Miracle-dz/UniFAR-FTUs huggingface dataset:
Influenza A Virus Infection Causes Chronic Lung Disease Linked to Sites of Active Viral RNA Remnants

Clinical and experimental observations suggest that chronic lung disease is linked to respiratory viral infection. However, the long-term aspect of this relationship is not yet defined using a virus that replicates at properly high levels in humans and a corresponding animal model. In this study, we show that influenza A virus infection achieves 1 × 106–fold increases in viral load in the lung and dose-dependent severity of acute illness in mice. Moreover, these events are followed by persistence of negative- and positive-strand viral RNA remnants for 15 wk and chronic lung disease for at least 26 wk postinfection. The disease is manifested by focal areas of bronchiolization and mucus production that contain increased levels of viral RNA remnants along with mucin Muc5ac and Il13 mRNA compared with uninvolved areas of the lung. Excess mucus production and associated airway hyperreactivity (but not fibrosis or emphysema) are partially attenuated with loss of IL-13 production or signaling (using mice with IL-13 or STAT6 deficiency). These deficiencies cause reciprocal increases in l17a mRNA and neutrophils in the lung; however, none of these disease endpoints are changed with IL-13/IL-17a compared with IL-13 deficiency or STAT6/IL-17a compared with STAT6 deficiency. The results establish the capacity of a potent human respiratory virus to produce chronic lung disease focally at sites of active viral RNA remnants, likely reflecting locations of viral replication that reprogram the region. Viral dose dependency of disease also implicates high-level viral replication and severity of acute infection as determinants of chronic lung diseases such as asthma and COPD with IL-13–dependent and IL-13/IL-17–independent mechanisms.
3D Printing of Ka band Orthomode Transducers

In this paper, the advance of an on-going research activity jointly developed by CNR-IEIIT, IIT and Politecnico di Torino in the design and SLM manufacturing of high performances microwave devices is reported. In particular, two prototypes of Ka-band Orthomode Transducers, based on a novel AM-oriented layout, are presented. All the component building blocks, such as, coupling junction, bends and twists are integrated in a single mechanical part suitable for 3D printing.
Collision Risk Assessment Algorithm via Lane-Based Probabilistic Motion Prediction of Surrounding Vehicles

In order to ensure reliable autonomous driving, the system must be able to detect future dangers in sufficient time to avoid or mitigate collisions. In this paper, we propose a collision risk assessment algorithm that can quantitatively assess collision risks for a set of local path candidates via the lane-based probabilistic motion prediction of surrounding vehicles. First, we compute target lane probabilities, which represent how likely a driver is to drive or move toward each lane, based on lateral position and lateral velocity in curvilinear coordinates. And then, collision risks are computed by incorporating both model probability distribution of lanes and a time-to-collision between a pair of predicted trajectories. Finally, collision risks are plotted on a trajectory plane that represents each set of the tangential acceleration and the final lateral offset of local path candidates. This collision risk map provides intuitive risk measures, and can also be utilized to determine a control strategy for a collision avoidance maneuver. Validation of the model is conducted by comparing the model probabilities with the maneuver probabilities derived from the next generation simulation database. Furthermore, the effectiveness of the proposed algorithm is verified in two driving scenarios, preceding vehicle braking and cut-in, on a curved highway with multiple vehicles.
Field-Aware Matrix Factorization for Recommender Systems

Predicting user response is one of the core machine learning tasks in recommender systems (RS). The matrix factorization (MF)-based model has been proved to be a useful tool to improve the performance of recommendation. Many existing matrix factorization-based models mainly rely on adding some side information into basic MF to enable the model to fully express the data. However, most of the side information is measured based on the statistics or empirical formula. Also, the latent features of side information cannot be deeply mined. In this paper, we focus on mining the influence of field information (useful side information) to improve the performance of prediction. Based on the MF framework, we propose a field-aware matrix factorization (FMF) model. In FMF, the interactions between user/item and field can be captured and learned in the latent vector spaces. We propose efficient implementations to train FMF. Then, we comprehensively analyze FMF and compare this model with the state-of-the-art models. The analysis of experiments on two large data sets demonstrates that our method is very useful in RS.
Machine learning for intraoperative prediction of viability in ischemic small intestine

Objective: Evaluation of intestinal viability is essential in surgical decision-making in patients with acute intestinal ischemia. There has been no substantial change in the mortality rate (30%–93%) of patients with acute mesenteric ischemia (AMI) since the 1980s. As the accuracy from the first laparotomy alone is 50%, the gold standard is a second-look laparotomy, increasing the accuracy to 87%–89%. This study investigates the use of machine learning to classify intestinal viability and histological grading in pig jejunum, based on multivariate time-series of bioimpedance sensor data. Approach: We have previously used a bioimpedance sensor system to acquire electrical parameters from perfused, ischemic and reperfused pig jejunum (7 + 15 pigs) over 1–16 h of ischemia and 1–8 h of reperfusion following selected durations of ischemia. In this study we compare the accuracy of using end-point bioimpedance measurements with a feedforward neural network (FNN), versus the accuracy when using a recurrent neural network with long short-term memory units (LSTM-RNN) with bioimpedance data history over different periods of time. Main results: Accuracies in the range of what has been reported clinically can be achieved using FNN’s on a single bioimpedance measurement, and higher accuracies can be achieved when employing LSTM-RNN on a sequence of data history. Significance: Intraoperative bioimpedance measurements on intestine of suspect viability combined with machine learning can increase the accuracy of intraoperative assessment of intestinal viability. Increased accuracy in intraoperative assessment of intestinal viability has the potential to reduce the high mortality and morbidity rate of the patients.
Recurrent Broad Learning Systems for Time Series Prediction

The broad learning system (BLS) is an emerging approach for effective and efficient modeling of complex systems. The inputs are transferred and placed in the feature nodes, and then sent into the enhancement nodes for nonlinear transformation. The structure of a BLS can be extended in a wide sense. Incremental learning algorithms are designed for fast learning in broad expansion. Based on the typical BLSs, a novel recurrent BLS (RBLS) is proposed in this paper. The nodes in the enhancement units of the BLS are recurrently connected, for the purpose of capturing the dynamic characteristics of a time series. A sparse autoencoder is used to extract the features from the input instead of the randomly initialized weights. In this way, the RBLS retains the merit of fast computing and fits for processing sequential data. Motivated by the idea of “fine-tuning” in deep learning, the weights in the RBLS can be updated by conjugate gradient methods if the prediction errors are large. We exhibit the merits of our proposed model on several chaotic time series. Experimental results substantiate the effectiveness of the RBLS. For chaotic benchmark datasets, the RBLS achieves very small errors, and for the real-world dataset, the performance is satisfactory.
Influence of power amplifier's intermodulation distortion on transmitted information quality

This paper describes intermodulation distortion influence on a transmitted information quality. We examine an analog predistortion linearizer for TWTA that lowers third-order intermodulation distortion by 9.5 dB, improves BER of QPSK signal in AWGN channel from 9.3·10<sup>-3</sup> to 2·10<sup>-6</sup> at E<inf>b</inf>/N0=10 dB and scales up jam resistance of a system by 1.5 times.
The lipid environment determines the activity of the Escherichia coli ammonium transporter AmtB

The movement of ammonium across biologic membranes is a fundamental process in all living organisms and is mediated by the ubiquitous ammonium transporter/methylammonium permease/rhesus protein (Amt/Mep/Rh) family of transporters. Recent structural analysis and coupled mass spectrometry studies have shown that the Escherichia coli ammonium transporter AmtB specifically binds 1‐palmitoyl‐2‐oleoyl phosphatidylglycerol (POPG). Upon POPG binding, several residues of AmtB undergo a small conformational change, which stabilizes the protein against unfolding. However, no studies have so far been conducted, to our knowledge, to explore whether POPG binding to AmtB has functional consequences. Here, we used an in vitro experimental assay with purified components, together with molecular dynamics simulations, to characterize the relation between POPG binding and AmtB activity. We show that the AmtB activity is electrogenic. Our results indicate that the activity, at the molecular level, of Amt in archaebacteria and eubacteria may differ. We also show that POPG is an important cofactor for AmtB activity and that, in the absence of POPG, AmtB cannot complete the full translocation cycle. Furthermore, our simulations reveal previously undiscovered POPG binding sites on the intracellular side of the lipid bilayer between the AmtB subunits. Possible molecular mechanisms explaining the functional role of POPG are discussed. —Mirandela, G. D. , Tamburrino, G. , Hoskisson, P. A. , Zachariae, U. , Javelle, A. The lipid environment determines the activity of the Escherichia coli ammonium transporter AmtB. FASEB J. 33, 1989–1999 (2019). www.fasebj. org
Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution

Convolutional neural networks (CNNs) have recently achieved great success in single-image super-resolution (SISR). However, these methods tend to produce over-smoothed outputs and miss some textural details. To solve these problems, we propose the Super-Resolution CliqueNet (SRCliqueNet) to reconstruct the high resolution (HR) image with better textural details in the wavelet domain. The proposed SRCliqueNet firstly extracts a set of feature maps from the low resolution (LR) image by the clique blocks group. Then we send the set of feature maps to the clique up-sampling module to reconstruct the HR image. The clique up-sampling module consists of four sub-nets which predict the high resolution wavelet coefficients of four sub-bands. Since we consider the edge feature properties of four sub-bands, the four sub-nets are connected to the others so that they can learn the coefficients of four sub-bands jointly. Finally we apply inverse discrete wavelet transform (IDWT) to the output of four sub-nets at the end of the clique up-sampling module to increase the resolution and reconstruct the HR image. Extensive quantitative and qualitative experiments on benchmark datasets show that our method achieves superior performance over the state-of-the-art methods.
Precoding Design for Energy Efficiency of Multibeam Satellite Communications

Instead of merely improving the spectral efficiency, improving the energy efficiency (EE) is another important concern for multibeam satellite systems, due to the power constraint of satellites. However, so far there has been no detailed work on the precoding design concerning the EE for multibeam satellite. In this letter, the EE maximization problem is investigated for multibeam satellite systems under the total power constraint as well as the quality of service constraints. Precoding design algorithms based on zero-forcing (ZF) and sequential convex approximation (SCA) are presented, respectively. In particular, these algorithms are verified by the real measured channel data of multibeam satellite systems. Numerical results show that the precoding algorithm based on SCA outperforms that based on ZF. It is also implied that the EE cannot be always improved by solely increasing the power of the satellite, while reducing the satellite operation power is an effective way for the EE improvement.
“To Approach Humans?”: A Unified Framework for Approaching Pose Prediction and Socially Aware Robot Navigation

We propose a unified framework for approaching pose prediction, and socially aware robot navigation, which enables a mobile service robot to safely and socially approach a dynamic human or human group in a social environment. The proposed framework is composed of four major functional blocks: 1) human detection and human features extraction to estimate the human states, and the social interaction information from the socio-spatio-temporal characteristics of a human and a group of humans; 2) a dynamic social zone (DSZ) consisting of an extended personal space and a social interaction space is modeled by the human states and social interaction information to represent space around the human and human group; 3) the approaching pose of the robot to a human or a human group is predicted using the DSZ and the environmental surroundings; and 4) the DSZ and the estimated approaching pose are incorporated into a motion planning system, comprising a local path planner and dynamic window approach technique, to generate the motion control commands for the mobile robot. We evaluate the developed framework through both simulation and real-world experiments under the newly proposed human safety and comfort indices, including the social individual index, social group index, and social direction index. The results show that the unified framework is fully capable of driving a mobile robot to approach both stationary and moving humans and human groups in a socially acceptable manner while guaranteeing human safety and comfort.
The Role of Heavy Metals in Plant Response to Biotic Stress

The present review discusses the impact of heavy metals on the growth of plants at different concentrations, paying particular attention to the hormesis effect. Within the past decade, study of the hormesis phenomenon has generated considerable interest because it was considered not only in the framework of plant growth stimulation but also as an adaptive response of plants to a low level of stress which in turn can play an important role in their responses to other stress factors. In this review, we focused on the defence mechanisms of plants as a response to different metal ion doses and during the crosstalk between metal ions and biotic stressors such as insects and pathogenic fungi. Issues relating to metal ion acquisition and ion homeostasis that may be essential for the survival of plants, pathogens and herbivores competing in the same environment were highlighted. Besides, the influence of heavy metals on insects, especially aphids and pathogenic fungi, was shown. Our intention was also to shed light on the relationship between heavy metals deposition in the environment and ecological communities formed under a strong selective pressure.
Fatores psicossociais associados ao desenvolvimento de transtornos psiquiátricos em crianças e adolescentes

Introducao: o objetivo deste estudo foi avaliar a prevalencia de comportamentos e sintomas relacionados ao desenvolvimento de transtornos psiquiatricos em criancas e adolescentes em uma populacao de 307 criancas acompanhadas em ambulatorios de pediatria do Hospital Infantil Albert Sabin (HIAS), em Fortaleza, Ceara. Metodos: o Questionario de Capacidades e Dificuldades (SDQ) foi o instrumento principal do estudo, alem de um formulario sociodemografico desenvolvido pelos autores, contendo possiveis fatores associados ao desenvolvimeto de transtornos mentais. O SDQ possui 25 questoes que compoem cinco subgrupos de sintomas psiquiatricos. Resultados: encontramos percentuais elevados de criancas com comportamento pro-social nao normal (10,1%), hiperatividade (30,3%), sintomas emocionais (61,6%), problemas de conduta (45,9%) e problemas de relacionamento com colegas (32,9%). A analise univariada revelou os seguintes fatores como estatisticamente significativos (p<0,05) para o surgimento potencial de transtornos mentais: doenca mental dos pais, depressao materna no periodo ate 03 meses apos o nascimento da crianca e pais que nao vivem juntos atualmente. Discussao: a prevalencia de transtornos mentais em criancas e adolescentes acompanhados por outras comorbidades clinicas e consideravelmente maior que na populacao geral; e os fatores significativos para o desenvolvimento de transtornos mentais na crianca estao relacionados aos pais.
ST-Accel: A High-Level Programming Platform for Streaming Applications on FPGA

In recent years we have witnessed the emergence of the FPGA in many high-performance systems. This is due to FPGA's high reconfigurability and improved user-friendly programming environment. OpenCL, supported by major FPGA vendors, is a high-level programming platform that liberates hardware developers from having to deal with the complex and error-prone HDL development. While OpenCL exposes a GPU-like programming model, which is well-suited for compute-intensive tasks, in many state-of-art systems that deploy FPGA, we observe that the workloads are streaming-like, which is communication-intensive. This mismatch leads to low throughput and high end-to-end latency. In this paper, we propose ST-Accel, a new high-level programming platform for streaming applications on FPGA. It has the following advantages: (i) ST-Accel adopts the multiprocessing programming model to capture the inherent pipeline-level parallelism of streaming applications while reducing the end-to-end latency. (ii) A message-passing-based host/FPGA communication model is used to avoid the coherency issue of shared memory, thus enabling host/FPGA communication during kernel execution. (iii) ST-Accel provides a high-level abstraction for I/O devices to support direct I/O device access that eliminates the overhead of host CPU and reduces the I/O latency. (iv) ST-Accel enables the decoupled access/execute architecture to maximize the utilization of I/O devices. (v) The host/FPGA communication interface is redesigned to cater to the demands of both latency-critical and throughput-critical scenarios. The experimental results on the Amazon AWS cloud and local machine show that ST-Accel can achieve 1.6X-166X throughput and 1/3 latency for typical streaming workloads when compared to OpenCL.
Label-less Learning for Traffic Control in an Edge Network

With the development of intelligent applications (e.g., self-driving, real-time emotion recognition), there are higher requirements for cloud intelligence. However, cloud intelligence depends on the multi-modal data collected by user equipments (UEs). Due to the limited capacity of network bandwidth, offloading all data generated from the UEs to the remote cloud is impractical. Thus, in this article, we consider the challenging issue of achieving a certain level of cloud intelligence while reducing network traffic. In order to solve this problem, we design a traffic control algorithm based on label-less learning on the edge cloud, which is dubbed LLTC. By use of the limited computing and storage resources at the edge cloud, LLTC evaluates the value of data that will be offloaded. Specifically, we first give a statement of the problem and the system architecture. Then we design the LLTC algorithm in detail. Finally, we set up the system testbed. Experimental results show that the proposed LLTC can guarantee the required cloud intelligence while minimizing the amount of data transmission.
Lambda-Blocks: Data Processing with Topologies of Blocks

We present and evaluate λ-blocks, a novel framework to write data processing programs in a descriptive manner. The main idea behind this framework is to separate the semantics of a program from its implementation. For that purpose, we define a data schema, able to describe, parameterize, compose, and link together blocks of code, storing a directed graph which represents the data transformations. Along this data schema lies an execution engine, able to read such a program, give feedback on potential errors, and finally execute it. In our reference implementation, a computation graph is described in YAML, linking together vertices of Python code blocks defined in separate libraries. The advantages of this approach are manyfold: faster, less error-prone programming; reuse of code blocks; computation graph manipulations; mixing of different specialized libraries; and finally middleware for potential front-ends (such as graphical interfaces) and back-ends (other execution engines). The main goal of λ-blocks is to bring complex data processing computations to non-specialists, by providing a simple abstraction over large-scale data processing systems. Our contributions lie within a description of the schema, and an analysis of the reference execution engine. For that purpose we describe λ-blocks' internals and its main abstractions (blocks and topologies), and evaluate the framework performances. We measured the framework overhead to have a maximum value of 50 ms, a negligible amount compared to the average duration of data processing jobs.
(WKSP) Sentiment Analysis of Twitter Samples That Differentiates Impact of User Participation Levels

The microblogging social media platform Twitter, accounting for millions of 'tweets' per day, provides an effective platform for sampling conversations on a wide array of topics and influences a variety of research areas. Coupled with the presupposition that online conversations often mirror offline conversations, many researchers leverage Twitter samples to justify conclusions about the larger population. More recently, researchers are sampling Twitter for sentiment analysis or opinion mining on products and services and, relevant to this work, for political and social commentary that may lead to election prediction. Traditionally, sentiment analysis has been visualized as an aggregation of opinions expressed in the content discussed online, while neglecting the presence of the creators of the content and the impact of their varying levels of participation. This paper illustrates and proposes an alternative model for evaluating and visualizing sentiment using Twitter samples while leveraging and highlighting user participation and impact.
Allocation of Publisher/Subscriber Data Links on a Set of Virtual Machines

There is an increasing interest in applications where sensors or other devices (acting as publishers) may generate data that are processed and analyzed by specialized software components (acting as subscribers to this data) that extract useful information in a variety of scientific or industrial settings. As a result of the large volumes of data that are often generated, Cloud infrastructures may be used to handle the data links between publishers and subscribers. Assuming that a certain number of virtual machines of some given bandwidth have been booked for this purpose, the problem that this paper considers is how to allocate data links to the virtual machines so that the amount of data received by subscribers is maximized. An Integer Linear Programming formulation of the problem and two heuristics are presented, which are evaluated in a range of experiments.