Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular Markers, Genetic Maps, and QTLs for Molecular Breeding in Peanut

Integration of plant breeding, genetics and genomics promises-to foster genetic enhancement leading to increased productivity, oil quality and resistance/tolerance to biotic and abiotic stresses. Recent advances in peanut have resulted in the development of genomic resources such as SSR markers, and genetic maps for diploids and tetraploids. Even though the tetraploid species have both the genomes, the genetic diversity observed in cultivated peanut maps has been low. Therefore, only partial (<100 loci) to low-moderate (<300 loci) genetic maps could be constructed. Consensus genetic maps were, therefore, constructed with thousands of marker loci using mapping information of multiple mapping populations in order to integrate as many markers as possible on a single genetic map. Development of SNP markers should lead to even more dense genetic maps and use of these markers in routine breeding and genetic applications. Efforts with the available limited genomic resources led to the identification of linked markers for drought tolerance, oil quality and disease resistance in peanut through trait mapping. These developments also led to deployment of linked markers to improve disease resistance and oil quality. Ongoing efforts should lead to the availability of the whole-genome sequence in the near future, providing huge genomic resources, which will hasten the much needed linking of phenotype with markers/genome sequences. However, this can only be achieved with precise and high-throughput phenotyping for complex traits. Recent advances in peanut genomics and molecular breeding efforts provide hope for efficient genetic enhancement of peanut for production as well as quality constraints.
Effects of valence and arousal on working memory performance in virtual reality gaming

The role of affective states in cognitive performance has long been an area of interest in cognitive science. Recent research in game-based cognitive training suggest that cognitive games should incorporate real-time adaptive mechanisms. These adaptive mechanisms would change the game's difficulty according to the player's performance in order to provide appropriate challenges and thus, achieve a real cognitive improvement. However, these mechanisms currently ignore the effects of valence and arousal on the player's cognitive skills. In this paper we investigate how working memory (WM) performance is affected when playing a VR game, and the effects of valence and arousal in this context. To this aim, a custom video game was created for Desktop and VR. Three difficulty levels were designed to evoke different levels of arousal while maintaining the same memory load for each difficulty level. We found an improvement in WM performance when playing in VR compared to Desktop. This effect was particularly pronounced in those with a low WM capacity. Significantly higher levels of valence and arousal were self-reported when playing in VR. We explore the impact that reported affective states could have in the player's WM performance. We suggest that high levels of arousal and positive valence can lead players to a flow state [1] that may have a positive impact on the player's WM performance.
SCNet: Learning Semantic Correspondence

This paper addresses the problem of establishing semantic correspondences between images depicting different instances of the same object or scene category. Previous approaches focus on either combining a spatial regularizer with hand-crafted features, or learning a correspondence model for appearance only. We propose instead a convolutional neural network architecture, called SCNet, for learning a geometrically plausible model for semantic correspondence. SCNet uses region proposals as matching primitives, and explicitly incorporates geometric consistency in its loss function. It is trained on image pairs obtained from the PASCAL VOC 2007 keypoint dataset, and a comparative evaluation on several standard benchmarks demonstrates that the proposed approach substantially outperforms both recent deep learning architectures and previous methods based on hand-crafted features.
A comparison between bootstrap and dropout for uncertainty estimates of time series forecast using a convolutional neural network

Forecasting the future is important in many applications, including forecasting future sales volumes. However, point forecasts without any estimates of the uncertainty are not as useful as if the uncertainty in the forecast is included. Uncertainty estimates allow a range of possible outcomes to be considered, not only the most likely as in the case of point forecasts. In this study the aim is to produce uncertainty estimations for forecasts made 120 days into the future for three different products. The three products are usually used for several years by the consumers and all three products belong to the same category. For the purpose of estimating the uncertainty in forecasts made with a neural network, two different methods were used, bootstrapping residuals and dropout, respectively. The methods were applied to a convolutional neural network, CNN, with dilated causal convolutions. The two methods were compared based one their coverage probability when constructing an interval which aims for 95 % coverage probability. In brief, bootstrapping residual performed better on all three products with a mean coverage probability of 76 % compared to 43 % for dropout. The study shows that it is possible to estimate the uncertainty in forecasts made with a CNN using bootstrap and dropout. Bootstrap seems more suitable than dropout, however the study was only made on three products in the same product category which makes it hard to draw any general conclusions.
Convolutional neural networks on surfaces via seamless toric covers

The recent success of convolutional neural networks (CNNs) for image processing tasks is inspiring research efforts attempting to achieve similar success for geometric tasks. One of the main challenges in applying CNNs to surfaces is defining a natural convolution operator on surfaces. In this paper we present a method for applying deep learning to sphere-type shapes using a global seamless parameterization to a planar flat-torus, for which the convolution operator is well defined. As a result, the standard deep learning framework can be readily applied for learning semantic, high-level properties of the shape. An indication of our success in bridging the gap between images and surfaces is the fact that our algorithm succeeds in learning semantic information from an input of raw low-dimensional feature vectors. We demonstrate the usefulness of our approach by presenting two applications: human body segmentation, and automatic landmark detection on anatomical surfaces. We show that our algorithm compares favorably with competing geometric deep-learning algorithms for segmentation tasks, and is able to produce meaningful correspondences on anatomical surfaces where hand-crafted features are bound to fail.
Workload prediction based on supply current tracking : a fuzzy logic approach

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication
Motion Recovery from Radon Transformed Image Using Neural Networks

In this paper, we address motion correction in image reconstruction. Patient motion, including breathing, is a persistent problem in medical imaging. Cardiac motion is particularly enigmatic and often ignored, except in high-speed imaging modalities like MRI. Motions may create artifacts to the extent that the image may have to be discarded. Since the beginning of medical imaging, motion correction remained an important subcategory of research. Motion corrections may be applied during or after tomographic image reconstruction. In this work, we considered motion as a Gaussian blur at the image level. Discrete radon transform is applied to the blurred images to create corresponding noisy sinograms that mimic real imaging scenarios. Our deep learning-based tool recovered accurately (1) the blurring functions with an artificial convolutional neural network (CNN) directly from the sinograms, and (2) successfully reconstructed (inverse radon transformed) the noise-free images utilizing an adaptation of the convolutional encoder-decoder network (CED) from the literature. Our work shows that neural networks are not only capable of eliminating systematic noise in reconstruction but can also recover the noise model.
On the Limits of Aligning OpenIE Extractions with Knowledge Bases

Open information extraction is the task of extracting relations and their corresponding arguments from a natural language sentence in an unsupervised manner. Outputs of such systems are used for downstream tasks, such as open question answering, slot ﬁlling and automatic knowledge base construction. Many of these downstream tasks (as well as the design of some OpenIE systems themselves) rely on aligning the OpenIE triples with reference knowledge bases (KBs). In this paper, we explore how OpenIE extractions are related to KBs w.r. t. information content. First, we investigate OpenIE triples and KB facts having the same arguments by comparing the information on the OpenIE surface relation with the KB relation. Second, we study the expressibility of any OpenIE triple w.r. t. a KB. We investigate whether (and how ) a given OpenIE triple can be mapped to a single KB fact. We found that such mappings are possible, though the information in the OpenIE triples tends to be more speciﬁc. Our study suggests that signiﬁcant part of such speciﬁc OpenIE triples can be expressed by using KB formulas. Finally, we publicly release all golden datasets, code and additional resources from our study.
Strategies for Designing Ideal Pt/Ionomer Interfaces in Polymer Electrolyte Fuel Cells

© Toyota Central R&D Labs. , Inc. 2018 Report received on Sep. 21, 2018 Kensaku Kodama, Ryosuke Jinnouchi, Akihiro Shinohara and Yu Morimoto Strategies for Designing Ideal Pt/Ionomer Interfaces in Polymer Electrolyte Fuel Cells Research Report Special Feature: Popularizing Fuel Cell Vehicles: Designing and Controlling Electrochemical Reactions in the MEA 1 R&D Review of Toyota CRDL, Vol.49 No.4 (2018) 1-11
Lattice Boltzmann Method and Vortex Methods for Fluid Animation

Most fluid solvers for visual effects are based on splitting Navier-Stokes (N-S) PDEs. In the N-S solver, a projection step is often applied to correct the minor errors from advection and produces divergence free constraint for incompressible fluid flow in order to output flows with swirls. It is one of the key steps in classic N-S solver, giving us turbulence visual effects. We follow the conventional N-S based solver, but mainly focus on a growing popular method named as LBM (Lattice Boltzmann Method). Many visual effect researches have proposed to utilize LBM’s straightforward parallelizable feature but neglected its capabilities for generating appealing visual effects. By investigating popular vortex methods of classic N-S solvers, we presented hybrid methods for enriching visual complexity for LBM. They require additional steps for vortex calculation and boundary conditions processing on macroscopic and microscopic levels, but are able to provide rich details for the simualtion with acceptable computation cost. Compared to introducting a complex turbulence model for LBM, our approach is easy-to-implement and straightforward.
Regret Bounds for Gaussian Process Bandit Problems

Bandit algorithms are concerned with trading exploration with exploitation where a number of options are available but we can only learn their quality by experimenting with them. We consider the scenario in which the reward distribution for arms is modelled by a Gaussian process and there is no noise in the observed reward. Our main result is to bound the regret experienced by algorithms relative to the a posteriori optimal strategy of playing the best arm throughout based on benign assumptions about the covariance function dening the Gaussian process. We further complement these upper bounds with corresponding lower bounds for particular covariance functions demonstrating that in general there is at most a logarithmic looseness in our upper bounds.
Integration of a Multilingual Preordering Component into a Commercial SMT Platform

Abstract We present a multilingual preordering component tailored for a commercial Statistical Machine translation platform. In commercial settings, issues such as processing speed as well as the ability to adapt models to the customers’ needs play a significant role and have a big impact on the choice of approaches that are added to the custom pipeline to deal with specific problems such as long-range reorderings. We developed a fast and customisable preordering component, also available as an open-source tool, which comes along with a generic implementation that is restricted neither to the translation platform nor to the Machine Translation paradigm. We test preordering on three language pairs: English →Japanese/German/Chinese for both Statistical Machine Translation (SMT) and Neural Machine Translation (NMT). Our experiments confirm previously reported improvements in the SMT output when the models are trained on preordered data, but they also show that preordering does not improve NMT.
Lygus lineolaris (Palisot), Tarnished Plant Bug (Hemiptera: Miridae)

spp. overwinter as adults in refuges such as shelterbelts, which provide maximum winter protection (Cleveland, 1982; Craig and Loan, 1987; Gerber and Wise, 1995; Schwartz and Foottit, 1998). When they become active in spring they move to the ﬁ rst plants in ﬂ ower, generally weeds and volunteer crop plants, e.g. canola, where the females lay eggs. The ﬁ rst generation develops on these plant hosts and new adults disperse to the next group of ﬂ owering plants, which includes many agricultural crops. Depend-ing on climatic zone, from one to ﬁ ve generations of
Quantitative analysis of computer interaction movements

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication
A Real-time Mobile Notification System for Inventory Stock out Detection using SIFT and RANSAC

Object detection and tracking is one of the most relevant computer technologies related to computer vision and image processing. It may mean the detection of an object within a frame and classify it (human, animal, vehicle, building, etc) by the use of some algorithms. It may also be the detection of a reference object within different frames (under different angles, different scales, etc.). The applications of the object detection and tracking are numerous; most of them are in the security field. It is also used in our daily life applications, especially in developing and enhancing business management. Inventory or stock management is one of these applications. It is considered to be an important process in warehousing and storage business because it allows for stock in and stock out products control. The stock-out situation, however, is a very serious issue that can be detrimental to the bottom line of any business. It causes an increased risk of lost sales as well as it leads to reduced customer satisfaction and lowered loyalty levels. On this note, a smart solution for stock-out detection in warehouses is proposed in this paper, to automate the process using inventory management software. The proposed method is a machine learning based real-time notification system using the exciting Scale Invariant Feature Transform feature detector (SIFT) and Random Sample Consensus (RANSAC) algorithms. Consequently, the comparative study shows the overall good performance of the system achieving 100% detection accuracy with features’ rich model and 90% detection accuracy with features’ poor model, indicating the viability of the proposed solution.
Resistance of Arabica coffee cultivars to leaf wounds and Pseudomonas syringae under field conditions

The aims of this study were: (a) to evaluate the resistance of coffee cultivars to Pseudomonas syringae (PS); (b) verify if there are coffee genotypes that present less wounds on the leaves; (c) to study the correlation between amount of wounds and PS severity. The field trial was installed in April 2014 at the IAPAR’s experimental station (Londrina, Paraná, Brazil). 18 Arabica coffee cultivars were evaluated. The cultivars Mundo Novo IAC 376-4 and Catuaí Vermelho IAC 81 were the susceptible controls and IPR 102 was the resistant control. After 32 months of planting the field trial, resistance to PS was evaluated in December 2016. The evaluation of the PS severity was carried out under conditions of natural infections of P. syringae pv. garcae and P. syringae pv. tabaci based on the symptoms, using a grading scale from 1 to 5. The number of wounds on the leaves of the eight cultivars were counted. The results showed that IPR 102 was resistant to Pseudomonas syringae, whereas IPR 99, Arara, IPR 107, Acauã, Sabiá, Catucaiam 24137, Japy and Catuaí Vermelho IAC 81 were susceptible. Mundo Novo and Catucaí Amarelo 2SL were more susceptible than Catuaí Vermelho. IPR 106, Japiam, Catiguá MG 2, Catiguá MG 1 and IBC Palma 2 showed moderate resistance, while IPR 103 and Catucaiam 2015479 were moderately susceptible. The cultivars IPR 102 and IPR 106 presented resistance to wounding because they had lower wounds than other cultivars. Increased leaf wounds was associated with increased P. syringae severity.
Visual memories are stored along a compressed timeline

In continuous recognition the recency effect manifests as a decrease in accuracy and a sublinear increase in response time (RT) with the lag of a repeated stimulus. The recency effect could result from the gradual weakening of mnemonic traces. Alternatively, the recency effect could result from a search through a compressed timeline of recent experience. These two hypotheses make very different predictions about the shape of response time distributions. Using highly-memorable pictures to mitigate changes in accuracy enabled a detailed examination of the effect of recency on retrieval dynamics. The recency at which pictures were repeated ranged over two orders of magnitude across three experiments. Analysis of the RT distributions showed that the time at which memories became accessible changed with the recency of the probe, as predicted by a serial search model suggesting that visual memories can be accessed by sequentially scanning along a compressed representation of the past.
Whole-body Vibration Training as a Supportive Therapy During Allogeneic Haematopoietic Stem Cell Transplantation – A Randomised Controlled Trial

1. Institute of Sport Science & University Medical Center Göttingen, Faculty of Rehabilitation and Sports Medicine, Göttingen, Germany; 2. Institute of Sports Medicine, Hannover Medical School (MHH), Hannover, Germany; 3. Clinic for Hematology and Medical Oncology with Department of Stem Cell Transplantation, Göttingen Comprehensive Cancer Center, University Medical Center Göttingen, Göttingen, Germany; 4. Medical Biometry and Statistical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany
The humanistic burden of advanced non-small cell lung cancer: What are the key drivers of caregiver burden?

149Background: Advanced Non-Small Cell Lung Cancer (aNSCLC) impacts the lives of patients and their caregivers. This analysis aimed to provide insight into the clinical, patient and caregiver factors associated with increased caregiver burden. Methods: Data from a cross-sectional study of aNSCLC patients and informal (unpaid) caregivers in France, Germany and Italy, were collected from May 2015 to June 2016, consisting of a chart review and patient and caregiver surveys. Caregiver burden was assessed using the Zarit Burden Interview (ZBI), measured on a 0-88 scale. An ordinary least square regression model was constructed to assess clinical, patient and caregiver factors associated with ZBI. Measured at time of survey completion, covariates included: country, patient age, sex, body mass index, stage of NSCLC, Eastern Cooperative Oncology Group Performance Status (ECOG-PS), disease duration, smoking status, presence of brain metastases, histology, comorbidities, chemotherapy treatment, caregiver age and se.
Range-Clustering Queries

In a geometric k-clustering problem the goal is to partition a set of points in R^d into k subsets such that a certain cost function of the clustering is minimized. We present data structures for orthogonal range-clustering queries on a point set S: given a query box Q and an integer k > 2, compute an optimal k-clustering for the subset of S inside Q. We obtain the following results. * We present a general method to compute a (1+epsilon)-approximation to a range-clustering query, where epsilon>0 is a parameter that can be specified as part of the query. Our method applies to a large class of clustering problems, including k-center clustering in any Lp-metric and a variant of k-center clustering where the goal is to minimize the sum (instead of maximum) of the cluster sizes. * We extend our method to deal with capacitated k-clustering problems, where each of the clusters should not contain more than a given number of points. * For the special cases of rectilinear k-center clustering in R^1, and in R^2 for k = 2 or 3, we present data structures that answer range-clustering queries exactly.
Improving Black-box Speech Recognition using Semantic Parsing

Speech is a natural channel for human-computer interaction in robotics and consumer applications. Natural language understanding pipelines that start with speech can have trouble recovering from speech recognition errors. Black-box automatic speech recognition (ASR) systems, built for general purpose use, are unable to take advantage of in-domain language models that could otherwise ameliorate these errors. In this work, we present a method for re-ranking black-box ASR hypotheses using an in-domain language model and semantic parser trained for a particular task. Our re-ranking method significantly improves both transcription accuracy and semantic understanding over a state-of-the-art ASR’s vanilla output.
AES International Conference Semantic Audio 2017, Erlangen, Germany, June 22-24, 2017

We present results of speech rhythm analysis for automatic speaker identification. We expand previous experiments using similar methods for language identification. Features describing the rhythmic properties of salient changes in signal components are extracted and used in an speaker identification task to determine to which extent they are descriptive of speaker variability. We also test the performance of state-of-the-art but simple-to-extract frame-based features. The paper focus is the evaluation on one corpus (swiss german, TEVOID) using support vector machines. Results suggest that the general spectral features can provide very good performance on this dataset, whereas the rhythm features are not as successful in the task, indicating either the lack of suitability for this task or the dataset specificity.