parallel CNTs for CNFET with multiple CNTs. In order to be compatible with both large-(digital) and small-signal (analog) applications, a complete transcapacitance network is implemented to deliver the real-time dynamic response. This model is implemented with an HSPICE. Using this model, we project a 13 times CV/I improvement of the intrinsic CNFET with (19, 0) CNT over the bulk n-type MOSFET at the 32-nm node. The model described in this paper serves as a starting point toward the complete CNFET-device model incorporating the additional device/circuit-level non-idealities and multiple CNTs reported in the paper of Deng and Wong.