Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing the Resource Allocation of Periodic Messages With Different Sizes in LTE-V2V

Recently, 3GPP has added new features to long term evolution (LTE) that allow vehicles to communicate directly with each other and with surrounding objects. These short-range communications will play a key role in the so-called cellular vehicle-to-anything (C-V2X). As a particular service, which will be the basis of most applications for automated and connected cars, each vehicle will periodically broadcast information on its identity, status, and movements through short-range vehicle-to-vehicle (V2V) communications. Given the importance of this service, great attention has been given to the associated resource allocation procedures and the number of vehicles that can be simultaneously communicating in the network. However, little attention has been paid to handling messages of different sizes, which is actually foreseen in practice in order to transmit static information with a reduced periodicity. With this in mind, we evaluate the number of vehicles that can be allocated when parameters are optimized for messages of different sizes. This objective is pursued taking into account the numerous constraints imposed by the standard, through the formalization and solution of a combinatorial optimization problem. Example results, based on 3GPP indications, show that, with respect to the optimization of the allocation based on the larger packets, it is possible to obtain an increase that varies between 10% and 30%, depending on the conditions, with peaks above 150% in specific cases.
Characterizing Spatial Patterns of Amazon Rainforest Wildfires and Driving Factors by Using Remote Sensing and GIS Geospatial Technologies

Known as the “lung of the planet,” the Amazon rainforest produces more than 20% of the Earth’s oxygen. Once a carbon pool for mitigating climate change, the Brazilian Amazônia Biome recently has become a significant carbon emitter due to increasingly frequent wildfires. Therefore, it is of crucial importance for authorities to understand wildfire dynamics to manage them safely and effectively. This study incorporated remote sensing and spatial statistics to study both the spatial distribution of wildfires during 2019 and their relationships to 15 environmental and anthropogenic factors. First, broad-scale spatial patterns of wildfire occurrence were explored using kernel density estimation, Moran’s I, Getis-Ord Gi*, and optimized hot spot analysis (OHSA). Second, the relationships between wildfire occurrence and the environmental and anthropogenic factors were explored using several regression models, including Ordinary Least Squares (OLS), global (quasi) Poisson, Geographically-weighted Gaussian Regression (GWGR), and Geographically-weighted Poisson Regression (GWPR). The spatial analysis results indicate that wildfires exhibited pronounced regional differences in spatial patterns in the vast and heterogeneous territory of the Amazônia Biome. The GWPR model outperformed the other regression models and explained the distribution and frequency of wildfires in the Amazônia Biome as a function of topographic, meteorologic, and environmental variables. Environmental factors like elevation, slope, relative humidity, and temperature were significant factors in explaining fire frequency in localized hotspots, while factors related to deforestation (forest loss, forest fragmentation measures, agriculture) explained wildfire activity over much of the region. Therefore, this study could improve a comprehensive study on, and understanding of, wildfire patterns and spatial variation in the target areas to support agencies as they prepare and plan for wildfire and land management activities in the Amazônia Biome.
Bidirectional LSTM-CRF for biomedical named entity recognition

Bio-medical entity recognition extracts significant entities, for instance cells, proteins and genes, which is an arduous task in an automatic system that mine knowledge in bioinformatics texts. In this thesis, we utilized a bidirectional long short-term memory (Bi-LSTM) combined with conditional random fields (CRFs) approach to automatically obtain word representation, obliterated the need for a marvelous number of feature engineering tasks. The consequences of this experiment represent the word representation method can effectually acquire potential semantic information. Without relying on any artificial features, the result on the test dataset obtained 76.81 % F-score. Therefore, the proposed method is expected to advance biomedical text mining in bioinformatics entity recognition.
Lexical Sememe Prediction with RNN and Modern Chinese Dictionary

Knowledge base HowNet defines sememes as the minimum semantic units of words or phrases. Linguists have put many efforts into manually annotating sememes for words. Although automatically methods have been proposed to help solve this labor-intensive and time-consuming work of manually annotating sememes, the field is not mature enough. To the best of our knowledge, only three models have been proposed to solve automatically sememe prediction, and some input information and label structure are not fully used. We propose Sememe Prediction with Sentence Embedding and Chinese Dictionary (SPSECD), an end-to-end neural network which implements sentence embedding and sememe prediction in a unified model. To the best of our knowledge, SPSECD is the first model which treats words with polysemy differently on sememe prediction task. Before predicting sememes, our model adds the definition sentence from a word in a Chinese Dictionary, and we use Recurrent Neural Network to learning the embedding of the sentence. With the help of dictionary auxiliary information, our model can aware which meaning the word with polysemy focus on because of the different meanings of a word have a different definition in the Chinese Dictionary, and then our model can choose better sememes for the specific meaning of a word. Experiments show that our model achieves state-of-the-art performances.
Digital Literacy of Employees in Production Process - Analyse of Production Stability and Productivity in INDUSTRY 4.0 Concept

Important part of the higher productivity and effectiveness in today’s industrial enterprises confronted with growing automation and digitization of production processes is strong connected with the ability of employees to be prepare communicate with newest digitized production technologies. Skills and abilities of employees influence radical the productivity, performance, value added of production flows. Presented paper analyze important parameters of digital literacy of employees in production processes and confronts the results with the core requirements on continuously process performance and improvement. Results achieved by presented analyze are useful for development of digitized process management schemas, prediction of effective value stream in production process.Important part of the higher productivity and effectiveness in today’s industrial enterprises confronted with growing automation and digitization of production processes is strong connected with the ability of employees to be prepare communicate with newest digitized production technologies. Skills and abilities of employees influence radical the productivity, performance, value added of production flows. Presented paper analyze important parameters of digital literacy of employees in production processes and confronts the results with the core requirements on continuously process performance and improvement. Results achieved by presented analyze are useful for development of digitized process management schemas, prediction of effective value stream in production process.
MongoDB Clustering using K-means for Real-Time Song Recognition

Recently, the increased competition in song recognition has led to the necessity to identify songs within very huge databases compared to previous years. Therefore, information retrieval technique requires a more efficient and scalable data storage framework. In this work, we propose an approach exploiting K-means clustering and describe strategies for improving accuracy and speed. In collaboration with an audio expert company providing us with 2.4 billion fingerprints data, we evaluated the performance of the proposed clustering and recognition algorithm.
Exercise Overrides Blunted Hypoxic Ventilatory Response in Prematurely Born Men

Purpose Pre-term birth provokes life-long anatomical and functional respiratory system sequelae. Although blunted hypoxic ventilatory response (HVR) is consistently observed in pre-term infants, it remains unclear if it persists with aging and, moreover, if it influences hypoxic exercise capacity. In addition, it remains unresolved whether the previously observed prematurity-related alterations in redox balance could contribute to HVR modulation. Methods Twenty-one prematurely born adult males (gestational age = 29 ± 4 weeks], and 14 age matched controls born at full term (gestational age = 39 ± 2 weeks) underwent three tests in a randomized manner: (1) hypoxia chemo-sensitivity test to determine the resting and exercise poikilocapnic HVR and a graded exercise test to volitional exhaustion in (2) normoxia (FiO2 = 0.21), and (3) normobaric hypoxia (FiO2 = 0.13) to compare the hypoxia-related effects on maximal aerobic power (MAP). Selected prooxidant and antioxidant markers were analyzed from venous samples obtained before and after the HVR tests. Results Resting HVR was lower in the pre-term (0.21 ± 0.21 L ⋅ min-1 ⋅ kg-1) compared to full-term born individuals (0.47 ± 0.23 L ⋅ min-1 ⋅ kg-1; p < 0.05). No differences were noted in the exercise HVR or in any of the measured oxidative stress markers before or after the HVR test. Hypoxia-related reduction of MAP was comparable between the groups. Conclusion These findings indicate that blunted resting HVR in prematurely born men persists into adulthood. Also, active adults born prematurely seem to tolerate hypoxic exercise well and should, hence, not be discouraged to engage in physical activities in hypoxic environments. Nevertheless, the blunted resting HVR and greater desaturation observed in the pre-term born individuals warrant caution especially during prolonged hypoxic exposures.
Iterative Spatial Updating During Forward Linear Walking Revealed Using a Continuous Pointing Task

Abstract The continuous pointing task uses target-directed pointing responses to determine how perceived distance traveled is estimated during forward linear walking movements. To more precisely examine the regulation of this online process, the current study measured upper extremity joint angles and step-cycle kinematics in full vision and no-vision continuous pointing movements. Results show perceptual under-estimation of traveled distance in no-vision trials compared to full vision trials. Additionally, parsing of the shoulder plane of elevation trajectories revealed discontinuities that reflected this perceptual under-estimation and that were most frequently coupled with the early portion of the right foot swing phase of the step-cycle. This suggests that spatial updating may be composed of discrete iterations that are associated with gait parameters.
Toward a Consensus for Musculoskeletal Ultrasonography Education in Physical Medicine and Rehabilitation

Supplemental digital content is available in the text. Objectives The aims of the study were to evaluate integration of musculoskeletal ultrasonography education in physical medicine and rehabilitation training programs in 2014–2015, when the American Academy of Physical Medicine & Rehabilitation and Accreditation Council for Graduate Medical Education Residency Review Committee both recognized it as a fundamental component of physiatric practice, to identify common musculoskeletal ultrasonography components of physical medicine and rehabilitation residency curricula, and to identify common barriers to integration. Design Survey of 78 Accreditation Council for Graduate Medical Education–accredited physical medicine and rehabilitation residency programs was conducted. Results The 2015 survey response rate was more than 50%, and respondents were representative of programs across the United States. Most programs (80%) reported teaching musculoskeletal ultrasonography, whereas a minority (20%) required mastery of ultrasonography skills for graduation. Ultrasonography curricula varied, although most programs agreed that the scope of resident training in physical medicine and rehabilitation should include diagnostic and interventional musculoskeletal ultrasonography, especially for key joints (shoulder, elbow, knee, wrist, hip, and ankle) and nerves (median, ulnar, fibular, tibial, radial, and sciatic). Barriers to teaching included insufficient expertise of instructors, poor access to equipment, and lack of a structured curriculum. Conclusions Musculoskeletal ultrasonography has become a required component of physical medicine and rehabilitation residency training. Based on survey responses and expert recommendations, we propose a structure for musculoskeletal ultrasonography curricular standards and milestones for trainee competency.
Single-Cell Transcriptomic Analysis of the Mouse Pancreas: Characteristic Features of Pancreatic Ductal Cells in Chronic Pancreatitis

Chronic pancreatitis (CP) is a fibroinflammatory disorder of the pancreas. Our understanding of CP pathogenesis is partly limited by the incomplete characterization of pancreatic cell types. Here, we performed single-cell RNA sequencing on 3825 cells from the pancreas of one control mouse and mice with caerulein-induced CP. An analysis of the single-cell transcriptomes revealed 16 unique clusters and cell type-specific gene expression patterns in the mouse pancreas. Sub-clustering of the pancreatic mesenchymal cells from the control mouse revealed four clusters of cells with specific gene expression profiles (combinatorial expressions of Smoc2, Cxcl14, Tnfaip6, and Fn1). We observed that immune cells in the pancreas of the CP mice were abundant and diverse in cellular type. Compared to the control, 547 upregulated genes (including Mmp7, Ttr, Rgs5, Adh1, and Cldn2) and 257 downregulated genes were identified in ductal cells from the CP group. The elevated expression levels of MMP7 and TTR were further verified in the pancreatic ducts of CP patients. This study provides a preliminary description of the single-cell transcriptome profiles of mouse pancreata and accurately demonstrates the characteristics of pancreatic ductal cells in CP. The findings provide insight into novel disease-specific biomarkers and potential therapeutic targets of CP.
Real-Time Polarization-Resolved Imaging of Living Tissues Based on Two-Photon Excitation Spinning-Disk Confocal Microscopy

Laser scanning microscopy using high-peak-power ultrashort near infrared light pulses can visualize biological microstructures by utilizing non-linear optical processes, such as multi-photon excitation and sum frequency generation. Here we introduced a polarization-resolving detection methodology for a laser scanning microscopy system equipped with a spinning disk confocal scanner. The developed system achieved high-speed intravital imaging of living tissues with resolving their signals to orthogonally polarized components. First, we applied the system to a liposomal vesicle labeled with the fluorescent lipophilic dye and confirmed the orientation map of the lipid bilayer. Next, by detecting polarization-resolved second harmonic generation signals, the structural orientations of the collagen fibers in fixed mouse tissues were visualized without exogenous or genetic fluorophore labeling. Finally, we demonstrated in vivo polarization-resolved second harmonic generation imaging of the collagen fibers in the mouse skeletal muscles at a 56-Hz temporal resolution. We expect that our developed methodology can achieve real-time visualization, thus, revealing the conformational changes of supramolecular structures in living animals.
Impulsivity Moderates Skin Conductance Activity During Decision Making in a Modified Version of the Balloon Analog Risk Task

Individual differences in traits such as impulsivity and processing of risk and reward have been linked to decision making and may underlie divergent decision making strategies. It is, however, unclear whether and how far individual differences in these characteristics jointly influence decision making. Here, we aimed to investigate the roles of skin conductance responses, a psychophysiological marker of risk processing and impulsivity, as assessed by the Barratt Impulsiveness Scale 11 on decision making. Forty-six healthy participants performed a modified version of the Balloon Analog Risk Task (BART), where reward and explosion risk are manipulated separately. Participants are informed about whether they play a high versus low reward and high versus low explosion risk condition. The exact risk and reward contingencies are, however, unknown to participants. Participants were less risk-taking under high, compared to low explosion risk and under high reward, compared to low reward on the modified BART, which served as a validation of the paradigm. Risk-taking was negatively related to skin conductance responses under high explosion risk. This relationship was primarily driven by individuals with relatively high levels of impulsivity. However, impulsivity alone was not found to be related to decision making on the modified BART. These results extend evidence that skin conductance responses may guide decision making in situations, where participants are informed about risk level (high vs. low), which might be differentially moderated by different levels of impulsivity.
Cholinergic Stimulation of the Adult Zebrafish Brain Induces Phosphorylation of Glycogen Synthase Kinase-3 β and Extracellular Signal-Regulated Kinase in the Telencephalon

The sequencing of the zebrafish genome, the emergence of powerful gene-editing tools, and the development of in vivo imaging techniques have propelled the economical zebrafish into prominence as a biomedical research model. Neurodegenerative disorders with a cholinergic component, such as Alzheimer’s and Parkinson’s diseases, are currently modeled using zebrafish. Still, the utility of zebrafish as a research model will not be fully realized until their neurophysiological properties are thoroughly characterized. In mammals, the coupling of cholinergic receptors to the phosphorylation of glycogen synthase kinase-3 β (GSK3β) and extracellular signal-regulated kinase 1/2 (ERK1/2) is of critical importance to cognitive processes and imparts protection against neuropathogenic events. Similarly, it is known that cholinergic receptors are required for learning and memory in zebrafish and that in vivo activation of cholinergic receptors induces transient changes in evoked synaptic transmission in the telencephalon. However, the intracellular events mediating cholinergic processes in zebrafish have yet to be elucidated. In the current study, an ex vivo drug treatment assay was used to demonstrate that carbachol (CCh)-mediated cholinergic stimulation of the intact adult zebrafish brain induces phosphorylation of GSK3β and ERK1/2 in the zebrafish telencephalon. These findings suggest GSK3β and ERK1/2 may underly cognitive processes in zebrafish.
Faster and More Accurate Computation of the ℋ∞ Norm via Optimization

In this paper, we propose an improved method for computing the $\mathcal{H}_\infty$ norm of linear dynamical systems that results in a code that is often several times faster than existing methods. By using standard optimization tools to rebalance the work load of the standard algorithm due to Boyd, Balakrishnan, Bruinsma, and Steinbuch, we aim to minimize the number of expensive eigenvalue computations that must be performed. Unlike the standard algorithm, our modified approach can also calculate the $\mathcal{H}_\infty$ norm to full precision with little extra work, and also offers more opportunity to further accelerate its performance via parallelization. Finally, we demonstrate that the local optimization we have employed to speed up the standard globally-convergent algorithm can also be an effective strategy on its own for approximating the $\mathcal{H}_\infty$ norm of large-scale systems.
Vision-Based In Situ Monitoring of Plankton Size Spectra Via a Convolutional Neural Network

Plankton size spectra monitoring is crucial for managing and conserving aquatic ecosystems. Thus, we develop an <italic>in situ</italic> size spectra monitoring system to obtain the size spectra of plankton and the information of their living status underwater. The system consists of an imaging unit and an information processing unit. The imaging part applies a darkfield illumination to enhance the image contrast. Three lenses with different magnifications are alternated by a motor automatically to capture sizes of plankton from <inline-formula><tex-math notation="LaTeX">$3\,\mu$</tex-math></inline-formula>m to <inline-formula><tex-math notation="LaTeX">$3\,\text{mm}$</tex-math></inline-formula>. Moreover, the system can analyze the captured images in real time using the proposed multitask size spectra convolutional neural network, obtaining size spectra and density distribution of plankton simultaneously. Field test confirms that our system performs well both in imaging and information processing. Furthermore, the system can provide the living behavior of plankton, thereby helping biologists to study the aquatic ecosystem effectively and precisely.
Mobile Traffic Anonymization Through Probabilistic Distribution

Current implementations of mobile apps offer limited security assurances against traffic analysis. Encryption is not effective in hiding particular patterns within packets that can be used as side-channel information to classify specific apps. What we need is an anonymity system that ensures strong security with acceptable computational overhead and latency for interactive app usage. Recently, we undertook this problem by mutating an app traffic that we try to defend so that it resembles the traffic of another app. Our goal in this paper is to develop a simpler and more scalable system to anonymize mobile app packet traffic without the need of another app’s model traffic. This could happen using probabilistic distribution of packet sizes. We propose first a scheme that regenerates statistical modeling of app packet lengths. Then, we present a privacy preserving technique that implements defense against traffic analysis and confines the incurred overhead by mutation of packet lengths of the incoming traffic to the regenerated ones. Experiments show that using this confusion technique, we are able to reduce 91.1% classification accuracy to 0.9% with only 12.51% overhead. On the other hand, we demonstrate a second technique for anonymization where we thwart traffic analysis of mobile app traffic by modifying its packet sizes probability distribution to another dissimilar distribution.