Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian hierarchical nonparametric inference for change-point problems

SUMMARY Bayesian nonparametric inference for a nonsequential change-point problem is studied. We use a mixture of products of Dirichlet processes as our prior distribution. This allows the data before and after the change-point to be dependent, even when the change point is known. A Gibbs sampler algorithm is also proposed in order to overcome analytic diiculties in computing the posterior distributions of interest, some of which have support on the space of all distribution functions.
Fault Detection for Vibration Signals on Rolling Bearings Based on the Symplectic Entropy Method

Bearing vibration response studies are crucial for the condition monitoring of bearings and the quality inspection of rotating machinery systems. However, it is still very difficult to diagnose bearing faults, especially rolling element faults, due to the complex, high-dimensional and nonlinear characteristics of vibration signals as well as the strong background noise. A novel nonlinear analysis method—the symplectic entropy (SymEn) measure—is proposed to analyze the measured signals for fault monitoring of rolling bearings. The core technique of the SymEn approach is the entropy analysis based on the symplectic principal components. The dynamical characteristics of the rolling bearing data are analyzed using the SymEn method. Unlike other techniques consisting of high-dimensional features in the time-domain, frequency-domain and the empirical mode decomposition (EMD)/wavelet-domain, the SymEn approach constructs low-dimensional (i.e., two-dimensional) features based on the SymEn estimate. The vibration signals from our experiments and the Case Western Reserve University Bearing Data Center are applied to verify the effectiveness of the proposed method. Meanwhile, it is found that faulty bearings have a great influence on the other normal bearings. To sum up, the results indicate that the proposed method can be used to detect rolling bearing faults.
Methods of operating and implementing wireless otfs communciations systems

The automatically determined by the structure of the data channel, as well as changes in the channel structure in order to automatically compensate the signal distortions caused by the different structural aspects of the data channel, and the floating time shifting of the one group frequency-shifting the waveform in a variety of ways, including the sending of the data symbols by weighting the bursts, or modulation, a pilot symbol method of the error detection methods, MIMO methods, and a computer automatically using a combination of various methods, including other methods the encrypted radio transmitter / receiver system is disclosed. Often the data channel is a two-dimensional or three-dimensional space are moved to a variety of radio transmitters, receivers and signal reflector. A modulation method of the invention detects other channel impairments and the positions and speeds of the various reflectors. The error detection method, variations of the modulation method, and a MIMO techniques to detect and compensate for the additional disorder. The present invention can be automatically optimized for his operating parameters, non deterministic in that might be other ways to decrease environment can generate a fading signal.
Evolution of Mobile Base Station Architectures

Abstract With the proliferation of wireless standards such as GSM/EGPRS, WLAN, WiMAX, WCDMA, HSDPA/HSUPA, and WiBRO — future wireless radio base stations will need to support multiple air-interfaces, frequency bands and modulation formats. Radio technology that enables reconfigurable hardware across multiple standards is a big challenge for base station developers. In this paper base station architecture evolution towards full software defined radio technologies is analyzed.
Predicting short-term Bitcoin price fluctuations from buy and sell orders

Bitcoin is the first decentralized digital cryptocurrency, which has showed significant market capitalization growth in last few years. It is important to understand what drives the fluctuations of the Bitcoin exchange price and to what extent they are predictable. In this paper, we study the ability to make short-term prediction of the exchange price fluctuations (measured with volatility) towards the United States dollar. We use the data of buy and sell orders collected from one of the largest Bitcoin digital trading offices in 2016 and 2017. We construct a generative temporal mixture model of the volatility and trade order book data, which is able to out-perform the current state-of-the-art machine learning and time-series statistical models. With the gate weighting function of our generative temporal mixture model, we are able to detect regimes when the features of buy and sell orders significantly affects the future high volatility periods. Furthermore, we provide insights into dynamical importance of specific features from order book such as market spread, depth, volume and ask/bid slope to explain future short-term price fluctuations.
Simultaneous Bayesian Inference for Linear, Nonlinear and Semiparametric Mixed-Effects Models with Skew-Normality and Measurement Errors in Covariates

In recent years, various mixed-effects models have been suggested for estimating viral decay rates in HIV dynamic models for complex longitudinal data. Among those models are linear mixed-effects (LME), nonlinear mixed-effects (NLME), and semiparametric nonlinear mixed-effects (SNLME) models. However, a critical question is whether these models produce coherent estimates of viral decay rates, and if not, which model is appropriate and should be used in practice. In addition, one often assumes that a model random error is normally distributed, but the normality assumption may be unrealistic, particularly if the data exhibit skewness. Moreover, some covariates such as CD4 cell count may be often measured with substantial errors. This paper addresses these issues simultaneously by jointly modeling the response variable with skewness and a covariate process with measurement errors using a Bayesian approach to investigate how estimated parameters are changed or different under these three models. A real data set from an AIDS clinical trial study was used to illustrate the proposed models and methods. It was found that there was a significant incongruity in the estimated decay rates in viral loads based on the three mixed-effects models, suggesting that the decay rates estimated by using Bayesian LME or NLME joint models should be interpreted differently from those estimated by using Bayesian SNLME joint models. The findings also suggest that the Bayesian SNLME joint model is preferred to other models because an arbitrary data truncation is not necessary; and it is also shown that the models with a skew-normal distribution and/or measurement errors in covariate may achieve reliable results when the data exhibit skewness.
Optimized but Not Maximized Cue Integration for 3D Visual Perception

Reconstructing three-dimensional (3D) scenes from two-dimensional (2D) retinal images is an ill-posed problem. Despite this, 3D perception of the world based on 2D retinal images is seemingly accurate and precise. The integration of distinct visual cues is essential for robust 3D perception in humans, but it is unclear whether this is true for non-human primates (NHPs). Here, we assessed 3D perception in macaque monkeys using a planar surface orientation discrimination task. Abstract Reconstructing three-dimensional (3D) scenes from two-dimensional (2D) retinal images is an ill-posed problem. Despite this, 3D perception of the world based on 2D retinal images is seemingly accurate and precise. The integration of distinct visual cues is essential for robust 3D perception in humans, but it is unclear whether this is true for non-human primates (NHPs). Here, we assessed 3D perception in macaque monkeys using a planar surface orientation discrimination task. Perception was accurate across a wide range of spatial poses (orientations and distances), but precision was highly dependent on the plane’s pose. The monkeys achieved robust 3D perception by dynamically reweighting the integration of stereoscopic and perspective cues according to their pose-dependent reliabilities. Errors in performance could be explained by a prior resembling the 3D orientation statistics of natural scenes. We used neural network simulations based on 3D orientation-selective neurons recorded from the same monkeys to assess how neural computation might constrain perception. The perceptual data were consistent with a model in which the responses of two independent neuronal populations representing stereoscopic cues and perspective cues (with perspective signals from the two eyes combined using nonlinear canonical computations) were optimally integrated through linear summation. Perception of combined-cue stimuli was optimal given this architecture. However, an alternative architecture in which stereoscopic cues, left eye perspective cues, and right eye perspective cues were represented by three independent populations yielded two times greater precision than the monkeys. This result suggests that, due to canonical computations, cue integration for 3D perception is optimized but not maximized.
Potential of Electrospun Nanofibers for Biomedical and Dental Applications

Electrospinning is a versatile technique that has gained popularity for various biomedical applications in recent years. Electrospinning is being used for fabricating nanofibers for various biomedical and dental applications such as tooth regeneration, wound healing and prevention of dental caries. Electrospun materials have the benefits of unique properties for instance, high surface area to volume ratio, enhanced cellular interactions, protein absorption to facilitate binding sites for cell receptors. Extensive research has been conducted to explore the potential of electrospun nanofibers for repair and regeneration of various dental and oral tissues including dental pulp, dentin, periodontal tissues, oral mucosa and skeletal tissues. However, there are a few limitations of electrospinning hindering the progress of these materials to practical or clinical applications. In terms of biomaterials aspects, the better understanding of controlled fabrication, properties and functioning of electrospun materials is required to overcome the limitations. More in vivo studies are definitely required to evaluate the biocompatibility of electrospun scaffolds. Furthermore, mechanical properties of such scaffolds should be enhanced so that they resist mechanical stresses during tissue regeneration applications. The objective of this article is to review the current progress of electrospun nanofibers for biomedical and dental applications. In addition, various aspects of electrospun materials in relation to potential dental applications have been discussed.
Health education and agency: A comprehensive program for young women in the Mixteca region of Mexico

Abstract The Si yo estoy bien, mi familia también (“If I am OK then so is my family”) is a health and agency education program that was provided over a period of 3 years to 39,000 rural women in Oaxaca, Mexico. The purpose of the article is to describe the development, implementation and evaluation of the program. The theoretical rationale and strategy of this intervention are described, as well as the construction of the program and the implementation. Moreover, an evaluation of the various stages of the program is provided, to enable an assessment of its efficacy, and the scope for dissemination and scaling up.
Denoising and interpolation of noisy Bayer data with adaptive cross-color filters

We propose a novel approach for joint denoising and interpolation of noisy Bayer-patterned data acquired from a digital imaging sensor (e.g., CMOS, CCD). The aim is to obtain a full-resolution RGB noiseless image. The proposed technique is specifically targeted to filter signal-dependant, e.g. Poissonian, or heteroscedastic noise, and effectively exploits the correlation between the different color channels. The joint technique for denoising and interpolation is based on the concept of local polynomial approximation (LPA) and intersection of confidence intervals (ICI). These directional filters utilize simultaneously the green, red, and blue color channels. This is achieved by a linear combination of complementary-supported smoothing and derivative kernels designed for the Bayer data grid. With these filters, the denoised and the interpolated estimates are obtained by convolutions over the Bayer data. The ICI rule is used for data-adaptive selection of the length of the designed cross-color directional filter. Fusing estimates from multiple directions provides the final anisotropic denoised and interpolated values. The full-size RGB image is obtained by placing these values into the corresponding positions in the image grid. The efficiency of the proposed approach is demonstrated by experimental results with simulated and real camera data.
Albuterol Isomers Modulate Platelet-Activating Factor Synthesis and Receptor Signaling in Human Bronchial Smooth Muscle Cells

Background: Racemic albuterol is a 50:50 mixture of the (R)- and (S)-enantiomers of albuterol. Its clinical efficacy resides in the (R)-enantiomer (levalbuterol). Studies have shown that (S)-albuterol induces human bronchial smooth muscle cell (HBSMC) proliferation via a pathway linked to platelet-activating factor (PAF), but the underlying mechanism by which (S)-albuterol augments PAF effects is not clear. In this study, we compared effect of levalbuterol and (S)-albuterol on PAF receptor (PAFr)-mediated signaling and PAF metabolism by HBSMCs after incubation with the albuterol isomers. Methods: PAF binding and inositol phosphate (IP3) release were studied on adherent cultured cells. PAFr protein expression was measured by Western blotting, PAF synthesis and catabolism were measured in membrane and cytosolic proteins of cells incubated with albuterol isomers. Results: Compared to control conditions, (S)-albuterol increased PAF binding by 70% after 30 min of preincubation and by 150% after 24 h of preincubation. Levalbuterol had no effect on PAF binding under both conditions. (S)-albuterol also augmented PAF stimulation of IP3 release, while levalbuterol and the racemic mixture had no effect. WEB 2170, a PAFr antagonist, inhibited the ability of (S)-albuterol to increase PAF binding or stimulate IP3 release. (S)-albuterol stimulated PAFr protein expression. With PAF metabolism, (S)-albuterol treatment augmented PAF synthesis, but significantly inhibited PAF catabolism. Conclusions: Our data suggest that one mechanism by which (S)-albuterol stimulates HBSMC proliferation involves upregulation of PAFr-mediated effects including increased PAF synthesis and decreased PAF catabolism.
Stochastic Ml Simplex-structured Matrix Factorization under the Dirichlet Mixture Model

Simplex-structured matrix factorization (SSMF) is a problem of recovering a basis matrix and the corresponding coefficient vectors from data, where the coefficient vectors are constrained to lie in the unit simplex. SSMF has attracted growing attention in recent years, with numerous applications such as hyperspectral unmixing and document clustering. In this work, we develop a maximum-likelihood (ML) approach for SSMF. Specifically, by modeling the coefficient vectors as random variables following a Dirichlet mixture distribution—which allows us to model more complex data distributions in real-life data, a probabilistic model for SSMF is employed. We consider a marginalized likelihood with respect to the coefficient vectors, and use ML estimation to learn the basis matrix and unknown Dirichlet mixture parameters. The marginalized likelihood does not admit a closed form and is non-concave, and this makes the problem challenging to solve. To handle this challenge, an effective algorithm using sample average approximation and block successive upper-bound minimization is proposed. We consider the aforementioned two real-world applications by simulations. Numerical results show that the proposed algorithm delivers appealing performance in both applications.
DOCK180, a major CRK-binding protein, alters cell morphology upon translocation to the cell membrane

CRK belongs to a family of adaptor proteins that consist mostly of SH2 and SH3 domains. Far Western blotting with CRK SH3 has demonstrated that it binds to 135- to 145-, 160-, and 180-kDa proteins. The 135- to 145-kDa protein is C3G, a CRK SH3-binding guanine nucleotide exchange protein. Here, we report on the molecular cloning of the 180-kDa protein, which is designated DOCK180 (180-kDa protein downstream of CRK). The isolated cDNA contains a 5,598-bp open reading frame encoding an 1,866-amino-acid protein. The deduced amino acid sequence did not reveal any significant homology to known proteins, except that an SH3 domain was identified at its amino terminus. To examine the function of DOCK180, a Ki-Ras farnesylation signal was fused to the carboxyl terminus of DOCK180, a strategy that has been employed successfully for activation of adaptor-binding proteins in vivo. Whereas wild-type DOCK180 accumulated diffusely in the cytoplasm and did not have any effect on cell morphology, farnesylated DOCK180 was localized on the cytoplasmic membrane and changed spindle 3T3 cells to flat, polygonal cells. These results suggest that DOCK180 is a new effector molecule which transduces signals from tyrosine kinases through the CRK adaptor protein.
Object identification by 3D LIDAR using nested infinite Gaussian mixture model

For an autonomous mobile robot that works in real-world environment, recognition of its surrounding environment is necessary. This paper presents an object identification system, which can identify known and unknown objects and estimate their locations using 3D point cloud data acquired from a 3D LIDAR sensor mounted on the mobile robot. The proposed system is divided into two main steps; the first step is segmentation based on the 3D point cloud and the second step is identification of the extracted objects. The 3D LIDAR sensor gives sparse 3D shape information accurately, and covers a wide range. One of the main problems of such data is that the object shape information varies according to object's orientation and distance from the sensor. To solve this problem, we use nested infinite Gaussian mixture models for object identification. The experimental results show that the proposed system can extract various types of objects and identify both known and unknown objects.
Clustering Verbs Semantically According to their Alternation Behaviour

Verbs were clustered semantically on the basis of their alternation behaviour, as characterised by their syntactic subcategorisation frames extracted from maximum probability parses of a robust statistical parser, and completed by assigning WordNet classes as selectional preferences to the frame arguments. The clustering was achieved (a) iteratively by measuring the relative entropy between the verbs' probability distributions over the frame types, and (b) by utilising a latent class analysis based on the joint frequencies of verbs and frame types.
Curvilinear Image Regions Detection: Applications to Mobile Robotics

This paper proposes a novel approach for visual features detection, which is based on the presence of objects whose shape can be modelled using cylinders or generalized cylinders. These specific structures are commonly found on indoor and outdoor scenarios, and their image representations, the so-called curvilinear regions, automatically deform with changing viewpoint as to keep on covering identical physical parts of a scene. The method is based on Marr's visual theory that proposes that visual objects can be decomposed in generalized cylinders. Also, part of the method can be compared to the behavior of AOS neurons, placed in the caudal intraparietal sulcus, that respond when an elongated object is visualized. Our detector reliably finds the same curvilinear regions under different viewing conditions. Evaluation results are given to demonstrate the performance of the approach and its ability to be applied for visual features detection in a mobile robot navigation framework.
Tuning the resonance properties of 2D carbon nanotube networks towards a mechanical resonator

The capabilities of the mechanical resonator-based nanosensors in detecting ultra-small mass or force shifts have driven a continuing exploration of the palette of nanomaterials for such application purposes. Based on large-scale molecular dynamics simulations, we have assessed the applicability of a new class of carbon nanomaterials for nanoresonator usage, i.e. the single-wall carbon nanotube (SWNT) network. It is found that SWNT networks inherit excellent mechanical properties from the constituent SWNTs, possessing a high natural frequency. However, although a high quality factor is suggested from the simulation results, it is hard to obtain an unambiguous Q-factor due to the existence of vibration modes in addition to the dominant mode. The nonlinearities resulting from these extra vibration modes are found to exist uniformly under various testing conditions including different initial actuations and temperatures. Further testing shows that these modes can be effectively suppressed through the introduction of axial strain, leading to an extremely high quality factor in the order of 109 estimated from the SWNT network with 2% tensile strain. Additional studies indicate that the carbon rings connecting the SWNTs can also be used to alter the vibrational properties of the resulting network. This study suggests that the SWNT network can be a good candidate for applications as nanoresonators.
Properties for Nonlinear Fractional SubLaplace Equations on the Heisenberg Group

The aim of the paper is to study properties of solutions to the nonlinear fractional subLaplace equations on the Heisenberg group. Based on the method of moving planes to the Heisenberg group, we prove the Liouville property of solutions on a half space and the symmetry and monotonicity of the solutions on the whole group respectively. AMS Subject Classifications: 35A01, 35J57, 35D99 Chinese Library Classifications: O175.2
The effect of periodontal surgery on bite force, occlusal contact area and bite pressure.

BACKGROUND Tooth mobility resulting from the loss of periodontal support or trauma induced by periodontal surgery may change the amount of bite force (BF) and bite pressure (BP) and number of occlusal contact areas (OCAs). The aim of the authors' study was to compare BF, BP and OCA of teeth with periodontal disease before and after periodontal surgery with similar values of healthy teeth. METHODS The authors performed quantitative analysis of BF, BP and OCA using a pressure measurement film. Ten patients with periodontitis who needed periodontal surgery served as the test group. The authors took measurements of BF, BP, OCA and mobility (using Miller's Mobility Index) just before surgery and at one, four and 12 weeks after surgery. They also measured clinical attachment levels (CAL) before surgery and 12 weeks after surgery. Ten subjects without periodontitis served as the control group. RESULTS Although BF and OCA increased the first week after periodontal surgery, analysis of variance (ANOVA) showed no statistically significant differences at a 95 percent confidence interval. There were statistically significant differences between first-week mobility and that at four and 12 weeks (P = .001). A factorial ANOVA showed significant interaction between BF and mobility (P < .05). CONCLUSIONS The authors' findings suggest that changes in BF, BP and OCA were not affected by periodontal surgery. However, mean mobility values and BF are correlated. Further investigations of this measurement method involving larger study populations and a longer follow-up period are needed. CLINICAL IMPLICATIONS It seems to be helpful to follow occlusal changes after periodontal surgery using a pressure measurement film. It also may be suggested that this measurement method could be used to evaluate the treatment prognosis.