nils-herrmann/clean_scirepeval huggingface dataset:
Psychophysics in a Web browser? Comparing response times collected with JavaScript and Psychophysics Toolbox in a visual search task
Venue: Behavior research methods

Behavioral researchers are increasingly using Web based software such as JavaScript to conduct response time experiments. Although there has been some research on the accuracy and reliability of response time measurements collected using JavaScript, it remains unclear how well this method performs relative to standard laboratory software in psychologically relevant experimental manipulations. Here we present results from a visual search experiment in which we measured response time distributions with both Psychophysics Toolbox (PTB) and JavaScript. We developed a methodology that allowed us to simultaneously run the visual search experiment with both systems, interleaving trials between two independent computers, thus minimizing the effects of factors other than the experimental software. The response times measured by JavaScript were approximately 25 ms longer than those measured by PTB. However, we found no reliable difference in the variability of the distributions related to the software, and both software packages were equally sensitive to changes in the response times as a result of the experimental manipulations. We concluded that JavaScript is a suitable tool for measuring response times in behavioral research.
Parallel Randomized Block Coordinate Descent for Neural Probabilistic Language Model with High Dimensional Output Targets
Venue: CCPR

Training a large probabilistic neural network language model, with typical high dimensional output is excessively time consuming, which is one of the main reasons that more simplified models such as n gram is often more popular despite the inferior performance. In this paper a Chinese neural probabilistic language model is trained using the Fudan Chinese Language Corpus. As hundreds of thousands of distinct words have been tokenized from the raw corpus, the model contains tens of millions of parameters. To address the challenge, popular parallel computing platform MPI (Message Passing Interface) based on cluster is employed to implement the parallel neural network language model. Specifically, we propose a new method termed as Parallel Randomized Block Coordinate Descent (PRBCD) to train this model cost effectively. Different from traditional coordinate descent method, our new method could be employed in network with multiple layers, allowing scaling up the gradients with respect to hidden units proportionally based on sampled parameters. We empirically show that our PRBCD is stable and is well suited for language models, which contain only a few layers while often have a large amount of parameters and extremely high dimensional output targets.
Drowsiness Detection Using RASPBERRY PI Model Based On Image Processing Miss

There are various reasons for accidents on road. The major reasons for accidents on road are fatigueless of driver and second one is the alcohol intoxication. These are the two reasons due to which probability of road accidents increases. These road accidents can be overcome by different ways. In recent years, technologies are developed and various techniques are generated for the detecting the road accidents and also preventing the road accidents. The different technologies which are developed are depending on vehicle, behavioral and physiological. In vehicle based approach, it continuously monitored position of steering wheels, position of lane and pressure on acceleration pedal. In behavioral based approach, it continuously sensed mouth's opening area, eye closure, blinking frequency of eye and head posture. In physiological based approach, it checks ECG, EOG .The proposed system used to detect real time drowsiness along with alcohol intake of car driver. Computer vision and embedded system are used to design the system. The system is based on behavioral based approach, in which we consider eye closing rate, eye blink frequency, and pupil and iris edge detection. Image processing techniques such as Haar features and Hough transform are used for acquiring details of given eye object and further processing. On the other hand, an alcohol gas sensor is used to calculate Blood alcohol content (BAC) from Breath alcohol content (BrAC) Raspberry pi processor is used for image processing.
Optimal Local Approximation Spaces for Component Based Static Condensation Procedures
Venue: SIAM J. Sci. Comput.

In this paper we introduce local approximation spaces for component based static condensation (sc) procedures that are optimal in the sense of Kolmogorov. To facilitate simulations for large structures such as aircraft or ships, it is crucial to decrease the number of degrees of freedom on the interfaces, or "ports," in order to reduce the size of the statically condensed system. To derive optimal port spaces we consider a (compact) transfer operator that acts on the space of harmonic extensions on a two component system and maps the traces on the ports that lie on the boundary of these components to the trace of the shared port. Solving the eigenproblem for the composition of the transfer operator and its adjoint yields the optimal space. For a related work in the context of the generalized finite element method, we refer the reader to [I. Babuska and R. Lipton, Multiscale Model. Simul. 9 (2011) pp. 373 406] We further introduce a spectral greedy algorithm to generalize the procedure to the parameter.
A Component Based Approach to Hybrid Systems Safety Verification
Venue: IFM

We study a component based approach to simplify the challenges of verifying large scale hybrid systems. Component based modeling can be used to split large models into partial models to reduce modeling complexity. Yet, verification results also need to transfer from components to composites. In this paper, we propose a component based hybrid system verification approach that combines the advantages of component based modeling e.g. reduced model complexity with the advantages of formal verification e.g. guaranteed contract compliance. Our strategy is to decompose the system into components, verify their local safety individually and compose them to form an overall system that provably satisfies a global contract, without proving the whole system. We introduce the necessary formalism to define the structure and behavior of components and a technique how to compose components such that safety properties provably emerge from component safety.
A Grey Model Based on First Differences in the Application of Satellite Clock Bias Prediction

In consideration of the characteristics of satellite clock bias (SCB) a grey GM(1,1) model based on the first difference method is proposed. The first differences between the SCB values of two adjacent epoches are firstly derived to obtain the corresponding first difference sequence. Then, a grey model is made based on the sequence to predict the first difference values for the time following. Finally, the predicted first difference sequence is recovered to be the corresponding predicted SCB. The clock bias data provided by the IGS (International GNSS Service) are used as the experimental data, and the cases with different lengths of modeling data and different prediction step lengths are compared and analyzed. The result shows that the prediction accuracy of this method is higher than that of traditional grey GM(1,1) model, especially for the PRN01 satellite clock, whose forecast effect is the best. With the first difference method, the model prediction accuracy and stability can be effectively improved and enhanced. This method is feasible and reliable for the relatively long term SCB prediction.
Indium Oxide as a Superior Catalyst for Methanol Synthesis by CO2 Hydrogenation.
Venue: Angewandte Chemie

Methanol synthesis by CO2 hydrogenation is attractive in view of avoiding the environmental implications associated with the production of the traditional syngas feedstock and mitigating global warming. However, there still is a lack of efficient catalysts for such alternative processes. Herein, we unveil the high activity, 100 selectivity, and remarkable stability for 1000 h on stream of In2 O3 supported on ZrO2 under industrially relevant conditions. This strongly contrasts to the benchmark Cu ZnO Al2 O3 catalyst, which is unselective and experiences rapid deactivation. In depth characterization of the In2 O3 based materials points towards a mechanism rooted in the creation and annihilation of oxygen vacancies as active sites, whose amount can be modulated in situ by co feeding CO and boosted through electronic interactions with the zirconia carrier. These results constitute a promising basis for the design of a prospective technology for sustainable methanol production.
Highly Linear mm Wave CMOS Power Amplifier
Venue: IEEE Transactions on Microwave Theory and Techniques

A Ka band highly linear power amplifier (PA) is implemented in 28 nm bulk CMOS technology. Using a deep class AB PA topology with appropriate harmonic control circuit, highly linear and efficient PAs are designed at millimeter wave band. This PA architecture provides a linear PA operation close to the saturated power. Also elaborated harmonic tuning and neutralization techniques are used to further improve the transistor gain and stability. A two stack PA is designed for higher gain and output power than a common source (CS) PA. Additionally, average power tracking (APT) is applied to further reduce the power consumption at a low power operation and, hence, extend battery life. Both the PAs are tested with two different signals at 28.5 GHz; they are fully loaded long term evolution (LTE) signal with 16 quadrature amplitude modulation (QAM) a 7.5 dB peakto average power ratio (PAPR) and a 20 MHz bandwidth (BW) and a wireless LAN (WLAN) signal with 64 QAM, a 10.8 dB PAPR, and an 80 MHz BW. The CS/two stack PAs achieve power added efficiency (PAE) of 27%/25% error vector magnitude (EVM) of 5.17%/3.19% and adjacent channel leakage ratio (ACLRE UTRA) of 33/ 33 dBc, respectively, with an average output power of 11/14.6 dBm for the LTE signal. For the WLAN signal, the CS/2 stack PAs achieve the PAE of 16.5%/17.3% and an EVM of 4.27%/4.21% respectively, at an average output power of 6.8/11 dBm.
Structure of a bd oxidase indicates similar mechanisms for membrane integrated oxygen reductases
Venue: Science

Peering into a membrance oxidase Microorganisms have evolved a number of enzymes to reduce oxygen and prevent oxidative stress. Cytochrome bd oxidases serve this role and also protect pathogenic bacteria from nitric acid; however, this class of enzymes so far has eluded high resolution crystallography. Safarian et al. were able to resolve the three dimensional structure of cytochrome bd oxidase from a thermophilic bacterium (see the Perspective by Cook and Poole) The overall structure and triangular arrangement of its heme cofactors bear little structural resemblance to those of other membrane spanning oxidases, despite serving a similar function. Science, this issue p. 583; see also p. 518 Structural details reveal how pathogens protect against oxidative stress and nitric oxide. The cytochrome bd oxidases are terminal oxidases that are present in bacteria and archaea. They reduce molecular oxygen (dioxygen) to water, avoiding the production of reactive oxygen species. In addition to their contribution to the proton motive force, they mediate viability under oxygen related stress conditions and confer tolerance to nitric oxide, thus contributing to the virulence of pathogenic bacteria. Here we present the atomic structure of the bd oxidase from Geobacillus thermodenitrificans, revealing a pseudosymmetrical subunit fold. The arrangement and order of the heme cofactors support the conclusions from spectroscopic measurements that the cleavage of the dioxygen bond may be mechanistically similar to that in the heme copper containing oxidases, even though the structures are completely different.
The Terminal Oxidase Cytochrome bd Promotes Sulfide resistant Bacterial Respiration and Growth
Venue: Scientific reports

Hydrogen sulfide (H2S) impairs mitochondrial respiration by potently inhibiting the heme copper cytochrome c oxidase. Since many prokaryotes, including Escherichia (E. coli, generate H2S and encounter high H2S levels particularly in the human gut, herein we tested whether bacteria can sustain sulfide resistant O2 dependent respiration. E. coli has three respiratory oxidases, the cyanide sensitive heme copper bo3 enzyme and two bd oxidases much less sensitive to cyanide. Working on the isolated enzymes, we found that, whereas the bo3 oxidase is inhibited by sulfide with half maximal inhibitory concentration IC50 1.1 0.1 mM, under identical experimental conditions both bd oxidases are insensitive to sulfide up to 58 mM. In E. coli respiratory mutants, both O2 consumption and aerobic growth proved to be severely impaired by sulfide when respiration was sustained by the bo3 oxidase alone, but unaffected by =200 mM sulfide when either bd enzyme acted as the only terminal oxidase. Accordingly, wild type E. coli showed sulfide insensitive respiration and growth under conditions favouring the expression of bd oxidases. In all tested conditions, cyanide mimicked the functional effect of sulfide on bacterial respiration. We conclude that bd oxidases promote sulfide resistant O2 consumption and growth in E. coli and possibly other bacteria. The impact of this discovery is discussed.
The cytochrome bd oxidase of Escherichia coli prevents respiratory inhibition by endogenous and exogenous hydrogen sulfide
Venue: Molecular microbiology

When sulfur compounds are scarce or difficult to process, Escherichia coli adapts by inducing the high level expression of sulfur compound importers. If cystine then becomes available, the cystine is rapidly overimported and reduced, leading to a burgeoning pool of intracellular cysteine. Most of the excess cysteine is exported, but some is adventitiously degraded, with the consequent release of sulfide. Sulfide is a potent ligand of copper and heme moieties, raising the prospect that it interferes with enzymes. We observed that when cystine was provided and sulfide levels rose, E. coli became strictly dependent upon cytochrome bd oxidase for continued respiration. Inspection revealed that low micromolar levels of sulfide inhibited the proton pumping cytochrome bo oxidase that is regarded as the primary respiratory oxidase. In the absence of the back up cytochrome bd oxidase, growth failed. Exogenous sulfide elicited the same effect. The potency of sulfide was enhanced when oxygen concentrations were low. Natural oxic anoxic interfaces are often sulfidic, including the intestinal environment where E. coli dwells. We propose that the sulfide resistance of the cytochrome bd oxidase is a key trait that permits respiration in such habitats.
Fixed Combination Aerosol Foam Calcipotriene 0.005% (Cal) Plus Betamethasone Dipropionate 0.064% (BD) is More Efficacious than Cal or BD Aerosol Foam Alone for Psoriasis Vulgaris: A Randomized, Double blind, Multicenter, Three arm, Phase 2 Study.
Venue: The Journal of clinical and aesthetic dermatology

To evaluate the efficacy of fixed combination aerosol foam calcipotriene 0.005% (Cal) plus betamethasone dipropionate 0.064% (BD) DESIGN Patients were randomized (100:101:101) to receive Cal/BD foam, Cal foam, or BD foam once daily for four weeks.  Twenty eight United States centers. PARTICIPANTS 302 patients =18 years) with Psoriasis vulgaris (plaque Psoriasis; =mild disease severity by physicians global assessment) MEASUREMENTS Treatment success of the body "clear""almost clear" from baseline moderate/severe disease; "clear" from baseline mild disease) Involved scalp treatment success was an additional endpoint.  Most patients (76% had moderate Psoriasis of the body (66% for scalp) At Week 4, 45 percent of Cal/BD foam patients achieved treatment success, significantly more than Cal foam (14.9% OR 4.34 [95%CI 2.16,8.72] P<0.001) or BD foam (30.7% 1.81 [1.00,3.26] P=0.047) Fifty three percent of Cal/BD foam patients achieved treatment success of the scalp, significantly greater than Cal foam (35.6% 1.91 [1.09,3.35] P=0.021) but not BD foam (47.5% 1.24 [0.71,2.16] P=0.45) Mean modified Psoriasis area and severity index (population baseline 7.6) improved in all groups, with statistically significant differences in Week 4 Cal/BD foam score (2.37) versus Cal foam (4.39; mean difference 2.03 2.63] 1.43] P<0.001) and BD foam (3.37; 1.19 1.80] 0.59] P<0.001) Four (Cal/BD) 10 (Cal) and 8 (BD) adverse drug reactions were reported.  Cal/BD foam was significantly more effective than Cal foam and BD foam in providing treatment success at Week 4 and effective on involved scalp.
Cytochrome bd Displays Significant Quinol Peroxidase Activity
Venue: Scientific reports

Cytochrome bd is a prokaryotic terminal oxidase that catalyses the electrogenic reduction of oxygen to water using ubiquinol as electron donor. Cytochrome bd is a tri haem integral membrane enzyme carrying a low spin haem b558, and two high spin haems: b595 and d. Here we show that besides its oxidase activity, cytochrome bd from Escherichia coli is a genuine quinol peroxidase (QPO) that reduces hydrogen peroxide to water. The highly active and pure enzyme preparation used in this study did not display the catalase activity recently reported for E. coli cytochrome bd. To our knowledge, cytochrome bd is the first membrane bound quinol peroxidase detected in E. coli. The observation that cytochrome bd is a quinol peroxidase, can provide a biochemical basis for its role in detoxification of hydrogen peroxide and may explain the frequent findings reported in the literature that indicate increased sensitivity to hydrogen peroxide and decreased virulence in mutants that lack the enzyme.
Stiffness modeling of machine tools based on machining space analysis

Machining stiffness, which serves the overall stability of a machine tool, plays a significant role in determining the resulting machining errors in a machine tool. Therefore, the machine tools can thus be improved to get high levels of precisions directly from what we behold as the most important determinant, the overall stiffness. In this paper, a novel approach modeling the static stiffness field of the machining space is being discussed. Within this method, a parametric model, considering six directional static stiffness, is established to design and evaluate the static stiffness model of a high performance computerized numerical control (CNC) machine tool. The result is then used for further discussions on the estimating and reducing of the machining errors. The machining errors are predicted by using the same CNC model under various load bearings considering the practical machining positions and stress conditions. Moreover, the effect of the six directional static stiffness on the machining errors according to each position in the machining space is researched, where we investigate the possible solutions to reduce the machining errors with the processing technology put into consideration. In the end, by experimenting on boring milling machining center, we verify the accuracy and efficiency of the proposed error prediction method by comparing the results from experimental data and those from finite element analysis. The proposed model is then proved to have great efficiency in optimizing the machine tool's layout and structural design based. This improved modeling strongly suggests a favorable application for future manufacturing processes of machine tools.
Machining process parameters optimization for heavy duty CNC machine tools in sustainable manufacturing

Machining process parameters (MPP) directly affect the machining quality and efficiency of heavy duty CNC machine tools (HCMT) The selection of MPP is very important to effectively improve machining performance. Machining performance has been closely related to the HCMT running state. In order to maintain HCMT sustainably manufacturing with high accuracy and low consumption after machining performance degradation for a long time running, MPP should be re optimized according to the current state of the machine tools. Thus, this paper proposed a MPP optimization method for running HCMT to obtain optimal MPP based on current running state. A multi objective optimization model was built, considering both the linear factors such as machining time and machining cost and nonlinear factors such as chatter in machining process. The nonlinear factors were reflected by the nonlinear dynamic model of machining process. Furthermore, a grid optimization algorithm was introduced to search the optimal MPP from the multi objective optimization model. Finally, a case study was implemented to verify the feasibility of the nonlinear dynamic model and the superiority of the multi objective optimization method compared with single objective optimization method.
A performance evaluation methodology for robotic machine tools used in large volume manufacturing

The manufacture of large components in various industries can create health, safety and economic challenges as a result of machining operations. A potential solution is the replacement of conventional large machine tools with low cost and portable robotic machine tools, although these lack accuracy and precision in comparison. Effort is therefore required to develop robotic machining technology to a state where it can be implemented in high tolerance applications using a variety of materials. A barrier to implementation is that there is not a standardised procedure available to robustly assess current robotic machining performance, which makes it challenging to assess the impact of technological developments. This paper develops a methodology to determine robotic machining performance based upon reviews of standards available that currently specify such guidelines for robotics and machine tool technologies used independently. It is found that useful elements from each theme can be combined and applied to robotic machine tool performance evaluation. These are presented here with the aim of forming a conceptual foundation on which the technology can be developed for large volume manufacturing.
SNAP: A General Purpose Network Analysis and Graph Mining Library
Venue: ACM Trans. Intell. Syst. Technol.

Large networks are becoming a widely used abstraction for studying complex systems in a broad set of disciplines, ranging from social network analysis to molecular biology and neuroscience. Despite an increasing need to analyze and manipulate large networks, only a limited number of tools are available for this task. Here, we describe Stanford Network Analysis Platform (SNAP) a general purpose, high performance system that provides easy to use, high level operations for analysis and manipulation of large networks. We present SNAP functionality, describe its implementational details, and give performance benchmarks. SNAP has been developed for single big memory machines and it balances the trade off between maximum performance, compact in memory graph representation, and the ability to handle dynamic graphs where nodes and edges are being added or removed over time. SNAP can process massive networks with hundreds of millions of nodes and billions of edges. SNAP offers over 140 different graph algorithms that can efficiently manipulate large graphs, calculate structural properties, generate regular and random graphs, and handle attributes and meta data on nodes and edges. Besides being able to handle large graphs, an additional strength of SNAP is that networks and their attributes are fully dynamic, they can be modified during the computation at low cost. SNAP is provided as an open source library in C+ as well as a module in Python. We also describe the Stanford Large Network Dataset, a set of social and information real world networks and datasets, which we make publicly available. The collection is a complementary resource to our SNAP software and is widely used for development and benchmarking of graph analytics algorithms.