Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian Neural Networks for Image Restoration

INTRODUCTION Numerical methods commonly employed to convert experimental data into interpretable images and spectra commonly rely on straightforward transforms, such as the Fourier transform (FT), or quite elaborated emerging classes of transforms,, and so forth. Yet experimental data are incomplete and noisy due to the limiting constraints of digital data recording and the finite acquisition time. The pitfall of most transforms is that imperfect data are directly transferred into the transform domain along with the signals of interest. The traditional approach to data processing in the transform domain is to ignore any imperfections in data, set to zero any unmeasured data points, and then proceed as if data were perfect. Contrarily, the maximum entropy (ME) principle needs to proceed from frequency domain to space (time) domain. The ME techniques are used in data analysis mostly to reconstruct positive distributions, such as images and spectra, from blurred, noisy, and/or corrupted data. The ME methods may be developed on axiomatic foundations based on the probability calculus that has a special status as the only internally consistent language of inference (Skilling 1989; Daniell 1994). Within its framework, positive distributions ought to be assigned probabilities derived from their entropy. Bayesian statistics provides a unifying and self-consistent framework for data modeling. Bayesian modeling deals naturally with uncertainty in data explained by marginalization in predictions of other variables. Data overfitting and poor generalization are alleviated by incorporating the principle of Occam's razor, which controls model complexity and set the preference for simple models (MacKay, 1992). Bayes-ian inference satisfies the likelihood principle (Berger, 1985) in the sense that inferences depend only on the probabilities assigned to data that were measured and not on the properties of some admissible data that had never been acquired. Artificial neural networks (ANNs) can be conceptualized as highly flexible multivariate regression and multiclass classification non-linear models. However, over-flexible ANNs may discover non-existent correlations in data. Bayesian decision theory provides means to infer how flexible a model is warranted by data and suppresses the tendency to assess spurious structure in data. Any probabilistic treatment of images depends on the knowledge of the point spread function (PSF) of the imaging equipment, and the assumptions on noise, image statistics, and prior knowledge. Contrarily, the neural approach only requires relevant training examples where true scenes are known, irrespective of our inability or bias to express prior distributions. Trained ANNs are much faster image restoration means, especially …
Unsteady Laminar Forced-Convective Tube Flow Under Dynamic Time-Dependent Heat Flux

A new all-time model is developed to predict transient laminar forced convection heat transfer inside a circular tube under arbitrary time-dependent heat flux. Slug flow (SF) condition is assumed for the velocity profile inside the tube. The solution to the timedependent energy equation for a step heat flux boundary condition is generalized for arbitrary time variations in surface heat flux using a Duhamel’s integral technique. A cyclic time-dependent heat flux is considered and new compact closed-form relationships are proposed to predict (i) fluid temperature distribution inside the tube, (ii) fluid bulk temperature and (iii) the Nusselt number. A new definition, cyclic fully developed Nusselt number, is introduced and it is shown that in the thermally fully developed region the Nusselt number is not a function of axial location, but it varies with time and the angular frequency of the imposed heat flux. Optimum conditions are found which maximize the heat transfer rate of the unsteady laminar forced-convective tube flow. We also performed an independent numerical simulation using ANSYS FLUENT to validate the present analytical model. The comparison between the numerical and the present analytical model shows great agreement; a maximum relative difference less than 5.3%. [DOI: 10.1115/1.4026119]
Networks and the Macroeconomy: An Empirical Exploration

The propagation of macroeconomic shocks through input-output and geographic networks can be a powerful driver of macroeconomic fluctuations. We first exposit that in the presence of Cobb-Douglas production functions and consumer preferences, there is a specific pattern of economic transmission whereby demand-side shocks propagate upstream (to input-supplying industries) and supply-side shocks propagate downstream (to customer industries) and that there is a tight relationship between the direct impact of a shock and the magnitudes of the downstream and the upstream indirect effects. We then investigate the short-run propagation of four different types of industry-level shocks: two demand-side ones (the exogenous component of the variation in industry imports from China and changes in federal spending) and two supply-side ones (TFP shocks and variation in knowledge/ideas coming from foreign patenting). In each case, we find substantial propagation of these shocks through the input-output network, with a pattern broadly consistent with theory. Quantitatively, the network-based propagation is larger than the direct effects of the shocks. We also show quantitatively large effects from the geographic network, capturing the fact that the local propagation of a shock to an industry will fall more heavily on other industries that tend to collocate with it across local markets. Our results suggest that the transmission of various different types of shocks through economic networks and industry interlinkages could have first-order implications for the macroeconomy.
A new class of regulatory genes underlying the cause of pear-shaped tomato fruit

A common, recurring theme in domesticated plants is the occurrence of pear-shaped fruit. A major quantitative trait locus (termed ovate) controlling the transition from round to pear-shaped fruit has been cloned from tomato. OVATE is expressed early in flower and fruit development and encodes a previously uncharacterized, hydrophilic protein with a putative bipartite nuclear localization signal, Von Willebrand factor type C domains, and an ≈70-aa C-terminal domain conserved in tomato, Arabidopsis, and rice. A single mutation, leading to a premature stop codon, causes the transition of tomato fruit from round- to pear-shaped. Moreover, ectopic, transgenic expression of OVATE unevenly reduces the size of floral organs and leaflets, suggesting that OVATE represents a previously uncharacterized class of negative regulatory proteins important in plant development.
Resource recombinations in the firm: knowledge structures and the potential for schumpeterian innovation

Building on the resource-based view of the firm, this paper explores the notion of ‘resource recombinations’ within the firm. We suggest such recombinations can occur when competencies within the firm (which are interpreted as organized clusters of firm resources) either combine to synthesize novel competencies (synthesis-based recombinations) or experience a reconfiguration or relinking with other competencies (reconfiguration-based recombinations). Central to this paper is an examination of the antecedents necessary for such innovation to occur, and in particular the nature of knowledge in the firm. We argue that several characteristics of knowledge (tacitness, context specificity, dispersion) and its social organization (the way competencies come to be formed and institutionalized) will have important consequences on the likelihoods of resource recombinations. Our paper develops a model of resource recombination likelihoods and propositions. © 1998 John Wiley & Sons, Ltd.
Visual Enhancements for Improved Interactive Rendering on Light Field Displays

Rendering of complex scenes on a projector-based light field display requires 3D content adaptation in order to provide comfortable viewing experiences in all conditions. In this paper we report about our approach to improve visual experiences while coping with the limitations in the effective field of depth and the angular field of view of the light field display. We present adaptation methods employing non-linear depth mapping and depth of field simulation which leave large parts of the scene unmodified, while modifying the other parts in a non-intrusive way. The methods are integrated in an interactive visualization system for the inspection of massive models on a large scale 35MPixel light field display. Preliminary results of subjective evaluation demonstrate that our rendering adaptation techniques improve visual comfort without affecting the overall depth perception.
Social welfare versus inequality aversion in an incomplete contract experiment

We explore experimentally how power asymmetries between partners affect relationship-specific investments. We find that on average playersâ€™ investments are larger than equilibrium investments. In contrast to social dilemma experiments, in our experiment preferences for social welfare and those for equality call for different actions. Surprisingly, even disadvantaged players care more for social welfare and less for equality. As a result social welfare increases but so does inequality. We then study conditions under which power-advantaged players give up power. Power-sharing can be successful in the experiment, even when it is not in a selfish world.
A spur-reduction Delta-Sigma Modulator with efficient dithering for fractional frequency synthesizer

A spur-reduction dither structure is presented to achieve low fractional spurs for a wideband frequency synthesizer. The dither structure is added to the digital multi-stage noise shaping (MASH) Delta-Sigma Modulator (DSM) for the wideband fractional-N frequency synthesizer. Traditionally, the MASH can generate spur tone in fractional frequency synthesizers. We propose to add a simple 8-bit linear feedback shift register (LFSR) to reduce spur tone. The MASH Structure is dithered using FPGA to reduce the spur. It improves in-band fractional spur about 13dBc/Hz at 50 kHz offset and achieves high frequency resolution of 0.001Hz.
HIV-1 Specific IgA Detected in Vaginal Secretions of HIV Uninfected Women Participating in a Microbicide Trial in Southern Africa Are Primarily Directed Toward gp120 and gp140 Specificities

Background Many participants in microbicide trials remain uninfected despite ongoing exposure to HIV-1. Determining the emergence and nature of mucosal HIV-specific immune responses in such women is important, since these responses may contribute to protection and could provide insight for the rational design of HIV-1 vaccines. Methods and Findings We first conducted a pilot study to compare three sampling devices (Dacron swabs, flocked nylon swabs and Merocel sponges) for detection of HIV-1-specific IgG and IgA antibodies in vaginal secretions. IgG antibodies from HIV-1-positive women reacted broadly across the full panel of eight HIV-1 envelope (Env) antigens tested, whereas IgA antibodies only reacted to the gp41 subunit. No Env-reactive antibodies were detected in the HIV-negative women. The three sampling devices yielded equal HIV-1-specific antibody titers, as well as total IgG and IgA concentrations. We then tested vaginal Dacron swabs archived from 57 HIV seronegative women who participated in a microbicide efficacy trial in Southern Africa (HPTN 035). We detected vaginal IgA antibodies directed at HIV-1 Env gp120/gp140 in six of these women, and at gp41 in another three women, but did not detect Env-specific IgG antibodies in any women. Conclusion Vaginal secretions of HIV-1 infected women contained IgG reactivity to a broad range of Env antigens and IgA reactivity to gp41. In contrast, Env-binding antibodies in the vaginal secretions of HIV-1 uninfected women participating in the microbicide trial were restricted to the IgA subtype and were mostly directed at HIV-1 gp120/gp140.
A Reconfigurable Low Power High Throughput Streaming Architecture for Big Data Processing

General purpose computing systems are used for a large variety of applications. Extensive supports for flexibility in these systems limit their energy efficiencies. Given that big data applications are among the main emerging workloads for computing systems, specialized architectures for big data processing are needed to enable low power and high throughput execution. Several big data applications are particularly focused on classification and clustering tasks. In this paper we propose a multicore heterogeneous architecture for big data processing. This system has the capability to process key machine learning algorithms such as deep neural network, autoencoder, and k-means clustering. Memristor crossbars are utilized to provide low power high throughput execution of neural networks. The system has both training and recognition (evaluation of new input) capabilities. The proposed system could be used for classification, unsupervised clustering, dimensionality reduction, feature extraction, and anomaly detection applications. The system level area and power benefits of the specialized architecture is compared with the NVIDIA Telsa K20 GPGPU. Our experimental evaluations show that the proposed architecture can provide four to six orders of magnitude more energy efficiency over GPGPUs for big data processing.
A brain-computer interface for shared vehicle control on TORCS car racing game

Brain-computer interface (BCI), an actively progressing field in brain engineering, refers to a platform that measures the specific intent of the user and issues commands to the computer by using EEG. This kind of interface can be used on various applications such as gaming, psychotherapy, or even treatment of patients suffering from amyotrophic lateral sclerosis (ALS). In this paper, we develop a BCI environment that controls an online 3D car racing game simulator, as known as TORCS, using EEG. Using sensorimotor rhythm (SMR) as command paradigm, we extract EEG signals corresponding to the user's right hand, left hand, and both hands. Moreover, because there is a limit in acquiring information from the user when using SMR, in this paper, we introduce a shared vehicle control system based on EEG to provide faster intention cognition than that of the electromyography (EMG) signals response. The first experiment of the shared vehicle control system is experimented to solve the EEG binary classification problem. The second experiment is for the classification of left, right, and both left and right EEG signals. The shared vehicle control system uses the spatial filter for accuracy of controlling the car on EEG. The non-spatial filter, full matrix, sparse matrix, and common average reference are analyzed by each experiment. Results conducted on a track experiments obtained from 10 participants show that using the CAR spatial filter method produces a faster average lap time of 1.3 seconds than when using only controller (not using EEG signals). T-test result shows no difference between using CAR spatial filter method and using only controller module.
Expectation Propagation in Gaussian Process Dynamical Systems

Rich and complex time-series data, such as those generated from engineering systems, financial markets, videos, or neural recordings are now a common feature of modern data analysis. Explaining the phenomena underlying these diverse data sets requires flexible and accurate models. In this paper, we promote Gaussian process dynamical systems as a rich model class that is appropriate for such an analysis. We present a new approximate message-passing algorithm for Bayesian state estimation and inference in Gaussian process dynamical systems, a non-parametric probabilistic generalization of commonly used state-space models. We derive our message-passing algorithm using Expectation Propagation and provide a unifying perspective on message passing in general state-space models. We show that existing Gaussian filters and smoothers appear as special cases within our inference framework, and that these existing approaches can be improved upon using iterated message passing. Using both synthetic and real-world data, we demonstrate that iterated message passing can improve inference in a wide range of tasks in Bayesian state estimation, thus leading to improved predictions and more effective decision making.
Ensemble learning for robust prediction of microRNA-mRNA interactions

Different microRNA target prediction tools produce different results. Motivated by this fact, here we present an ensemble-learning approach that combines the outcomes from multiple tools to reduce prediction error. We test this approach with a dataset derived from a public database containing human microRNAs and microRNA-mRNA pairs. According to our experimental result, using the proposed method tends to be significantly better than using individual prediction tools in terms of increasing the area under curve (AUC) defined on a receiver operating characteristic curve.
Effect of a 6-week yoga intervention on swing mechanics during the golf swing: a feasibility study

ABSTRACT Recent evidence suggests that participating in physical conditioning programmes can improve golf performance; however, the effectiveness of a yoga intervention is yet to be investigated. The aim of the current study was to investigate the effectiveness of a 6-week yoga intervention on golf swing mechanics. Ten male golfers participated in the laboratory-based study. Golf swing mechanics were collected from two testing sessions, before and after the 6-week yoga intervention, using the Vicon motion capture system. Following the 6-week yoga intervention, significant changes were observed between the yoga and control group in X-Factor (P ≤ 0.05) and a medium effect (d ≥ 0.50) observed. No significant changes (P > 0.05) and no effect (d < 0.20) were observed in the X-Factor stretch. Significant changes (P ≤ 0.05) and a medium effect (d > 0.50) were observed for the pelvis rotations following the yoga intervention; however, no differences were observed in torso rotations or hand velocities (P > 0.05). The findings of this feasibility study suggest that yoga may be a promising intervention in helping to improve golf swing mechanics; however, future research is required to confirm the effect of the use of yoga during the golf swing due to the sample size.
A high-throughput screening of genes that encode proteins transported into the endoplasmic reticulum in mammalian cells

The compartments of eukaryotic cells maintain a distinct protein composition to perform a variety of specialized functions. We developed a new method for identifying the proteins that are transported to the endoplasmic reticulum (ER) in living mammalian cells. The principle is based on the reconstitution of two split fragments of enhanced green fluorescent protein (EGFP) by protein splicing with DnaE from Synechocystis PCC6803. Complementary DNA (cDNA) libraries fused to the N-terminal halves of DnaE and EGFP are introduced in mammalian cells with retroviruses. If an expressed protein is transported into the ER, the N-terminal half of EGFP meets its C-terminal half in the ER, and full-length EGFP is reconstituted by protein splicing. The fluorescent cells are isolated using fluorescence-activated cell sorting and the cDNAs are sequenced. The developed method was able to accurately identify cDNAs that encode proteins transported to the ER. We identified 27 novel proteins as the ER-targeting proteins. The present method overcomes the limitation of the previous GFP- or epitope-tagged methods, using which it was difficult to identify the ER-targeting proteins in a high-throughput manner.
Skin-Derived Precursors Generate Myelinating Schwann Cells for the Injured and Dysmyelinated Nervous System

Although neural stem cells hold considerable promise for treatment of the injured or degenerating nervous system, their current human sources are embryonic stem cells and fetally derived neural tissue. Here, we asked whether rodent and human skin-derived precursors (SKPs), neural crest-related precursors found in neonatal dermis, represent a source of functional, myelinating Schwann cells. Specifically, cultured SKPs responded to neural crest cues such as neuregulins to generate Schwann cells, and these Schwann cells proliferated and induced myelin proteins when in contact with sensory neuron axons in culture. Similar results were obtained in vivo; 6 weeks after transplantation of naive SKPs or SKP-derived Schwann cells into the injured peripheral nerve of wild-type or shiverer mutant mice (which are genetically deficient in myelin basic protein), the majority of SKP-derived cells had associated with and myelinated axons. Naive rodent or human SKPs also generated Schwann cells that myelinated CNS axons when transplanted into the dysmyelinated brain of neonatal shiverer mice. Thus, neonatal SKPs generate functional neural progeny in response to appropriate neural crest cues and, in so doing, provide a highly accessible source of myelinating cells for treatment of nervous system injury, congenital leukodystrophies, and dysmyelinating disorders.
Impact of Spike Train Autostructure on Probability Distribution of Joint Spike Events

The discussion whether temporally coordinated spiking activity really exists and whether it is relevant has been heated over the past few years. To investigate this issue, several approaches have been taken to determine whether synchronized events occur significantly above chance, that is, whether they occur more often than expected if the neurons fire independently. Most investigations ignore or destroy the autostructure of the spiking activity of individual cells or assume Poissonian spiking as a model. Such methods that ignore the autostructure can significantly bias the coincidence statistics. Here, we study the influence of the autostructure on the probability distribution of coincident spiking events between tuples of mutually independent non-Poisson renewal processes. In particular, we consider two types of renewal processes that were suggested as appropriate models of experimental spike trains: a gamma and a log-normal process. For a gamma process, we characterize the shape of the distribution analytically with the Fano factor (FFc). In addition, we perform Monte Carlo estimations to derive the full shape of the distribution and the probability for false positives if a different process type is assumed as was actually present. We also determine how manipulations of such spike trains, here dithering, used for the generation of surrogate data change the distribution of coincident events and influence the significance estimation. We find, first, that the width of the coincidence count distribution and its FFc depend critically and in a nontrivial way on the detailed properties of the structure of the spike trains as characterized by the coefficient of variation CV. Second, the dependence of the FFc on the CV is complex and mostly nonmonotonic. Third, spike dithering, even if as small as a fraction of the interspike interval, can falsify the inference on coordinated firing.
Approximation and compression of piecewise smooth functions

Wavelet or sub–band coding has been quite successful in compression applications, and this success can be attributed in part to the good approximation properties of wavelets. In this paper, we revisit rate–distortion (RD) bounds for the wavelet approximation of piecewise smooth functions, and piecewise polynomial functions in particular. We contrast these results with RD bounds achievable using an oracle–based method. We then introduce a practical dynamic programming algorithm, which achieves performance similar to the oracle method, and present experimental results.