nils-herrmann/clean_scirepeval huggingface dataset:
Numerical analysis of unsteady cavitating turbulent flow and shedding horse shoe vortex structure around a twisted hydrofoil
Year: 2013

Cavitating turbulent flow around hydrofoils was simulated using the Partially Averaged Navier Stokes (PANS) method and a mass transfer cavitation model with the maximum density ratio r l r v ,clip effect between the liquid and the vapor. The predicted cavity length and thickness of stable cavities as well as the pressure distribution along the suction surface of a NACA66(MOD) hydrofoil compare well with experimental data when using the actual maximum density ratio r l r v ,clip 43391) at room temperature. The unsteady cavitation patterns and their evolution around a Delft twisted hydrofoil were then simulated. The numerical results indicate that the cavity volume fluctuates dramatically as the cavitating flow develops with cavity growth, destabilization, and collapse. The predicted three dimensional cavity structures due to the variation of attack angle in the span wise direction and the shedding cycle as well as its frequency agree fairly well with experimental observations. The distinct side lobes of the attached cavity and the shedding U shaped horse shoe vortex are well captured. Furthermore, it is shown that the shedding horse shoe vortex includes a primary U shaped vapor cloud and two secondary U shaped vapor clouds originating from the primary shedding at the cavity center and the secondary shedding at both cavity sides. The primary shedding is related to the collision of a radially diverging re entrant jet and the attached cavity surface, while the secondary shedding is due to the collision of side entrant jets and the radially diverging re entrant jet. The local flow fields show that the interaction between the circulating flow and the shedding vapor cloud may be the main mechanism producing the cavitating horse shoe vortex. Two side views described by iso surfaces of the vapor volume fraction for a 10% vapor volume, and a non dimensional Q criterion equal to 200 are used to illustrate the formation, roll up and transport of the shedding horse shoe vortex. The predicted height of the shedding horse shoe vortex increases as the vortex moves downstream. It is shown that the shape of the horse shoe vortex for the non dimensional Q criterion is more complicated than that of the 10% vapor fraction iso surface and is more consistent with the experiments. Further, though the time averaged lift coefficient predicted by the PANS calculation is about 12% lower than the experimental value, it is better than other predictions based on RANS solvers.
Transforming growth factor beta induced alpha smooth muscle cell actin expression in renal proximal tubular cells is regulated by p38beta mitogen activated protein kinase, extracellular signal regulated protein kinase1,2 and the Smad signalling during epithelial myofibroblast transdifferentiation.
Venue: Nephrology, dialysis, transplantation official publication of the European Dialysis and Transplant Association European Renal Association
Year: 2008

Transforming growth factor beta (TGFbeta) induced epithelial myofibroblast transdifferentiation is a central mechanism contributing to the pathogenesis of progressive tubulo interstitial fibrosis. We wanted to dissect the role of extracellular signal regulated protein kinase (ERK1,2) p38 mitogen activated protein kinase (p38 MAPK) and the receptor regulated Smad proteins in the regulation of alpha smooth muscle cell actin (alphaSMA) expression, a hallmark of myofibroblast formation, induced by TGFbeta in renal proximal tubular cells.  Activation of signalling molecules was assessed by western blotting using phospho specific antibodies. To specifically interfere with signalling cascades, porcine proximal tubular cells (LLC PK/AT1) were infected with recombinant replication deficient adenoviruses. In other experiments, specific kinase inhibitors were used. The alphaSMA synthesis was assessed by western blotting or immunofluorescent staining of cellular alphaSMA. To assess the regulation of the alphaSMA promoter, tubular cells were transiently transfected with a 785 bp alphaSMA promoter luciferase reporter construct and vectors interfering with the Smad pathway.  Blocking ERK1,2 activation with PD98059 or p38 MAPK with SB 203580 potently inhibited the TGFbeta induced alphaSMA synthesis in renal tubular cells. Adenoviral expression of dominant negative (DN) p38beta but not of p38alpha potently inhibited alphaSMA expression. Furthermore, adenoviral expression of DN MKK6b but not of DN MKK3b caused a substantial inhibition of the TGFbeta effect, confirming the role of p38beta in the regulation of TGFbeta induced alphaSMA expression. Finally, inhibiting the Smad pathway with adenovirally delivered Smad7 and DN Smad3 also blocked TGFbeta induced alphaSMA synthesis.  TGFbeta induced alphaSMA expression is regulated by the coordinated activation of a complex system of parallel MAPK and Smad signalling pathways in renal proximal tubular cells during epithelial mesenchymal transdifferentiation.
Decentralized Statistical Inference with Unrolled Graph Neural Networks
Venue: ArXiv
Year: 2021

In this paper, we investigate the decentralized statistical inference problem, where a network of agents cooperatively recover a (structured) vector from private noisy samples without centralized coordination. Existing optimizationbased algorithms suffer from issues of model mismatch and poor convergence speed, and thus their performance would be degraded, provided that the number of communication rounds is limited. This motivates us to propose a learning based framework, which unrolls well noted decentralized optimization algorithms (e.g. Prox DGD and PG EXTRA) into graph neural networks (GNNs) By minimizing the recovery error via end toend training, this learning based framework resolves the model mismatch issue. Our convergence analysis (with PG EXTRA as the base algorithm) reveals that the learned model parameters may accelerate the convergence and reduce the recovery error to a large extent. The simulation results demonstrate that the proposed GNN based learning methods prominently outperform several state of the art optimization based algorithms in convergence speed and recovery error.
A dynamic model for replication protein A (RPA) function in DNA processing pathways
Venue: Nucleic acids research
Year: 2006

Processing of DNA in replication, repair and recombination pathways in cells of all organisms requires the participation of at least one major single stranded DNA (ssDNA) binding protein. This protein protects ssDNA from nucleolytic damage, prevents hairpin formation and blocks DNA reannealing until the processing pathway is successfully completed. Many ssDNA binding proteins interact physically and functionally with a variety of other DNA processing proteins. These interactions are thought to temporally order and guide the parade of proteins that 'trade places' on the ssDNA, a model known as 'hand off' as the processing pathway progresses. How this hand off mechanism works remains poorly understood. Recent studies of the conserved eukaryotic ssDNA binding protein replication protein A (RPA) suggest a novel mechanism by which proteins may trade places on ssDNA by binding to RPA and mediating conformation changes that alter the ssDNA binding properties of RPA. This article reviews the structure and function of RPA, summarizes recent studies of RPA in DNA replication and other DNA processing pathways, and proposes a general model for the role of RPA in protein mediated hand off.
TSM: Temporal Shift Module for Efficient Video Understanding.
Venue: IEEE transactions on pattern analysis and machine intelligence
Year: 2020

The explosive growth in video streaming requires video understanding at high accuracy and low computation cost. Conventional 2D CNNs are computationally cheap but cannot capture temporal relationships; 3D CNN based methods can achieve good performance but are computationally intensive. In this paper, we propose a generic and effective Temporal Shift Module (TSM) that enjoys both high efficiency and high performance. The key idea of TSM is to shift part of the channels along the temporal dimension, thus facilitate information exchanged among neighboring frames. It can be inserted into 2D CNNs to achieve temporal modeling at zero computation and zero parameters. TSM offers several unique advantages. Firstly, TSM has high performance; it ranks the first on the Something Something leaderboard upon submission. Secondly, TSM has high efficiency; it achieves a high frame rate of 74fps and 29fps for online video recognition on Jetson Nano and Galaxy Note8. Thirdly, TSM has higher scalability compared to 3D networks, enabling large scale Kinetics training on 1,536 GPUs in 15 minutes. Lastly, TSM enables action concepts learning, which 2D networks cannot model; we visualize the category attention map and find that spatial temporal action detector emerges during the training of classification tasks. The code is publicly available.
AN ANALYSIS OF STUDENTS' WRITING SKILLS: FOCUS ON GRAMMATICAL AND DISCOURSE COMPETENCE
Year: 2019

This paper presents an analysis of the students' writing skills. It is believed that writing is a powerful device to reflect language structure and to promote language learning. However, many students still experience difficulties in writing since it requires the appropriate grammatical and textual structures. Therefore, this study tries to analyse the students' writing skills that will help them to improve their communicative competence. Data were obtained through documentation of students' texts and interviews. The theories of Systemic Functional Linguistics, explanation text, and communicative competence were employed as the framework of the text analysis. The findings revealed the social function, the generic structure, the linguistic features, and also the grammatical and discourse competence in students' explanation texts. It was concluded that the students perceived the importance of the use of correct grammar and textual structure in their writing. Regarding some grammatical mistakes in students' explanation texts, it is recommended that teacher give explicit teaching and more exercises to them so that the students' communicative competence can be developed. DOI 10.24071/llt.2019.220206
Utilizing Blockchain Trade Finance to Promote Financial Inclusion
Year: 2019

This paper examines the use of blockchain, or distributed ledger, technology for the potential supplantation of the antiquated process of international trade financing. Using the technology for this purpose has the potential to narrow the enormous gap in unmet demand for trade finance experienced by small and medium sized enterprises in the developing world. The current process of trade finance is still paper based and relies heavily on manual labor. After the 2008 Global Financial Crisis, banks became restrictive in their lending, especially to small and medium sized enterprises in developing countries, leading to the aforementioned trade finance gap. Blockchain technology could narrow this gap by digitizing and automating key steps in the trade finance process, which will lead to efficiency gains along the trade finance process. By allowing users to establish a verifiable identity, blockchain also increases compliance with 'know your customer' and anti money laundering requirements. Currently, permissioned blockchains are better suited for trade finance as evidenced through recent initiatives, whereas permissionless blockchains have more to offer to individuals at the "bottom of the financial pyramid" who are typically excluded from the formal financial sector. Financial inclusion refers to the delivery of basic financial services in a nondiscriminatory way. Blockchain can help lift the large unbanked and financially underserved populations in the developing world out of poverty and into the global economy, contributing to sustainable economic growth.
Modified space vector pwm transformerless inverters with reduced common mode voltage and leakage current Freddy Tan Kheng Suan
Year: 2015

Small, highly efficient and low cost photovoltaic (PV) system can be achieved when the transformer is removed from the PV inverter. Nevertheless, transformerless PV inverters generates dangerous leakage currents. Therefore, various transformerless PV inverters topologies are proposed recently to reduce the leakage current to meet the requirement of the standard. In this project, two types of recently proposed transformerless PV inverters are investigated, i.e. single phase and three phase inverters. A common mode model circuit for each type is developed to study the common mode behavior of the transformerless PV inverters. It is shown that the leakage current is directly depending on the common mode voltage (CMV) Based on the analysis of the studies, a novel transformerless PV inverter topology is proposed for both single phase and three phase PV systems respectively. For single phase PV systems, a simple modified H bridge zero voltage state rectifier (HBZVR D) is proposed to eliminate the leakage current. A fast recovery diode is added to the existing HBZVR topology to improve the clamping branch performance. It is shown that the improved clamping branch of the proposed topology completely clamps the CMV to constant to eliminate the leakage current. On the other hand, a three phase transformerless inverter (H7) adapted from the single phase H5 topology, is investigated. An additional switch is added to the conventional full bridge structure to provide galvanic isolation. Here, a novel modulation technique based on conventional discontinuous pulsewidth modulation is proposed. It is shown that the proposed topology is able to reduce the CMV in order to reduce the leakage current. The validity of the proposed inverters are verified via simulations and laboratory prototypes. DSP TMS320F28335 is used to program the modulation techniques. The performances of the proposed topologies, in terms of CMV, leakage current, total harmonic distortion (THD) and efficiency, are compared with various recently proposed transformerless PV inverters. It is experimentally proven that the proposed transformerless single and three phase inverters are able to reduce the leakage current with superior overall performance among the recently proposed topologies.
The Effect of Audit Opinion, Financial Distress, Client Size, Management Turn and KAP Size on Auditor Switching
Year: 2017

The issue of Auditor Independence, the most frequently, makes the debate of auditor rotation. This auditor rotation is related to the company activity for doing auditor switching. Some previous studies showed that switching auditors leads to the various result and it needs to hold rediscovery and to verify the theory of auditor switching. The study Aimed to test empirically the auditor opinion, financial distress, the client company size, management turnover, and KAP size toward auditor switching on manufacturing companies listed on the Indonesian Stock Exchange (BEI) in 2010 until 2014. The study is an explanatory quantitative research with purposive sampling technique for the collection of data. The 39 manufacturing companies used as a sample. The study used SPSS Application with 18 version and regression logistics to test the hypothesis because the independent variable is the combination between metric and non metric. The result of the study Showed that the independent variable has an effect on auditor switching and auditor's opinion Financial distress, the client company size, management turnover and the firm size do not affect the auditor switching.
Control of Output and Circulating Current of Modular Multilevel Converter Using a Sliding Mode Approach
Venue: Energies
Year: 2019

The modular multilevel converter (MMC) has been prominently used in medium and high power applications. This paper presents the control of output and circulating current of MMC using sliding mode control (SMC) The design of the proposed controller and the relation between control parameters and validity condition are based on the system dynamics. The proposed designed controller enables the system to track its reference values. The controller is designed to control both output current and circulating current along with suppression of second harmonics contents in circulating current. Furthermore, the capacitor voltage and energy of the converter are also regulated. The control of output current is carried out in dq axis as well as in ab axis with first order switching law. However, a second order switching law based super twisting algorithm is used for controlling circulating current and suppression of its second harmonics contents. The stability of the controlled system is numerically calculated and verified by Lyapunov stability conditions. Moreover, the simulation results of the proposed controller are critically compared with the conventional proportional resonant (PR) controller to verify the effectiveness of the proposed control strategy. The proposed controller attains faster dynamic response and minimizes steady state error comparatively. The simulation of the MMC model is carried out in MATLAB/Simulink
Multi agent reinforcement learning
Year: 2016

Reinforcement learning deals with the problem of how to map situations (states) to actions so as to maximize a numerical reward while interacting with dynamical and uncertain environment. Within the framework of Markov Decision Processes (MDPs) these methods are typically based on approximate dynamic programming using appropriate calculation/approximation of the value function. In this work we propose new algorithms for multi agent distributed iterative value function approximation where the agents are allowed to have different behavior policies while evaluating the response to a single target policy. The algorithms assume linear parametrization of the value function and are based on consensus based distributed stochastic approximation. Under appropriate assumptions on the time varying network topology and the overall state visiting distributions of the agents we prove weak convergence of the parameter estimates to the globally optimal point. It is demonstrated that the agents are able to together reach this solution even when the individual agents cannot.
An analysis of performance evolution of Linux's core operations
Venue: SOSP
Year: 2019

This paper presents an analysis of how Linux's performance has evolved over the past seven years. Unlike recent works that focus on OS performance in terms of scalability or service of a particular workload, this study goes back to basics: the latency of core kernel operations (e.g. system calls, context switching, etc. To our surprise, the study shows that the performance of many core operations has worsened or fluctuated significantly over the years. For example, the select system call is 100% slower than it was just two years ago. An in depth analysis shows that over the past seven years, core kernel subsystems have been forced to accommodate an increasing number of security enhancements and new features. These additions steadily add overhead to core kernel operations but also frequently introduce extreme slowdowns of more than 100% In addition, simple misconfigurations have also severely impacted kernel performance. Overall, we find most of the slowdowns can be attributed to 11 changes. Some forms of slowdown are avoidable with more proactive engineering. We show that it is possible to patch two security enhancements (from the 11 changes) to eliminate most of their overheads. In fact, several features have been introduced to the kernel unoptimized or insufficiently tested and then improved or disabled long after their release. Our findings also highlight both the feasibility and importance for Linux users to actively configure their systems to achieve an optimal balance between performance, functionality, and security: we discover that 8 out of the 11 changes can be avoided by reconfiguring the kernel, and the other 3 can be disabled through simple patches. By disabling the 11 changes with the goal of optimizing performance, we speed up Redis, Apache, and Nginx benchmark workloads by as much as 56% 33% and 34% respectively.
Processing contradictory brand information from advertising and social media: an application of the multiple motive heuristic systematic model
Year: 2018

This study tested two major theoretical assumptions of the heuristic systematic model (1) concurrent information processing and (2) multiple processing motives in a common advertising situation where contradictory brand information from different sources (e.g. a positive advertising claim [a persuasive argument] vs. negative consensus information from a third party source [a persuasion cue] was available to consumers with varying motivation (accuracy and defense motivation) Results showed that individuals employed different information processing strategies, depending on motivation type. Accuracy motivated individuals engaged in concurrent (both cue and argument based) processing when involvement was high. Defense motivated individuals sought positive valence, regardless of the level of involvement and type of information.
Building Trust and Cooperation through Technology Adaptation in Virtual Teams: Empirical Field Evidence
Venue: Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008)
Year: 2008

This article reports findings of an empirical, field study of how virtual information systems development teams project managers improve team trust and cooperation by managing adaptation of information and communications tools. We examine the effect of leader style on the efficacy of interaction interventions. Results indicate how theory X (command and control) and theory Y (facilitate and support) leadership approaches enable and contradict effective technology adaptation, trust building, and outcomes. Successful technology adaptation led to increased trust. In order to achieve technology adaptation during interventions, leaders taking employing actions associated with a theory Y approach achieved greater success. Implications for virtual project management are introduced.
Towards Shape Based Knee Osteoarthritis Classification Using Graph Convolutional Networks
Venue: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
Year: 2020

We present a transductive learning approach for morphometric osteophyte grading based on geometric deep learning. We formulate the grading task as semi supervised node classification problem on a graph embedded in shape space. To account for the high dimensionality and non Euclidean structure of shape space we employ a combination of an intrinsic dimension reduction together with a graph convolutional neural network. We demonstrate the performance of our derived classifier in comparisons to an alternative extrinsic approach.