Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical coherence tomography retinal image reconstruction via nonlocal weighted sparse representation

Abstract. We present a nonlocal weighted sparse representation (NWSR) method for reconstruction of retinal optical coherence tomography (OCT) images. To reconstruct a high signal-to-noise ratio and high-resolution OCT images, utilization of efficient denoising and interpolation algorithms are necessary, especially when the original data were subsampled during acquisition. However, the OCT images suffer from the presence of a high level of noise, which makes the estimation of sparse representations a difficult task. Thus, the proposed NWSR method merges sparse representations of multiple similar noisy and denoised patches to better estimate a sparse representation for each patch. First, the sparse representation of each patch is independently computed over an overcomplete dictionary, and then a nonlocal weighted sparse coefficient is computed by averaging representations of similar patches. Since the sparsity can reveal relevant information from noisy patches, combining noisy and denoised patches’ representations is beneficial to obtain a more robust estimate of the unknown sparse representation. The denoised patches are obtained by applying an off-the-shelf image denoising method and our method provides an efficient way to exploit information from noisy and denoised patches’ representations. The experimental results on denoising and interpolation of spectral domain OCT images demonstrated the effectiveness of the proposed NWSR method over existing state-of-the-art methods.
Affordance learning and inference based on vision-speech association in human-robot interactions

Human-robot interactions is important for a robot to learn the environment and finish tasks. However, humans are difficult to teach a robot all the required information so the robot needs to infer some new knowledge based on that has been learned. Humans and other animals understand the world based on affordances which have been introduced into robotics to promote a robot's cognitive capabilities in planning, recognition and control. An affordance, which is jointly determined by the object and robot, encodes a potential action that the robot might execute upon the object. Most existing works make a robot build its affordance knowledge in only one dimension such as vision. In order to guide a robot to develop its intelligence like humans as much as possible, we propose an affordance-based interaction model that learns affordances based on vision-speech association. Our model has the following features: (i) use speech to abstractly represent behavioral and visual information in a high level without considering the detail; (ii) reduce the number of parameters in human-robot communications; (iii) infer the unknown information through table association; (iv) promote affordance learning from one dimension to double dimensions. The experiment is carried out on a NAO humanoid robot, and it is proved that our method supports affordance learning and inference correctly and effectively.
Orientation planning in task space using quaternion polynomials

This paper introduces a computationally fast method for orientation trajectory planning in point-to-point motion tasks when the angular velocity and acceleration at the endpoints are constrained. Addressing such a problem with existing spherical-interpolation-based methods (in the context of unit quaternion) is not straightforward, since the inherent complexities of spherical curves necessitate task-specific tunings for satisfying all the boundary conditions. To tackle such a difficulty, we propound an interpolation function on the basis of standard polynomials in time with quaternion coefficients. We introduce a novel algorithm to determine varying polynomial coefficients in a way that the unit length of interpolated quaternion can be guaranteed. The performance of the developed planning algorithms is illustrated through a functional analysis and via simulation experiments on an anthropomorphic robotic arm. The results corroborate the merits of the presented approach especially in respecting arbitrary boundary conditions.
Overlap hyper-sphere clustering-based outlier mining algorithm

Traditional clustering-based outlier mining algorithm only qualitatively judge whether a data point is an outlier, unable to quantitatively describe the outlier degree. It results to a low mining precision. In order to improve the performance of existing outlier mining algorithms, this paper proposes an overlap hyper-sphere clustering-based outlier mining algorithm. The experiment shows compared with the traditional outlier mining algorithms, it has higher precision and recall rate.
Bioconcentration and half-life of quinalphos pesticide in rice-fish integration system in the Mekong Delta, Vietnam

ABSTRACT In order to determine the distribution and enable the elimination of quinalphos, a popular active pesticide compound used in the Mekong Delta, an experiment was set up in a rice-fish integration system in Can Tho City, Vietnam. Fish was stocked into the field when the rice was two-months old. Quinalphos was applied twice in doses of 42.5 g per 1000 m2. Water, fish and sediment samples were collected at time intervals and analyzed by a Gas Chromatography Electron Capture Detector system. The results show that quinalphos residues in fish muscles were much higher than those of the water and the bioconcentration factor (logBCF) was above 2 for the fish. The half-life of first and second quinalphos applications were 12.2 and 11.1 days for sediment, 2.5 and 1.1 days for silver barb, 1.9 and 1.3 days for common carp, and 1.1 and 1.0 days for water, respectively.
Recommendation based on object typicality

Current recommendation methods are mainly classified into content-based, collaborative filtering and hybrid methods. These methods are based on similarity measurements among items or users. In this paper, we investigate recommendation systems from a new perspective based on object typicality and propose a novel typicality-based recommendation approach. Experiments show that our method outperforms compared methods on recommendation quality.
COX-2 gene variants in bipolar disorder-I.

BACKGROUND Bipolar disorder-I (BD-I) is a complex illness, and multiple genes and environmental factors determine its pathogenesis. Several studies have ascertained that BD-I and inflammation are linked through shared genetic polymorphisms and gene expression, as well as altered cytokine levels. COX-2 gene polymorphisms affecting COX-2 levels may be associated with BD-I by altering the inflammatory response. SUBJECTS AND METHODS We investigated COX-2-765G→C and COX-2-1195A→G gene polymorphisms, which might be related for BD-I. The present analyses are based on 180 subjects with bipolar I disorder-I and 170 non-bipolar subjects. Genotyping of COX-2 gene polymorphisms (COX-2-765G→C, COX-2-1195A→G) were detected by PCR-RFLP. RESULTS We found a positive association of COX-2 gene variants for development of BD-I. There were statistically significant differences in COX-2-1195A→G genotypes and alleles between the controls and patients (p:0.000; p:0.000). The indivuals with COX-2-1195A→G AA genotype had seems to be associated for BD-I (p:0.000). CONCLUSIONS It seems that there is a protective role of COX-2-1195A→G G+ genotype against BD-I (p:0.000). In addition, there was a weak linkage disequilibrium between COX-2-765G→C and COX-2-1195A→G polymorphisms. Our findings suggest that COX-2-1195A→G AA genotype may faciliate the development of BD-I.
Concentration of heavy metals in sediments and bivalves (Soletellina sp.) in the mangrove environments of Tok Bali, Kelantan, Malaysia.

An assessment on marine contamination due to heavy metals was conducted in the mangrove areas based on marine bivalves and sediments collected from Tok Bali, Kelantan. The sediments were collected at the intertidal zone during low tide. Six sampling stations were randomly chosen for sampling in the whole study area. The sediments collected were analyzed for sedimentological characteristics using particle size analyzer (PSA) for sediments <63 mm, organic carbon, using wet dichromate method and heavy metals using Inductive Couple Plasma-Mass Spectroscopy (ICP-MS). Results on the sedimentological characteristics revealed that the sediments ranged from fine sand (2.43Φ) to silt (6.97Φ), moderately sorted (0.66Φ) to poorly sorted (1.65Φ) and strongly negatively skewed (-0.06 Φ) to positively skewed (0.11Φ). Organic carbon contents ranged from 0.9% to 1.3%. For heavy metals in sediments, results revealed that the average concentrations for Cr is 83. 8µg/g; 139. 4µg/g for Mn; 11. 5µg/g for Cu; 82. 9µg/g for Zn; 45. 1µg/g for Sr; 31. 5µg/g for Ba and 35. 1µg/g for Pb. For the bivalve on the other hand, Cr has an average concentrations of 2. 1µg/g; 47. 8µg/g for Mn; 4. 7µg/g for Cu; 80. 0µg/g for Zn; 34. 4µg/g for Sr; 1. 4µg/g for Ba and 1. 6µg/g for Pb. Results from normalization revealed that the heavy metals were derived from natural source and no anthropogenic input was observed
Improving the Color Features of Hemp Fibers After Chemical Preparation for Textile Applications

The renewed interest in bast fibers for textile products has focused attention on research in different methods of bast fiber processing. The color properties of bast fibers must be improved during fiber preparation, especially when standard textile processes such as cotton and wool methods are considered. The color properties of hemp fibers are investigated in this paper after chemical processing based on experimental design tech niques. The effects on the fiber color of hemp raw materials and chemical processes are discussed. The results of alkali boiling and peroxide bleaching are analyzed for their significant effects on hemp fiber color.
Evaluation of viscoelastic materials for MEMS by creep compliance analysis

This thesis advances the current state-of-the-art in microscale material testing in two aspects. First, a new methodology is established to determine the long term creep compliance of non-linear viscoelastic thin films using fully clamped diaphragms under tensile stress. Second, the mechanical properties creep compliance, fracture strength and intrinsic in-plane stress of several photosensitive polymers are investigated and quantified with the introduced approach. The measurement methodology employs the bulge test method, the resonant vibrometry test method and the uniaxial tensile test method. Theoretical models for nonlinear viscoelastic behavior of membranes are established and experimentally verified. They include the influence of the specimens’ geometry as well as the complex loading conditions. The intrinsic tensile in-plane stress and strain of the polymeric thin films are integrated. Simulations support the model assumption of plain-strain conditions in the chosen geometry of the diaphragms. A statistically firm experimental analysis of time-dependent mechanical behavior of MEMS-relevant polymers is performed under consideration of material constraints and the measurement technique. The materials are chosen on the basis of a physical property evaluation which reviews their suitability to serve in mechanically active microelectromechanical systems (MEMS). Three different negative photoresist formulations are thoroughly characterized. Experimental results are presented for the highly cross-linked epoxy resin SU-82002, the polyimide PI2737 and for GLM2060, a composite version of SU-8, filled with silica nanoparticles. Based on a series of creep experiments, the isothermal long-term creep behavior of the polymeric thin films in their non-linear viscoelastic regime is extrapolated according to the time-stress superposition principle. Master creep compliance curves, predicting the creep compliance up to t > 1010s are constructed. The shift factors of the individual creep compliance curves can be described by the Eyring theory of stress activated plastic flow for all experiments. The fracture strengths are quantified and the von-Mises model is verified for the description of the latter under complex loading conditions for SU-8. Reference measurements of the fracture strength and the glassy modulus are taken on bone shaped SU-8 samples with an uniaxial tensile test setup. Furthermore, the time and temperature dependent characteristics of the intrinsic in-plane stress of SU-82002 membranes are investigated. Additionally, a fourth material is subject to experimental studies: GCM3060, a conductive SU-8 composite filled with silver microparticles. It is characterized in its mechanical short term behavior. All experimentally obtained material properties are summarized in the following table.
Blending Irregularity in Blended Yarns

Two methods of analysis of fiber composition is a cross section of hlended yarns are presented. One is based on the observation of cross sections of yarn in the microscope, and the second is based on the separation of fibers going through the cross section of the yarn, then counting the total number of fibers in the cross section, dissolving of fibers of one com ponent, and then counting the remaining fibers. To analyze which of the components influences more the blend irregu larity, the.use of Huberty's coefficient for both components is proposed. The coefficient of correlation between the num bers of fibers of both components can offer insight into the irregularity of local composition of blended yarns.
Overcoming the Curse of Dimensionality When Clustering Multivariate Volume Data

Visual analytics of multidimensional data suffer from the curse of dimensionality, i.e., that even large numbers of data points will be scattered in a high-dimensional space. The curse of dimensionality prohibits the proper use of clustering algorithms in the high-dimensional space. Projecting the space before clustering imposes a loss of information and possible mixing of separated clusters. We present an approach where we overcome the curse of dimensionality for a particular type of multidimensional data, namely for attribute spaces of multivariate volume data. For multivariate volume data, it is possible to interpolate between the data points in the high-dimensional attribute space based on their spatial relationship in the volumetric domain (or physical space). We apply this idea to a histogram-based clustering algorithm. We create a uniform partition of the attribute space in multidimensional bins and compute a histogram indicating the number of data samples belonging to each bin. Only non-empty bins are stored for efficiency. Without interpolation, the analysis is highly sensitive to the cell sizes yielding inaccurate clustering for improper choices: Large cells result in no cluster separation, while clusters fall apart for small cells. Using tri-linear interpolation in physical space, we can refine the data by generating additional samples. The refinement scheme can adapt to the data point distribution in attribute space and the histogram’s bin size. As a consequence, we can generate a density computation, where clusters stay connected even when using very small cell sizes. We exploit this result to create a robust hierarchical cluster tree. It can be visually explored using coordinated views to physical space visualizations and to parallel coordinates plots. We apply our technique to several datasets and compare the results against results without interpolation.
CNN in MRF: Video Object Segmentation via Inference in a CNN-Based Higher-Order Spatio-Temporal MRF

This paper addresses the problem of video object segmentation, where the initial object mask is given in the first frame of an input video. We propose a novel spatiotemporal Markov Random Field (MRF) model defined over pixels to handle this problem. Unlike conventional MRF models, the spatial dependencies among pixels in our model are encoded by a Convolutional Neural Network (CNN). Specifically, for a given object, the probability of a labeling to a set of spatially neighboring pixels can be predicted by a CNN trained for this specific object. As a result, higher-order, richer dependencies among pixels in the set can be implicitly modeled by the CNN. With temporal dependencies established by optical flow, the resulting MRF model combines both spatial and temporal cues for tackling video object segmentation. However, performing inference in the MRF model is very difficult due to the very high-order dependencies. To this end, we propose a novel CNN-embedded algorithm to perform approximate inference in the MRF. This algorithm proceeds by alternating between a temporal fusion step and a feed-forward CNN step. When initialized with an appearance-based one-shot segmentation CNN, our model outperforms the winning entries of the DAVIS 2017 Challenge, without resorting to model ensembling or any dedicated detectors.
Masked Signal Decomposition Using Subspace Representation and Its Applications

Signal decomposition is a classical problem in signal processing, which aims to separate an observed signal into two or more components each with its own property. Usually each component is described by its own subspace or dictionary. Extensive research has been done for the case where the components are additive, but in real world applications, the components are often non-additive. For example, an image may consist of a foreground object overlaid on a background, where each pixel either belongs to the foreground or the background. In such a situation, to separate signal components, we need to find a binary mask which shows the location of each component. Therefore it requires to solve a binary optimization problem. Since most of the binary optimization problems are intractable, we relax this problem to the approximated continuous problem, and solve it by alternating optimization technique, which solves the mask and the subspace projection for each component, alternatingly and iteratively. We show the application of the proposed algorithm for three applications: separation of text from background in images, separation of moving objects from a background undergoing global camera motion in videos, separation of sinusoidal and spike components in one dimensional signals. We demonstrate in each case that considering the non-additive nature of the problem can lead to significant improvement.
A psychometric evaluation of the Pediatric Anesthesia Emergence Delirium scale

Emergence delirium and emergence agitation have been a subject of interest since the early 1960s. This behavior has been associated with increased risk of injury in children and dissatisfaction with anesthesia care in their parents. The Pediatric Anesthesia Emergence Delirium Scale is a commonly used instrument for codifying and recording this behavior.
Distributed power allocation for cooperative localization: A potential game approach

In wireless sensor networks (WSNs), the availability of position information is a critical problem for many commercial and military applications. Network cooperation between agents can improve the localization accuracy but the power consumption will increase at the same time. Then the optimal trade-off between localization accuracy and power consumption for cooperation is a challenging task. In this paper, we investigate the problem of distributed power allocation for cooperative localization in WSNs localization systems, using a potential game approach. First, we propose a local altruistic power allocation game, in which each agent not only consider localization accuracy and power consumption of itself but also localization accuracy of its neighbors. Then, we prove that the proposed game is an exact potential game with at least one pure Nash equilibrium (NE). Moreover, the ε-equilibrium solution as a refinement of NE can be reached by better response dynamics. Numerical results show that the proposed strategies can give an instructional trade-off between power consumption and localization accuracy for cooperation.
Single‐channel recordings of three types of calcium channels in chick sensory neurones.

1. T‐, and L‐type Ca2+ channels were studied in cell‐attached patch recordings from the cell bodies of chick dorsal root ganglion neurones. All experiments were performed with isotonic BaCl2 (110 mM) in the recording pipette and with isotonic potassium aspartate in the bathing solution to zero the cell membrane potential. 2. L‐type channels are distinguished by a unitary slope conductance of 25 pS, activation over the range of membrane potentials between 0 and +40 mV, little inactivation over the course of a 136 ms depolarization, and availability for opening even at depolarized holding potentials (h.p. greater than ‐40 mV). L channels show a predominant mode of gating (mode 1) characterized by brief openings (approximately 1 ms), occasionally interspersed with another pattern of gating characterized by much longer openings (mode 2). 3. The dihydropyridine (DHP) Ca2+ agonist Bay K 8644 promotes mode 2 activity and shifts the voltage dependence of L‐type channel activation towards more negative potentials. It leaves the unitary current‐voltage relation unchanged. 4. Nifedipine, a DHP Ca2+ antagonist, strongly inhibits L‐type channel activity through an increase in the proportion of blank sweeps. 5. T‐type Ca2+ channels are distinguished by a much smaller unitary slope conductance (8 pS) and by activation and inactivation over relatively negative ranges of potential. Inactivation is complete by the end of 136 ms pulses to test potentials beyond ‐20 mV. 6. N‐type Ca2+ channels are distinguished by an intermediate unitary slope conductance (13 pS), and by activation over a range of potentials between those of T‐ and L‐type channels. Inactivation of N‐type channels takes place over an exceptionally broad range of holding potentials (‐80 to ‐20 mV). 7. Cell‐attached patch data on the voltage dependence of activation and inactivation of T‐ and N‐type channels are in excellent agreement with results from whole‐cell recordings (Fox, Nowycky & Tsien, 1987) if allowances are made for variations in external surface potential. 8. Patches containing one or two channels of a single type were used for analysis of gating kinetics. The predominant pattern of activity for each of the channel types is an exponential distribution of relatively brief (approximately 1 ms) openings, and a bi‐exponential distribution of short and long closings. 9. Patches containing all possible combinations of channel types were observed. However, preliminary evidence suggests that channels are distributed unevenly over the cell body; clustering of N‐type channels is particularly prominent. (ABSTRACT TRUNCATED AT 400 WORDS)
Facilitation of IL-22 production from innate lymphoid cells by prostaglandin E2 prevents experimental lung neutrophilic inflammation

Acute lung injury is a neutrophil-dominant, life-threatening disease without effective therapies and better understanding of the pathophysiological mechanisms involved is an urgent need. Here we show that interleukin (IL)-22 is produced from innate lymphoid cells (ILC) and is responsible for suppression of experimental lung neutrophilic inflammation. Blocking prostaglandin E2 (PGE2) synthesis reduces lung ILCs and IL-22 production, resulting in exacerbation of lung neutrophilic inflammation. In contrast, activation of the PGE2 receptor EP4 prevents acute lung inflammation. We thus demonstrate a mechanism for production of innate IL-22 in the lung during acute injury, highlighting potential therapeutic strategies for control of lung neutrophilic inflammation by targeting the PGE2/ILC/IL-22 axis.