Miracle-dz/UniFAR-FTUs huggingface dataset:
GeoMF++

Location recommendation is an important means to help people discover attractive locations. However, extreme sparsity of user-location matrices leads to a severe challenge, so it is necessary to take implicit feedback characteristics of user mobility data into account and leverage the location’s spatial information. To this end, based on previously developed GeoMF, we propose a scalable and flexible framework, dubbed GeoMF++, for joint geographical modeling and implicit feedback-based matrix factorization. We then develop an efficient optimization algorithm for parameter learning, which scales linearly with data size and the total number of neighbor grids of all locations. GeoMF++ can be well explained from two perspectives. First, it subsumes two-dimensional kernel density estimation so that it captures spatial clustering phenomenon in user mobility data; Second, it is strongly connected with widely used neighbor additive models, graph Laplacian regularized models, and collective matrix factorization. Finally, we extensively evaluate GeoMF++ on two large-scale LBSN datasets. The experimental results show that GeoMF++ consistently outperforms the state-of-the-art and other competing baselines on both datasets in terms of NDCG and Recall. Besides, the efficiency studies show that GeoMF++ is much more scalable with the increase of data size and the dimension of latent space.
The usefulness of competitive PCR: airway gene expression of IL-5, IL-4, IL-4δ2, IL-2, and IFNγ in asthma

BACKGROUND Asthma has been described as an eosinophilic bronchitis driven by interleukin(IL)-4 and IL-5. The quantification of cytokine mRNA levels in airway samples has been confounded by housekeeping gene expression which differs between and within asthmatics and controls. METHODS The usefulness of competitive reverse transcriptase-polymerase chain reaction (RT-PCR) that is independent of housekeeping gene expression for quantitating the mRNA for interferon (IFN)γ, IL-2, IL-5, IL-4 and its receptor antagonist encoding splicing variant IL-4δ2 was determined in a cross sectional study of 45 normal control subjects and 111 with asthma. RESULTS Atopic controls and atopic asthmatic subjects expressed more IL-5 than non-atopic controls (p<0.02) in bronchoalveolar lavage (BAL) cells, but not in biopsy specimens. IL-5 mRNA expression in BAL cells from asthmatic subjects using inhaled corticosteroids (ICS) was significantly lower than those not receiving ICS (p=0.04). IL-2 mRNA levels differed with steroid use in biopsy specimens but not in BAL cells. IFNγ, IL-4, and IL-4δ2 mRNA levels did not differ between any groups and were not affected by steroid use. IL-4 and IL-4δ2 mRNA levels were positively correlated (p<0.0001), suggesting coordinated transcription. CONCLUSIONS While the signal differentiation of competitive PCR in asthma may rival that of in situ hybridisation and immunohistochemistry, the method is expensive and wasteful of material.
Combining Cultural Heritage Related Web Resources in 3D Information Landscapes

In recent years, the number of data collections that are publicly available via the Internet has dramatically increased. Web based semantic databases like Wikipedia derivate DbPedia extend the functionality of Wikipedia by allowing semantic annotations to the information that is usually in form of free text. Since these data collections also cover broad topics related to the Cultural Heritage (CH) domain, they might be well suited to serve as data sources for Cultural Information Systems. Besides the mentioned community based knowledge bases, a number of dedicated Web galleries, for example the Web Gallery of Art (WGA), offer huge digital collections of artworks and relevant metadata. Moreover, renowned institutions like the Getty Foundation provide rich vocabularies of CH related terms that are rather aimed at professional users. We believe that a combination of such different sources might provide interesting insights that would not be available when using each source alone. We therefore propose a system that combines these sources and displays them by using methods from Information Visualisation. We apply the metaphor of 3D Information Landscapes, based on a graph visualisation showing artists and their artworks as nodes and their mutual relationships such as teacher/student, parent/child etc. as edges. The resulting network is drawn in chronological order, thus allowing users to explore art history in a new way.
Analysis of Biometric Authentication Protocols in the Blackbox Model

In this paper we analyze different biometric authentication protocols considering an internal adversary. Our contribution takes place at two levels. On the one hand, we introduce a new comprehensive framework that encompasses the various schemes we want to look at. On the other hand, we exhibit actual attacks on recent schemes such as those introduced at ACISP 2007, ACISP 2008, and SPIE 2010, and some others. We follow a blackbox approach in which we consider components that perform operations on the biometric data they contain and where only the input/output behavior of these components is analyzed.
Gravitational Collapse in Turbulent Molecular Clouds. II. Magnetohydrodynamical Turbulence

Hydrodynamic supersonic turbulence can only prevent local gravitational collapse if the turbulence is driven on scales smaller than the local Jeans lengths in the densest regions, which is a very severe requirement (see Paper I). Magnetic fields have been suggested to support molecular clouds either magnetostatically or via magnetohydrodynamic (MHD) waves. Whereas the first mechanism would form sheetlike clouds, the second mechanism not only could exert a pressure onto the gas counteracting the gravitational forces but could lead to a transfer of turbulent kinetic energy down to smaller spatial scales via MHD wave interactions. This turbulent magnetic cascade might provide sufficient energy at small scales to halt local collapse. We test this hypothesis with MHD simulations at resolutions up to 2563 zones done with ZEUS-3D. We first derive a resolution criterion for self-gravitating, magnetized gas: to prevent collapse of magnetostatically supported regions caused by numerical diffusion, the minimum Jeans length must be resolved by four zones. Resolution of MHD waves increases this requirement to roughly six zones. We then find that magnetic fields cannot prevent local collapse unless they provide magnetostatic support. Weaker magnetic fields do somewhat delay collapse and cause it to occur more uniformly across the supported region in comparison to the hydrodynamical case. However, they still cannot prevent local collapse for much longer than a global free-fall time.
Mycobacterium tuberculosis Reporter Strains as Tools for Drug Discovery and Development

Reporter strains have proven to be powerful tools to study Mycobacterium tuberculosis (Mtb) physiology. Transcriptional and translational reporter strains are engineered by fusing a readout gene, encoding a fluorescent, luminescent or enzymatic protein, downstream of a promoter or in‐frame with a gene of interest. When the reporter is expressed, it generates a signal that acts as a synthetic phenotype, enabling the study of physiologies that might have otherwise been hidden. This review will discuss approaches for generating reporter strains in Mtb and how they can be used as tools for high‐throughput genetic and small molecule screening and as biomarkers for examining Mtb responses to drug or immune stresses during animal infections. Fluorescent reporter strains have an added benefit in that they can be used for single‐cell studies both in vitro and in vivo, thus enabling the study of mechanisms underlying phenotypic heterogeneity. Recent examples of the use of Mtb reporter strains will be presented with a focus on how they can be used as tools for drug discovery and development. © 2018 IUBMB Life, 70(9):818–825, 2018
Towards a Security Metrics Taxonomy for the Information and Communication Technology Industry

To obtain evidence of the security of different products or organizations, systematic approaches to measuring security are needed. We introduce a high abstraction level taxonomy to support the development of feasible security metrics, along with a survey of the emerging security metrics from the academic, governmental and industrial perspectives. With our taxonomy, we strive to bridge the gap between information security management and ICT products, and services security engineering. We believe that if common metrics approaches between different security disciplines can be found, this will advance our holistic understanding and capabilities, both in security management and engineering. Our taxonomy is based on comparing earlier taxonomy approaches and analyzing types of security metrics. Based on the survey, a discussion of future research directions is given in order to prompt advances in the field.
Partial Sequence of a Novel Virus Isolated from Pelodiscus sinensis Hemorrhagic Disease

Outbreaks of hemorrhagic syndrome-like disease with high mortality rates have frequently occurred in Pelodiscus sinensis farms. The purpose of this study was to investigate the pathogen through challenge infection assays and partial sequencing of the genome of the pathogen. A 453-bp amplicon was obtained by random PCR using the nucleic acid extracted from the tissue homogenate filtrate and showed 32% identity at the amino acid level with the replicase polyprotein of the porcine reproductive and respiratory syndrome virus by Blastx. Multiple alignments indicated the putative protein sequence has some similarities to the replicase polyprotein of Arteriviridae, and the phylogenetic tree showed it was closely related to equine arteritis virus. This sequence was found in the lung of the diseased P. sinensis by in situ hybridization. Dot blot hybridization and quantitative RT-PCR showed that the lung had the highest content of virus. The peak replication of P. sinensis hemorrhagic syndrome virus (TSHSV) in the lung occurred 4 days after infection. The ribonucleic nature of the viral genome was confirmed by RNase A or DNase I treatments. We named the virus TSHSV in this study as P. sinensis is also known as Trionyx sinensis. These results provide a fundamental basis for further understanding the biology and the molecular mechanisms of TSHSV.
On Interference Networks Over Finite Fields

We present a framework to study linear deterministic interference networks over finite fields. Unlike the popular linear deterministic models introduced to study Gaussian networks, we consider networks where the channel coefficients are general scalars over some extension held F(pm) (scalar mth extension held models), m x m diagonal matrices over Fp (m-symbol extension ground held models), and m x m general nonsingular matrices (multiple-input and multiple-output (MIMO) ground held models). We use the companion matrix representation of the extension held to convert mth extension scalar models into MIMO ground held models, where the channel matrices have special algebraic structure. For such models, we consider the 2×2×2 topology (two-hop two-flow) and the three-user interference network topology. We derive achievability results and feasibility conditions for certain schemes based on the precoding-based network alignment (PBNA) approach, where intermediate nodes use random linear network coding (i.e., propagate random linear combinations of their incoming messages) and nontrivial precoding/decoding is performed only at the network edges, at the sources and destinations. Furthermore, we apply this approach to the scalar 2 × 2 × 2 complex Gaussian interference channel with fixed channel coefficients and show two competitive schemes outperforming other known approaches at any SNR, where we combine finite field linear precoding/decoding with lattice coding and the compute and forward approach at the signal level. As a side result, we also show significant advantages of vector linear network coding both in terms of feasibility probability (with random coding coefficients) and in terms of coding latency, with respect to standard scalar linear network coding, in PBNA schemes.
Adolescent Scoliosis Screening in Nara City Schools: A 23-Year Retrospective Cross-Sectional Study

Study Design Retrospective cross-sectional study. Purpose To determine the prevalence of idiopathic scoliosis, define the distribution of the curve magnitude, evaluate the accuracy of Moiré topography as a screening tool, and investigate the cost-effectiveness of our screening system. Overview of Literature Early detection of idiopathic scoliosis provides the opportunity for conservative treatment before the deformity is noticeable. We believe that scoliosis screening in schools is useful for detection; however, screening programs are controversial owing to over referral of students who do not require further testing or follow-up. In Japan, school scoliosis screening programs are mandated by law with individual policies determined by local educational committees. We selected Moiré topography as the scoliosis screening tool for schools in Nara City. Methods We selected Moiré topography as the scoliosis screening tool for schools in Nara City. We screened boys and girls aged 11-14 years and reviewed the school scoliosis screening results from 1990 to 2012. Results A total of 195,149 children aged 11-14 years were screened. The prevalence of scoliosis (defined as ≥10° curvature) was 0.057%, 0.010%, and 0.059% in fifth, sixth, and seventh grade boys and 0.337%, 0.369%, and 0.727% in fifth, sixth, and seventh grade girls, respectively. The false-positive rate of our Moiré topography was 66.7%. The minimum cost incurred for scoliosis detection in one student was 2,000 USD. Conclusions The overall prevalence of scoliosis was low in the students of Nara City schools. Over 23 years, the prevalence of scoliosis in girls increased compared to that in the first decade of the study.
Prediction of short term re-exacerbation in patients with acute exacerbation of chronic obstructive pulmonary disease

Background The objective of the study is to develop a scoring system for predicting a 90-day re-exacerbation in hospitalized patients with acute exacerbations of chronic obstructive pulmonary disease (AECOPD). Methods A total of 176 consecutive hospitalized patients with AECOPD were included. The sociodemographic characteristics, status before acute exacerbation (AE), presentations of and treatment for the current AE, and the re-exacerbation in 90 days after discharge from hospital were collected. Results The re-exacerbation rate in 90 days was 48.9% (86 out of 176). It was associated with the degree of lung function impairment (Global initiative for chronic Obstructive Lung Disease [GOLD] grades), frequency of AE in the previous year, and parameters of the current AE, including pleural effusion, use of accessory respiratory muscles, inhaled long-acting β-2-agonists, inhaled corticosteroids, controlled oxygen therapy, noninvasive mechanical ventilation, and length of hospital stay, but was not associated with body mass index, modified Medical Research Council scale, or chronic obstructive pulmonary disease assessment test. A subgroup of ten variables was selected and developed into the re-exacerbation index scoring system (age grades, GOLD grades, AE times in the previous year, pleural effusion, use of accessory respiratory muscles, noninvasive mechanical ventilation, controlled oxygen therapy, inhaled long-acting β-2-agonists and inhaled corticosteroids, and length of hospital stay). The re-exacerbation index showed good discrimination for re-exacerbation, with a C-statistic of 0.750 (P<0.001). Conclusion A comprehensive assessment integrating parameters of stable chronic obstructive pulmonary disease, clinical presentations at exacerbation, and treatment showed a strong predictive capacity for short-term outcome in patients with AECOPD. Further studies are required to verify these findings.
Seeing the Unseen: Revealing Mobile Malware Hidden Communications via Energy Consumption and Artificial Intelligence

Modern malware uses advanced techniques to hide from static and dynamic analysis tools. To achieve stealthiness when attacking a mobile device, an effective approach is the use of a covert channel built by two colluding applications to exchange data locally. Since this process is tightly coupled with the used hiding method, its detection is a challenging task, also worsened by the very low transmission rates. As a consequence, it is important to investigate how to reveal the presence of malicious software using general indicators, such as the energy consumed by the device. In this perspective, this paper aims to spot malware covertly exchanging data using two detection methods based on artificial intelligence tools, such as neural networks and decision trees. To verify their effectiveness, seven covert channels have been implemented and tested over a measurement framework using Android devices. Experimental results show the feasibility and effectiveness of the proposed approach to detect the hidden data exchange between colluding applications.
Skolemising Blank Nodes while Preserving Isomorphism

In this paper, we propose and evaluate a scheme to produce canonical labels for blank nodes in RDF graphs. These labels can be used as the basis for a Skolemisation scheme that gets rid of the blank nodes in an RDF graph by mapping them to globally canonical IRIs. Assuming no hash collisions, the scheme guarantees that two Skolemised graphs will be equal if and only if the two input graphs are isomorphic. Although the proposed scheme is exponential in the worst case, we claim that such cases are unlikely to be encountered in practice. To support these claims, we present the results of applying our Skolemisation scheme over a diverse collection of 43.5 million real-world RDF graphs (BTC-2014); we also provide results for some nasty synthetic cases.
Downlink Pilot Reduction for Massive MIMO Systems via Compressed Sensing

This letter addresses a problem of downlink pilot allocation for massive multiple-input multiple-output (MIMO) systems. When a massive MIMO is employed in frequency division duplex (FDD) systems, significant amount of radio resources are dedicated to the transmission of downlink pilots. Such huge pilot overhead leads to a substantial loss in the maximum data throughput, which motivates us to reduce the number of pilots. In this letter, we propose a pilot reduction strategy based on compressed sensing techniques for orthogonal frequency division multiplexing systems. The pilots are randomly located in a low density manner over the time and frequency domain. To estimate the channels with such low density pilots, we propose a novel sparse channel estimation technique that exploits the common support of the consecutive channel impulse responses over the certain time duration. The evaluation shows that for a massive MIMO with 128 antennas, the proposed scheme achieves significant reduction of pilot overhead, while maintaining good channel estimation performance.
Straightforward and accurate nonlinear device model parameter-estimation method based on vectorial large-signal measurements

To model nonlinear device behavior at microwave frequencies, accurate large-signal models are required. However, the standard procedure to estimate model parameters is often cumbersome, as it involves several measurement systems (DC, vector network analyzer, etc.). Therefore, we propose a new nonlinear modeling technique, which reduces the complexity of the model generation tremendously and only requires full two-port vectorial large-signal measurements. This paper reports on the results obtained with this new modeling technique applied to both empirical and artificial-neural-network device models. Experimental results are given for high electron-mobility transistors and MOSFETs. We also show that realistic signal excitations can easily be included in the optimization process.
RGB-D Salient Object Detection via Minimum Barrier Distance Transform and Saliency Fusion

Automatic detection of salient objects in images has gained its popularity in computer vision field for its usage in numerous vision tasks in recent years. Depth information plays an important role in the human vision system while it is underutilized in most existing two-dimensional (2-D) saliency detection methods. In this letter, a multistage salient object detection framework via minimum barrier distance transform and saliency fusion based on multilayer cellular automata (MCA) is proposed. First, we independently generate the 3-D spatial prior, depth bias, and RGB-produced and depth-induced saliency maps. Next, the two saliency maps are weighted by depth bias to obtain two initial maps. Then, we adopt a saliency optimization step to generate more precise depth-induced saliency map. Moreover, the initial RGB-produced and the optimized depth-induced maps are further fused with 3-D spatial prior. Finally, we utilize MCA to fuse all saliency maps generated previously and obtain the final saliency result with complete salient object. The proposed method is evaluated on the publicly available benchmark dataset, RGBD1000. Compared to several state-of-the-art 2-D and depth-aware approaches, the experimental results demonstrate the effectiveness and superiority of our method, which can accurately detect the salient objects from RGB-D images, and has the most satisfactory overall performance.
Mean-Shift Tracking Using Fuzzy Coding Histogram

In this paper, the fuzzy coding histogram representation for an image region is proposed for visual tracking. At the initial frame, we use the fuzzy clustering technology on all pixels within the initial detection box to get the cluster prototypes, which are used to construct the fuzzy codebook. During the tracking period, the candidate image region is also represented by a fuzzy coding histogram which is constructed by the fuzzy memberships. This representation can incorporate more feature information and enhance the tracking robustness. In addition, we use the cumulative distribution function between the fuzzy coding histograms to construct a cross-bin metric and use the mean-shift iteration to realize the robust visual tracking. Finally, some tracking experimental results are presented to demonstrate the effectiveness of the proposed approach.
Greedy partitioning based tree structured multiclass SVM for Odia OCR

There have been many proposals to extend the basic two-class SVM classifier for multiclass classification and it is established that among these extensions binary-structured hierarchical SVMs is the most efficient computationally. However, determining an effective binary structure by recursively dividing the classes is a major research issue. We describe a new classifier, GP-SVM, based on greedy partitioning of classes and demonstrate that GP-SVM gives better classification accuracy than all major combinational techniques besides having the computational advantages. The advantages of GP-SVM is better realized when the number of classes is large. We demonstrate this advantage in recognition of printed Odia character. We built a corpus of 10025 tagged Odia aksharas collected over multiple printed documents of different fonts. We used a very modest number of features. GP-SVM with 133 classes yielded 95% accuracy of recognition. During the learning process of GP-SVM, the proposed system could learn the taxonomy of character-shapes of Odia script.