Miracle-dz/UniFAR-FTUs huggingface dataset:
Angular-based Massive-MIMO User Clustering and Precoding

We consider the problems of user clustering and precoding in massive MIMO by exploiting the angles of departure (AoD) associated to the different users. First, we present a new clustering method that simultaneously finds the number of groups and associates the users to the appropriate group. In our approach, the users are modelled as a connected graph where the edges reflect the similarity in term of AoDs and we apply spectral analysis to form the clusters. Then a first precoder is obtained to point the energy in the direction of the user cluster with a second multi-user MIMO precoder to simultaneously serve the users within the same cluster. The direct application of the proposed method requires the number of RF chains to be equal to the degrees of freedom of the system. To reduce the number of RF chains down to the number of users, we propose a new hybrid precoder structure that approaches the performance of the traditional fully digital zero-forcing precoding.
Sub-Function Allocation for Computation over Wide-Band MAC

Future networks are expected to connect an enormous number of nodes wirelessly using wide-band transmission. This brings great challenges. To avoid collecting a large amount of data from the massive number of nodes, computation over multi-access channel (CoMAC) is proposed to compute a desired function over the air utilizing the signal-superposition property of multi-access channel (MAC). Due to frequency selective fading, wide-band CoMAC is more challenging and has never been studied before. In this work, we propose the use of orthogonal frequency division multiplexing (OFDM) in wide-band CoMAC to transmit functions in a similar way to bit sequences through division, allocation and reconstruction of functions. To prevent a vanishing computation rate from the increase of the number of nodes, a novel sub-function allocation (SFA) of sub-carriers is derived. The improved computation rate of the proposed framework and the corresponding allocation has been verified through both theoretical analysis and simulation.
Negative Stefan-Maxwell Diffusion Coefficients and Complete Electrochemical Transport Characterization of Homopolymer and Block Copolymer Electrolytes

Author(s): Villaluenga, I; Pesko, DM; Timachova, K; Feng, Z; Newman, J; Srinivasan, V; Balsara, NP | Abstract: © The Author(s) 2018. Published by ECS. Nanostructured block copolymers are of particular interest as electrolytes in batteries with lithium metal anodes. The performance of electrolytes in batteries can be predicted only if three transport coefficients (ionic conductivity, κ, salt diffusion coefficient, D, and cation transference number, t+0) are known. We present complete electrochemical transport characterization of a microphase-separated SEO block copolymer electrolyte by reporting κ, D, and t+0 as functions of salt concentration. We compare the properties of the block copolymer electrolyte with those of PEO homopolymer electrolytes. Negative values of t+0 are observed in many cases. Recasting the transport parameters in terms of Stefan-Maxwell coefficients provides insight into the nature of ion transport in these electrolytes.
MADS Based on DL Techniques on the Internet of Things (IoT): Survey

Technologically speaking, humanity lives in an age of evolution, prosperity, and great development, as a new generation of the Internet has emerged; it is the Internet of Things (IoT) which controls all aspects of lives, from the different devices of the home to the large industries. Despite the tremendous benefits offered by IoT, still there are some challenges regarding privacy and information security. The traditional techniques used in Malware Anomaly Detection Systems (MADS) could not give us as robust protection as we need in IoT environments. Therefore, it needed to be replaced with Deep Learning (DL) techniques to improve the MADS and provide the intelligence solutions to protect against malware, attacks, and intrusions, in order to preserve the privacy of users and increase their confidence in and dependence on IoT systems. This research presents a comprehensive study on security solutions in IoT applications, Intrusion Detection Systems (IDS), Malware Detection Systems (MDS), and the role of artificial intelligent (AI) in improving security in IoT.
ReFriction: Remote friction control on polystyrene foam by ultrasound phased array

We propose a system that can remotely change the friction feel of the polystyrene foam surface using an Airborne Ultrasound Tactile Display (AUTD). In previous studies, the superficial friction of an object is mainly controlled by ultrasonic vibration of an object to reduce the friction using the squeeze film effect or by static electricity to increase superficial friction. In contrast to these studies, our approach changes the friction remotely, thus no device is needed on the target surface. This allows the tactile display to be a large screen or disposable, which can be used as an intuitive touch panel in applications where hygiene is important, such as during surgery. Furthermore, changing the surface friction of three-dimensional objects of any shape is also considered as another application. In this system, the position of the fingertip is detected by a device (Xperia Touch) equipped with a projector and a fingertip detection sensor, and the ultrasound focus is changed according to the fingertip position in real time using AUTD. As a result, the feel of friction on the polystyrene foam is altered by the focused ultrasound. Various friction patterns can be presented according to the image of the projector.
Gallbladder dysfunction

Diagnostic criteria of the gallbladder dysfunction 1. Recurrent episodes of moderate or severe pain in the right hypochondrium or epigastrium which last for 20 minutes and more (pain is defined as moderate when it disrupts the patient’s daily activities, and as severe when medical consultation or medication is necessary). In addition, a patient may also experience one or more of the following symptoms: a. Nausea and vomiting b. Irradiation of pain in the right scapular region or in the right shoulder c. Pain occurs after a meal d. Pain occurs at night 2. Impaired gallbladder emptying (the gallbladder ejection fraction is less than 40%). 3. Absence of structural (morphological) changes explaining these symptoms.
Levitar: Real Space Interaction through Mid-Air CG Avatar

In this paper, we propose a system for using computer graphic (CG) avatars to re-design human-to-human interactions in real space. Virtual reality (VR) social networks enable users to interact with each other through CG avatars and to choose their appearances freely. However, this is only possible in VR space. We propose a system that takes the avatar from VR space to real space with the help of mid-air imaging technology. The video captured from the mid-air image position is presented to the user via a head-mounted display. Our technical contribution is the design of a mid-air stereo camera in which the gaze direction is synchronized with the user’s head movements. A simple mechanism that rotates the mirror and the camera independently enables a complex horizontal and vertical gaze control of the mid-air stereo camera.
Voltage regulation on grid fed by fuel cell and electrochemical capacitor sources

The paper focuses on the first experimental results of a research program addressed to study the possibility of using fuel cell source as back up electrical generator for urban application. The paper, starting from the experimental tests effected on fuel cell source and electrochemical capacitor, useful to indicate subsystem analytical models, examines the energy exchanges among the electrical sources, with the aim either to define design criteria for devices integration or to develop, based on system inputs, a control strategy suitable for the application.
Systemic deficiency of vitronectin is associated with aortic inflammation and plaque progression in ApoE‐Knockout mice

Optimal cell spreading and interplay of vascular smooth muscle cells (VSMC), inflammatory cells, and cell adhesion molecules (CAM) are critical for progressive atherosclerosis and cardiovascular complications. The role of vitronectin (VTN), a major cell attachment glycoprotein, in the pathogenesis of atherosclerosis remains elusive. In this study, we attempt to examine the pathological role of VTN in arterial plaque progression and inflammation. We found that, relative expression analysis of VTN from the liver of Apolipoprotein E (ApoE) Knockout mice revealed that atherosclerotic progression induced by feeding mice with high cholesterol diet (HCD) causes a significant downregulation of VTN mRNA as well as protein after 60 days. Promoter assay confirmed that cholesterol modulates the expression of VTN by influencing its promoter. Mimicking VTN reduction with siRNA in HCD fed ApoE Knockout mice, accelerated athero‐inflammation with an increase in NF‐kB, ICAM‐1, and VCAM‐1 at the site of the plaque along with upregulation of inflammatory proteins like MCP‐1 and IL‐1β in the plasma. Also, matrix metalloprotease (MMP)‐9 and MMP‐12 expression were increased and collagen content was decreased in the plaque, in VTN deficient condition. This might pose a challenge to plaque integrity. Human subjects with acute coronary syndrome or having risk factors of atherosclerosis have lower levels of VTN compared to healthy controls suggesting a clinical significance of plasma VTN in the pathophysiology of coronary artery disease. We establish that, VTN plays a pivotal role in cholesterol‐driven atherosclerosis and aortic inflammation and might be a useful indicator for atherosclerotic plaque burden and stability.
Ensemble Deep Learning Based Cooperative Spectrum Sensing with Semi-soft Stacking Fusion Center

In this paper, an ensemble learning (EL) framework is adopted for cooperative spectrum sensing (CSS) in an orthogonal frequency division multiplex (OFD-M) signal based cognitive radio system. Each secondary user (SU) is accordingly considered as a base learner, where the local spectrum sensing is for investigating the probability of PU being inactive or active. The convolution neural networks with simple architecture are applied given its strength in image recognition as well as the limited computation ability of each SU, meanwhile, the cyclic spectral correlation feature is introduced as the input data. Here, as for the supervised learning, the bagging strategy is helped to establish the training database. For the global decision, the fusion center employs the stacked generalization for further combination learning the SU output of the probability predictions of the PU status. Our method shows significant advantages over conventional CSS methods in term of the detection probability or false alarm probability performance.
STAR: Spatio-Temporal Taxonomy-Aware Tag Recommendation for Citizen Complaints

In modern cities, complaining has become an important way for citizens to report emerging urban issues to governments for quick response. For ease of retrieval and handling, government officials usually organize citizen complaints by manually assigning tags to them, which is inefficient and cannot always guarantee the quality of assigned tags. This work attempts to solve this problem by recommending tags for citizen complaints. Although there exist many studies on tag recommendation for textual content, few of them consider two characteristics of citizen complaints, i.e., the spatio-temporal correlations and the taxonomy of candidate tags. In this paper, we propose a novel Spatio-Temporal Taxonomy-Aware Recommendation model (STAR), to recommend tags for citizen complaints by jointly incorporating spatio-temporal information of complaints and the taxonomy of candidate tags. Specifically, STAR first exploits two parallel channels to learn representations for textual and spatio-temporal information. To effectively leverage the taxonomy of tags, we design chained neural networks that gradually refine the representations and perform hierarchical recommendation under a novel taxonomy constraint. A fusion module is further proposed to adaptively integrate contributions of textual and spatio-temporal information in a tag-specific manner. We conduct extensive experiments on a real-world dataset and demonstrate that STAR significantly performs better than state-of-the-art methods. The effectiveness of key components in our model is also verified through ablation studies.
Assessment of Multiple-Biomarker Classifiers: fundamental principles and a proposed strategy

The multiple-biomarker classifier problem and its assessment are reviewed against the background of some fundamental principles from the field of statistical pattern recognition, machine learning, or the recently so-called "data science". A narrow reading of that literature has led many authors to neglect the contribution to the total uncertainty of performance assessment from the finite training sample. Yet the latter is a fundamental indicator of the stability of a classifier; thus its neglect may be contributing to the problematic status of many studies. A three-level strategy is proposed for moving forward in this field. The lowest level is that of construction, where candidate features are selected and the choice of classifier architecture is made. At that point, the effective dimensionality of the classifier is estimated and used to size the next level of analysis, a pilot study on previously unseen cases. The total (training and testing) uncertainty resulting from the pilot study is, in turn, used to size the highest level of analysis, a pivotal study with a target level of uncertainty. Some resources available in the literature for implementing this approach are reviewed. Although the concepts explained in the present article may be fundamental and straightforward for many researchers in the machine learning community they are subtle for many practitioners, for whom we provided a general advice for the best practice in \cite{Shi2010MAQCII} and elaborate here in the present paper.
The glycine-rich flexible region in SSB is crucial for PriA stimulation

Single-stranded DNA-binding protein (SSB) is essential for all DNA-dependent cellular processes. The mechanism through which PriA helicase, an initiator protein in the DNA replication restart process, is stimulated by SSB in Escherichia coli (Ec) has been established. Nevertheless, whether or not PriA stimulated by SSB is conserved among Gram-negative bacteria remains unclear, and the SSB specificity for the stimulation effect on PriA is unknown. In this study, three similar SSBs from Klebsiella pneumoniae (KpSSB), Salmonella enterica (StSSB), and Pseudomonas aeruginosa (PaSSB) were used to analyze the stimulation effect. Two chimeric proteins, namely, KpSSBn-PaSSBc and KpSSBn-StSSBc, were also used. KpSSB, StSSB, and KpSSBn-StSSBc can stimulate KpPriA activity, but PaSSB and KpSSBn-PaSSBc cannot. The crystal structure of PaSSB solved at 2.04 Å resolution (PDB entry 5YUO) reveals the classic OB fold structure, similar to that of EcSSB. Comparison of SSBs through sequence analysis showed that the typical glycine-rich flexible region in PaSSB contains very few glycine residues. Through analyses of protein chimeragenesis, structure–sequence, and ATPase stimulation effects, we concluded that the inherent difference in the glycine-rich flexible region among SSB species is a determinant of PriA stimulation. Further research can directly focus on determining the type of glycine-rich hinge that can stimulate PriA and the reason why bacterial SSBs need to evolve different C-terminal domains during evolution.
Multispecies probiotics alter fecal short-chain fatty acids and lactate levels in weaned pigs by modulating gut microbiota

Abstract Short-chain fatty acids (SCFAs) are metabolic products produced during the microbial fermentation of non-digestible fibers and play an important role in metabolic homeostasis and overall gut health. In this study, we investigated the effects of supplementation with multispecies probiotics (MSPs) containing Bacillus amyloliquefaciens, Limosilactobacillus reuteri, and Levilactobacillus brevis on the gut microbiota, and fecal SCFAs and lactate levels of weaned pigs. A total of 38 pigs weaned at 4 weeks of age were fed either a basal diet or a diet supplemented with MSPs for 6 weeks. MSP administration significantly increased the fecal concentrations of lactate (2.3-fold; p < 0.01), acetate (1.8-fold; p < 0.05), and formate (1.4-fold; p < 0.05). Moreover, MSP supplementation altered the gut microbiota of the pigs by significantly increasing the population of potentially beneficial bacteria such as Olsenella, Catonella, Catenibacterium, Acidaminococcus, and Ruminococcaceae. MSP supplementation also decreased the abundance of pathogenic bacteria such as Escherichia and Chlamydia. The modulation of the gut microbiota was observed to be strongly correlated with the changes in fecal SCFAs and lactate levels. Furthermore, we found changes in the functional pathways present within the gut, which supports our findings that MSP modulates the gut microbiota and SCFAs levels in pigs. The results support the potential use of MSPs to improve the gut health of animals by modulating SCFAs production.
The Determinate ‘Tasti-Lee’ tomato competes with indeterminate, greenhouse cultivars for yield, fruit quality, and sensory analysis when produced hydroponically

Three cluster-type tomato Lycopersicon esculentum L. cultivars (‘Campari’, ‘Pitenza’, and ‘Tradiro’) and two beefsteak-type tomato cultivars (‘Tasti-Lee’ and ‘FA-574’) were grown in a passively-ventilated greenhouse from 30 Oct. 2007 until 1 May 2008. Both ‘Campari’ and ‘Tasti-Lee’ are marketed as high-lycopene fruit cultivars. All greenhouse cultivars were harvested 15 times from 17 Jan. until 1 May 2008; ‘Tasti-Lee’, a determinate field cultivar, was harvested 10 times from 17 Jan. until 27 Mar. 2008. ‘Campari’ produced the smallest fruit (90 g) and the greatest number of fruit per plant but not significantly different than ‘Pitenza’ (mean 210). ‘Tradiro’ and ‘Pitenza’ produced the greatest fruit weight per plant (mean 26 kg·m–2). ‘Tasti-Lee’ produced 11 kg·m–2 during 5 months of production. Fruit quality analysis was performed on ‘Campari’, ‘Tasti-Lee’, ‘Tradiro’, and a Florida-field-grown sample, ‘Sanibel’, which was purchased at a local retail store. ‘Campari’ fruits were softer (18 vs. 1.8–3.6 mm deformation), higher soluble solids (5.6 vs. 2.4–3.6 °Brix), higher titratable acidity (0.5% vs. 0.2%–0.3%), and lower pH (4.2 vs. 4.3–4.6) than the other cultivars. ‘Tasti-Lee’ fruits were highest in lycopene content (85 vs. 40–60 μg/g). Results from a sensory taste panel ranked ‘Tasti-Lee’ best for overall appearance and color, and was similar to ‘Campari’ for fi rmness, fl avor, and overall acceptability. ‘Campari’ was ranked best for sweetness followed by ‘Tasti-Lee’. The retail sample ‘Sanibel’ was usually ranked last in all categories by the sensory panel. The cultivar Tasti-Lee would be a good selection for greenhouse tomato growers interested in double-cropping in spring and fall to produce high-lycopene fruits with excellent quality and flavor.
Characterizing Bacteria and Methanogens
in a Balloon-Type Digester Fed with Dairy Cattle
Manure for Anaerobic Mono-Digestion

In this study, bacteria and methanogens involved in the decomposition of dairy cattle manure have been characterized via cultivation on selective microbiological media by the viable plate count technique. In addition, DNA was extracted from digested samples, and the 16S rRNA gene was amplified using six primer sets specific to bacterial and archaeal domain via PCR. The sequences of the PCR products were determined and compared to similar sequences in the GenBank database using the BLASTN tools to identify the closest relatives. By culture, E. coli, Salmonella, Shigella, and Campylobacter species were identified and belonged to the phylum Proteobacteria. Following, 16S rRNA analysis, Firmicutes (80%) was the most dominant bacterial phylum represented by the predominant order Clostridiales and genus Clostridium. Other members belonged to the phyla Proteobacteria and Spirochaetes. The phylum Euryarchaeota (100%) was the only observed archaeal domain with members that belonged to the dominant class Methanomicrobia and genus Methanocorpusculum. Other members were related to the order Methanobacteriales and Methanosarcinales. Results suggested that Clostridium sp, Clostridium related organisms, and other acidogens were responsible for the deconstruction of biomass-generating substrates metabolized by Methanocorpusculum and Methanobrevibacter species to produce methane via the fundamental hydrogenotrophic pathway.
Attention-Residual Network with CNN for Rumor Detection

Wide dissemination of unverified claims has negative influence on social lives. Rumors are easy to emerge and spread in the crowds especially in Online Social Network (OSN), due to its openness and extensive amount of users. Therefore, rumor detection in OSN is a very challenging and urgent issue. In this paper, we propose an Attention-Residual network combined with CNN (ARC), which is based on the content features for rumor detection. First, we build a data encoding model based on word-level data for contextual feature representation. Second, we propose a residual framework based on fine-tuned attention mechanism to capture long-range dependency. Third, we apply convolution neural network with varying window size to select important components and local features. Experiments on two twitter datasets demonstrate that the proposed model has better performance than other content-based methods both in rumor detection and early rumor verification. To the best of our knowledge, we are the first work that utilize attention model in conjunction with residual network on rumor detection.
Compression and Recovery Scheme for Cell-free Cloud Radio Access Network

As a novel wireless network architecture, cloud radio access network (C-RAN) can significantly reduce the cost of operations and maintenance as well as providing lower power consumption and higher spectral efficiency. However, the huge load on the fronthaul network which connects the baseband unit (BBU) and the large number of remote radio heads (RRHs) is a significant challenge. To reduce the fronthaul load, this paper proposes a data compression and recovery scheme based on compressive sensing (CS) for the uplink C-RAN scenario. The numerical results show that proposed compression scheme can be effectively applied to the uplink of a cell-free C-RAN system.
Data-driven Summarization and Synchronized Second-screen Enrichment of Cycling Races: Using Live and Historical Sports Data to Reinvent Traditional Reporting

Traditional broadcasters of cycling races are experiencing hard times as the numbers of spectators are decreasing each year. Other ways of reporting are needed to keep the viewer interested. In this paper, two possible solutions are proposed that have been evaluated during the Grand Depart of the Tour de France 2019 in Brussels. The first innovation focuses on data-driven summarization and allows end-users to query for personalized stories of a race, tailored to their wishes (such as the length of the clip and the teams and/or riders that they are interested in). The second innovation follows the second screen trend and synchronizes cycling heritage multimedia data with the riders' live location during the race. Both rich, interactive TV experiences are based on a combination of data mining and computer vision techniques which can also be applied to other sports with similar characteristics. Evaluation by a test audience showed that there is certainly potential in both formats.