Miracle-dz/UniFAR-FTUs huggingface dataset:
Superpixel Partitioning of Very High Resolution Satellite Images for Large-Scale Classification Perspectives with Deep Convolutional Neural Networks

Supervised classification is the fundamental task for land-cover map generation. Deep neural networks recently outperformed other state-of-the-art classifiers in many machine learning challenges, from semantic segmentation to speech recognition. Such strategies are now commonly employed in the literature for the purpose of land-cover mapping. This paper develops the strategy for the use of deep networks to label very high resolution satellite images, with the perspective of mapping regions at country scale. Therefore, a superpixel based method is introduced in order to (i) ensure correct delineation of objects and (ii) perform the classification in a dense way but with decent computing times.
Multi-class protein fold recognition using large margin logic based divide and conquer learning

Inductive Logic Programming (ILP) systems have been successfully applied to solve complex biological problem by viewing them as binary classification tasks. It remains an open question how an accurate solution to a multi-class problem can be obtained by using a logic based learning method. In this paper we present a novel logic based approach to solve complex and challenging multi-class classification problems in bioinformatics by focusing on a particular task, namely protein fold recognition. Our technique is based on the use of large margin kernel-based methods in conjunction with first order rules induced by an ILP system. The proposed approach learns a multi-class classifier by using a divide and conquer reduction strategy that splits multi-classes into binary groups and solves each individual problem recursively hence generating an underlying decision list structure. The method is applied to assigning protein domains to folds. Experimental evaluation of the method demonstrates the efficacy of the proposed approach to solving complex multi-class classification problems in bioinformatics.
Modulation of aquaporin gene expression by n-3 long-chain PUFA lipid structures in white and brown adipose tissue from hamsters

Abstract EPA (20 : 5n-3) and DHA (22 : 6n-3) fatty acids have weight-reducing properties with physiological activity depending on their molecular structure – that is, as TAG or ethyl esters (EE). Aquaporins (AQP) are membrane protein channels recognised as important players in fat metabolism, but their differential expression in white adipose tissue (WAT) and brown adipose tissue (BAT), as well as their modulation by dietary n-3 long-chain PUFA (LCPUFA) such as EPA and DHA, has never been investigated. In this study, the transcriptional profiles of AQP3, AQP5, AQP7 and selected lipid markers of WAT (subcutaneous and visceral) and BAT (interscapular) from hamsters fed diets containing n-3 LCPUFA in different lipid structures such as fish oil (FO, rich in EPA and DHA in the TAG form) and FO-EE (rich in EPA and DHA in the EE form) were used and compared with linseed oil (LSO) as the reference group. A clear effect of fat depot was observed for AQP3 and leptin (LEP), with the lowest values of mRNA found in BAT relative to WAT. The opposite occurred for PPARα. AQP7 was affected by diet, with FO-fed hamsters having higher mRNA levels compared with LSO-fed hamsters. The relative gene expression of AQP5, adiponectin (ADIPO), GLUT4 and PPARγ was influenced by both fat tissue and diet. Taken together, our results revealed a differential expression profile of AQP and some markers of lipid metabolism in both WAT and BAT in response to feeding n-3 LCPUFA in two different structural formats: TAG v. EE.
A Novel Graph Based Label Propagation Method for Hyperspectral Remote Sensing Data Classification

For hyperspectral image classification, we present a novel graph based semi-supervised classification method that learns from similarity and dissimilarity on labeled and unlabeled data, which contain both the adjacency graph and the dissimilar graph. Since manifold learning approach is capable of exploring the manifold geometry of data, it is suitable for calculating the adjacency graph with label similarity. A manifold learning method was utilized to calculate the adjacency graph. Dissimilarity among examples probably be used to construct the dissimilar graph, which is hard to grasp. The dissimilar probability was proposed to construct the dissimilar graph, which has effectively improved the classification accuracy of hyperspectral data in experiment.
Narrow Road Extraction from Remote Sensing Images Based on Super-Resolution Convolutional Neural Network

In remote sensing images, it is usually hard to extract narrow roads with only several pixels width. To address this problem, the original remote sensing images are processed with super-resolution to enlarge the details of the narrow roads by a convolutional neural network method. Then the One-Class Support Vector Machine (OCSVM) classifier is applied after super-resolution for exact extraction of narrow roads. Experiments are conducted on an open dataset of remote sensing images to verify the performance of the new method and the results are compared with the method without image super-resolution. The experimental results demonstrate the validity and superiority of the new method.
Automatic Insar Phase Modeling and Quality Assessment Using Machine Learning and Hypothesis Testing

PS-InSAR time series yield large volumes of data points, observed during many epochs. While traditional processing algorithms use a single parameterization for the behavior of all points, in reality this behavior will differ significantly between points and over time. It is a challenge to find the optimal parameterization for this behavior, and to assess the quality of the measurements per point and per epoch. Here we propose a post-processing method to improve the model estimation of PS-InSAR phase time series. The method combines machine learning (ML) algorithms and hypothesis testing (HT) into the ML/HT method efficiently leading to significant improvements in data interpretation, parameterization, as well as the quality of the estimated parameters. Moreover we show that we can find structure in the data regardless of spatial location and temporal complexity. In contrast to conventional assumptions that nearby points behave in the same way, with unchanged characteristics over time, a method is developed that takes individual behavior into account. Demonstrating that we can move from spatial and temporal analysis tools to semantic-based analysis.
Sleep Deprived and Lonely: The Moderating Effect of Social Exclusion on Insomnia and Cyberloafing

Drawing from the Conservation of Resources (COR) theory, this study examines the moderating effect of social exclusion on the relationship between insomnia and cyberloafing by conducting two studies. Study 1 was based on a lab experimental design that measured cyberloafing objectively. Results of Study 1 showed that sleep deprived participants who were lonely engaged in higher levels of cyberloafing compared to those who were not lonely. As experiments tend to lack external validity, we will conduct a second study to enhance the generalizability of our findings. Study 2 will be conducted using a survey of working adult sample. Implications of our findings are discussed.
PARS: Using Augmented 360-Degree Panoramas of Reality for Construction Safety Training

Improving the hazard-identification skills of construction workers is a vital step towards preventing accidents in the increasingly complex working conditions of construction jobsites. Training the construction workforce to recognize hazards therefore plays a central role in preparing workers to actively understand safety-related risks and make assertive safety decisions. Considering the inadequacies of traditional safety-training methods (e.g., passive lectures, videos, demonstrations), researchers have employed advanced visualization techniques such as virtual reality technologies to enable users to actively improve their hazard-identification skills in a safe and controlled environment. However, current virtual reality techniques sacrifice realism and demand high computational costs to reproduce real environments. Augmented 360-degree panoramas of reality offers an innovative alternative that creates low-cost, simple-to-capture, true-to-reality representations of the actual construction jobsite within which trainees may practice identifying hazards. This proof-of-concept study developed and evaluated a platform using augmented 360-degree panoramas of reality (PARS) for safety-training applications to enhance trainees’ hazard-identification skills for four types of sample hazards. Thirty subjects participated in a usability test that evaluated the PARS training platform and its augmented 360-degree images captured from real construction jobsites. The usability reviews demonstrate that the trainees found the platform and augmentations advantageously to learning hazard identification. The results of this study will foreseeably help researchers in developing engaging training platforms to improve the hazard-identification skills of workers.
Estimating Three-Dimensional Body Orientation Based on an Improved Complementary Filter for Human Motion Tracking

Rigid body orientation determined by IMU (Inertial Measurement Unit) is widely applied in robotics, navigation, rehabilitation, and human-computer interaction. In this paper, aiming at dynamically fusing quaternions computed from angular rate integration and FQA algorithm, a quaternion-based complementary filter algorithm is proposed to support a computationally efficient, wearable motion-tracking system. Firstly, a gradient descent method is used to determine a function from several sample points. Secondly, this function is used to dynamically estimate the fusion coefficient based on the deviation between measured magnetic field, gravity vectors and their references in Earth-fixed frame. Thirdly, a test machine is designed to evaluate the performance of designed filter. Experimental results validate the filter design and show its potential of real-time human motion tracking.
Testing the heat dissipation limitation hypothesis: basal metabolic rates of endotherms decrease with increasing upper and lower critical temperatures

Metabolic critical temperatures define the range of ambient temperatures where endotherms are able to minimize energy allocation to thermogenesis. Examining the relationship between metabolic critical temperatures and basal metabolic rates (BMR) provides a unique opportunity to gain a better understanding of how animals respond to varying ambient climatic conditions, especially in times of ongoing and projected future climate change. We make use of this opportunity by testing the heat dissipation limit (HDL) theory, which hypothesizes that the maximum amount of heat a species can dissipate constrains its energetics. Specifically, we test the theory’s implicit prediction that BMR should be lower under higher metabolic critical temperatures. We analysed the relationship of BMR with upper and lower critical temperatures for a large dataset of 146 endotherm species using regression analyses, carefully accounting for phylogenetic relationships and body mass. We show that metabolic critical temperatures are negatively related with BMR in both birds and mammals. Our results confirm the predictions of the HDL theory, suggesting that metabolic critical temperatures and basal metabolic rates respond in concert to ambient climatic conditions. This implies that heat dissipation capacities of endotherms may be an important factor to take into account in assessments of species’ vulnerability to climate change.
Outcomes of Anterior Cervical Fusion Using Polyetheretherketone Cage with Demineralized Bone Matrix and Plate for Management of Subaxial Cervical Spine Injuries

Objective We evaluated the usefulness of a polyetheretherketone (PEEK) cage filled with demineralized bone matrix (DBM) and plate fixation in anterior interbody fusions for subaxial cervical spine injuries. Methods A retrospective review of 98 patients (58 women, 40 men; mean age, 49.7 years; range, 17–78 years) who underwent single-level anterior cervical discectomy and fusion (ACDF) using a PEEK cage filled with DBM and plate fixation for subaxial cervical spine injuries from March 2005 to June 2018 was conducted. Bone fusion, interbody height (IBH), segmental lordosis, and adjacent segment degeneration (ASD) development were assessed with plain radiographs and computed tomography. Clinical outcomes were assessed using a visual analog scale (VAS) for pain and the Frankel grade for neurologic function. Results The mean follow-up period was 27.6 months (range, 6–142 months). Twenty-one patients (21.4%) had an improvement of at least one Frankel grade. The mean preoperative and final follow-up neck pain VAS scores were 8.3±0.9 and 2.6±1.5 (p<0.05). All patients showed solid fusion at the final follow-up. The mean preoperative and final Cobb's angles were −3.7±7.9° and 1.9±5.1° (p<0.05). The mean preoperative and final IBHs were 36.9±1.7 mm and 38.2±1.8 mm (p<0.05). Five patients (5%) showed ASD. Conclusion ACDF using a PEEK cage filled with DBM and plate fixation yielded high fusion rates and satisfactory clinical outcomes without donor-site morbidity. This procedure is safe and effective for single-level subaxial cervical spine injuries.
DCLab: A Web-based System for Digital Logic Experiment Teaching

This Research-to-Practice Work in Progress paper presents DCLab, a web-based system for conducting digital logic experiments online, to improve both the effectiveness and the efficiency of digital logic experiment teaching. DCLab covers all experimental contents required by traditional digital logic experiment classes. It allows the students to draw circuit diagrams or to write VHDL code to design their own circuits, and it provides complete simulation functions. DCLab records the progress of each student and makes it convenient for a student to review the history of his practice. Furthermore, for the instructors, they are able to post homework assignments on DCLab, and the system will automatically add homework projects to the students’ home pages. Statistics about how the students perform on the homework will be displayed to the instructors, which may help them develop more effective courses. We have tested DCLab among students in the digital logic course, and the results have confirmed its validity.
Value of Transvaginal Two-Dimensional Contrast-Enhanced Ultrasonography in Diagnosing Atypical Ovarian Corpus Luteum Hematoma

Objective To evaluate the value of transvaginal two-dimensional contrast-enhanced ultrasonography (2D-CEUS) in the diagnosis of atypical ovarian corpus luteum hematoma (AT-OCLH). Methods A prospective study was performed on 53 consecutive patients with suspected AT-OCLH, and the diagnostic results by transvaginal 2D-CEUS were statistically compared with the gold standard. The gold standard results were confirmed by surgical pathology or long-term follow-up. Results The characteristic perfusion patterns of AT-OCLH in 2D-CEUS showed no contrast agent perfusion within the tumor mass, and the capsule wall showed rapid, annular, high enhancement perfusion; perfusion patterns could be classified into type Ia and type IIa. AT-OCLH can be distinguished from ovarian tumors based on perfusion characteristics and perfusion pattern type, which can be diagnosed based on the significantly stronger cystic wall perfusion intensity, earlier arrival time, and thicker cystic wall than nonluteal cystic foci (P<0.05). The sensitivity of 2D-CEUS for diagnosing AT-OCLH was 95.7%, with a specificity of 96.6%. A 2D-CEUS scoring system for AT-OCLH was established. Lesions with scores >17.5 were diagnosed as AT-OCLH. Conclusion 2D-CEUS is an effective method for diagnosing AT-OCLH.
FullSWOF: Full Shallow-Water equations for Overland Flow

Numerical simulations of shallow flows are required in numerous applications and are typically performed by solving shallow-water equations. FullSWOF solves these equations by using up-to-date finite volume methods and well-balanced schemes. Several features make FullSWOF particularly suitable for surface water hydrologists: small water depths and wet-dry transitions are robustly addressed, rainfall and infiltration are incorporated, and grid-based digital topographies can be used directly. The modular structure of FullSWOF is also useful to numerical modelers willing to test new schemes or boundary conditions.
Acoustic phonon lifetimes limit thermal transport in methylammonium lead iodide

Significance Hybrid organic–inorganic perovskites are a promising class of materials for efficient and low-cost solar cells. Unlike conventional inorganic semiconductors such as silicon and gallium arsenide, hybrid perovskites feature significant dynamic disorder in their crystal structure. This dynamic disorder can be broadly classified into lattice vibrations (phonons) and molecular rotations. Phonons interact with charge carriers through electron–phonon coupling, which has substantial impacts on the operation of solar cells. Our study shows that acoustic phonons, the type responsible for transmitting heat in conventional semiconductors, have extraordinarily short lifetimes in the archetypal hybrid perovskite methylammonium lead iodide. These short lifetimes have direct implications on the cooling and transport of electrons and reflect a key difference between hybrid perovskites and conventional photovoltaic semiconductors. Hybrid organic–inorganic perovskites (HOIPs) have become an important class of semiconductors for solar cells and other optoelectronic applications. Electron–phonon coupling plays a critical role in all optoelectronic devices, and although the lattice dynamics and phonon frequencies of HOIPs have been well studied, little attention has been given to phonon lifetimes. We report high-precision momentum-resolved measurements of acoustic phonon lifetimes in the hybrid perovskite methylammonium lead iodide (MAPI), using inelastic neutron spectroscopy to provide high-energy resolution and fully deuterated single crystals to reduce incoherent scattering from hydrogen. Our measurements reveal extremely short lifetimes on the order of picoseconds, corresponding to nanometer mean free paths and demonstrating that acoustic phonons are unable to dissipate heat efficiently. Lattice-dynamics calculations using ab initio third-order perturbation theory indicate that the short lifetimes stem from strong three-phonon interactions and a high density of low-energy optical phonon modes related to the degrees of freedom of the organic cation. Such short lifetimes have significant implications for electron–phonon coupling in MAPI and other HOIPs, with direct impacts on optoelectronic devices both in the cooling of hot carriers and in the transport and recombination of band edge carriers. These findings illustrate a fundamental difference between HOIPs and conventional photovoltaic semiconductors and demonstrate the importance of understanding lattice dynamics in the effort to develop metal halide perovskite optoelectronic devices.
The Cellular NMD Pathway Restricts Zika Virus Infection and Is Targeted by the Viral Capsid Protein

Zika virus (ZIKV) is a significant global health threat, as infection has been linked to serious neurological complications, including microcephaly. Using a human stem cell-derived neural progenitor model system, we find that a critical cellular quality control process called the nonsense-mediated mRNA decay (NMD) pathway is disrupted during ZIKV infection. Importantly, disruption of the NMD pathway is a known cause of microcephaly and other neurological disorders. We further identify an interaction between the capsid protein of ZIKV and up-frameshift protein 1 (UPF1), the master regulator of NMD, and show that ZIKV capsid targets UPF1 for degradation. Together, these results offer a new mechanism for how ZIKV infection can cause neuropathology in the developing brain. ABSTRACT Zika virus (ZIKV) infection of neural progenitor cells (NPCs) in utero is associated with neurological disorders, such as microcephaly, but a detailed molecular understanding of ZIKV-induced pathogenesis is lacking. Here we show that in vitro ZIKV infection of human cells, including NPCs, causes disruption of the nonsense-mediated mRNA decay (NMD) pathway. NMD is a cellular mRNA surveillance mechanism that is required for normal brain size in mice. Using affinity purification-mass spectrometry, we identified multiple cellular NMD factors that bind to the viral capsid protein, including the central NMD regulator up-frameshift protein 1 (UPF1). Endogenous UPF1 interacted with the ZIKV capsid protein in coimmunoprecipitation experiments, and capsid expression posttranscriptionally downregulated UPF1 protein levels, a process that we confirmed occurs during ZIKV infection. Cellular fractionation studies show that the ZIKV capsid protein specifically targets nuclear UPF1 for degradation via the proteasome. A further decrease in UPF1 levels by RNAi significantly enhanced ZIKV infection in NPC cultures, consistent with a model in which NMD restricts ZIKV infection in the fetal brain. We propose that ZIKV, via the capsid protein, has evolved a strategy to lower UPF1 levels and dampen antiviral activities of NMD, which in turn contributes to neuropathology in vivo. IMPORTANCE Zika virus (ZIKV) is a significant global health threat, as infection has been linked to serious neurological complications, including microcephaly. Using a human stem cell-derived neural progenitor model system, we find that a critical cellular quality control process called the nonsense-mediated mRNA decay (NMD) pathway is disrupted during ZIKV infection. Importantly, disruption of the NMD pathway is a known cause of microcephaly and other neurological disorders. We further identify an interaction between the capsid protein of ZIKV and up-frameshift protein 1 (UPF1), the master regulator of NMD, and show that ZIKV capsid targets UPF1 for degradation. Together, these results offer a new mechanism for how ZIKV infection can cause neuropathology in the developing brain.
Self-Supervised Learning for Stereo Reconstruction on Aerial Images

Recent developments established deep learning as an inevitable tool to boost the performance of dense matching and stereo estimation. On the downside, learning these networks requires a substantial amount of training data to be successful. Consequently, the application of these models outside of the laboratory is far from straight forward. In this work we propose a self-supervised training procedure that allows us to adapt our network to the specific (imaging) characteristics of the dataset at hand, without the requirement of external ground truth data. We instead generate interim training data by running our intermediate network on the whole dataset, followed by conservative outlier filtering. Bootstrapped from a pre-trained version of our hybrid CNN-CRF model, we alternate the generation of training data and network training. With this simple concept we are able to lift the completeness and accuracy of the pre-trained version significantly. We also show that our final model compares favorably to other popular stereo estimation algorithms on an aerial dataset.