Miracle-dz/UniFAR-FTUs huggingface dataset:
AN CBRSIR FEATURES COMPRESSION APPROACH BASED ON DPSO AND SVM

The number of image features used by content-based remote sensing image retrieval (CBRSIR) system is not less than one hundred, and the image amount is very large, at the same time, the time cost is very important to the retrieval system. So the image feature compression is a crucial subject to CBRSIR. This paper proposed a high dimensional feature’s compression approach based on discrete particle swarm optimization (DPSO) and support vector machine (SVM). This approach trained the SVM classifier by DPSO, and gained the particle’s fitness by both the train data and the verification data. By iterative processing, the optimized high dimensional feature compression result achieved. This paper addressed the theory and the flow of the new approach in detail and the experiment verified the effectiveness of the new approach. * Corresponding author.
Evaluating and Optimizing Online Advertising: Forget the Click, but There Are Good Proxies

Online systems promise to improve advertisement targeting via the massive and detailed data available. However, there often is too few data on exactly the outcome of interest, such as purchases, for accurate campaign evaluation and optimization (due to low conversion rates, cold start periods, lack of instrumentation of offline purchases, and long purchase cycles). This paper presents a detailed treatment of proxy modeling, which is based on the identification of a suitable alternative (proxy) target variable when data on the true objective is in short supply (or even completely nonexistent). The paper has a two-fold contribution. First, the potential of proxy modeling is demonstrated clearly, based on a massive-scale experiment across 58 real online advertising campaigns. Second, we assess the value of different specific proxies for evaluating and optimizing online display advertising, showing striking results. The results include bad news and good news. The most commonly cited and used proxy is a click on an ad. The bad news is that across a large number of campaigns, clicks are not good proxies for evaluation or for optimization: clickers do not resemble buyers. The good news is that an alternative sort of proxy performs remarkably well: observed visits to the brand's website. Specifically, predictive models built based on brand site visits-which are much more common than purchases-do a remarkably good job of predicting which browsers will make a purchase. The practical bottom line: evaluating and optimizing campaigns using clicks seems wrongheaded; however, there is an easy and attractive alternative-use a well-chosen site-visit proxy instead.
Texture Free Radargrammetric Processing of Opposite-View TomoSAR Data for DEM Estimation

In the present work a new methodology to retrieve the absolute digital elevation model (DEM) of a scene from Tomographic SAR (TomoSAR) [1] data is proposed. First of all, two 3D images of the scene are generated from TomoSAR data acquired from opposite views. Tomographic data are phase calibrated with Phase Center Double Localization algorithm, that jointly estimates platform positioning errors and elevation of the targets in the scene [2]. From the two opposite view 3D images, two DEMs of the same area are thus obtained as corresponding to the surface layer location. The two DEMs obtained are affected from different low pass undulations corresponding to the null space of the calibration problem [2], which consists of a different residual rotation for each along track position of the sensors. For every azimuth, a pair of rotations can thus be fixed by maximizing the correlation between the elevation profiles resulting from the opposite views as a function of the pair of correction rotations applied. This step is analogous to the coregistration step of radargrammetric processing [3]; in this case, however, no scene texture is required, since surface elevation profiles obtained from TomoSAR are coregistered instead of 2D intensity images. This methodology is thus particularly useful in the case of scenes lacking features for the application of texture tracking techniques, such as land ice. Moreover, it exploits the accuracy of interferometric processing combined to the precise stereo localization of opposite-view radargrammetry for rotational ambiguity solving. The methodology can be applied even starting from partial topographic information. An absolute DEM in georeferenced coordinates may finally be obtained by fusing the two corrected DEMs. The methodology is illustrated and tested on real data from ESA IceSAR 2012 campaign [4] and lidar data for comparison [5].
Integrated Wi-Fi fingerprinting and inertial sensing for indoor positioning

Indoor positioning has emerged as a widely used application of Wi-Fi wireless networks. A region-based fingerprinting approach is presented for indoor positioning in Wi-Fi wireless networks. This proposed method compares the fingerprint of a Wi-Fi tag with that of a region-based group of reference points, instead of an individual reference point. With the fingerprinting position estimate obtained, and with an inertial measurement unit integrated with the Wi-Fi tag, a stochastic system model is adopted to track the target's position when it is in piecewise constant velocity motion in Wi-Fi wireless networks. The stochastic system model utilizes Wi-Fi fingerprinting position estimates as measurements and inertial sensing data as control inputs. Both simulation studies and experiment data have shown the positioning performance of the integrated mobile platform with improved accuracy, by using the proposed Wi-Fi and inertial sensing technologies.
IPS-1 Is Essential for the Control of West Nile Virus Infection and Immunity

The innate immune response is essential for controlling West Nile virus (WNV) infection but how this response is propagated and regulates adaptive immunity in vivo are not defined. Herein, we show that IPS-1, the central adaptor protein to RIG-I-like receptor (RLR) signaling, is essential for triggering of innate immunity and for effective development and regulation of adaptive immunity against pathogenic WNV. IPS-1−/− mice exhibited increased susceptibility to WNV infection marked by enhanced viral replication and dissemination with early viral entry into the CNS. Infection of cultured bone-marrow (BM) derived dendritic cells (DCs), macrophages (Macs), and primary cortical neurons showed that the IPS-1-dependent RLR signaling was essential for triggering IFN defenses and controlling virus replication in these key target cells of infection. Intriguingly, infected IPS-1−/− mice displayed uncontrolled inflammation that included elevated systemic type I IFN, proinflammatory cytokine and chemokine responses, increased numbers of inflammatory DCs, enhanced humoral responses marked by complete loss of virus neutralization activity, and increased numbers of virus-specific CD8+ T cells and non-specific immune cell proliferation in the periphery and in the CNS. This uncontrolled inflammatory response was associated with a lack of regulatory T cell expansion that normally occurs during acute WNV infection. Thus, the enhanced inflammatory response in the absence of IPS-1 was coupled with a failure to protect against WNV infection. Our data define an innate/adaptive immune interface mediated through IPS-1-dependent RLR signaling that regulates the quantity, quality, and balance of the immune response to WNV infection.
A face image hashing method based on optimal linear transform under colored Gaussian noise assumption

In this paper, we propose a novel face image hashing method based on an optimal linear transformation. In the proposed method, first, we apply a feature extraction method. Then, we define an optimal linear transformation matrix based on within-class covariance matrix which is the maximum likelihood estimate of the variations of the biometric data belonging to the same user. Next, we reduce the dimension of the feature vector by using this transform. Finally, we apply quantization and obtain a face image hash vector. We test the performance of the proposed method with AT&T and M2VTS face databases and compare the results with the random projection based biometric hashing methods. We perform the simulations by taking into account two scenarios: 1) Secret key is not known by attacker, 2) Attacker illegally acquires the secret key. The simulation results show the proposed method has better performance especially when the secret key has been compromised.
Analysis of color feature extraction techniques for pathology image retrieval system

Medical imaging has become an important tool not only in documenting patient presentation and clinical findings, but also understanding and managing various diseases. Image data provides tangible visual evidence of disease manifestation. The number of digital images that needs to be acquired, analyzed, classified, stored and retrieved in the medical centers is exponentially growing with the advances in medical imaging technology. The goal of this work is to develop a medical image retrieval system for pathology images that implements recent improvements in feature representation, efficient indexing, and similarity matching. The feature representation is analyzed using various color feature extraction techniques in HSV, CIE L∗u∗v∗, CIE L∗a∗b∗ color space. The image indexing is implemented based on the color descriptors. The efficiency of similarity matching is analyzed by means of Euclidean distance, Histogram intersection and hamming distance. The experimental results are compared for the color feature extraction techniques discussed in this paper. The image database considered for retrieval process consists of pathology images belonging to dermatology, dental, hematology, gastroscopic and cervical cancer. This paper suggests an optimal color feature extraction technique suitable for different pathology image categories chosen for the study.
COMBINATORIAL SCHEMA AND THEIR COHOMOLOGY; APPLICATIONS IN NATURAL AND ORGANIZATIONAL SYSTEMS.

This paper denes a new category of combinatorial modules. These are overlapping collections of small categories indexed by a directed set. They are specialized in applications to non-commuting rings acting on overlapping modules. Each combinatorial module is associated with an Abelian category of group valued sheaves which is its combinatorial scheme. An adaptation of Cech cohomology is used to analyze the structure of applications. The paper focuses on the applications of these ideas to supply chains, testing software and ecosystems. As these systems display a variety of phenomena or behaviors the area of interest is in the means of validating scenarios or assertions of capability. Cohomology is used to analyze techniques of validating scenarios in supply chains and assertions in systems. The characterization of scenarios in terms of when they can be satised are a start of understanding the many forms of failure (called fragmentation theory) that a supply chain or system can undergo. The intention is to motivate further development of combinatorial schema through applications that that aggregate or combine materials , validated data, concepts or nutrients along a directed graph.
Tweets Beget Propinquity: Detecting Highly Interactive Communities on Twitter Using Tweeting Links

Many community detection algorithms have been developed to detect communities on Online Social Networks (OSN). However, these algorithms are based only on topological links and researchers have observed that many topological links do not translate to actual user interaction. As such, many members of the detected communities do not communicate frequently to each other. This inactivity creates a problem in targeted advertising and viral marketing which requires the community to be highly active so as to allow the diffusion of product/service information. We propose an approach to detect highly interactive Twitter communities that share common interests, based on the frequency and patterns of direct tweeting among users, rather than the topological information implicit in follower/following links. From a topological aspect, we show that our method detects communities that are more cohesive and connected within different interest groups. We also show that the detected communities interact actively about the specific interests, based on the high frequency of #hash tags and @mentions related to this interest. In addition, we study the trends in their tweeting patterns such as how they follow and unfollow other users.
Chief Executive Cognition, Turnaround Strategy and Turnaround Attempts of Declining Firms

ABSTRACT This paper develops an interpretative model of turnaround management that describes how both the attention and attribution patterns of CEOs in declining firms influence the odds of successful turnaround attempts. This model is then tested on a sample of US-based, publicly traded single business firms that experienced significant performance declines between 1987 and 2005. The results show the odds of turnaround attempt success increased when firm CEOs paid more attention to issues external to the organizations and when the causes of decline were attributed to factors perceived as more controllable by the firm. In addition, while the amount of strategic change launched by declining firms during turnaround attempts had a positive association with the chances of successful turnaround, the effect of increased strategic change increased when CEOs attributed the causes of decline to stable sources.
Personality, coping with stress and quality of life in individuals at risk of type 2 diabetes in late adolescence - mediation model testing

ABSTRACT Chronic disease, their risk and associated stress are important factors in determining quality of life (QoL). Those at risk may prepare themselves for the difficulties connected with the disease by developing proactive strategies (PCS). The current study aimed to examine differences in QoL, PCS and personality traits (PT) between the group at risk of type 2 diabetes (DT2) and control group (CG) and the mediating role of PCS for relationships between PT and QoL. Participants included 72 at risk of DT2 and 72 healthy persons in the late adolescence. We used: WHOQoL-Brief, PCI and NEO-FFI. DT2 risk group and CG differ in terms of satisfaction with health, phisical domain and neuroticism. The strategy of emotional support seeking turned out to be amediatory factor in the relationship between psychological satisfaction and neuroticism and between psychological satisfaction and extraversion in DT2 risk group. PT and PCS seem to have agreat value when dealing with chronic disease like diabetes. Depending on what strategy the young person is using it can affect QoL in abig matter due to neuroticism and extraversion. That can be ahint how to support adolescents in working on adequate strategies to cope with stress.
Engagement with news on Twitter: insights from Australia and Korea

ABSTRACT This study examines the patterns of news engagement among news consumers with different political affiliation and cultural background. We use computational methods and data from Twitter in a cross-country comparison of engagement with six online news sources in Australia and South Korea. For our analysis, we used a subset of Twitter users who retweeted at least one political story during the period of collection, and for whom we were able to predict political affiliation using correspondence analysis and data on Twitter follower ties to politicians. We find that right-wing Australian retweeters are more intense in their news engagement, compared with their left-wing counterparts, whereas in South Korea it was the opposite. Australian right-wing political retweeters have more diverse information sources, while there was no difference in information diversity between the right and left in South Korea. We discuss how the political situation in South Korea at the time of data collection may have affected our analysis. We emphasise the methodological contributions of our research and its connection to on-going research into the behavioural foundations of ‘filter bubbles’.
Financial Contracting

This paper discusses how economists' views of firms' financial structure decisions have evolved from treating firms' profitability as given; to acknowledging that managerial actions affect profitability; to recognizing that firm value depends on the allocation of decision or control rights. The paper argues that the decision or control rights approach is useful, even though it is at an early stage of development, and that the approach has some empirical content: it can throw light on the structure of venture capital contracts and the reasons for the diversity of claims.
Ontology-driven Enrichment of Spatial Databases

Generalization is an abstraction process by which characteristics of spatial patterns should be preserved and highlighted. This requires the patterns to be detected beforehand. Additionally, automated enrichment of spatial data is of growing importance for many mapping agencies in order to respond to varying user needs. In this paper we present a framework for pattern recognition in urban environments that complements current algorithm-centered approaches by first formalizing spatial patterns in ontologies, and then deductively triggering appropriate low-level pattern recognition techniques. We start our paper by giving an introduction to the terminology of ontologies. Existing work on pattern recognition using semantic models is reviewed. We then outline our general framework and exemplify an ontological model of an urban structure for a case study we are currently working on. Finally, we discuss issues, benefits and challenges of the approach.
Protein-Protein Interaction Analysis Highlights Additional Loci of Interest for Multiple Sclerosis

Genetic factors play an important role in determining the risk of multiple sclerosis (MS). The strongest genetic association in MS is located within the major histocompatibility complex class II region (MHC), but more than 50 MS loci of modest effect located outside the MHC have now been identified. However, the relative candidate genes that underlie these associations and their functions are largely unknown. We conducted a protein-protein interaction (PPI) analysis of gene products coded in loci recently reported to be MS associated at the genome-wide significance level and in loci suggestive of MS association. Our aim was to identify which suggestive regions are more likely to be truly associated, which genes are mostly implicated in the PPI network and their expression profile. From three recent independent association studies, SNPs were considered and divided into significant and suggestive depending on the strength of the statistical association. Using the Disease Association Protein-Protein Link Evaluator tool we found that direct interactions among genetic products were significantly higher than expected by chance when considering both significant regions alone (p<0.0002) and significant plus suggestive (p<0.007). The number of genes involved in the network was 43. Of these, 23 were located within suggestive regions and many of them directly interacted with proteins coded within significant regions. These included genes such as SYK, IL-6, CSF2RB, FCLR3, EIF4EBP2 and CHST12. Using the gene portal BioGPS, we tested the expression of these genes in 24 different tissues and found the highest values among immune-related cells as compared to non-immune tissues (p<0.001). A gene ontology analysis confirmed the immune-related functions of these genes. In conclusion, loci currently suggestive of MS association interact with and have similar expression profiles and function as those significantly associated, highlighting the fact that more common variants remain to be found to be associated to MS.
Adaptive covariance estimation of locally stationary processes

It is shown that the covariance operator of a locally stationary process has approximate eigenvectors that are local cosine functions. We model locally stationary processes with pseudo-differential operators that are time-varying convolutions. An adaptive covariance estimation is calculated by searching first for a “best” local cosine basis which approximates the covariance by a band or a diagonal matrix. The estimation is obtained from regularized versions of the diagonal coefficients in the best basis.
A Simple Secure Signature Scheme Based on the Strong RSA Assumption without Random Oracle Model

Summary In this paper, we present a simple secure signature scheme which is based on the Strong RSA Assumption without random oracle model. This scheme is derived from the Cremer and Shoup strong RSA signature scheme. In contrast, the public key and signature sizes in our proposed scheme are smaller. Consequently, our scheme is somewhat more efficient.
Focusing the wavefield inside an unknown 1D medium: Beyond seismic interferometry

With seismic interferometry one can retrieve the response to a virtual source inside an unknown medium, if there is a receiver at the position of the virtual source. Using inverse scattering theory, we demonstrate that, for a 1D medium, the requirement of having an actual receiver inside the medium can be circumvented, going beyond seismic interferometry. In this case, the wavefield can be focused inside an unknown medium with independent variations in velocity and density using reflection data only.
GENERAL THEORY OF INFERENTIAL MODELS I . CONDITIONAL INFERENCE

As applied problems have grown more complex, statisticians have been gradually led to reconsider the foundations of statistical inference. The recently proposed inferential model (IM) framework of Martin, Zhang and Liu (2010) achieves an interesting compromise between the Bayesian and frequentist ideals. Indeed, inference is based on posterior probability-like quantities, but there are no priors and the inferential output satisfies certain desirable long-run frequency properties. In this two-part series, we further develop the theory of IMs into a general framework for statistical inference. Here, in Part I, we build on the idea of making inference by predicting unobserved auxiliary variables, focusing primarily on an intermediate step of conditioning, whereby the dimension of this auxiliary variable is reduced to a manageable size. This dimension reduction step leads to a simpler construction of IMs having the required longrun frequency properties. We show that under suitable conditions, this dimension reduction step can be made without loss of information, and that these conditions are satisfied in a wide class of models, including those with a group invariance structure. The important credibility theorem of Zhang and Liu (2010) is extended to handle the case of conditional IMs, and connections to conditional inference in the likelihood framework are made which, in turn, allow for numerical approximation of the conditional posterior belief functions using the well-known “magic formula” of Barndorff-Nielsen (1983). The conditional IM approach is illustrated on a variety of examples, including Fisher’s problem of the Nile.
Performance analysis of M-QAM in a non-Gaussian environment

| This paper considers a class of nonGaussian noise modeled as a combination of a white Gaussian process and a shot noise process as suggested by Bello and Esposito [1]. With this noise model as input of a quadrature demodulator, the noise output vector is shown to have circular symmetry. In the particular case of Rayleigh distribution for the noise envelope amplitude, the symbol error probability for M-QAM systems is exactly obtained and numerically computed with any desired precision to cover a large range of parameters values. General results show that the system performance depends on two parameters: the average number of impulses occurring in the detection interval, and the ratio of impulsive noise power to Gaussian noise power. Furthermore, in all situations, there is a threshold phenomena which separates into two distinct regions: for high SNR, it is mainly the impulse noise which a ects the performance of the systems; for low SNR, white Gaussian noise dominates.