nils-herrmann/clean_scirepeval huggingface dataset:
Numerical modelling of shoreline evolution in the Aveiro coast, Portugal climate change scenarios
Year: 2013

Pereira, C. Coelho, C. Ribeiro, A. Fortunato, A. Lopes, C.L. and Dias, J.M. 2013. Numerical modelling of shoreline evolution in the Aveiro coast, Portugal climate change scenarios In the Northwest coast of Portugal, the shoreline evolution is leading to several problems, such as loss of territory and infrastructural damages, mainly caused by the sea wave's action. This study aims to predict the impacts of climate change on the shoreline evolution in this coastal area. Coastal stretches studied were Esmoriz Furadouro and Vagueira Mira, due to their vulnerability related with the low lying sandy coast, the deficit of sediments, the presence of urban waterfronts and the narrow width of the sand spit that separates the ocean from the Aveiro lagoon. The paper describes the application of two shoreline evolution models (LTC and GENESIS) to the projection of shoreline evolution between 2010 and 2100. A reference scenario considering the present typical wave climate and the mean sea water level was compared with 3 other future scenarios that may result from climate change effects, combining different wave climates and mean sea levels (0.42 and 0.64 m above present level) Considering the numerical models' results, maps of the shoreline position were represented every 30 years. The results allowed the analysis of the shoreline retreat, the areas of lost territory and the alongshore sediment transport volumes in several cross sections. A strong trend of shoreline retreat was found for all the scenarios, although the retreat rates tend to decrease along time. Comparing the reference scenario with the climate change scenarios, a slight clockwise rotation of the shoreline was predicted, also increasing the average shoreline retreat from 6% to 11% depending on the considered scenario.
uso de HQs como ferramenta motivacional para o ensino de astronomia
Year: 2020

The Universe is something that has always caught the attention of mankind and has given rise to Astronomy, which is one of the oldest sciences in our history. However, teaching and learning this content comes up against some difficulties on the part of teachers' insecurity and differentiated material to work on this content. Thus, this study presents the results of a survey with students from elementary schools in the city of Coxim [state of Mato Grosso do Sul, Brazil] through the use of a Comic Book elaborated in the form of comic strips as a way of verifying its potential as a motivational tool for the teaching and learning processes about the subjects that involve the Universe and the Earth. To this end, evaluations were applied at the beginning and at the end of a teaching sequence designed to work on the topic in question. We evidenced that the use of Comics as a motivational tool presented positive results for teaching Astronomy, particularly with the students of the Elementary School.
Improved Prediction of Aqueous Solubility of Novel Compounds by Going Deeper With Deep Learning
Venue: Frontiers in Oncology
Year: 2020

Aqueous solubility is an important physicochemical property of compounds in anti cancer drug discovery. Artificial intelligence solubility prediction tools have scored impressive performances by employing regression, machine learning, and deep learning methods. The reported performances vary significantly partly because of the different datasets used. Solubility prediction on novel compounds needs to be improved, which may be achieved by going deeper with deep learning. We constructed deeper net models of ~20 layer modified ResNet convolutional neural network architecture, which were trained and tested with 9,943 compounds encoded by molecular fingerprints. Retrospectively tested by 62 recently published novel compounds, one deeper net model outperformed four established tools, shallow net models, and four human experts. Deeper net models also outperformed others in predicting the solubility values of a series of novel compounds newly synthesized for anti cancer drug discovery. Solubility prediction may be improved by going deeper with deep learning. Our deeper net models are accessible at http:/www.npbdb.net/solubility/index.jsp.
Instance Shadow Detection
Venue: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2020

Instance shadow detection is a brand new problem, aiming to find shadow instances paired with object instances. To approach it, we first prepare a new dataset called SOBA, named after Shadow OBject Association, with 3,623 pairs of shadow and object instances in 1,000 photos, each with individual labeled masks. Second, we design LISA, named after Light guided Instance Shadow object Association, an end to end framework to automatically predict the shadow and object instances, together with the shadow object associations and light direction. Then, we pair up the predicted shadow and object instances, and match them with the predicted shadow object associations to generate the final results. In our evaluations, we formulate a new metric named the shadow object average precision to measure the performance of our results. Further, we conducted various experiments and demonstrate our method's applicability on light direction estimation and photo editing.
Dynamique des filieres laitieres en zone sahelienne cas de l'offre et de la demande du lait en zone agropastorale centre du Senegal
Year: 2007

In Senegal, like in many developing countries in West Africa, the weight of imported milk and dairy products in the trade balance led to making a priority of improving local dairy production. An artificial insemination program was thus launched in 1994 in Senegal in order to improve domestic dairy products. This study was initiated within the context of the Concerted Research Project for the Development of Livestock in West Africa (PROCORDEL) It aimed at better understanding the dairy milk subsector in Senegal's peanut basin. The collected data concerned 96 farmers, 50 milk and dairy traders and 120 consumers located in the areas of Kaolack and Fatick. Results showed that animal production tended toward intensification with a decrease in the number of cows per farmer because of the reduced grazing space. In both study areas, the milk production of mixed breeds was on average 5.6 L/cow/day. Feed availability was identified by the agrofarmers as the main constraint to production, especially in the dry season. The marketing system was characterized by a short channel, operated by women who faced major constraints related to the insufficiency of the milk supply produced, and the frequent lack of transportation means, particularly in landlocked areas. In addition to a low milk supply, there was almost no milk processing units in the region, and farmers' organizations in the subsector were little effective. The demand in dairy products, however, is becoming more and more important and diversified with the increase in imports. It was also correlated to the level of income and consumers' food preferences and habits.
The Approximation Ratio of the 2 Opt Heuristic for the Euclidean Traveling Salesman Problem
Venue: STACS
Year: 2021

The 2 Opt heuristic is a simple improvement heuristic for the Traveling Salesman Problem. It starts with an arbitrary tour and then repeatedly replaces two edges of the tour by two other edges, as long as this yields a shorter tour. We will prove that for euclidean Traveling Salesman Problems with $n$ cities the approximation ratio of the 2 Opt heuristic is \Theta(\log n/ \log \log n)
Language Models are Few shot Multilingual Learners
Venue: ArXiv
Year: 2021

General purpose language models have demonstrated impressive capabilities, performing on par with state of the art approaches on a range of downstream natural language processing (NLP) tasks and benchmarks when inferring instructions from very few examples. Here, we evaluate the multilingual skills of the GPT and T5 models in conducting multi class classification on non English languages without any parameter updates. We show that, given a few English examples as context, pre trained language models can predict not only English test samples but also non English ones. Finally, we find the in context few shot cross lingual prediction results of language models are significantly better than random prediction, and they are competitive compared to the existing state of the art cross lingual models and translation models.
System architectures and algorithms for radar imaging by MIMO SAR
Venue: 2009 IEEE Radar Conference
Year: 2009

The multi input multi output (MIMO) principle is well known for communication applications, whereas at least the name 'MIMO' is relatively new for radar applications  Nevertheless, the principle has been analyzed and used in a few examples since the early 80th or even before. A MIMO radar is characterised by a number N of transmitting and a number M of receiving antennas forming N x M Tx/Rx pairs where each propagation path from the nth transmit antenna to the object to the mth receive antenna is made available to the signal processing. This can be achieved by temporal multiplexing, spatial coding and/or orthogonal waveforms. A further step is to transfer this technique to the SAR case. Additionally, the whole array is moving, SAR processing can be applied. Possible geometries of MIMO SAR are along track arrays (reduction of azimuth ambiguities, moving target indication, super resolution) or across track arrays (reduction of elevation ambiguities, interferometry, 3D down looking SAR) In this paper, some aspects of moving MIMO arrays for SAR will be addressed.
First principles study of structural, optoelectronic and photocatalytic properties of SnS, SnSe monolayers and their van der Waals heterostructure
Year: 2020

Electronic structure, optical, and photocatalytic properties of SnS, SnSe and their van der Waals heterostructures are investigated by first principle calculations. Thermal stability confirmed that SnS, SnSe and SnS SnSe van der Waals heterostructure are thermodynamically stable. The calculated band structure shows that SnS, SnSe and SnS SnSe van der Waals heterostructure are indirect band nature while the heterostructure are confirmed for type II band alignment. Bader charge analysis shows that the charges are transfer from SnS layer to SnSe layer. Furthermore, absorption spectra are calculated to understand the optical behavior of these systems, where the lowest energy transitions are lies in visible region. The valence and conduction band edges straddle the standard redox potentials in SnS, SnSe and their van der Waals heterostructures van der Waals heterostructures, making them promising candidates for water splitting in the acidic solution.
A test of the hydraulic vulnerability segmentation hypothesis in angiosperm and conifer tree species.
Venue: Tree physiology
Year: 2016

Water transport from soils to the atmosphere is critical for plant growth and survival. However, we have a limited understanding about many portions of the whole tree hydraulic pathway, because the vast majority of published information is on terminal branches. Our understanding of mature tree trunk hydraulic physiology, in particular, is limited. The hydraulic vulnerability segmentation hypothesis (HVSH) stipulates that distal portions of the plant (leaves, branches and roots) should be more vulnerable to embolism than trunks, which are nonredundant organs that require a massive carbon investment. In the current study, we compared vulnerability to loss of hydraulic function, leaf and xylem water potentials and the resulting hydraulic safety margins (in relation to the water potential causing 50% loss of hydraulic conductivity) in leaves, branches, trunks and roots of four angiosperms and four conifer tree species. Across all species, our results supported strongly the HVSH as leaves and roots were less resistant to embolism than branches or trunks. However, branches were consistently more resistant to embolism than any other portion of the plant, including trunks. Also, calculated whole tree vulnerability to hydraulic dysfunction was much greater than vulnerability in branches. This was due to hydraulic dysfunction in roots and leaves at less negative water potentials than those causing branch or trunk dysfunction. Leaves and roots had narrow or negative hydraulic safety margins, but trunks and branches maintained positive safety margins. By using branch based hydraulic information as a proxy for entire plants, much research has potentially overestimated embolism resistance, and possibly drought tolerance, for many species. This study highlights the necessity to reconsider past conclusions made about plant resistance to drought based on branch xylem only. This study also highlights the necessity for more research of whole plant hydraulic physiology to better understand strategies of plant drought tolerance and the critical control points within the hydraulic pathway.
Perceptual Video Coding Based on Visual Saliency Modulated Just Noticeable Distortion
Venue: 2019 Data Compression Conference (DCC)
Year: 2019

To reduce the perceptual redundancy in the video coding process, human visual system (HVS) based visual attention and visual sensitivity can be utilized due to their intrinsic natures. Just Noticeable Distortion (JND) is one of widely used models to simulate human visual sensitivity, while visual saliency map has been popular for years in image processing to describe the visual attention feature, which has been proved by the ability to enhance the visual sensitivity effect. In this paper, we proposed a perceptual video coding (PVC) scheme with visual saliency modulated JND model to suppress the DCT coefficient without resulting in noteworthy subjective quality degradation. The experimental results show that the PVC scheme with the proposed VS JND model can save bit rates up to 35.58% in high bit rates case with the similar subjective quality compared with that of HEVC software reference code HM 16.12.
PENGARUH RETURN ON EQUITY (ROE) TOTAL ASSET TURNOVER (TATO) DAN PRICE EARNING RATIO (PER) TERHADAP RETURN SAHAM SYARIAH (Studi Empiris Pada Perusahaan yang Termasuk Dalam Jakarta Islamic Index/JII Tahun 2014 2016)
Year: 2019

This study was to determine the effect of return on equity, total asset turnover and price earning ratio toward sharia stock return. The sample in this study was 17 corporate which include in the Jakarta Islamic Index (JII) for three years start form 2014 until 2016 so the total sample becomes 51 corporate. Methods of data analysis are using multiple linear regression analysis. The result showed that return on equity has significant positive effect on sharia stock return while total asset turnover and price earning ratio have no effect on sharia stock return. Keywords sharia stock return, return on equity, total asset turnover, price earning ratio. Penelitian ini bertujuan untuk mengetahui pengaruh return on equity, total asset turnover dan price earning ratio terhadap return saham syariah. Sampel dalam penelitian ini adalah 17 perusahaan yang termasuk dalam kelompok Jakarta Islamic Index (JII) selama 3 tahun yaitu 2014 2016 sehingga total keseluruhan sampel menjadi 51 perusahaan. Penelitian ini menggunakan metode analisis regresi linier berganda. Hasil penelitian ini menunjukan bahwa return on equity berpengaruh positif signifikan terhadap return saham syariah sedangkan total asset turnover dan price earning ratio tidak berpengaruh signifikan terhadap return saham syariah. Kata kunci return on equity, total asset turnover, price earning ratio, return saham syariah.
Biogeography of plant root associated fungal communities in the North Atlantic region mirrors climatic variability
Venue: Journal of Biogeography
Year: 2019

Polar and alpine ecosystems appear to be particularly sensitive to increasing temperatures and the altered precipitation patterns linked to climate change. However, little is currently known about how these environmental drivers may affect edaphic organisms within these ecosystems. In this study, we examined communities of plant root associated fungi (RAF) over large biogeographical scales and along climatic gradients in the North Atlantic region in order to gain insights into the potential effects of climate variability on these communities. We also investigated whether selected fungal traits were associated with particular climates.  Jan Mayen and Svalbard.  Root fungi associated with the ectomycorrhizal and herbaceous plant Bistorta vivipara.  DNA metabarcoding of the ITS1 region was used to characterize the RAF of 302 whole plant root systems, which were analysed by means of ordination methods and linear modelling. Fungal spore length, width, volume and shape, as well as mycelial exploration type (ET) of ectomycorrhizal (ECM) basidiomycetes were summarized at a community level.  The RAF communities exhibited strong biogeographical structuring, and both compositional variation as well as fungal species richness correlated with annual temperature and precipitation. In accordance with general island biogeography theory, the least species rich RAF communities were found on Jan Mayen, a remote and small island in the North Atlantic Ocean. Fungal spores tended to be more elongated with increasing latitude. We also observed a climate effect on which mycelial ET was dominating among the ectomycorrhizal fungi.  Both geographical and environmental variables were important for shaping root associated fungal communities at a North Atlantic scale, including the High Arctic. Fungal OTU richness followed general biogeographical patterns and decreased with decreasing size and/or increasing isolation of the host plant population. The probability of possessing more elongated spores increases with latitude, which may be explained by a selection for greater dispersal capacity among more isolated host plant populations in the Arctic.
Toolbox for 3D modelling and image reconstruction in electrical capacitance tomography
Year: 2017

Electrical Capacitance Tomography is used to visualize a spatial distribution of electric permittivity in a tomographic sensor. ECT is able to create even thousands of frames per second which is suitable for application in the industry, e.g. monitoring of multiphase flows or material mixing. A tool for sensor modelling and image reconstruction is needed in order to develop improved solutions and to better understand phenomena in ECT. A software for 2D and 2D modelling is developed in the Division of Nuclear and Medical Electronics. In this paper a Matlab toolbox called ECTsim for 3D modelling is presented.
Stable Antiphase Oscillations in a Network of Electrically Coupled Model Neurons
Venue: SIAM J. Appl. Dyn. Syst.
Year: 2013

We consider a model of two identical neurons with electrical coupling and give a rigorous analysis for when the network exhibits stable antiphase behavior. Each neuron is modeled as a relaxation oscillator, and the main technique used in the analysis is geometric singular perturbation theory. Using fast/slow analysis, we derive a one dimensional map; the antiphase solution corresponds to a fixed point of this map. Detailed analysis of the map, along with its derivative, leads to precise conditions on the duty cycle of an individual cell for when there exists an antiphase solution and when it is stable, for sufficiently weak electrical coupling. The results presented here extend our previous analysis [D. Terman et al.  (2011) ] to more biophysical neuronal models.
Testing a Reported Correlation between Arrival Directions of Ultra high energy Cosmic Rays and a Flux Pattern from nearby Starburst Galaxies using Telescope Array Data
Year: 2018

The Pierre Auger Collaboration (Auger) recently reported a correlation between the arrival directions of cosmic rays with energies above 39 EeV and the flux pattern of 23 nearby starburst galaxies (SBGs) In this Letter, we tested the same hypothesis using cosmic rays detected by the Telescope Array experiment (TA) in the 9 year period from May 2008 to May 2017. Unlike the Auger analysis, we did not optimize the parameter values but kept them fixed to the best fit values found by Auger, namely 9.7% for the anisotropic fraction of cosmic rays assumed to originate from the SBGs in the list and 12.9deg for the angular scale of the correlations. The energy threshold we adopted is 43 EeV, corresponding to 39 EeV in Auger when taking into account the energy scale difference between two experiments. We find that the TA data is compatible with isotropy to within 1.1{\sigma} and with the Auger result to within 1.4{\sigma} meaning that it is not capable to discriminate between these two hypotheses.
Sequence Level Consistency Training for Semi Supervised End to End Automatic Speech Recognition
Venue: ICASSP 2020 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2020

This paper presents a novel semi supervised end to end automatic speech recognition (ASR) method that employs consistency training with the use of unlabeled data. In consistency training, unlabeled data can be utilized for constraining a model such that it becomes invariant to small deformation. In fact, considering consistency can make the model robust to a variety of input examples. While previous studies have applied consistency training to primitive classification problems, no studies have employed consistency training to tackle sequence to sequence generation problems including end to end ASR. One problem is that existing consistency training schemes cannot take sequence level generation consistency into consideration. In this paper, we propose a sequence level consistency training scheme specialized to handle sequence to sequence generation problems. Our key idea is to consider the consistency of the generation function by utilizing beam search decoding results. For semi supervised learning, we adopt Transformer as the end to end ASR model, and SpecAugment as the deformation function in consistency training. Our experiments show that our semi supervised learning proposal with sequence level consistency training can efficiently improve ASR performance using unlabeled speech data.
Students' representations of a university professor's leadership qualities in conditions of e learning
Year: 2020

The article deals with the problem of university professor leadership qualities in conditions of e learning. The purpose of this study was to determine the leadership qualities of a university professor that are in demand in e learning, according to the students. The main method of research was a survey of students aimed at identifying their attitude to e learning, and their opinions on the necessary leadership qualities of the university professor in the condition of e learning. The respondents were 146 undergraduate students enrolled in teacher training programs at the Smolensk State University. The study shows the relationship between the presence of leadership qualities in the university professor and the positive attitude of students to e learning. The most important qualities of a university professor in conditions of e learning, according to students, are highlighted. The authors emphasize the importance of developing professors' leadership qualities as an important factor in quality education at the university.