Miracle-dz/UniFAR-FTUs huggingface dataset:
Recognition and tracking of temporary lanes in motorway construction sites

We propose a system for detecting a construction site on motorways, and subsequently track its individual lanes using kalman filters. Beacons and yellow lane markers are extracted from color images and provide indicators for a construction site situation. Yellow lane markers are used in this case for tracking the temporary lanes at road works. When leaving the construction site, again white road markers are extracted to track regular motorway lanes.
Throughput enhancement through dynamic fragmentation in wireless LANs

Many rate-adaptive MAC protocols have been proposed in the past for wireless local area networks (LANs) to enhance the throughput based on channel information. Most of these protocols are receiver based and employ the RTS/CTS collision avoidance handshake specified in the IEEE 802.11 standard. However, these protocols have not considered the possibility of bursty transmission of fragments in the corresponding rate adaptation schemes. In this article, a rate-adaptive protocol with dynamic fragmentation is proposed to enhance the throughput based on fragment transmission bursts and channel information. Instead of using one fragmentation threshold in the IEEE 802.11 standard, we propose to use multiple thresholds for different data rates so more data can be transmitted at higher data rates when the channel is good. In our proposed scheme, whenever the rate for the next transmission is chosen based on the channel information from the previous fragment transmission, a new fragment is then generated using the fragment threshold for the new rate. In this way, the channel condition can be more effectively used to squeeze more bits into the medium. We evaluate this scheme under a time-correlated fading channel model and show that the proposed scheme achieves much higher throughput than other rate-adaptive protocols.
Large-scale collaborative prediction using a nonparametric random effects model

A nonparametric model is introduced that allows multiple related regression tasks to take inputs from a common data space. Traditional transfer learning models can be inappropriate if the dependence among the outputs cannot be fully resolved by known input-specific and task-specific predictors. The proposed model treats such output responses as conditionally independent, given known predictors and appropriate unobserved random effects. The model is nonparametric in the sense that the dimensionality of random effects is not specified a priori but is instead determined from data. An approach to estimating the model is presented uses an EM algorithm that is efficient on a very large scale collaborative prediction problem. The obtained prediction accuracy is competitive with state-of-the-art results.
Quantitative temporal and spatial distribution of adenovirus type 2 correlates with disease manifestations and organ failure during disseminated infection

Disseminated adenovirus (HAdV) infections are serious complications in allogenic stem cell transplant (SCT) recipients. Quantitative HAdV DNA detection in blood samples demonstrated the association of high virus loads with disease and improved early diagnosis. However, the pathogenesis of disseminated HAdV disease, for example sources of HAdV DNA shedding in the blood stream and association of HAdV replication sites with disease manifestations, remained obscure. In this report, 24 bioptic and autoptic organ and tissue samples of an adult SCT recipient suffering from disseminated infection were quantitatively analyzed for HAdV DNA. Results indicate subsequent virus replication in the colon, bone marrow and liver as origin of HAdV DNAemia, which increased from 1.4 × 104 copies/ml to a peak of 2 × 109 copies/ml over a period of 84 days in spite of antiviral therapy. Symptoms as diarrhoea, bone marrow failure and hepatic failure were clearly linked to high HAdV DNA concentrations in affected organs. For example, the HAdV DNA level was 2.2 × 103 copies/cell in a colon biopsy when the patient suffered from diarrhoea whereas only 1.1 × 101 copies/cell were detected when symptoms had improved. Focal HAdV infection of the liver as demonstrated by laser microdissection was followed by fulminant virus replication with 1.3 × 105 copies of HAdV DNA/cell causing terminal hepatic failure. In conclusion, pathogenesis of disseminated HAdV disease was associated with virus replication in affected organs and not immune mediated as suggested recently by a fatal case of gene therapy with a non‐replication competent HAdV‐C5 vector. J. Med. Virol. 80:294–297, 2008. © 2007 Wiley‐Liss, Inc.
Conservation laws for the voter model in complex networks

We consider the voter model dynamics in random networks with an arbitrary distribution of the degree of the nodes. We find that for the usual node-update dynamics the average magnetization is not conserved, while an average magnetization weighted by the degree of the node is conserved. However, for a link-update dynamics the average magnetization is still conserved. For the particular case of a Barabási-Albert scale-free network, the voter model dynamics leads to a partially ordered metastable state with a finite-size survival time. This characteristic time scales linearly with system size only when the updating rule respects the conservation law of the average magnetization. This scaling identifies a universal or generic property of the voter model dynamics associated with the conservation law of the magnetization.
Using Frequent Item Set Mining and Feature Selection Methods to Identify Interacted Risk Factors - The Atrial Fibrillation Case Study

Disease risk prediction is highly important for early intervention and treatment, and identification of predictive risk factors is the key point to achieve accurate prediction. In addition to original independent features in a dataset, some interacted features, such as comorbidities and combination therapies, may have non-additive influence on the disease outcome and can also be used in risk prediction to improve the prediction performance. However, it is usually difficult to manually identify the possible interacted risk factors due to the combination explosion of features. In this paper, we propose an automatic approach to identify predictive risk factors with interactions using frequent item set mining and feature selection methods. The proposed approach was applied in the real world case study of predicting ischemic stroke and thromboembolism for atrial fibrillation patients on the Chinese atrial fibrillation registry dataset, and the results show that our approach can not only improve the prediction performance, but also identify the comorbidities and combination therapies that have potential influences on TE occurrence for AF.
A dop model for phrase structure trees

This chapter gives an instantiation of DOP (Scha 1990, 1992; Bod 1992) which is known as Tree-DOP or DOP1 and which will be extensively used in the rest of this book. Tree-DOP combines subtrees from a treebank to parse new sentences. It employs the relative frequency estimator to assign probabilities to subtrees, and computes the probability of a parse tree as the sum of the probabilities of its derivations.
Bank Stability and Market Discipline: The Effect of Contingent Capital on Risk Taking and Default Probability

This paper investigates the effects of financial institutions issuing contingent capital, a debt security that automatically converts into equity if assets fall below a predetermined threshold. We decompose bank liabilities into sets of barrier options and present closed-form solutions for their prices. We quantify the reduction in default probability associated with issuing contingent capital instead of subordinated debt. We then show that appropriate choice of contingent capital terms (in particular the conversion ratio) can virtually eliminate stockholders' incentives to risk-shift, a motivation that is present when bank liabilities instead include either subordinated debt or additional equity. Importantly, risk-taking incentives continue to be weak during times of financial distress. Our findings imply that contingent capital may be an effective tool for stabilizing financial institutions.
On phased delay stochastic Petri nets: definition and an application

We present a novel stochastic Petri net formalism where both discrete and continuous phase-type firing delays can appear simultaneously in the same model. By capturing non-Markovian behavior in discrete or continuous time, as appropriate, the formalism affords higher modeling fidelity. Alone, discrete or continuous phase-type Petri nets have simple underlying Markov chains, but mixing the two complicates matters. We show that, in a mixed model where discrete-time transitions are synchronized, the underlying process is semi-regenerative and we can employ Markov renewal theory to formulate stationary or time-dependent solutions. Also noteworthy are the computational trade-offs between the so-called embedded and subordinate Markov chains, which we employ to improve the overall solution efficiency. We present a preliminary stationary solution method that shows promise in terms of time and space efficiency and demonstrate it on an aeronautical data link system application.
Temporal and statistical properties of the ytterbium doped fiber laser

Recently, temporal and statistical properties of quasi-CW fiber lasers have attracted a great attention. In particular, properties of Raman fiber laser (RFLs) have been studied both numerically and experimentally. Experimental investigation is more challengeable, as the full generation optical bandwidth (typically hundreds of GHz for RFLs) is much bigger than real-time bandwidth of oscilloscopes (up to 60GHz for the newest models). So experimentally measured time dynamics is highly bandwidth averaged and do not provide precise information about overall statistical properties. To overpass this, one can use the spectral filtering technique to study temporal and statistical properties within optical bandwidth comparable with measurement bandwidth or indirect measurements. Ytterbium-doped fiber lasers (YDFL) are more suitable for experimental investigation, as their generation spectrum usually 10 times narrower. Moreover, recently ultra-narrow-band generation has been demonstrated in YDFL which provides in principle possibility to measure time dynamics and statistics in real time using conventional oscilloscopes.
An unknown input fractional-order observer design for fractional-order glucose-insulin system

In this paper, we introduce fractional-order derivatives into a generalized minimal model of glucose-insulin. A fractional-order state observer is designed for estimating the structure of a blood glucose-insulin with glucose rate disturbance to show the complete dynamics of the glucose-insulin system where the fractional-order α belonging to 0 <; α <; 1. A nonlinear fractional-order unknown input observer strategy is used where the glucose rate disturbance is considered as an unknown input to the perspective dynamical system. The developed method provides the observer estimation algorithm for a glucose-insulin system with unknown time-varying glucose rate disturbance. The stability analysis of the fractional-order error system is completed and showed that the fractional-order observer design is as stable as their integer order counterpart and guarantees the best convergence of the estimation error. Finally, numerical simulations are given to illustrate the effectiveness of the proposed method.
Structural Model Updating and Health Monitoring with Incomplete Modal Data Using Gibbs Sampler

Abstract: A new Bayesian model updating approach is presented for linear structural models. It is based on the Gibbs sampler, a stochastic simulation method that decomposes the uncertain model parameters into three groups, so that the direct sampling from any one group is possible when conditional on the other groups and the incomplete modal data. This means that even if the number of uncertain parameters is large, the effective dimension for the Gibbs sampler is always three and so high‐dimensional parameter spaces that are fatal to most sampling techniques are handled by the method, making it more practical for health monitoring of real structures. The approach also inherits the advantages of Bayesian techniques: it not only updates the optimal estimate of the structural parameters but also updates the associated uncertainties. The approach is illustrated by applying it to two examples of structural health monitoring problems, in which the goal is to detect and quantify any damage using incomplete modal data obtained from small‐amplitude vibrations measured before and after a severe loading event, such as an earthquake or explosion.
A Unified Framework for Measuring a Network's Mean Time-to-Compromise

Measuring the mean time-to-compromise provides important insights for understanding a network's weaknesses and for guiding corresponding defense approaches. Most existing network security metrics only deal with the threats of known vulnerabilities and cannot handle zero day attacks with consistent semantics. In this paper, we propose a unified framework for measuring a network's mean time-to-compromise by considering both known, and zero day attacks. Specifically, we first devise models of the mean time for discovering and exploiting individual vulnerabilities. Unlike existing approaches, we replace the generic state transition model with a more vulnerability-specific graphical model. We then employ Bayesian networks to derive the overall mean time-to-compromise by aggregating the results of individual vulnerabilities. Finally, we demonstrate the framework's practical application to network hardening through case studies.
To INT_MAX... and Beyond! Exploring Large-Count Support in MPI

In order to describe a structured region of memory, the routines in the MPI standard use a (count, datatype) pair. The C specification for this convention uses an int type for the count. Since C int types are nearly always 32 bits large and signed, counting more than 231 elements poses a challenge. Instead of changing the existing MPI routines, and all consumers of those routines, the MPI Forum asserts that users can build up large datatypes from smaller types. To evaluate this hypothesis and to provide a user-friendly solution to the large-count issue, we have developed BigMPI, a library on top of MPI that maps large-count MPI-like functions to MPI-3 standard features. BigMPI demonstrates a way to perform such a construction, reveals shortcomings of the MPI standard, and uncovers bugs in MPI implementations.
Acceptance driven Local Search and Evolutionary Algorithms

Local Search (LS) and Evolutionary Algorithms (EA) are probabilistic search algorithms, widely used in global optimization, where selection is important as it drives the search. In this paper, we introduce acceptance, a metric measuring the selective pressure in LS and EA, that is the tradeoff between exploration and exploitation. Informally, acceptance is the proportion of accepted non-improving transitions in a selection. We propose a new LS algorithm, SAAD, based on acceptance schedule (a schedule for the selective pressure). In EA, two new selection rules based on the Metropolis criterion are introduced. They allow two new EA (2MT and RT) based on acceptance schedule. They demonstrate a possible way of merging LS and EA technologies. Benchmarks show that the developed algorithms are more performant than standard SA and EA algorithms, and that SAAD is as efficient as the best SA algorithms while 2MT and RT are complementary to Evolution Strategies.
Efficient online learning for multitask feature selection

Learning explanatory features across multiple related tasks, or MultiTask Feature Selection (MTFS), is an important problem in the applications of data mining, machine learning, and bioinformatics. Previous MTFS methods fulfill this task by batch-mode training. This makes them inefficient when data come sequentially or when the number of training data is so large that they cannot be loaded into the memory simultaneously. In order to tackle these problems, we propose a novel online learning framework to solve the MTFS problem. A main advantage of the online algorithm is its efficiency in both time complexity and memory cost. The weights of the MTFS models at each iteration can be updated by closed-form solutions based on the average of previous subgradients. This yields the worst-case bounds of the time complexity and memory cost at each iteration, both in the order of O(d × Q), where d is the number of feature dimensions and Q is the number of tasks. Moreover, we provide theoretical analysis for the average regret of the online learning algorithms, which also guarantees the convergence rate of the algorithms. Finally, we conduct detailed experiments to show the characteristics and merits of the online learning algorithms in solving several MTFS problems.
Estimating Derivatives for Samples of Sparsely Observed Functions, With Application to Online Auction Dynamics

It is often of interest to recover derivatives of a sample of random functions from sparse and noise-contaminated measurements, especially when the dynamics of underlying processes is of interest. We propose a novel approach based on estimating derivatives of eigenfunctions and expansions of random functions into their eigenfunctions to obtain a representation for derivatives. In combination with estimates for functional principal component scores for sparse data, this leads to a viable solution of the challenging problem to recover derivatives for sparsely observed functions. We establish consistency results and demonstrate in simulations that the method is superior to alternative approaches (derivative estimation with random effects models based on B-spline bases, kernel smoothing, smoothing splines, or P-splines). Our study is motivated by an analysis of bidding histories for eBay auctions, for which bids are typically very sparse in the middle and somewhat more frequent near the beginning and end of an auction. We demonstrate the estimation of derivatives of price curves for individual auctions from the sparsely observed bidding histories and also derive a model-free first-order differential equation that applies in the case of Gaussian processes. This provides a data-driven dynamic model that we use to elucidate auction dynamics.
Active range imaging via random gating

Range Imaging (RI) has sparked an enthusiastic interest recently due to the numerous applications that can benefit from the presence 3D data. One of the most successful techniques for RI employs Time-of-Flight (ToF) cameras which emit and subsequently record laser pulses in order to estimate the distance between the camera and an object. A limitation of this class of RI is the requirement for a large number of frames that have to be captured in order to generate high resolution depth maps. In this work, we propose a novel approach for ToF based RI that utilizes the recently proposed framework of Compressed Sensing to dramatically reduce the number of necessary frames. Our technique employs a random gating function along with state-of-the-art minimization techniques in order to estimate the location of a returning laser pulse and infer the distance. To validate the theoretical motivation, software simulations were carried out. Our simulated results have shown that reconstruction of a depth map is possible from as low as 10% of the frames that traditional ToF cameras require with minimum reconstruction error while 20% sampling rates can achieve almost perfect reconstruction in low resolution regimes. Our experimental results have also shown that the proposed method is robust to various types of noise and applicable to realistic signal models.
Optimization of thermoreversible poloxamer gel system using QbD principle

Abstract The objective of this study was to optimize thermoreversible gel formulations with respect to their gelation temperature and solubilizing capacity, using quality by design (QbD) principles based on design of experiment (DoE). Independent variables, X1, X2 and X3, represent the weight percentages of poloxamer 407 (P407), poloxamer 188 (P188) and the polyethylene glycol 400, respectively (these polymers are either thermoreversible gelling agents or solubilizers). Emodin, a poorly water-soluble compound, was used as a model drug. Fifteen gel formulations were prepared based on the compositions generated by an extreme vertices type of mixture design using a DoE software, in which their gelation temperatures (Y1) and emodin solubility (Y2) were measured. The gelation temperature and emodin solubility were each described adequately using a full cubic model and a quadratic model, respectively. Theoretical and experimental responses showed linearity with R2=0.9943 for the gelation temperature and R2=0.9629 for emodin solubility. The design space was established from the models describing the gelation temperature (test accuracy: 104.5%) and emodin solubility (test accuracy: 96.6%). This study demonstrates successful application of DoE for prediction of gelation temperature and solubility of emodin thermoreversible poloxamer gel (PG). In conclusion, the present DoE approach has led to the establishment of a design space and manufacturing control strategy for gel formulations.
Comparison of a Variable-Number Tandem-Repeat (VNTR) Method for Typing Mycobacterium avium with Mycobacterial Interspersed Repetitive-Unit-VNTR and IS1245 Restriction Fragment Length Polymorphism Typing

ABSTRACT Mycobacterium avium complex (MAC) infections are increasing annually in various countries, including Japan, but the route of transmission and pathophysiology of the infection remain unclear. Currently, a variable-number tandem-repeat (VNTR) typing method using the Mycobacterium avium tandem repeat (MATR) loci (MATR-VNTR) is employed in Japan for epidemiological studies using clinical isolates of M. avium. In this study, the usefulness of this MATR-VNTR typing method was compared with that of the IS1245-restriction fragment length polymorphism (IS1245-RFLP) typing method and a mycobacterial interspersed repetitive-unit (MIRU)-VNTR typing method reported previously (V. C. Thibault, M. Grayon, M. L. Boschiroli, C. Hubbans, P. Overduin, K. Stevenson, M. C. Gutierrez, P. Supply, and F. Biet, J. Clin. Microbiol. 45:2404-2410, 2007). Seventy clinical isolates identified as M. avium from human immunodeficiency virus-negative patients with MAC infections were used. MATR-VNTR typing using 15 loci distinguished 56 patterns of different allele profiles, yielding a Hunter-Gaston discriminatory index (HGDI) of 0.990. However, IS1245-RFLP and MIRU-VNTR typing yielded HGDIs of 0.960 and 0.949, respectively, indicating that MATR-VNTR has an excellent discriminatory power compared with MIRU-VNTR and IS1245-RFLP typing. Moreover, concomitant use of the MATR-VNTR method and IS1245-RFLP typing increased the HGDI to 0.999. MATR-VNTR typing is inexpensive and easy to perform and could thus be useful in establishing a digital multifacility database that will greatly contribute to the clarification of the transmission route and pathophysiology of M. avium infections.