nils-herrmann/clean_scirepeval huggingface dataset:
The PRIDE database and related tools and resources in 2019: improving support for quantification data
Venue: Nucleic Acids Res.
Year: 2019

The PRoteomics IDEntifications (PRIDE) database (https:/www.ebi.ac.uk/pride/ is the world's largest data repository of mass spectrometry based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added value resources including Ensembl, UniProt and Expression Atlas.
Mental Well Being (Depression, Loneliness, Insomnia, Daily Life Fatigue) during COVID 19 Related Home Confinement A Study from Poland
Venue: International journal of environmental research and public health
Year: 2020

The COVID 19 pandemic is a great threat to both physical and mental health as it may lead to psychological stress connected with an economic crisis, threat of unemployment, or fear of losing family members. Emerging data shows that the general public may be vulnerable to the pandemic related stress and experience frequently prevalent anxiety. A study involving 471 subjects (85.6% female) was conducted online during the COVID 19 pandemic. We used the following scales: Insomnia Severity Index (ISI) Beck Depression Inventory (BDI) Revised University of California, Los Angeles (R UCLA) Loneliness Scale, and Daily Life Fatigue scale (DLF) Women had higher mean scores of depression, loneliness, and daily life fatigue and more often than males started exercising. Among people professionally active before the pandemic, there were more cases of increased alcohol consumption than among students. No differences in alcohol consumption patterns were found between genders. People living alone had higher scores of loneliness and daily life fatigue compared to those living with someone. Respondents who started taking any new drugs during COVID 19 home confinement had higher outcomes in all questionnaires. During home confinement, high scores of depression, insomnia, loneliness, and everyday fatigue were observed.
Robust Face Recognition under Difficult Lighting Conditions
Year: 2012

This paper addresses the problem of illumination effects on Face recognition and works for an approach to reduce their effect on recognition performance. More broadly, a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition. Using local ternary patterns (LTP) a generalization of the local binary pattern (LBP) local texture descriptor that is more discriminant and less sensitive to noise in uniform regions. We also show that replacing comparisons based on local spatial histograms with a distance transform based similarity metric further improves the performance of LBP/LTP based face recognition; and Robustness is still improved by adding Kernel principal component analysis (PCA) feature extraction. KeywordsFace recognition, local ternary patterns, local binary pattern, and Kernel principal component analysis
Utilizing the platform economy effect through EWOM: Does the platform matter?
Year: 2020

Businesses use the platform economy and electronic word of mouth generated by online reviews to attract consumers. Based on the communication accommodation theory, we examine both the contents of online consumer reviews and managerial responses to analyze what consumers and managers write and the associated linguistic characteristics to determine how they write. We examine and compare online consumer reviews and managerial responses on social media platforms, third party booking platforms, and direct sales platforms in four dimensions. These include (a) linguistic characteristics, (b) content, (c) the interaction between consumers and managers, and (d) the mechanism of the reflection of online reviews in consumer satisfaction. We use a text mining approach, latent semantic analysis, and text regressions to analyze data from the hotel industry. The findings suggest that both the linguistic characteristics and content of the consumer reviews and managerial responses differ depending on the platform. However, the factors influencing consumer satisfaction are the same among the three platforms. Consumer reviews on social media platforms have greater subjectivity and length; reviews on direct platforms have higher polarity, diversity, and readability. Consumer reviews focus more on interpersonal and intangible attributes on social media, on economic attributes on third party platforms, and on tangible attributes on direct platforms. Managers' responses have similar linguistic styles in terms of subjectivity, polarity, and readability, but they use more words with greater diversity and focus more on operations and facility issues on direct platforms than on the other two platforms. We provide implications for managers to understand consumer reviews and write responses.
Critical points of solutions to elliptic problems in planar domains
Year: 2010

Given a planar domain \Omega$ and an analytic function $f$ we describe the set of critical points for the solution $u$ of the semilinear elliptic problem \Delta u f(u) in \Omega$ $u=0$ on \partial\Omega$ For simply connected domains we establish that the set of critical points is finite while for non simply connected domains we show that this set is made up of finitely many isolated points and finitely many analytic Jordan curves. Further results are given in the case that \Omega$ is an annular domain whose border has nonzero curvature.
Rendering Speed Improvement for Particle Based Fluid Simulation on GVDB Voxels using Whitted Raytracing
Venue: 2019 International Conference on Data and Software Engineering (ICoDSE)
Year: 2019

Fluid simulation is the representation of fluids and its displacement as in the real world on digital media such as computers. This research specifically discuss problem regarding visual rendering for fluid simulation. A common approach to represent fluid simulation is through groups of point representing volumetric fluid particles. This data representation approach is called as point cloud data. GVDB Voxels is a new framework that may aid storing, calculating, and rendering of fluid simulation data stored with the framework. In this research, it is specifically discussed techniques to render fluids stored in GVDB Voxels. This research explains how point cloud data is stored in GVDB Voxels, converted into level set data, rendered to images using raytracing, and lastly modified to perform trade off between render speed and render quality. Raytracing was performed using NVIDIA OptiX Engine integration with GVDB Voxels. Using OptiX Engine, we implement a Whitted Ray tracer as alternative to the provided Monte Carlo Path Tracer. Moreover, we propose Quincunx antialiasing method to deal with appearing aliasing problem. Rendering with the proposed solution is able to reach realtime performance with FPS count 30.47 and higher SNR (signal to noise) value than the provided Monte Carlo Path Tracing method in particular cases. Future work related with this topic should aim to modify secondary ray tracer by incorporating depth bounding which may generate for FPS gains.
Partial Discharge Signal Denoising Based on Wavelet Pair and Block Thresholding
Venue: IEEE Access
Year: 2020

In electrical engineering, it is of great importance to monitor partial discharge (PD) of high voltage apparatus. However, on site PD signal easily gets corrupted by white noise, and hence denoising the measured PD signal is necessary to acquire pure PD. In traditional method, single wavelet and regular thresholding are utilized. By combining wavelets of the same family, dual tree complex wavelet pairs can be constructed. Specific combinations are approximately translation invariant, and they are also able to achieve better denoising effect without sophisticated computation. Meanwhile, considering the correlation between adjacent wavelet coefficients, the block thresholding is employed. In this paper, it is proposed to combine wavelet pair and block thresholding for PD denoising. There are four combinations based on single wavelet/wavelet pair and regular thresholding/block thresholding, and they are compared with each other. Based on a numerical study, the results demonstrate that the proposed algorithm outperforms the traditional method.
Explainable artificial intelligence (XAI) interactively working with humans as a junior cyber analyst
Year: 2021

There are many applications where artificial intelligence (AI) can add a benefit, but this benefit may not be fully realized, if the human cannot understand and interact with the output as required by their context. Allowing AI to explain its decisions can potentially mitigate this issue. To develop effective explainable AI methods to support this need, we need to understand both what the human needs for decision making, as well as what information the AI has and can make available. This paper presents an example case of capturing those requirements. We explore how an operational planner (senior human analyst) for a cyber protection team could use a junior analyst virtual agent to scour, analyze, and present the data available on vulnerabilities and incidents on both the target systems as well as similar systems. We explore the interactions required to understand these outputs and to integrate additional knowledge held by the human. This is an exemplar case for integrating XAI into the real world bi directional workflow: the senior analyst needs to be able to understand the junior analysts results, particularly the assumptions and implications, in order to create a plan and brief it up the command chain. He or she may have further questions, or analysis needs to achieve this understanding. The application is the junior analyst agent and senior human analysts working together to create this understanding of threats, vulnerabilities, incidents, likely future attacks, and counteractions on the mission relevant cyber terrain that their unit has been assigned a mission on.
Diagnostico de fasciola hepatica en animales faenados en el camal Municipal de Macara, a traves de tres metodos de sedimentacion.
Year: 2015

This research was held in the Municipal slaughterhouse of Macara to determine the prevalence of hepatic Fasciola in the species;  Cattle and goats. There were analysed 317 samples of stool from which 170 belonged to pigs, 81 samples of cattle and 66 samples from goats, they were taken from the viscera of the animals slaughtered in the Municipal slaughterhouse of Macara Canton, on Fridays. From the ten weeks there were analysed by the following techniques: Dennis, sedimentation by centrifugation and simple sedimentation. The prevalence of hepatic Fasciola by domestic species analysed it was 0% for pigs and goats and 25% for cattle.The prevalence of hepatic Fasciola by their origin was 0% for pigs and goats, in the case of cattle the highest percentage of positive animals to the disease were from the West of Macara Canton sector with 12.5% and the rest with the other 12.5% distributed to the north, south and east of Macara Canton. By age, in pigs and goats 0% and the highest percentage cattle were older than two years with 28% According to sex in pigs and goats for both males and females 0% for cattle sex they presented higher percentage were females with 30%.In the case of the correlation of the results with the body condition of the animals in pigs and goats 0% in cattle was the highest percentage of regulating animal body condition with 55% .And for the effectiveness of the methods applied the method more effective was that of Dennis with 100% effectiveness
Multi layer silver nanowire/polyethylene terephthalate mesh structure for highly efficient transparent electromagnetic interference shielding.
Venue: Nanotechnology
Year: 2020

Electromagnetic interference protection in optoelectronic devices is challenging because of the dual requirements of optical transmittance and high shielding effectiveness. Herein, we propose a novel silver nanowire (AgNW)/polyethylene terephthalate (PET) multi layer mesh pattern structure for transparent electromagnetic shielding obtained via laser marking and transfer printing. A three layer composite shielding film with an optical transmittance of 67.8% exhibits a shielding effectiveness of 44 dB at 10 GHz, which is superior to most of the reported transparent shielding films composed of AgNWs to date. The newly designed multi layer composite structure can enhance the transparent shielding properties of the shielding film via optimization of the AgNW distribution and the shielding film structure. It is expected that this multi layer mesh composite structure will have splendid application prospects in electromagnetic shielding films, which require both light transmittance and high shielding effectiveness.
A Very low phase noise ku band coupled VCO in 0.25 mm SiGe:C BiCMOS technology with low frequency pushing
Venue: 2017 IEEE Bipolar/BiCMOS Circuits and Technology Meeting (BCTM)
Year: 2017

This paper presents a fully integrated Ku band coupled Voltage Controlled Oscillator (VCO) with very low phase noise performances, implemented in the QUBiC4X 0.25 mm SiGe:C BiCMOS process of NXP semiconductors. The originality of this design consists in using two coupled VCOs with a new bias circuit to improve the pushing and the phase noise. Under 5 V supply voltage and a maximum total power dissipation of 231.5 mW, the proposed VCO features a phase noise of 121.4 dBc/Hz at 1 MHz frequency offset. The VCO is tuned from 13.53 GHz to 14.79 GHz with a tuning voltage varying from 1 V to 4.5 V and occupies 0.84x1.85 mm2.
Orientation gradient on surface of non oriented electrical steel annealed by g a transformation
Venue: Journal of Iron and Steel Research International
Year: 2019

The microstructures and textures of the non oriented electrical steel sheets under various annealing conditions were investigated. The orientation gradient in the interior of grains appeared on the surface of steel sheets after a g a transformation in various atmospheres. The orientation gradient that appeared on the surface was inherited into the center of the steel sheet in H 2 atmosphere, while that appeared only in the grains of the surface layer during g a transformation with N 2 atmosphere. The thin oxide scale was generated in various atmospheres containing traces of O 2 and H 2 O. The formation mechanism of orientation gradient is related to the thermal stress caused by the difference of thermal expansion coefficients between the oxide scale and the thin ferrite on the surface. The combined effect of phase transformation stress and thermal stress caused the plastic deformation of the thin ferrite grains on the surface in the initial stage of g a transformation, and then the orientations of the thin ferrite grains gradually rotated and caused the orientation gradient in the interior of grains.
Students' Acceptance of Technology Mediated Teaching How It Was Influenced During the COVID 19 Pandemic in 2020: A Study From Germany
Venue: Frontiers in Psychology
Year: 2021

In response to the impending spread of COVID 19, universities worldwide abruptly stopped face to face teaching and switched to technology mediated teaching. As a result, the use of technology in the learning processes of students of different disciplines became essential and the only way to teach, communicate and collaborate for months. In this crisis context, we conducted a longitudinal study in four German universities, in which we collected a total of 875 responses from students of information systems and music and arts at four points in time during the spring summer 2020 semester. Our study focused on (1) the students' acceptance of technology mediated learning, (2) any change in this acceptance during the semester and (3) the differences in acceptance between the two disciplines. We applied the Technology Acceptance Model and were able to validate it for the extreme situation of the COVID 19 pandemic. We extended the model with three new variables (time flexibility, learning flexibility and social isolation) that influenced the construct of perceived usefulness. Furthermore, we detected differences between the disciplines and over time. In this paper, we present and discuss our study's results and derive short and long term implications for science and practice.
Recent advances in efficient computation of deep convolutional neural networks
Venue: Frontiers of Information Technology Electronic Engineering
Year: 2018

Deep neural networks have evolved remarkably over the past few years and they are currently the fundamental tools of many intelligent systems. At the same time, the computational complexity and resource consumption of these networks continue to increase. This poses a significant challenge to the deployment of such networks, especially in real time applications or on resource limited devices. Thus, network acceleration has become a hot topic within the deep learning community. As for hardware implementation of deep neural networks, a batch of accelerators based on a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC) have been proposed in recent years. In this paper, we provide a comprehensive survey of recent advances in network acceleration, compression, and accelerator design from both algorithm and hardware points of view. Specifically, we provide a thorough analysis of each of the following topics: network pruning, low rank approximation, network quantization, teacher student networks, compact network design, and hardware accelerators. Finally, we introduce and discuss a few possible future directions.
Bootstrap learning for accurate onset detection
Venue: Machine Learning
Year: 2006

Supervised learning models have been applied to create good onset detection systems for musical audio signals. However, this always requires a large set of labeled training examples, and hand labeling is quite tedious and time consuming. In this paper, we present a bootstrap learning approach to train an accurate note onset detection model. Audio alignment techniques are first used to find the correspondence between a symbolic music representation (such as MIDI data) and an acoustic recording. This alignment provides an initial estimate of note boundaries which can be used to train an onset detector. Once trained, the detector can be used to refine the initial set of note boundaries and training can be repeated. This iterative training process eliminates the need for hand labeled audio. Tests show that this training method can improve an onset detector initially trained on synthetic data.
Study habits of Canadian urology residents: Implications for development of a competence by design curriculum.
Venue: Canadian Urological Association journal Journal de l'Association des urologues du Canada
Year: 2017

The purpose of this study was to evaluate the study habits of Canadian urology residents throughout their residency training.  A survey was administered to all final year Canadian urology residents over a two year period. Sixty seven respondents answered a survey consisting of 54 questions scored on a 10 point Likert score. The survey addressed study habits throughout training, motivations for studying, and preferred resources used.  Dedication to studying was directly correlated with proximity to the Royal College of Physicians and Surgeons of Canada (RCPSC) exam. Ninety six percent of residents reported studying over 10 hours per week during their chief year compared to 0% during their junior year. As residents progressed in their training, preparation for the Royal College exam became the greatest motivator for studying. There was considerable variability in study methods and study resources used throughout training. In their chief year, residents found such resources as the textbook Campbell Walsh, AUA updates, CUA and AUA guidelines, and the study notes of former trainees to be valuable for their preparation. Teaching rounds, journal clubs, and reading current urological literature were found to be les helpful. Forty six percent of all residents surveyed indicated that they would prefer writing their RCPSC exam one year earlier than the current timing.  This study provides insight into study habits of Canadian urology residents. This data may be helpful in shaping the future of urology training programs and examinations within Canada and elsewhere.
Making attributes from the Linked Open Data (LOD) cloud a part of Spatial Data Infrastructures (SDIS) for thematic mapping
Year: 2018

The creation of the linked open data (LOD) cloud has enhanced the availability of interlinked open statistical data associated with geographic regions (attributes) through the Web. Spatial data infrastructures (SDIs) are widely used to share, discover, visualise and retrieve geospatial data. Geoportals are the visible parts of SDIs focused on interoperability through implementing standards such OGC web services for discovery and use of geographic data and services. In the current form OGC services cannot be directly connected to the LOD cloud. This research aimed at finding novel ways of visualising linked data in the form of thematic maps over the Internet. This paper provided answers to the question of how an OGC Web Map Service (WMS) can create thematic maps by combining attributes from the LOD cloud with geometry stored in a spatial database server (SDS) This research contributed to bridging the gap between linked data, SDI and web thematic maps and further showed how existing web mapping and OGC technologies can benefit from the Semantic Web. First, the design of a geospatial web service (representing the visible part of an SDI) that accesses attribute data from the LOD cloud referred to in this paper as SDI LOD is presented. SDI LOD produces web thematic maps by programmatically combining attributes from the LOD cloud with geometry in an SDS. Next, the author presented implementation results of SDI LOD and concluded with a discussion of the implementation. This paper has motivated future work on SDI LOD to integrating data from internet of things.  thematic map; LOD cloud; geospatial data; linked open data; geospatial web service; SDI