nils-herrmann/clean_scirepeval huggingface dataset:
Visualization of hydrodynamic pilot-wave phenomena

The reflection of an object can be distorted by undulations of the reflector, be it a funhouse mirror or a fluid surface. Painters and photographers have long exploited this effect, for example, in imaging scenery distorted by ripples on a lake. Here, we use this phenomenon to visualize micrometric surface waves generated as a millimetric droplet bounces on the surface of a vibrating fluid bath (Bush 2015b). This system, discovered a decade ago (Couder et al. 2005), is of current interest as a hydrodynamic quantum analog; specifically, the walking droplets exhibit several features reminiscent of quantum particles (Bush 2015a).
On the Convergence of Learning-based Iterative Methods for Nonconvex Inverse Problems

Numerous tasks at the core of statistics, learning and vision areas are specific cases of ill-posed inverse problems. Recently, learning-based (e.g., deep) iterative methods have been empirically shown to be useful for these problems. Nevertheless, integrating learnable structures into iterations is still a laborious process, which can only be guided by intuitions or empirical insights. Moreover, there is a lack of rigorous analysis about the convergence behaviors of these reimplemented iterations, and thus the significance of such methods is a little bit vague. This paper moves beyond these limits and proposes Flexible Iterative Modularization Algorithm (FIMA), a generic and provable paradigm for nonconvex inverse problems. Our theoretical analysis reveals that FIMA allows us to generate globally convergent trajectories for learning-based iterative methods. Meanwhile, the devised scheduling policies on flexible modules should also be beneficial for classical numerical methods in the nonconvex scenario. Extensive experiments on real applications verify the superiority
Intercultural Sensitivity and Cross-cultural Adjustment among Malaysian Students Abroad

Effective socio-cultural integration has formed Malaysia into a nation with harmonious ethnic diversification and living. Cultural understanding is a foundation to sustain a respectful interethnic relationship among multi-ethnic populations in this country. However, Malaysian may experience cross-cultural difficulties when facing a monoculture or unusual cultural environment overseas. Thus, main purpose of the present study is to examine the extent of intercultural sensitivity support the effectiveness of cross-cultural adjustments among Malaysian students while studying and living abroad. Key finding found in the present study shows the magnitude of intercultural sensitivity is moderately facilitate cross-cultural adjustment among Malaysian students abroad. The implications towards the effectiveness of intercultural relations are also highlighted and discussed.
Hair Mercury Association with Selenium, Serum Lipid Spectrum, and Gamma-Glutamyl Transferase Activity in Adults

The primary objective of the research is to estimate the dependence between hair mercury content, hair selenium, mercury-to-selenium ratio, serum lipid spectrum, and gamma-glutamyl transferase (GGT) activity in 63 adults (40 men and 23 women). Serum triglyceride (TG) concentration in the high-mercury group significantly exceeded the values obtained for low- and medium-mercury groups by 72 and 42 %, respectively. Serum GGT activity in the examinees from high-Hg group significantly exceeded the values of the first and the second groups by 75 and 28 %, respectively. Statistical analysis of the male sample revealed similar dependences. Surprisingly, no significant changes in the parameters analyzed were detected in the female sample. In all analyzed samples, hair mercury was not associated with hair selenium concentrations. Significant correlation between hair mercury content and serum TG concentration (r = 0.531) and GGT activity (r = 0.524) in the general sample of the examinees was detected. The respective correlations were observed in the male sample. Hair mercury-to-selenium ratios significantly correlated with body weight (r = 0.310), body mass index (r = 0.250), serum TG (r = 0.389), atherogenic index (r = 0.257), and GGT activity (r = 0.393). The same correlations were observed in the male sample. Hg/Se ratio in women did not correlate with the analyzed parameters. Generally, the results of the current study show the following: (1) hair mercury is associated with serum TG concentration and GGT activity in men, (2) hair selenium content is not related to hair mercury concentration, and (3) mercury-to-selenium ratio correlates with lipid spectrum parameters and GGT activity.
Evaluating knowledge management failure factors using intuitionistic fuzzy FMEA approach

The twenty-first century organizations are characterized by an emphasis on knowledge and information. Today’s organizations also require the acquisition, management, and exploitation of knowledge and information in order to improve their own performance. In the current economy, the foundations of organizational competitiveness have turned former tangible and intangible resources into knowledge and the focus of information systems has also changed from information management to knowledge management. Besides, the most important step in the implementation of knowledge management is to examine the significant factors in this regard and to identify the causes of failure. Therefore, the present study evaluated knowledge management failure factors in an intuitionistic fuzzy environment as a case study in Khuzestan Oil and Gas Company. For this purpose, a series of failure factors affecting knowledge management in organizations were identified based on a review of the related literature and similar studies. Then, 16 failure factors in the implementation of knowledge management in the given organization were determined on the basis of interviews with company experts. According to the specified factors as well as the integration of multiple criteria decision-making techniques in an intuitionistic fuzzy environment, knowledge management failure factors in Khuzestan Oil and Gas Company were investigated. The results indicated that lack of management commitment and leadership was the most important factor affecting the failure of knowledge management in the given company.
Meta-learning in Reinforcement Learning

Meta-parameters in reinforcement learning should be tuned to the environmental dynamics and the animal performance. Here, we propose a biologically plausible meta-reinforcement learning algorithm for tuning these meta-parameters in a dynamic, adaptive manner. We tested our algorithm in both a simulation of a Markov decision task and in a non-linear control task. Our results show that the algorithm robustly finds appropriate meta-parameter values, and controls the meta-parameter time course, in both static and dynamic environments. We suggest that the phasic and tonic components of dopamine neuron firing can encode the signal required for meta-learning of reinforcement learning.
Agency mechanics: gameplay design in survival horror video games

This article proposes the notion of ‘agency mechanics’ as an innovative design element of video games. It begins by exploring the well-documented interactive elements of horror cinema, and goes on to explore how strategies such as the sadistic/masochistic gaze and narrative perspective have been further developed in video games. It then explores work to date on the manipulation of interactivity in video games before closely analysing how agency and control are managed in the survival horror franchise Dead Space. It concludes with reflections on further research in the nexus between film and games, and further possibilities for game design through the explicit management of
BarrierPoint: Sampled simulation of multi-threaded applications

Sampling is a well-known technique to speed up architectural simulation of long-running workloads while maintaining accurate performance predictions. A number of sampling techniques have recently been developed that extend well-known single-threaded techniques to allow sampled simulation of multi-threaded applications. Unfortunately, prior work is limited to non-synchronizing applications (e.g., server throughput workloads); requires the functional simulation of the entire application using a detailed cache hierarchy which limits the overall simulation speedup potential; leads to different units of work across different processor architectures which complicates performance analysis; or, requires massive machine resources to achieve reasonable simulation speedups. In this work, we propose BarrierPoint, a sampling methodology to accelerate simulation by leveraging globally synchronizing barriers in multi-threaded applications. BarrierPoint collects microarchitecture-independent code and data signatures to determine the most representative inter-barrier regions, called barrierpoints. BarrierPoint estimates total application execution time (and other performance metrics of interest) through detailed simulation of these barrierpoints only, leading to substantial simulation speedups. Barrierpoints can be simulated in parallel, use fewer simulation resources, and define fixed units of work to be used in performance comparisons across processor architectures. Our evaluation of BarrierPoint using NPB and Parsec benchmarks reports average simulation speedups of 24.7× (and up to 866.6×) with an average simulation error of 0.9% and 2.9% at most. On average, BarrierPoint reduces the number of simulation machine resources needed by 78×.
Early Seizure Detection Algorithm Based on Intracranial EEG and Random Forest Classification

The goal of this study is to provide a seizure detection algorithm that is relatively simple to implement on a microcontroller, so it can be used for an implantable closed loop stimulation device. We propose a set of 11 simple time domain and power bands features, computed from one intracranial EEG contact located in the seizure onset zone. The classification of the features is performed using a random forest classifier. Depending on the training datasets and the optimization preferences, the performance of the algorithm were: 93.84% mean sensitivity (100% median sensitivity), 3.03 s mean (1.75 s median) detection delays and 0.33/h mean (0.07/h median) false detections per hour.
BPMN AND BPR: A NEW SHAPE OF BUSINESS PROCESSES

The aim of this paper is to perform the main part of the restructuring processes with Business Process Reengineering (BPR) methodology. The first step was to choose the processes for analysis. Two business processes, which occur in most of the manufacturing companies, have been selected. Afterwards, current state of these processes was examined. The conclusions were used to propose own changes in accordance with assumptions of the BPR. This was possible through modelling and simulation of selected processes with iGrafx modeling software.
A compact FPGA-based single-phase cascaded H-bridge multilevel inverter for teaching and research activities

In this paper is presented the implementation of a compact FPGA-based single-phase cascaded H-bridge multilevel inverter suitable for teaching and research activities. The softwares Matlab/Simulink and Quartus II were used to describe and simulate the PWM algorithm in hardware description language (VHDL), before experimental implementation. A Terasic DE0-Nano board with an Intel Cyclone IV EP4CE22F17C6N FPGA was used to generate the 2.4 kHz PWM switching control signals, which are fed to isolated gate drivers with the HCPL-3180 optocoupler, before being applied to the eight IRF640N MOSFETs in a developed low power prototype inverter board. To validate the proposed inverter, two amplitude modulation indexes were evaluated (0.4 and 0.99) using the phase opposition carriers disposition (POD) technique. Simulation and experimental results to synthesize a three- and a five-level PWM voltage waveform across a resistive and a resistive-inductive-capacitive load show that both are in close agreement, validating the proposed low-cost inverter.
Very Short-Term Nonparametric Probabilistic Forecasting of Renewable Energy Generation— With Application to Solar Energy

Due to the inherent uncertainty involved in renewable energy forecasting, uncertainty quantification is a key input to maintain acceptable levels of reliability and profitability in power system operation. A proposal is formulated and evaluated here for the case of solar power generation, when only power and meteorological measurements are available, without sky-imaging and information about cloud passages. Our empirical investigation reveals that the distribution of forecast errors do not follow any of the common parametric densities. This therefore motivates the proposal of a nonparametric approach to generate very short-term predictive densities, i.e., for lead times between a few minutes to one hour ahead, with fast frequency updates. We rely on an Extreme Learning Machine (ELM) as a fast regression model, trained in varied ways to obtain both point and quantile forecasts of solar power generation. Four probabilistic methods are implemented as benchmarks. Rival approaches are evaluated based on a number of test cases for two solar power generation sites in different climatic regions, allowing us to show that our approach results in generation of skilful and reliable probabilistic forecasts in a computationally efficient manner.
Multiple anthropogenic stressors exert complex, interactive effects on a coral reef community

Multiple natural and anthropogenic stressors impact coral reefs across the globe leading to declines of coral populations, but the relative importance of different stressors and the ways they interact remain poorly understood. Because coral reefs exist in environments commonly impacted by multiple stressors simultaneously, understanding their interactions is of particular importance. To evaluate the role of multiple stressors we experimentally manipulated three stressors (herbivore abundance, nutrient supply, and sediment loading) in plots on a natural reef in the Gulf of Panamá in the Eastern Tropical Pacific. Monitoring of the benthic community (coral, macroalgae, algal turf, and crustose coralline algae) showed complex responses with all three stressors impacting the community, but at different times, in different combinations, and with varying effects on different community members. Reduction of top–down control in combination with sediment addition had the strongest effect on the community, and led to approximately three times greater algal biomass. Coral cover was reduced in all experimental units with a negative effect of nutrients over time and a synergistic interaction between herbivore exclosures and sediment addition. In contrast, nutrient and sediment additions interacted antagonistically in their impacts on crustose coralline algae and turf algae so that in combination the treatments limited each other’s effects. Interactions between stressors and temporal variability indicated that, while each stressor had the potential to impact community structure, their combinations and the broader environmental conditions under which they acted strongly influenced their specific effects. Thus, it is critical to evaluate the effects of stressors on community dynamics not only independently but also under different combinations or environmental conditions to understand how those effects will be played out in more realistic scenarios.
Network performance evaluation for RIP, OSPF and EIGRP routing protocols

The main function of a network layer is to route packets from the source machine to the destination machine. Algorithms that are used for route selection and data structure are the main parts for the network layer. In this paper we examine the network performance when using three routing protocols, RIP, OSPF and EIGRP. Video, HTTP and Voice application where configured for network transfer. We also examine the behaviour when using link failure/recovery controller between network nodes. The simulation results are analyzed, with a comparison between these protocols on the effectiveness and performance in network implemented.
Cytokinin signaling networks.

Despite long-standing observations on diverse cytokinin actions, the discovery path to cytokinin signaling mechanisms was tortuous. Unyielding to conventional genetic screens, experimental innovations were paramount in unraveling the core cytokinin signaling circuitry, which employs a large repertoire of genes with overlapping and specific functions. The canonical two-component transcription circuitry involves His kinases that perceive cytokinin and initiate signaling, as well as His-to-Asp phosphorelay proteins that transfer phosphoryl groups to response regulators, transcriptional activators, or repressors. Recent advances have revealed the complex physiological functions of cytokinins, including interactions with auxin and other signal transduction pathways. This review begins by outlining the historical path to cytokinin discovery and then elucidates the diverse cytokinin functions and key signaling components. Highlights focus on the integration of cytokinin signaling components into regulatory networks in specific contexts, ranging from molecular, cellular, and developmental regulations in the embryo, root apical meristem, shoot apical meristem, stem and root vasculature, and nodule organogenesis to organismal responses underlying immunity, stress tolerance, and senescence.
Resource allocation for spectrum underlay in cognitive radio networks

A resource allocation framework is presented for spectrum underlay in cognitive wireless networks. We consider both interference constraints for primary users and quality of service (QoS) constraints for secondary users. Specifically, interference from secondary users to primary users is constrained to be below a tolerable limit. Also, signal to interference plus noise ratio (SINR) of each secondary user is maintained higher than a desired level for QoS insurance. We propose admission control algorithms to be used during high network load conditions which are performed jointly with power control so that QoS requirements of all admitted secondary users are satisfied while keeping the interference to primary users below the tolerable limit. If all secondary users can be supported at minimum rates, we allow them to increase their transmission rates and share the spectrum in a fair manner. We formulate the joint power/rate allocation with proportional and max-min fairness criteria as optimization problems. We show how to transform these optimization problems into a convex form so that their globally optimal solutions can be obtained. Numerical results show that the proposed admission control algorithms achieve performance very close to that of the optimal solution. Also, impacts of different system and QoS parameters on the network performance are investigated for the admission control, and rate/power allocation algorithms under different fairness criteria.
Reference-Conditioned Super-Resolution by Neural Texture Transfer

With the recent advancement in deep learning, we have witnessed a great progress in single image super-resolution. However, due to the significant information loss of the image downscaling process, it has become extremely challenging to further advance the state-of-theart, especially for large upscaling factors. This paper explores a new research direction in super resolution, called reference-conditioned superresolution, in which a reference image containing desired high-resolution texture details is provided besides the low-resolution image. We focus on transferring the high-resolution texture from reference images to the super-resolution process without the constraint of content similarity between reference and target images, which is a key difference from previous example-based methods. Inspired by recent work on image stylization, we address the problem via neural texture transfer. We design an end-to-end trainable deep model which generates detail enriched results by adaptively fusing the content from the low-resolution image with the texture patterns from the reference image. We create a benchmark dataset for the general research of reference-based super-resolution, which contains reference images paired with low-resolution inputs with varying degrees of similarity. Both objective and subjective evaluations demonstrate the great potential of using reference images as well as the superiority of our results over other state-of-the-art methods.
Antiangiogenic drugs in oncology: a focus on drug safety and the elderly - a mini-review.

Angiogenesis is essential for normal tissue and even more so for solid malignancies. At present, inhibition of tumor angiogenesis is a major focus of anticancer drug development. Bevacizumab, a humanized antibody against VEGF, was the first antiangiogenic agent to be approved for advanced non-small cell lung cancer, breast cancer and colorectal cancer. The most commonly observed adverse events are hypertension, proteinuria, bleeding and thrombosis. Sunitinib, a small molecule blocking intracellular VEGF, KIT, Flt3 and PDGF receptors, which regulate angiogenesis and cell growth, is approved for the treatment of advanced renal cell cancer (RCC) and malignant gastrointestinal stromal tumor. The most frequent adverse events include hand-foot syndrome, stomatitis, diarrhea, fatigue, hypothyroidism and hypertension. Sorafenib, an oral multikinase inhibitor, is approved for the second-line treatment of advanced RCC and upfront treatment of advanced hepatocellular carcinoma. Most common adverse events with sorafenib are dermatologic (hand-foot skin reaction, rash, desquamation), fatigue, diarrhea, nausea, hypothyroidism and hypertension. More recently, cardiovascular toxicity has increasingly been recognized as a potential adverse event associated with sunitinib and sorafenib treatment. Elderly patients are at increased risk of thromboembolic events when receiving bevacizumab, and potentially for cardiac dysfunction when receiving sunitinib or sorafenib. The safety of antiangiogenic drugs is of special concern when taking these agents for longer-term adjuvant or maintenance treatment. Furthermore, newer investigational antiangiogenic drugs are briefly reviewed.
Solar PV powered SRM driven water pumping system using Landsman converter

This work is primarily focused on the design and development of an efficient and cost effective solar photovoltaic generator (PVG) based water pumping system implying a switched reluctance motor (SRM) drive. The maximum extraction of available power from PVG is attained by introducing an incremental conductance (IC) maximum power point tracking (MPPT) controller with Landsman DC-DC converter as a power conditioning stage. The CCM (continuous conduction mode) operation of Landsman DC-DC converter helps to reduce the current and voltage stress on its components and to realize the DC-DC conversion ratio independent of the load. The efficient utilization of SPV array and limiting the high initial inrush current in the motor drive is the primary concern of a Landsman converter. The inclusion of start-up control algorithm in the motor drive system facilitates the smooth self-starting of an 8/6 SRM drive. A novel approach to regulate the speed of the motor-pump system by controlling the DC link voltage of split capacitors converter helps in eliminating the voltage or current sensors required for speed control of SRM drive. The electronic commutated operation of mid-point converter considerably reduces its switching losses. This topology is designed and modeled in Matlab/Simulink platform and a laboratory prototype is developed to validate its performance under varying environmental conditions.