Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating multilevel regional variation in excess mortality of cancer patients using integrated nested Laplace approximation

Models of excess mortality with random effects were used to estimate regional variation in relative or net survival of cancer patients. Statistical inference for these models based on the Markov chain Monte Carlo (MCMC) methods is computationally intensive and, therefore, not feasible for routine analyses of cancer register data. This study assessed the performance of the integrated nested Laplace approximation (INLA) in monitoring regional variation in cancer survival. Poisson regression model of excess mortality including both spatially correlated and unstructured random effects was fitted to the data of patients diagnosed with ovarian and breast cancer in Finland during 1955‐2014 with follow up from 1960 through 2014 by using the period approach with five‐year calendar time windows. We estimated standard deviations associated with variation (i) between hospital districts and (ii) between municipalities within hospital districts. Posterior estimates based on the INLA approach were compared to those based on the MCMC simulation. The estimates of the variation parameters were similar between the two approaches. Variation within hospital districts dominated in the total variation between municipalities. In 2000‐2014, the proportion of the average variation within hospital districts was 68% (95% posterior interval: 35%‐93%) and 82% (60%‐98%) out of the total variation in ovarian and breast cancer, respectively. In the estimation of regional variation, the INLA approach was accurate, fast, and easy to implement by using the R‐INLA package.
Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT

The accurate identification of malignant lung nodules on chest CT is critical for the early detection of lung cancer, which also offers patients the best chance of cure. Deep learning methods have recently been successfully introduced to computer vision problems, although substantial challenges remain in the detection of malignant nodules due to the lack of large training data sets. In this paper, we propose a multi-view knowledge-based collaborative (MV-KBC) deep model to separate malignant from benign nodules using limited chest CT data. Our model learns 3-D lung nodule characteristics by decomposing a 3-D nodule into nine fixed views. For each view, we construct a knowledge-based collaborative (KBC) submodel, where three types of image patches are designed to fine-tune three pre-trained ResNet-50 networks that characterize the nodules’ overall appearance, voxel, and shape heterogeneity, respectively. We jointly use the nine KBC submodels to classify lung nodules with an adaptive weighting scheme learned during the error back propagation, which enables the MV-KBC model to be trained in an end-to-end manner. The penalty loss function is used for better reduction of the false negative rate with a minimal effect on the overall performance of the MV-KBC model. We tested our method on the benchmark LIDC-IDRI data set and compared it to the five state-of-the-art classification approaches. Our results show that the MV-KBC model achieved an accuracy of 91.60% for lung nodule classification with an AUC of 95.70%. These results are markedly superior to the state-of-the-art approaches.
Spatial Effects of Video Compression on Classification in Convolutional Neural Networks

A collection of computer vision applications reuse pre-learned features to analyse video frame-by-frame. Those features are classically learned by Convolutional Neural Networks (CNN) trained on high quality images. However, available video content is almost always subject to compression which is nearly never considered during the analysis process. In this paper, we present an empirical study to measure how the visual discrepancy of compressed data limit the learning performance of the CNN model. The learning performance is evaluated using a benchmark of synthetic datasets compressed at various levels using H.264/AVC. We measure the image quality quantitatively using classical evaluation metrics such as Peak Signal to Noise Ratio and Structural SIMilarity. A cross-evaluation is performed to measure the robustness of the CNN model in processing for a wide range of quality-varying visual data. Our experimental results have shown that the performance of the CNN depends on the compression rate. The results show that, in general, higher compression results in lower performance. However performance on lower quality test data can be improved by using lower quality data for CNN training. Finally, our work demonstrates that conditioning the CNN with the compression properties could potentially lead to better learning.
Diversified gut microbiota in newborns of mothers with gestational diabetes mellitus

Gestational diabetes mellitus (GDM), a high-risk pregnancy complication of great effect on the perinatal health of women and newborns, may cause changes of gut microbiota in mothers and further affect gut microbiota in newborns. This study aimed to investigate the potential effect of mother GDM on newborns’ gut microbiota. Meconium DNA was extracted from a total of 34 full-term and C-sectioned newborns, in which 20 newborns had mothers diagnosed with GDM, while 14 had unaffected mothers. Sequencing and bioinformatics analysis of 16S rRNA indicated that the gut microbiota of GDM newborns showed differences compared to control newborns. The taxonomy analyses suggested that the overall bacterial content significantly differed by maternal diabetes status, with the microbiome of the GDM group showing lower alpha-diversity than that of control group. The phyla of Proteobacteria and Actinobacteria in GDM newborns increased, while that of Bacteroidetes significantly reduced (P<0.05). Moreover, several unique gut microbiota in phylum of Proteobacteria, Firmicutes, Actinobacteria, Bacteroidetes, Chloroflexi, Acidobacteria, and Planctomycetes found in control newborns were absent in GDM ones. At genus level, the relative abundance of Prevotella and Lactobacillus significantly decreased (P<0.05) in GDM newborns. Correlation analysis indicated that maternal fasting glucose levels were positively correlated with the relative abundance of phylum Actinobacteria and genus Acinetobacter, while negatively correlated with that of phylum Bacteroidetes and genus Prevotella. However, bacteria in GDM grade A2 (GDM_A2) newborns did not show any statistical variation compared to those from control newborns, which might be attributed to the additional intervention by insulin. The results of this study have important implications for understanding the potential effects of GDM on the gut microbiota of newborns and thus possibly their metabolism at later stages in their lives.
Frequency and coincidence of geriatric syndromes according to age groups: single-center experience in Turkey between 2013 and 2017

Background Geriatric syndromes are complex clinical manifestations that are not an isolated disease in older adults and have common risk factors within themselves. The syndromes are significant causes of mortality, morbidity, and increased health care costs. Objective To determine the frequency of geriatric syndromes such as malnutrition, dementia, depression, falls, polypharmacy, urinary incontinence, pressure ulcer, sarcopenia, and frailty in community-dwelling older adults. Methods A total of 2,816 patients, who applied to geriatric outpatient clinic and were evaluated by comprehensive geriatric assessment, were included in this cross-sectional retrospective study. Falls in the last year and urinary incontinence were recorded. Polypharmacy was accepted as concurrent use of more than four drugs. Diagnosis of dementia and depression was defined according to Diagnostic and Statistical Manual of Mental Disorders Fifth Edition diagnostic criteria. In addition, frailty and sarcopenia were evaluated according to Fried’s physical frailty scale and European Working Group on Sarcopenia criteria, respectively. Results The frequency of polypharmacy was 54.5%, urinary incontinence 47.6%, malnutrition 9.6%, depression 35.1%, dementia 21.6%, falls 33.6%, sarcopenia 31.7%, and frailty 28.3%. When all the participants were divided into three groups (60–69, 70–79, ≥80 years), all syndromes were significantly increased with age, except for depression. While 20% of cases in 60–69 years age group did not have any syndromes, 48% of cases in ≥80 years had more than four syndromes simultaneously. Conclusion The frequency and coincidence of geriatric syndromes, except for depression, increases with age. Therefore, clinicians other than geriatricians taking care of older people should be aware of these syndromes as well as their treatment mechanisms.
Crystal structure of the bacterial acetate transporter SatP reveals that it forms a hexameric channel

Acetate is found ubiquitously in the natural environment and can be used as an exogenous carbon source by bacteria, fungi, and mammalian cells. A representative member of the acetate uptake transporter (AceTr) family named SatP (also yaaH) has been preliminarily identified as a succinate–acetate/proton symporter in Escherichia coli. However, the molecular mechanism of acetate uptake by SatP still remains elusive. Here, we report the crystal structure of SatP from E. coli at 2.8 Å resolution, determined with a molecular replacement approach using a previously developed predicted model algorithm, which revealed a hexameric UreI-like channel structure. Structural analysis identified six transmembrane (TM) helices surrounding the central channel pore in each protomer and three conserved hydrophobic residues, FLY, located in the middle of the TM region for pore constriction. According to single-channel conductance recordings, performed with purified SatP reconstituted into lipid bilayer, three conserved polar residues in the TM1 facing to the periplasmic side are closely associated with acetate translocation activity. These analyses provide critical insights into the mechanism of acetate translocation in bacteria and a first glimpse of a structure of an AceTr family transporter.
Residue management alters microbial diversity and activity without affecting their community composition in black soil, Northeast China

Residue management is an important agricultural practice for improving soil fertility. To reveal the impact of residue management on soil microbial community, we conducted a field experiment with three treatments: no straw returning (control, CK), straw returning (SR), and straw returning combined with cow manure (SM). Our results indicated that soil organic matter content was significantly higher in SR treatment than CK in both seedling and jointing stages. In seedling stage, the lowest total nitrogen content was observed in CK treatment, and significantly lower than that in SM and SR treatment. Furthermore, soil available phosphorus content was significantly higher in SM and SR treatment than CK in jointing stage. In the seedling stage, the soil microbial average wellcolor development (AWCD) value, microbial McIntosh index, and Shannon index of CK and SM treatments were significantly higher than those in SR treatment. The AWCD value and McIntosh index in the jointing stage showed similar patterns: SM > CK > SR. Permutational multivariate analysis of variance indicated that soil microbial community was significantly affected by growth stage, but unaffected by residue management. The partial Mantel test revealed that the available potassium and the C/N ratio had independent effects on soil microbial community. Overall, our results indicated that straw returning combined with cow manure had a beneficial effect on soil fertility, microbial activity and diversity.
3DBodyTex: Textured 3D Body Dataset

In this paper, a dataset, named 3DBodyTex, of static 3D body scans with high-quality texture information is presented along with a fully automatic method for body model fitting to a 3D scan. 3D shape modelling is a fundamental area of computer vision that has a wide range of applications in the industry. It is becoming even more important as 3D sensing technologies are entering consumer devices such as smartphones. As the main output of these sensors is the 3D shape, many methods rely on this information alone. The 3D shape information is, however, very high dimensional and leads to models that must handle many degrees of freedom from limited information. Coupling texture and 3D shape alleviates this burden, as the texture of 3D objects is complementary to their shape. Unfortunately, high-quality texture content is lacking from commonly available datasets, and in particular in datasets of 3D body scans. The proposed 3DBodyTex dataset aims to fill this gap with hundreds of high-quality 3D body scans with high-resolution texture. Moreover, a novel fully automatic pipeline to fit a body model to a 3D scan is proposed. It includes a robust 3D landmark estimator that takes advantage of the high-resolution texture of 3DBodyTex. The pipeline is applied to the scans, and the results are reported and discussed, showcasing the diversity of the features in the dataset.
Lateral Representation Learning in Convolutional Neural Networks

We explore a type of transfer learning in Convolutional Neural Networks where a network trained on a primary representation of examples (e.g. photographs) is capable of generalizing to a secondary representation (e.g. sketches) without fully training on the latter. We show that the network is able to improve classification on classes for which no examples in the secondary representation were provided, an evidence that the model is exploiting and generalizing concepts learned from examples in the primary representation. We measure this lateral representation learning on a CNN trained on the ImageNet dataset and use overlapping classes in the TU-Berlin and Caltech- 256 datasets as secondary representations, showing that the effect can’t be fully explained by the network learning newly specialized kernels. This phenomenon can potentially be used to train classes in domain adaptation tasks for which few examples in a target representation are available.
Active Learning for Input Space Exploration in Traffic Simulators

Urban environments are systems of overwhelming complexity and dynamism, involving numerous variables and idiosyncrasies which not usually easy to model from a functional perspective. Simulation modeling is a common and well-accepted approach to study such systems, specially those that prove to be too complex to be analyzed by standard analytic methods. However, such urban simulation models can become computationally very expensive to run. To address this drawback, simulation metamodels can be employed to approximate the underlying simulation function. In this paper, we propose a batch-mode active learning strategy based on Gaussian Processes metamodeling that searches for the most informative data points in batches with respect to their corresponding predictive variances. These points are selected in such a way that they originate from different high variance neighborhoods. Eventually, this allows us to analyze the simulation output behavior with fewer simulation requests. Using an illustrative traffic simulation example, the results show that the proposed restricted batch-mode strategy is able to increase the simulation input space exploration efficiency in comparison with standard batch-mode strategies.
MNRD: A Merged Neural Model For Rumor Detection In Social Media

With the continuous development of social media and the diversity of its contents, rumors can quickly spread widely and cause serious damage, which makes the early detection of rumors a key task in social media analysis. Most existing methods for rumor detection exploit handcrafted features and ignore the different roles original post and retweets play in information production and spreading process. In fact, original post usually contains detailed content information about event itself, while retweets reflect the diffusion of information in online interaction. Recently, several studies manifest the effectiveness of deep neural network in rumor detection. However, these previous research has made no distinction between original post and retweets for rumor detection. In this paper, we propose a Merged Neural Rumor Detection (MNRD) model that consider three aspects of rumor data: content of original post, diffusion of retweet and user information. We propose content attention to aggregate pivotal words in original post and diffusion attention to focus on most informative retweets during the diffusion process. We also incorporate user information by identifying features to capture user’s reliability and social influence. Experimental results on real world dataset show the effectiveness of our model to detect rumors in social media.
Gene pair based prognostic signature for colorectal colon cancer

Background: The identification of high-risk colorectal cancer (CRC) patient is key to individualized treatment after surgery and reliable prognostic biomarkers are needed identifying high-risk CRC patients. Methods: We developed a gene pair based prognostic signature that could can the prognosis risk in patients with CRC. This study retrospectively analyzed 4 public CRC datasets, and 1123 patients with CRC were divided into a training cohort (n = 300) and 3 independent validation cohorts (n = 507, 226, and 90 patients). Results: A signature of 9 prognosis-related gene pairs (PRGPs) consisting of 17 unique genes was constructed. Then, a PRGP index (PRGPI) was constructed and divided patients into high- and low-risk groups according to the signature score. Patients in the high-risk group showed a poorer relapse-free survival than the low-risk group in both the training cohort [hazard ratio (HR) range, 4.6, 95% confidence interval (95% CI), 2.55–8.32; P < . 0001] and meta-validation set (hazard ratio range, 4.09, 95% CI, 1.99–8.39; P < .0001). The PRGPI signature achieved a higher accuracy [mean concordance index (C-index): 0.6∼0.74] than a commercialized molecular signature (mean C-index, 0.48∼0.56) for estimation of relapse-free survival in comparable validation sets. Conclusion: The gene pair based prognostic signature is a promising biomarker for estimating relapse-free survival of CRC.
3D Head Pose Estimation Using Tensor Decomposition and Non-linear Manifold Modeling

Head pose estimation is a challenging computer vision problem with important applications in different scenarios such as human-computer interaction or face recognition. In this paper, we present an algorithm for 3D head pose estimation using only depth information from Kinect sensors. A key feature of the proposed approach is that it allows modeling the underlying 3D manifold that results from the combination of pitch, yaw and roll variations. To do so, we use tensor decomposition to generate separate subspaces for each variation factor and show that each of them has a clear structure that can be modeled with cosine functions from a unique shared parameter per angle. Such representation provides a deep understanding of data behavior and angle estimations can be performed by optimizing combination of these cosine functions. We evaluate our approach on two publicly available databases, and achieve top state-of-the-art performance.
The Newton Scheme for Deep Learning

We introduce a neural network (NN) strictly governed by Newton's Law, with the nature required basis functions derived from the fundamental classic mechanics. Then, by classifying the training model as a quick procedure of 'force pattern' recognition, we developed the Newton physics-based NS scheme. Once the force pattern is confirmed, the neuro network simply does the checking of the 'pattern stability' instead of the continuous fitting by computational resource consuming big data-driven processing. In the given physics's law system, once the field is confirmed, the mathematics bases for the force field description actually are not diverged but denumerable, which can save the function representations from the exhaustible available mathematics bases. In this work, we endorsed Newton's Law into the deep learning technology and proposed Newton Scheme (NS). Under NS, the user first identifies the path pattern, like the constant acceleration movement.The object recognition technology first loads mass information, then, the NS finds the matched physical pattern and describe and predict the trajectory of the movements with nearly zero error. We compare the major contribution of this NS with the TCN, GRU and other physics inspired 'FIND-PDE' methods to demonstrate fundamental and extended applications of how the NS works for the free-falling, pendulum and curve soccer balls.The NS methodology provides more opportunity for the future deep learning advances.
Attitude Estimation of Unmanned Aerial Vehicle Based on LSTM Neural Network

In this paper, a novel attitude estimation for unmanned aerial vehicle (UAV) is proposed based on long and short term memory neural network (LSTM NN). The UAV is a strong coupling and multi-variable nonlinear complex system, in which the attitude estimation is nonlinear and the attitude data of the UAV is a time series sequence. LSTM NN is therefore selected due to its satisfied performance in time-based data prediction. The data samples to train the LSTM NN are collected during the test flight of a quadrotor. To improve the accuracy of the model, different configurations of the LSTM NNs are used for comparison. Experimental results demonstrate that the method for the UAV attitude estimation has higher accuracy and the potential of applying deep learning technique to the online UAV attitude estimation.
A High Accuracy Time-Reversal Based WiFi Indoor Localization Approach with a Single Antenna †

In this paper, we study the influence of multipath magnitude, bandwidth, and communication link number on the performance of the existing time-reversal (TR) based fingerprinting localization approach and find that the localization accuracy deteriorates with a limited bandwidth. To improve the localization performance, by exploiting two unique location-specified signatures extracted from Channel State Information (CSI), we propose a high accuracy TR fingerprint localization approach, HATRFLA. Furthermore, we employ a density-based spatial clustering algorithm to minimize the storage space of the fingerprint database by adaptively selecting the optimal number of fingerprints for each location. Experimental results confirm that the proposed approach can efficiently mitigate accuracy deterioration caused by a limited bandwidth and consequently, achieve higher accuracy compared with the existing TR localization approach.
An Experimental Comparison of Two Navigation Techniques for Requirements Modeling Tools

In Requirements Engineering, many modeling tasks require viewing different parts of a model concurrently. However, traditional zoom+scroll navigation uses a single focus zoom, i.e., at a given point in time, a user can zoom in on a single spot in the model only. Therefore, new focus+context navigation techniques have been proposed that allow multiple foci at the same time. In this paper, we report on an experiment with students where we compare the participants' performance when using a requirements modeling tool with traditional zoom+scroll navigation vs. one with so-called FlexiView navigation which is a focus+context technique with multiple foci. The participants had to perform typical modeling tasks such as searching, editing, and traversing a model. All tasks were performed on medium-sized tablets with a tool for manipulating so-called ImitGraphs. ImitGraphs are enriched node-and-edge diagrams that can mimic various diagram types such as class, activity, or goal decomposition diagrams. We found that navigation with FlexiView outperformed zoom+scroll navigation with respect to task completion time, number of mistakes, cognitive load, and user satisfaction.