Miracle-dz/UniFAR-FTUs huggingface dataset:
DETECTION AND ROBUST ESTIMATION OF CYLINDER FEATURES IN POINT CLOUDS

The objective of this work is to develop new methods for efficient automatic 3D modeling of existing industrial installations from point cloud data. Traditionally, cylinder feature extraction algorithms utilize 5D Hough transforms, resulting in impractically high computational complexity. A more efficient approach uses a 2D Hough transform to estimate orientation followed by a 3D Hough transform to detect position, but still has extensive runtimes and lacks robustness in dense point cloud data. This work endeavors to (1) further decrease the runtime for cylinder feature extraction by implementing a coarse-to-fine approach, and (2) improve the robustness of the algorithm in detecting multiple cylinders by applying a clustering algorithm. In the coarse-to-fine approach, an initial estimate of the cylinder feature is quickly generated by coarsely sampling the Hough space. Subsequently, the search space is iteratively restricted based on the previous estimate while increasing sampling density to generate continually improving feature estimates until a stop criterion is reached. Results show that the implemented coarseto-fine approach yielded an improvement in orientation estimate accuracy of 20% while reducing runtime by 74%. To improve the robustness of the Hough transform in the presence of multiple cylinders, a clustering technique is implemented on the accumulator. First, cells in the accumulator with small number of accumulations are discarded to facilitate the computation of a hierarchical tree. Clustering is then applied to group the remaining cells into clusters representing different cylinders. This method improves robustness as well as accuracy of feature extraction in point cloud data with diverse cylinders.
Strengthening Schedules through Uncertainty Analysis Agents

In this paper, we describe an approach to scheduling under uncertainty that achieves scalability through a coupling of deterministic and probabilistic reasoning. Our specific focus is a class of oversubscribed scheduling problems where the goal is to maximize the reward earned by a team of agents in a distributed execution environment. There is uncertainty in both the duration and outcomes of executed activities. To ensure scalability, our solution approach takes as its starting point an initial deterministic schedule for the agents, computed using expected duration reasoning. This initial agent schedule is probabilistically analyzed to find likely points of failure, and then selectively strengthened based on this analysis. For each scheduled activity, the probability of failing and the impact that failure would have on the schedule's overall reward are calculated and used to focus schedule strengthening actions. Such actions generally entail fundamental trade-offs; for example, modifications that increase the certainty that a high-reward activity succeeds may decrease the schedule slack available to accommodate uncertainty during execution. We describe a principled approach to handling these trade-offs based on the schedule's "expected reward," using it as a metric to ensure that all schedule modifications are ultimately beneficial. Finally, we present experimental results obtained using a multi-agent simulation environment, which confirm that executing schedules strengthened in this way result in significantly higher rewards than are achieved by executing the corresponding initial schedules.
Distributed multimedia systems

A distributed multimedia system (DMS) is an integrated communication, computing, and information system that enables the processing, management, delivery, and presentation of synchronized multimedia information with quality-of-service guarantees. Multimedia information may include discrete media data, such as text, data, and images, and continuous media data, such as video and audio. Such a system enhances human communications by exploiting both visual and aural senses and provides the ultimate flexibility in work and entertainment, allowing one to collaborate with remote participants, view movies on demand, access on-line digital libraries from the desktop, and so forth. In this paper, we present a technical survey of a DMS. We give an overview of distributed multimedia systems, examine the fundamental concept of digital media, identify the applications, and survey the important enabling technologies.
Internal Properties of Ultracompact Dwarf Galaxies in the Virgo Cluster

We present new imaging and spectroscopic observations of six ultracompact dwarf (UCD) galaxies in the Virgo Cluster, along with reanalyzed data for five Fornax Cluster UCDs. These are the most luminous UCDs: -14 mag < MV < -12 mag. Our Hubble Space Telescope imaging shows that most of the UCDs have shallow or steep cusps in their cores; only one UCD has a flat "King" core. None of the UCDs show tidal cutoffs down to our limiting surface brightness. Spectroscopic analysis shows that Virgo UCDs are old (older than 8 Gyr) and have metallicities in the range from [Z/H] = -1.35 to +0.35 dex. Five Virgo UCDs have supersolar [α/Fe] abundance ratios, and one Virgo UCD has a solar abundance ratio. The supersolar [α/Fe] abundances are typical of old stellar populations found in globular clusters and elliptical galaxies. We find that Virgo UCDs have structural and dynamical properties similar to Fornax UCDs. The Virgo and Fornax UCDs all have masses ≈(2-9) × 107 M⊙ and mass-to-light ratios ≈(3-5) M⊙/L⊙,V. The dynamical mass-to-light ratios for Virgo UCDs are consistent with simple stellar population model predictions: the Virgo UCDs do not require dark matter to explain their mass-to-light ratios. We conclude that the internal properties of Virgo UCDs are consistent with them being the high-mass/high-luminosity extreme of known globular cluster populations. We refrain from any firm conclusions on Fornax UCD origins until accurate age, metallicity, and α-abundance estimates are obtained for them. Some of our results, notably the fundamental plane projections, are consistent with the formation of UCDs by the simple removal of the halo from the nuclei of nucleated dwarf galaxies. However, the ages, metallicities, and abundances for Virgo UCDs are not consistent with this simple stripping model. It might be consistent with more sophisticated models of the stripping process that include the effects of gas removal on the chemical evolution of the nuclei.
A parallel genetic algorithm for the optimal design of multi-body model vehicle suspensions

Based on an improved genetic algorithm, a parallel genetic algorithm is presented and the running environment is constituted in this paper. The parallel genetic algorithm of multi-body model vehicle suspension optimization is implemented establishing an interface between ADAMS software and the genetic algorithm. The results show that the parallel genetic algorithm developed in this paper is efficient.
Parallel Entity Resolution with Dedoop Preprint , accepted for publication in “ Datenbank-Spektrum ”

We provide an overview of Dedoop (Deduplication with Hadoop), a new tool for parallel entity resolution (ER) on cloud infrastructures. Dedoop supports a browserbased specification of complex ER strategies and provides a large library of blocking and matching approaches. To simplify the configuration of ER strategies with several similarity metrics, training-based machine learning approaches can be employed with Dedoop. Specified ER strategies are automatically translated into MapReduce jobs for parallel execution on different Hadoop clusters. For improved performance, Dedoop supports redundancy-free multi-pass blocking as well as advanced load balancing approaches. To illustrate the usefulness of Dedoop, we present the results of a comparative evaluation of different ER strategies on a challenging real-world dataset.
α3β1 Integrin Is Required for Normal Development of the Epidermal Basement Membrane

Integrins α3β1 and α6β4 are abundant receptors on keratinocytes for laminin-5, a major component of the basement membrane between the epidermis and the dermis in skin. These integrins are recruited to distinct adhesion structures within keratinocytes; α6β4 is present in hemidesmosomes, while α3β1 is recruited into focal contacts in cultured cells. To determine whether differences in localization reflect distinct functions of these integrins in the epidermis, we studied skin development in α3β1-deficient mice. Examination of extracellular matrix by immunofluorescence microscopy and electron microscopy revealed regions of disorganized basement membrane in α3β1-deficient skin. Disorganized matrix was first detected by day 15.5 of embryonic development and became progressively more extensive as development proceeded. In neonatal skin, matrix disorganization was frequently accompanied by blistering at the dermal-epidermal junction. Laminin-5 and other matrix proteins remained associated with both the dermal and epidermal sides of blisters, suggesting rupture of the basement membrane itself, rather than detachment of the epidermis from the basement membrane as occurs in some blistering disorders such as epidermolysis bullosa. Consistent with this notion, primary keratinocytes from α3β1-deficient skin adhered to laminin-5 through α6 integrins. However, α3β1-deficient keratinocytes spread poorly compared with wild-type cells on laminin-5, demonstrating a postattachment requirement for α3β1 and indicating distinct roles for α3β1 and α6β4. Our findings support a novel role for α3β1 in establishment and/or maintenance of basement membrane integrity, while α6β4 is required for stable adhesion of the epidermis to the basement membrane through hemidesmosomes.
Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions

We consider the problem of constructing a function whose zero set is to represent a surface, given sample points with surface normal vectors. The contributions include a novel means of regularising multi-scale compactly supported basis functions that leads to the desirable properties previously only associated with fully supported bases, and show equivalence to a Gaussian process with modified covariance function. We also provide a regularisation framework for simpler and more direct treatment of surface normals, along with a corresponding generalisation of the representer theorem. We demonstrate the techniques on 3D problems of up to 14 million data points, as well as 4D time series data.
Video-Based Pedestrian Re-Identification by Adaptive Spatio-Temporal Appearance Model

Pedestrian re-identification is a difficult problem due to the large variations in a person’s appearance caused by different poses and viewpoints, illumination changes, and occlusions. Spatial alignment is commonly used to address these issues by treating the appearance of different body parts independently. However, a body part can also appear differently during different phases of an action. In this paper, we consider the temporal alignment problem, in addition to the spatial one, and propose a new approach that takes the video of a walking person as input and builds a spatiotemporal appearance representation for pedestrian re-identification. Particularly, given a video sequence, we exploit the periodicity exhibited by a walking person to generate a spatiotemporal body-action model, which consists of a series of body-action units corresponding to certain action primitives of certain body parts. Fisher vectors are learned and extracted from individual body-action units and concatenated into the final representation of the walking person. Unlike previous spatiotemporal features that only take into account local dynamic appearance information, our representation aligns the spatiotemporal appearance of a pedestrian globally. Extensive experiments on public data sets show the effectiveness of our approach compared with the state of the art.
Call dropping performance of the relay station (RS)-first channel access scheme in LTE-Advanced relay networks

Both the Third Generation Partnership Project (3GPP) Long Term Evolution (LTE-Advanced) and IEEE 802.16m rely on relay technique to ameliorate cell edge throughput. Despite the throughput benefit achieved by relay technique, LTE-Advanced relay network is vulnerable to frequent handoffs, which can degrade the dropping performance, an important quality-of-service metric concerning realtime communications. This paper proposes a mathematical model to analyze the call dropping performance of the relay station (RS)-first channel access scheme, which prefers a RS-channel over an eNode-B (eNB)-channel when determining an access channel for a new and a handoff call. Closed-form analytical result is derived for the dropping probability of the RS-first policy and based on the result a numerical study is conducted to evaluate the influences of diverse parameters on the dropping performance. The result obtained in this paper can be availed of to develop appropriate call admission strategies in LTE-Advanced relay networks.
Similarity Analysis between Transcription Factor Binding Sites by Bayesian Hypothesis Test

Transcription factor binding sites (TFBS) in promoter sequences of higher eu- karyotes are commonly modeled using position frequency matrices (PFM). The ability to compare PFMs representing binding sites is especially important for de novo sequence motif discovery, where it is desirable to compare putative matrices to one another and to known matrices. We propose to identify and group similar profiles using Bayesian hy- pothesis test between PFMs, describing a column-by-column method for PFM similarity quantification based on Bayes factor and posterior probability of null model that aligned columns are independent and identically distributed observation from the same multino- mial distribution. We group TFBS frequency matrices from less redundant JASPAR into matrix families by cluster analysis according to Bayes factors and posterior probability of similar PFMs. Clusters of highly similar matrices are identified. We further compare the performance of this method to Pearson χ 2 test on simulated data. The proposed method is very simple, easily implemented and outperforms the other method in our test. Taking Pearson product moment correlation coefficient as an objective criterion of the performance, results indicate that Bayesian test performs better than the classical meth- ods on average.
Imaging of the Lamina Cribrosa in Glaucoma: Perspectives of Pathogenesis and Clinical Applications

Abstract The lamina cribrosa (LC) is a sieve-like structure in the sclera where retinal ganglion cell axons exit from the eye. The LC has been known to play a critical role in the pathogenesis of glaucoma. With the advent of imaging technologies, such as enhanced depth imaging, spectral-domain optical coherence tomography (OCT) enables us to unveil the LC in vivo features. The application of adaptive optics technology and a compensatory image-processing algorithm has further improved the visualization of the beams and pores and neural pathways of the LC and the scleral insertion sites. Monitoring the changes of these structures in relation to acute and chronic elevation of intraocular pressure would be germane to decipher the relationship between the stress and strain response of the LC and optic nerve damage and improve our understanding of glaucoma pathophysiology. While the impact of investigating the integrity of LC is substantive, considerable challenges remain for imaging the LC. Nevertheless, with the rapid development of the OCT technology, it is expected that some of these limitations can be overcome and the potentials of LC imaging will be unraveled.
E-Health Design of EEG Signal Classification for Epilepsy Diagnosis

Epilepsy, which is caused by abnormal discharges in the brain, is one of the most common neurological disorders. To diagnose efficiently epilepsy, it is valuable to classify electroencephalogram signal. In this paper, we proposed a new e-health design of Electroencephalogram (EEG) signal classification for epilepsy diagnosis. The design is based on support vector machine to classify electroencephalogram signal. We first decompose electroencephalogram signal into bands by using discrete wavelet transform and compute the approximate entropy values in the bands. Next, by proposed feature selection method, the feature vectors are selected adaptively from statistical wavelet coefficients and approximate entropy values. Finally, the support vector machine is used to classify the selected features. The experimental results showed the proposed system has great performance and reliability and the total accuracy of classification can achieve 98%.
The Relevance of Statistical Tests in Cryptography

The evaluation of a cryptographic primitive (such as a block/stream cipher algorithm or pseudorandom bit generator) involves statistical testing meant to identify nonrandom behavior that can be exploited by cryptanalyzing the primitive. This article presents statistical requirements for validating the security of cryptographic primitives. Because validation by statistical methods is prone to errors due to the samples used in testing, we must look at statistical testing assumptions, types of errors, and sample construction and requirements.
Síndrome metabólica e padrões alimentares de indivíduos idosos do Estudo SABE: Saúde, Bem-estar e Envelhecimento.

Introduction: Metabolic syndrome (MS) is common in elderly people. The feeding influences its prevalence constituting one of the modifiable risk factor, with positive impact on the disease control. Objective: To investigate the association between MS and dietary patterns of older adults from Sao Paulo city. Methods: Cross-sectional study that used data from the SABE survey: epidemiological, cohort based and home-dweling. The study population included elderly individuals (≥ 60 years old), both sexes, selected by probability sampling, and interviewed in 2010, belonging to three cohorts A/2000 (n = 630), B/2006 (n = 214) and C / 2010 (n = 311). The study variables were: a) referred: gender, age, education, alcohol consumption, smoking, physical activity, number of chronic noncommunicable diseases; b) measured: MS, identified according to the criteria of the National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATPIII): waist circumference > 102 cm for men, and > 88 cm for women; blood pressure ≥ 130 and/or ≥ 85 mmHg (obtained by trained interviewers); triglycerides ≥ 150 mg/dL; high density lipoprotein cholesterol 100 mg/dL (through blood samples); obesity, c classified by body mass index ≥ 30 kg/m 2 . The feeding data were obtained by one specific food frequency questionnaire with 107 foods, classified into 18 food groups. Dietary patterns were determined by exploratory factor analysis of principal components. Rao & Scott and multiple logistic regression for complex sample were used, with 5% significance level. The statistical software STATA 13.1 was adopted for calculations. Results: 1155 subjects were studied. The prevalence of MS was 57.9%. Four dietary patterns were identified: pattern 1, composed by fried foods, sausages, canned foods, sweets, tubers, spices/sauces and processed eggs, called inappropriated; pattern 2, composed by low-fat dairy products, whole grain cereals and breads, and light, diet or zero food, called modified; pattern 3, composed by vegetable oils, rice, refined cereals and white bread, meat and legumes, called Brazilian traditional pattern; and pattern 4, fruits, vegetables and tubers, called beneficial. The modified pattern was positively associated to MS. 4th quartile modified pattern older adults had increased in 60% the risk of MS. The beneficial pattern was negatively associated to MS, in the older individuals (≥ 70 y, cohort A). The inappropriated and Brazilian traditional patterns were not significantly associated to MS. Conclusion: The modified and beneficial dietary patterns were associated to MS. Modified individuals pattern showed increased risk of MS. Beneficial pattern reduced the risk to the oldest older individuals. The modified pattern could be due to dietary changes implemented in the older adults daily life after diagnosis of metabolic abnormalities.
Exploiting Class Bias for Discovery of Topical Experts in Social Media

Discovering contexts of user's expertise can be a challenging task, especially if there is no explicit attribution provided. With more professionals adopting social networks as a mean of communicating with their colleagues and broadcasting updates on the area of their competence, it is crucial to detect such individuals automatically. This would not only allow for better follower recommendation, but would also help to mine valuable insights and emerging signals in different communities. We posit that topical groups have their unique semantic signatures. Hence, we can treat identification of expert's topical attribution as a binary classification task, exploiting the class bias to generate training sample without any manual labor. In thiswork, we present profile-and behavior-based models to explore experts topicality. While the former focuses on the static profile of user activity, the latter takes into account consistency and dynamics of a topic in user feed. We also propose a naive baseline tailored to a domain used in evaluation. All models are assessed on a case study of Twitter investment community.
The Elevation of Boiling Points in H_2O and D_2O Electrolytes

The excess enthalpy effect in cold fusion experiments for Pd/D2O + LiOD systems is subject to positive feedback, i.e., increasing the cell temperature increases the excess enthalpy. Therefore, the largest excess enthalpy effects are often observed near or at the boiling point corresponding to that of the electrolyte solution in the cell. The exact boiling temperatures at different electrolyte concentrations are needed for cold fusion experiments. These studies of LiOH in H2O and LiOD in D2O both show boiling point elevations at higher concentrations that are significantly lower than theoretical calculations based on ideal solutions. Activity coefficients of LiOD in D2O at the boiling point of D2O (101.42°C) are also reported. These boiling point measurements suggest a purity problem with heavy water samples that may contribute to the reproducibility problem for excess enthalpy.
THE EFFECTS OF SELF-DETERMINATION ON LEARNING OUTCOMES IN A BLENDED LEARNING

The purpose of the paper is to examine whether the sub-constructs of self-determination, that is, learners’ perceived level of autonomy, competence, and relatedness, predict learning flow, persistence, and achievement in a blended learning context. Participants are 102 adult learners who voluntarily registered for a Chinese language learning seminar, which consisted of weekly offline meetings and supplementary online activities for 24 weeks. A series of regression analysis revealed that autonomy, competence, and relatedness significantly predicted learning flow, which significantly predicted persistence. Also, competence and relatedness, and learning flow predicted achievement.