Miracle-dz/UniFAR-FTUs huggingface dataset:
Public key cryptosystem and signature schemes over the ring of eisenstein integers

In this paper, the extended ElGamal public key cryptosystem and digital signature schemes with appendix and message recovery are described in the setting of the groups of units of the ring of Eisenstein integers modulo Eisenstein primes. This approach has many advantages over the classical ElGamal cryptosystem and digital signature scheme.Many examples are given to show the validity of the proposed extensions of the ElGamal public key cryptosystem and digital signature schemes.
Research, part of a Special Feature on Social Network Analysis in Natural Resource Governance Synapses in the Network: Learning in Governance Networks in the Context of Environmental Management

In the face of apparent failures to govern complex environmental problems by the central state, new modes of governance have been proposed in recent years. Network governance is an emerging concept that has not yet been consolidated. In network governance, processes of (collective) learning become an essential feature. The key issue approached here is the mutual relations between network structure and learning, with the aim of improving environmental management. Up to now, there have been few attempts to apply social network analysis (SNA) to learning and governance issues. Moreover, little research exists that draws on structural characteristics of networks as a whole, as opposed to actor-related network measures. Given the ambiguities of the concepts at stake, we begin by explicating our understanding of both networks and learning. In doing so, we identify the pertinent challenge of individual as opposed to collective actors that make up a governance network. We introduce three learning-related functions that networks can perform to different degrees: information transmission, deliberation, and resilience. We address two main research questions: (1) What are the characteristics of networks that foster collective learning in each of the three dimensions? To this end, we consider SNA-based network measures such as network size, density, cohesion, centralization, or the occurrence of weak as opposed to strong ties. (2) How does collective learning alter network structures? We conclude by outlining a number of open issues for further research.
LASP1, a Newly Identified Melanocytic Protein with a Possible Role in Melanin Release, but Not in Melanoma Progression

The LIM and SH3 protein 1 (LASP1) is a focal adhesion protein. Its expression is increased in many malignant tumors. However, little is known about the physiological role of the protein. In the present study, we investigated the expression and function of LASP1 in normal skin, melanocytic nevi and malignant melanoma. In normal skin, a distinct LASP1 expression is visible only in the basal epidermal layer while in nevi LASP1 protein is detected in all melanocytes. Melanoma exhibit no increase in LASP1 mRNA compared to normal skin. In melanocytes, the protein is bound to dynamin and mainly localized at late melanosomes along the edges and at the tips of the cell. Knockdown of LASP1 results in increased melanin concentration in the cells. Collectively, we identified LASP1 as a hitherto unknown protein in melanocytes and as novel partner of dynamin in the physiological process of membrane constriction and melanosome vesicle release.
Leveraging Mobile Technology to Increase the Permanent Adoption of Shelter Dogs

We present the results of an 8-week pilot study with 55 dogs investigating whether using quantimetric monitors and a companion smartphone application can reduce returns and increase the perceived strength of bonds between newly adopted dogs from the Humane Society of Silicon Valley and their adopters. Through this pilot study, we developed guidelines for future research and discovered promising results indicating that providing dog quantimetric data to adopters through the use of a smartphone application could yield reduced rates of re-relinquishment. Additionally, respondents indicated that they felt using the smartphone application helped them to better meet the activity needs of their dog and increased the bond between themselves and their newly adopted dog.
Oligonucleotide Chip for Detection of Lamivudine-Resistant Hepatitis B Virus

ABSTRACT Hepatitis B virus (HBV) is one of the major causes of liver disease worldwide. It is important to conduct antiviral therapy against chronic hepatitis B to minimize the amount of liver damage. Lamivudine has been known to be an effective antiviral agent for the treatment of HBV infection. However, the emergence of viral mutants resistant to lamivudine is the main concern during the treatment of HBV-infected patients. Therefore, the detection of lamivudine-resistant mutants is of clinical importance. We have developed an oligonucleotide chip for the detection of lamivudine-resistant HBV which is rapid and accurate. The oligonucleotide chip consists of quality control probes, negative control probes, and specific oligonucleotide probes for the detection of lamivudine-resistant HBV. The specific probes consist of five probes for the detection of wild-type rtL180, rtM204, and rtV207 sequences and seven probes for the detection of HBV mutations. We tested 123 serum samples from patients with chronic HBV infection who had received lamivudine therapy. Eighty samples contained mutants with YMDD mutations. Among these, 17 contained rtM204V (YVDD), 24 contained rtM204I3 (YIDD3), 3 contained rtM204I2 (YIDD2), and 36 contained mixed types. We compared the results obtained with our oligonucleotide chip with those obtained by PCR-restriction fragment length polymorphism (RFLP) analysis and sequencing. The rate of concordance between the assay with the oligonucleotide chip and PCR-RFLP analysis for detection of the YMDD motif was 96.7%. The rate of concordance between the results obtained with the oligonucleotide chip for the detection of rtL180 and rtV207 and the results obtained by sequencing was 100%. Thus, the oligonucleotide chip is a reliable and useful tool for the detection of antiviral-resistant HBV.
Data-Driven Detection of Prominent Objects

This article deals with the detection of prominent objects in images. As opposed to the standard approaches based on sliding windows, we study a fundamentally different solution by formulating the supervised prediction of a bounding box as an image retrieval task. Indeed, given a global image descriptor, we find the most similar images in an annotated dataset, and transfer the object bounding boxes. We refer to this approach as data-driven detection (DDD). Our key novelty is to design or learn image similarities that explicitly optimize some aspect of the transfer unlike previous work which uses generic representations and unsupervised similarities. In a first variant, we explicitly learn to transfer, by adapting a metric learning approach to work with image and bounding box pairs. Second, we use a representation of images as object probability maps computed from low-level patch classifiers. Experiments show that these two contributions yield in some cases comparable or better results than standard sliding window detectors - despite its conceptual simplicity and run-time efficiency. Our third contribution is an application of prominent object detection, where we improve fine-grained categorization by pre-cropping images with the proposed approach. Finally, we also extend the proposed approach to detect multiple parts of rigid objects.
Finding k-dominant Skyline cube based on sharing-strategy

K-dominant skyline query has been proposed as an important operator for multi-criteria decision making, data mining and so on, this technology can reduce the large result sets of skyline query in high dimensional space. In this paper, a new concept was firstly proposed: k-dominant Skyline cube, which consists of all the k-dominant skylines. Although existing algorithms can compute every k-dominant skyline, they lead to much repeat work because of no sharing result. We develop two computation sharing strategies—ASCEND sharing strategy and DESCEND sharing strategy. Based on these two sharing strategies, two novel algorithms—BUA (Bottom-Up Algorithm) and UBA (Up-Bottom Algorithm) are proposed to compute k-dominant skyline cube. Furthermore, detailed theoretical analyses and extensive experiments demonstrate that our algorithms are both efficient and effective.
Towards Polypharmacokinetics: Pharmacokinetics of Multicomponent Drugs and Herbal Medicines Using a Metabolomics Approach

Determination of pharmacokinetics (PKs) of multicomponent pharmaceuticals and/or nutraceuticals (polypharmacokinetics, poly-PKs) is difficult due to the vast number of compounds present in natural products, their various concentrations across a wide range, complexity of their interactions, as well as their complex degradation dynamics in vivo. Metabolomics coupled with multivariate statistical tools that focus on the comprehensive analysis of small molecules in biofluids is a viable approach to address the challenges of poly-PK. This paper discusses recent advances in the characterization of poly-PK and the metabolism of multicomponent xenobiotic agents, such as compound drugs, dietary supplements, and herbal medicines, using metabolomics strategy. We propose a research framework that integrates the dynamic concentration profile of bioavailable xenobiotic molecules that result from in vivo absorption and hepatic and gut bacterial metabolism, as well as the human metabolic response profile. This framework will address the bottleneck problem in the pharmacological evaluation of multicomponent pharmaceuticals and nutraceuticals, leading to the direct elucidation of the pharmacological and molecular mechanisms of these compounds.
The shared and specific mechanism of four autoimmune diseases

Interaction between genetic and epigenetic mechanisms may lead to autoimmune diseases. The features of these diseases show familial aggregation. The generality and specificity are keys to studying pathogenesis and etiology of them. This research integrated data of genetics and epigenetics, to find disease-related genes based on the levels of expression and regulation, and explored then to the shared and specific mechanism of them by analyzing shared and specific pathways of common four autoimmune diseases, including Type 1 Diabetes Mellitus (T1D), Multiple Sclerosis (MS), Rheumatoid Arthritis (RA) and Systemic Lupus Erythematosus (SLE). The results showed that Lysosome and Fc gamma R-mediated phagocytosis are shared pathways of the four diseases. It means that the occurrence and development of them may associate with lysosomes and phagocytosis. And there were 2 pathways are shared pathways of three diseases, ribosome pathway associated with susceptibility to MS, RA and SLE, and Pathogenic Escherichia coli infection associated with susceptibility to T1D, MS and RA; 9 pathways are shared pathways of two diseases. The corporate underlying causes of these diseases may be these shared pathways activated. Furthermore, we found that T1D-related specific pathways (Insulin signaling,etc.) were 9, MS (Proteasome,etc.) is also 9, RA and SLE is 10 and 6 respectively. These pathways could help us to reveal shared and specific mechanisms of the four diseases.
Active Annotation Translation

We introduce a general framework for quickly annotating an image dataset when previous annotations exist. The new annotations (e.g. part locations) may be quite different from the old annotations (e.g. segmentations). Human annotators may be thought of as helping translate the old annotations into the new ones. As annotators label images, our algorithm incrementally learns a translator from source to target labels as well as a computer-vision-based structured predictor. These two components are combined to form an improved prediction system which accelerates the annotators' work through a smart GUI. We show how the method can be applied to translate between a wide variety of annotation types, including bounding boxes, segmentations, 2D and 3D part-based systems, and class and attribute labels. The proposed system will be a useful tool toward exploring new types of representations beyond simple bounding boxes, object segmentations, and class labels, and toward finding new ways to exploit existing large datasets with traditional types of annotations like SUN [36], Image Net [11], and Pascal VOC [12]. Experiments on the CUB-200-2011 and H3D datasets demonstrate 1) our method accelerates collection of part annotations by a factor of 3-20 compared to manual labeling, 2) our system can be used effectively in a scheme where definitions of part, attribute, or action vocabularies are evolved interactively without relabeling the entire dataset, and 3) toward collecting pose annotations, segmentations are more useful than bounding boxes, and part-level annotations are more effective than segmentations.
A Zero-Voltage-Switching Bidirectional DC–DC Converter With State Analysis and Soft-Switching-Oriented Design Consideration

This paper proposes a new soft-switching bidirectional DC/DC converter. The proposed converter achieves zero-voltage switching (ZVS) for the entire main switches and zero-current switching for the rectifier diodes in the large-load range. These features reduce switching loss, voltage and current stresses, and diode reverse-recovery effect. The simple electrical isolated topology with the soft-switching characteristic provides an attractive solution for a battery charge/discharge system in an electric vehicle, distributed power system, or uninterruptible power system. This paper describes the operation principle and the ZVS condition in detail. The mathematical model based on the state-space averaging method is also deduced to depict the performance characteristic. Then, design guidelines are presented to ensure the ZVS condition for all the switches. Finally, simulation and experimental results obtained from a 1-kW prototype verify the discussed theoretical analysis.
A Maturity Model for Business Transformation Management

Business Transformation Management Methodology (BTM2) is a recent methodology aimed at dealing with growing complexity, globalization and uncertainty organizations are faced with due to continuous change processes. Meta Management is the foundation of this methodology and is focused on providing a framework that helps to manage the complexity of business transformation. The difficulty lies on the different ways you can apply Meta Management and how far you can go. In fact, the way this layer of management will be conducted is decisive for the effectiveness and efficiency of the transformation process. A maturity model for Meta Management helps leaders and managers derive and prioritize improvement measures, as well as control progress, by providing them with the as-is assessment. In our research we applied the proposed Meta Management Maturity Model to the Portuguese eProcurement Interoperability project, a complex and critical national challenge. According to the practitioners, this is an important step for the success of the project.
MODE OF TEXT PRESENTATION AND ITS INFLUENCE ON READING EFFICIENCY: SCROLLING VERSUS PAGINATION

In the last decades, the Internet has developed into a proliferating and flourishing source of information. This phenomenon requires complex pieces of writing to be presented in a way that expedites their efficient processing. This paper presents an experiment studying how text presentation affects reading efficiency and text recall. We compared different types of text presentations - scrolling and text pagination with and without callouts. A word recognition task was used to assess the recognition of a presented text. Discrepancies in reading efficiency were apparent in the results obtained from the eye tracking data; namely, we noted that scrolling is more demanding than reading paginated text in terms of the processing time. Our findings provide support for claims of more efficient processing of paginated texts. Such text presentation ap- pears to have a strong influence on cognition that should be taken into account by designers whenever visualizing complex texts online.
Predicting relational security among early adolescent girls

The current study used multiple informants to examine the impact of relationship experiences with parents and romantic partners on relational security among early adolescent girls, over a 1-year period. Seventy-one early adolescent girls and their primary caregivers participated at Time 1 and 1 year later (Time 2). Findings suggest that both parental and romantic relationship experiences impact relational security during early adolescence. The most consistent findings from the current study were the ability of romantic rejection to alter the relational security of early adolescent girls as well as the importance of parent–adolescent attachment security in predicting comfort with intimacy.
A Solid State Nanopore Device for Investigating the Magnetic Properties of Magnetic Nanoparticles

In this study, we explored magnetic nanoparticles translocating through a nanopore in the presence of an inhomogeneous magnetic field. By detecting the ionic current blockade signals with a silicon nitride nanopore, we found that the translocation velocity that is driven by magnetic and hydrodynamic forces on a single magnetic nanoparticle can be accurately determined and is linearly proportional to the magnetization of the magnetic nanoparticle. Thus, we obtained the magneto-susceptibility of an individual nanoparticle and the average susceptibility over one hundred particles within a few minutes.
Trade-off analysis meets probabilistic model checking

Probabilistic model checking (PMC) is a well-established and powerful method for the automated quantitative analysis of parallel distributed systems. Classical PMC-approaches focus on computing probabilities and expectations in Markovian models annotated with numerical values for costs and utility, such as energy and performance. Usually, the utility gained and the costs invested are dependent and a trade-off analysis is of utter interest. In this paper, we provide an overview on various kinds of non-standard multi-objective formalisms that enable to specify and reason about the trade-off between costs and utility. In particular, we present the concepts of quantiles, conditional probabilities and expectations as well as objectives on the ratio between accumulated costs and utility. Such multi-objective properties have drawn very few attention in the context of PMC and hence, there is hardly any tool support in state-of-the-art model checkers. Furthermore, we broaden our results towards combined quantile queries, computing conditional probabilities those conditions are expressed as formulas in probabilistic computation tree logic, and the computation of ratios which can be expected on the long-run.
Contour propagation for lung tumor delineation in 4D-CT using tensor-product surface of uniform and non-uniform closed cubic B-splines

A robust contour propagation method is proposed to help physicians delineate lung tumors on all phase images of four-dimensional computed tomography (4D-CT) by only manually delineating the contours on a reference phase. The proposed method models the trajectory surface swept by a contour in a respiratory cycle as a tensor-product surface of two closed cubic B-spline curves: a non-uniform B-spline curve which models the contour and a uniform B-spline curve which models the trajectory of a point on the contour. The surface is treated as a deformable entity, and is optimized from an initial surface by moving its control vertices such that the sum of the intensity similarities between the sampling points on the manually delineated contour and their corresponding ones on different phases is maximized. The initial surface is constructed by fitting the manually delineated contour on the reference phase with a closed B-spline curve. In this way, the proposed method can focus the registration on the contour instead of the entire image to prevent the deformation of the contour from being smoothed by its surrounding tissues, and greatly reduce the time consumption while keeping the accuracy of the contour propagation as well as the temporal consistency of the estimated respiratory motions across all phases in 4D-CT. Eighteen 4D-CT cases with 235 gross tumor volume (GTV) contours on the maximal inhale phase and 209 GTV contours on the maximal exhale phase are manually delineated slice by slice. The maximal inhale phase is used as the reference phase, which provides the initial contours. On the maximal exhale phase, the Jaccard similarity coefficient between the propagated GTV and the manually delineated GTV is 0.881 ± 0.026, and the Hausdorff distance is 3.07 ± 1.08 mm. The time for propagating the GTV to all phases is 5.55 ± 6.21 min. The results are better than those of the fast adaptive stochastic gradient descent B-spline method, the 3D + t B-spline method and the diffeomorphic demons method. The proposed method is useful for helping physicians delineate target volumes efficiently and accurately.
An Overview on 3GPP Device-to-Device Proximity Services

Device-to-device (D2D) communication will likely be added to LTE in 3GPP Release 12. In principle, exploiting direct communication between nearby mobile devices will improve spectrum utilization, overall throughput, and energy consumption, while enabling new peer-to-peer and location-based applications and services. D2D-enabled LTE devices can also become competitive for fallback public safety networks, that must function when cellular networks are not available, or fail. Introducing D2D poses many challenges and risks to the long-standing cellular architecture, which is centered around the base station. We provide an overview on D2D standardization activities in 3GPP, identify outstanding technical challenges, draw lessons from initial evaluation studies, and summarize"best practices"in the design of a D2D-enabled air interface for LTE-based cellular networks.
Numerical Simulation of the Full-Polarimetric Emissivity of Vines and Comparison with Experimental Data

Abstract: Surface soil moisture is a key variable needed to understand and predict the climate. L-band microwave radiometry seems to be the best technique to remotely measure the soil moisture content, since the influence of other effects such as surface roughness and vegetation is comparatively small. This work describes a numerical model developed to efficiently compute the four elements of the Stokes emission vector ( T h , T v , T U and T V ) of vegetation-covered soils at low microwave frequencies, as well as the single-scattering albedo and the extinction coefficient of the vegetation layer over a wide range of incidence angles. A comparison with L-band (1.400–1.427 MHz) experimental radiometric data gathered during the SMOS REFLEX 2003 field experiment over vines is presented and discussed. The measured and simulated emissivities at vertical polarization agree very well. However, at horizontal polarization there is some disagreement introduced by the soil emission model. Important radiometric parameters, such as the albedo, the attenuation and the transmissivity are computed and analyzed in terms of their values and trends, as well as their dependence on the observation and scene parameters. It is found that the vegetation attenuation is mainly driven by the presence of branches and leaves, while the albedo is