Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-scale Generative Adversarial Network for Automatic Sublingual Vein Segmentation

Sublingual vein segmentation is an essential yet challenging task in computer-aided Traditional Chinese Medicine (TCM) tongue diagnosis. The most intricate part of sublingual vein segmentation is the strong diversity of sublingual vein images (e.g., various exposure of vein and complex background) and the natural connectivity of veins. To this end, we propose a novel and effective approach based on generative adversarial networks (GANs). Specifically, the framework comprises two modules: a segmentation generator network with multi-scale outputs and a scale-consistent discriminator. The former segmentation generator learns the overall structure of the segmentation, which reduces the training difficulties of stride 1 output by multi-scale outputs. The latter scale-consistent discriminator regularizes the segmentation maps from different output strides, which keeps the natural connectivity of veins and avoids generated noise. Additionally, we have constructed and publicized a well-annotated sublingual vein dataset (FDU-SV) which we believe will promote the significant development of this area. Extensive experimental results confirm that our method outperforms other representative segmentation models with a remarkable margin, achieving the state-of-the-art performance of 64.53% mIoU score. Code and dataset are available at https://github.com/echobear313/FDUVEIN.
Anomaly detection in systems with periodic outputs

This paper presents a strategy for detecting anomalies—behavior that is different from nominal—in systems with periodic outputs. Different from classical detectors available in the literature which require the computation of a residual, the detection algorithms proposed here are based on the concept of the Poincaré map and on the notion of cyclic group. The performance of the detectors is illustrated by numerical simulations and validated by experiments on an actuated mass-spring-damper oscillator.
Vehicle Detection in Aerial Images Based on 3D Depth Maps and Deep Neural Networks

Object detection in aerial images, particularly of vehicles, is highly important in remote sensing applications including traffic management, urban planning, parking space utilization, surveillance, and search and rescue. In this article, we investigate the ability of three-dimensional (3D) feature maps to improve the performance of deep neural network (DNN) for vehicle detection. First, we propose a DNN based on YOLOv3 with various base networks, including DarkNet-53, SqueezeNet, MobileNet-v2, and DenseNet-201. We assessed the base networks and their performance in combination with YOLOv3 on efficiency, processing time, and the memory that each architecture required. In the second part, 3D depth maps were generated using pairs of aerial images and their parallax displacement. Next, a fully connected neural network (fcNN) was trained on 3D feature maps of trucks, semi-trailers and trailers. A cascade of these networks was then proposed to detect vehicles in aerial images. Upon the DNN detecting a region, coordinates and confidence levels were used to extract the corresponding 3D features. The fcNN used 3D features as the input to improve the DNN performance. The data set used in this work was acquired from numerous flights of an unmanned aerial vehicle (UAV) across two industrial harbors over two years. The experimental results show that 3D features improved the precision of DNNs from 88.23 % to 96.43 % and from 97.10 % to 100 % when using DNN confidence thresholds of 0.01 and 0.05, respectively. Accordingly, the proposed system was able to successfully remove 72.22 % to 100 % of false positives from the DNN outputs. These results indicate the importance of 3D features utilization to improve object detection in aerial images for future research.
Defect detection on the surface of the technical ceramics using image processing and deep learning algorithms

A digital microscope is considered as an instrument for ceramic disks evaluation process automatization in the production cycle. It provides an opportunity for visual defect detection in range 50 to 500 µm, The use of digital filtration, adaptive histogram equalization, threshold function, feature detection as a classical approach showed a great result. Deep learning has proven its effectiveness, selecting features of the defect and presented more stable results in defect detection.
THE CONCEPTUAL LANDSCAPE OF ANCIENT DNA RESEARCH: CURRENT STATE AND FUTURE PROSPECTS

Recent developments in next generation sequencing technologies have triggered a rapid growth in both the volume of ancient DNA (aDNA) sequence data and the number of publications concerning this interdisciplinary field. As the size of scientific literature regarding aDNA and complexity of the field have increased tremendously, it has become difficult to decipher research trends and identify central issues within it based on personal knowledge alone. In this article we’ve used a bibliometric approach to outline the scope and structure of aDNA research. Publication records concerning aDNA, paleogenetics and paleogenomics from the Web of Science were used to generate a dataset for the creation of bibliographic maps based on text co-occurrence, bibliographic coupling and author cocitation patterns. Three major research areas, five narrower research fields and 13 smaller highly interconnected sub-fields of aDNA studies were identified at different levels of clustering resolution in text co-occurrence analysis. Although the majority of the studies are centered around human population history, the highest average number of citations is associated with the articles, regarding the application of aDNA to evolutionary research. At the same time the most rapidly developing avenue of aDNA research is connected with reconstruction of environmental history (particularly with the use of environmental DNA approach) and conservation genetics.
Baricitinib Receives Emergency Use Authorization from the FDA for the Treatment of Hospitalized Patients with COVID-19

INDIANAPOLIS, Nov. 19, 2020 /PRNewswire/ -Eli Lilly and Company (NYSE: LLY) and Incyte (NASDAQ: INCY) announced today that the U.S. Food and Drug Administration (FDA) issued an Emergency Use Authorization (EUA) for the distribution and emergency use of baricitinib to be used in combination with remdesivir in hospitalized adult and pediatric patients two years of age or older with suspected or laboratory confirmed COVID-19 who require supplemental oxygen, invasive mechanical ventilation, or extracorporeal membrane oxygenation (ECMO).
REAL-TIME VEHICLE AND PEDESTRIAN DETECTION ON EMBEDDED PLATFORMS

Real-time pedestrian and vehicle detection on embedded devices play crucial role in many intelligent transport systems because of the limited hardware in autonomous driving devices. This paper presents a lightweight two-stage detector for real-time pedestrian and vehicle detection. The proposed detector includes a lightweight backbone at first stage and a lightweight detection network at second stage. The proposed lightweight backbone is designed based on the ShuffleNetv2 network, which achieves the best accuracy in very limited computational budgets. The proposed lightweight detection network consists of an improved R-CNN to improve the computational cost and a separable convolution module to increase the receptive field. In addition, a lightweight region proposal network is used to improve both accuracy and inference speed of proposals generation stage. The lightweight region proposal network includes pointwise convolution to reduce the number of channels of input features and dilated convolution to enlarge the receptive field. The KITTI dataset is adopted to evaluate the effectiveness of the proposed detector. Experimental results on recent embedded devices, including Raspberry Pi 4 and NVIDIA Jetson TX2, and GPU-based computer show that the proposed method achieves a much better trade-off between accuracy and efficiency compared with recent methods and meets the requirement for real-time object detection on embedded platforms.
HEALTH PROFILE OF PRUT ICHTHYOFAUNA – A BRIEF STUDY

The aim of our investigation was to assess the fish health condition from wild population from the Prut River in order to evaluate the quality of its aquatic environment conditions. Analysed fish species were: Cyprinus carpio, Silurus glanis, Stizostedion lucioperca, Abramis brama. Parasitological analyses were carried out in the Research Centre MoRAS-UDJ Galati (http://moras.ugal.ro). Parasitological investigations were performed on fresh samples by classic methods and the obtained results were expressed in grades of extensity and intensity. In the analysed fish, 10 parasitic species were identified belonging to 7 systematic groups: Mycophyta, Monogenea, Trematoda, Cestoda, Nematoda, Acanthocephala, Crustacea. The extensity of the parasitosis varied among examined fish species, as the following Abramis brama ˃ Silurus glanis ˃ Cyprinus carpio ˃ Stizostedion lucioperca. The most frequently found helminths were monogeneans and nematodes. Although a higher number of parasites has been identified in the analysed fish, the parasitic intensity was lower.
Non-destructive microstructural analysis with depth resolution: application to seashells

Energy-variable X-ray diffraction at a synchrotron beamline has been used to control the X-ray penetration depth and thus to study structural parameters in polycrystalline and textured materials with depth resolution. This approach was applied to the investigation of the depth evolution of microstructure in the nacre layer of bivalvia seashells. According to conventional X-ray diffraction and scanning electron microscopy, the nacre layer in the seashells of Acanthocardia tuberculata under investigation consists of large [001]-oriented lamellae packed nearly parallel to the inner shell surface. In this paper, attention is focused on the microstructural information that can be extracted from the shapes of diffraction profiles (line profile analysis) measured at X-ray energies that are varied by small steps. Depth dependences of the thickness of the lamellae and the average microstrain fluctuation are revealed.
Risk factors for mortality of critically ill patients with COVID-19 receiving invasive ventilation

Rationale: Early invasive ventilation may improve outcomes for critically ill patients with COVID-19. The objective of this study is to explore risk factors for 28-day mortality of COVID-19 patients receiving invasive ventilation. Methods: 74 consecutive adult invasively ventilated COVID-19 patients were included in this retrospective study. The demographic and clinical data were compared between survivors and non-survivors, and Cox regression analysis was used to explore risk factors for 28-day mortality. The primary outcome was 28-day mortality after initiation of invasive ventilation. Secondary outcome was the time from admission to intubation. Results: Of 74 patients with COVID-19, the median age was 68.0 years, 53 (71.6%) were male, 47 (63.5%) had comorbidities with hypertension, and diabetes commonly presented. The most frequent symptoms were fever and dyspnea. The median time from hospital admission to intubation was similar in survivors and non-survivors (6.5 days vs. 5.0 days). The 28-day mortality was 81.1%. High Sequential Organ Failure Assessment (SOFA) score (hazard ratio [HR], 1.54; 95% confidence interval [CI], 1.23-1.92; p < 0.001) and longer time from hospital admission to intubation (HR, 2.41; 95% CI, 1.15-5.07; p = 0.020) were associated with 28-day mortality in invasively ventilated COVID-19 patients. Conclusions: The mortality of invasively ventilated COVID-19 patients was particularly striking. Patients with high SOFA score and receiving delayed invasive ventilation were at high risk of mortality.
Selecting Time Series Clustering Methods based on Run-Time Costs

Clustering time series, e.g., of monitoring data from software systems, can reveal important insights and interesting hidden patterns. However, choosing the right method is not always straightforward, especially as not only clustering quality but also run-time costs must be considered. In this paper, we thus present an approach that aids users in selecting the best methods in terms of quality as well as computational costs. Given a set of candidate methods, we evaluate their clustering performance and robustly measure their actual run times, i.e., the execution time on a speciﬁc machine. We evaluate our approach using data from the UCR time series archive and show its usefulness in determining the best clustering methods while also taking costs into account.
Critical Factors of Post-Harvest Nitrous Oxide Emissions from Oilseed Rape – Cereal Rotations-Evaluations Based on Field Studies and Stable Isotope Labeling -

The sustainable production of bioenergy from crops like oilseed rape, barley, and maize presents a significant option to mitigate climate change by reducing fossil CO2 emissions. Greenhouse gas emissions (specifically N2O) during the energy crop production need to be quantified precisely for reliable life cycle analysis of bioenergy cropping systems. Energy crops (specifically oilseed rape) have a very high N demand, which results in a higher N-fertilizer application and thus higher risk of N2O emissions not only during the vegetation period but also after crop harvest due to i) incorporation of N rich plant residue to soil and/or ii) residual N. An incubation experiment was conducted under conditions favoring denitrification (80% water-holding capacity), to study the drivers of N2O emissions specifically during the post-harvest period. Here we compared two different plant residues varying in C/N ratio (oilseed rape (RST) and barley straw (BST)) with or without N supply and measured CO2, and N2O emissions. Stable isotope labeling (N) was used to quantify soiland residue-born N2O. Incorporation of both plant residues alone induced significant increases in CO2 emissions compared to control soil without straw addition (p < .05). However, the increase in CO2 emissions was less pronounced when straw was incorporated in conjunction with mineral N. There was a clear increase in cumulative N2O emissions (p < .05) when soil amended with BST or RST (6and 9-fold) was compared to control, however, the increase of cumulative N2O emissions was drastic when mineral N was added (15and 23-fold). No significant differences in N2O emission were observed when comparing residue types (p > .05). Stable isotope labeling of barley straw clearly showed that the share of residue-born N2O was very low (1.35 or 0.4%) in the overall N2O fluxes in BST and BST+N. The present study suggests that N fertilization in autumn should be avoided to minimize N2O fluxes regardless of type of straw.
Emotion Intensity and Gender Detection via Speech and Facial Expressions

Human emotion detection has received increasing attention over the last decades for a variety of applications and systems. However, detecting the intensity of the expressed emotion has not been investigated as much as detecting the type of the expressed emotion. To ﬁll this gap, we investigate the utility of different facial and speech features for emotion intensity detection. To this end, we designed different Deep Neural Network based models and applied them to the RAVDESS dataset. Obtained results show that speech signal features are better indicators of emotion intensity than facial features. However, in the absence of speech signals, ﬁnding emotion intensity by facial expressions is more accurate for males in comparison to females. The difference between the accuracy of emotion intensity detection for two genders motivated us to use speech signals for the gender detection task. The obtained results conﬁrm that the proposed model achieves higher accuracy in emotion intensity detection and is more robust in gender detection than the state-of-the-art.
Change of Hydrodynamic Loads due to Fluid-Structure Interaction during Aircraft Ditching

Aviation is an ever growing market with a strong demand for robust and safe product certification. Since seventy percent of the earth’s surface is covered with water, an important saftey aspect is the emergency landing on water, so-called ditching. To support the related comissioning an design needs, a potential flow based simulation method has been suggested in [1]. The approach was subsequently refined to cover various effects like ventilation, cavitation or deep immersion influences. The simulation of A/C loads usually follows a rigid body assumption, which are subsequently imposed on a deformable A/C to investigate the structure response. The present study is devoted to an investigation of load changes induced by a deformation of the fuselage structure. To investigate the two-way FSIcoupling, a partioned Finite Volume Method (FVM) / Finite Element Method (FEM) [2] is used. Following a validation of the procedure, an application of a generic fuselage section impacting on a free water surface with a vertical speed of 1.5 m/s is presented. The hydrodynamic loads are compared to the impact of the same but rigid fuselage section.
Pruning Deep Neural Networks with $\ell_{0}$-constrained Optimization

Deep neural networks (DNNs) give state-of-the-art accuracy in many tasks, but they can require large amounts of memory storage, energy consumption, and long inference times. Modern DNNs can have hundreds of million parameters, which make it difficult for DNNs to be deployed in some applications with low-resource environments. Pruning redundant connections without sacrificing accuracy is one of popular approaches to overcome these limitations. We propose two $\ell_{0}$-constrained optimization models for pruning deep neural networks layer-by-layer. The first model is devoted to a general activation function, while the second one is specifically for a ReLU. We introduce an efficient cutting plane algorithm to solve the latter to optimality. Our experiments show that the proposed approach achieves competitive compression rates over several state-of-the-art baseline methods.
OPTIMUM WLAN PROTOCOL AND NETWORK ARCHITECTURE IDENTIFICATION FOR VOIP APPLICATION

This research developed a novel algorithm to evaluate Voice over Internet Protocol (VoIP) metrics of different IEEE 802.11 technologies in order to identify the optimum network architecture among Basic Service Set (BSS), Extended Service Set (ESS), and the Independent Basic Service Set (IBSS). The proposed algorithm will yield the rank order of different IEEE 802.11 technologies. By selecting the optimum network architecture and technology, the best overall network performance that provides a good voice quality is guaranteed. Furthermore, it meets the acceptance threshold values for the VoIP quality metrics. This algorithm was applied to various room sizes ranging from 2x3m to 10x14m and the number of nodes ranged from one to forty. The spatial distributions considered were circular, uniform, and random. The Quality of Service (QoS) metrics used were delay, jitter, throughput and packet loss.
Responses to the reviewers’ comments for manuscript Size segregated particle number and mass emissions in urban Beijing Title revised to “Size segregated particle number and mass concentrations of emission sources in urban Beijing” according to Reviewer #2’s comment

Jing Cai, Biwu Chu, Lei Yao, Chao Yan, Liine M. Heikkinen, Feixue Zheng, Chang Li, Xiaolong Fan, Shaojun Zhang, Daoyuan Yang, Yonghong Wang, Tom V. Kokkonen, Tommy Chan, Ying Zhou, Lubna Dada, Yongchun Liu, Hong He, Pauli Paasonen, Joni T. Kujansuu, Tuukka Petäjä, Claudia Mohr, Juha Kangasluoma, Federico Bianchi, Yele Sun, Philip L. Croteau, Douglas R. Worsnop, Veli-Matti Kerminen, Wei Du*, Markku Kulmala*, Kaspar R. Daellenbach* 1 Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, China
Treatment of heterogeneous electrocatalysis in modeling transport-reaction phenomena in PEFCS

Thermal and water management is a critical issue in PEFCs. In this research, the thermal behavior of PEFC is focused. The objective is to understand the influence of heat on cell performance both by experiment and theoretical analysis, as well as improving cell performance and reliability. In order to investigate the theoretical behavior, especially in the catalyst layer where the electrochemical reactions occur, a detailed modeling of heterogeneous surface reaction coupled with reactant transport is needed. In this paper, a theoretical model that improves the dependency of the exchange current density with reactant concentrations by applying data from a known surface reaction steps found in catalysis is developed. It served as a preliminary step before the thermal-electrochemical behavior of a PEFC can be fully understood.Copyright © 2007 by ASME
An External-Memory Work-Depth Model and Its Applications to Massively Parallel Join Algorithms

The PRAM is a fundamental model in parallel computing, but it is seldom directly used in parallel algorithm design. Instead, work-depth models have enjoyed much more popularity, as they relieve algorithm designers and programmers from worrying about how various tasks should be assigned to each of the processors. Mean-while, they also make it easy to study the fundamental parallel complexity of the algorithm, namely work and depth , which are irrelevant to the number of processors available. The massively parallel computation (MPC) model, which is a simplified version of the BSP model, has drawn a strong interest in recent years, due to the widespread popularity of many big data systems based on such a model. However, the current state of art for MPC algorithm design is still akin to that of PRAM, i.e., one has to specify, in each round, what each processor should compute and to which other processors messages should be sent. Being a coarse-grained parallel model, we observe that the MPC/BSP can be viewed as a combination of a certain variant of the external memory (EM) model and the PRAM. Seeing the impact of work-depth models on parallel algorithm design, we propose an EM work-depth (EWD) model, by incorporating elements from the EM model into an internal memory work-depth model (we choose the multi-thread model). We show that algorithms designed in the EWD model can be optimally simulated in the MPC/BSP model, and illustrate how it can be used to more easily design MPC/BSP algorithms by parallelizing the corresponding EM algorithms. In particular, we show how many of the EM join algorithms can be converted fairly easily to the EWD model, which allows us to either recover or improve the best known MPC/BSP results.