Miracle-dz/UniFAR-FTUs huggingface dataset:
Pigment Pattern Formation in the Guppy, Poecilia reticulata, Involves the Kita and Csf1ra Receptor Tyrosine Kinases

Males of the guppy (Poecilia reticulata) vary tremendously in their ornamental patterns, which are thought to have evolved in response to a complex interplay between natural and sexual selection. Although the selection pressures acting on the color patterns of the guppy have been extensively studied, little is known about the genes that control their ontogeny. Over 50 years ago, two autosomal color loci, blue and golden, were described, both of which play a decisive role in the formation of the guppy color pattern. Orange pigmentation is absent in the skin of guppies with a lesion in blue, suggesting a defect in xanthophore development. In golden mutants, the development of the melanophore pattern during embryogenesis and after birth is affected. Here, we show that blue and golden correspond to guppy orthologs of colony-stimulating factor 1 receptor a (csf1ra; previously called fms) and kita. Most excitingly, we found that both genes are required for the development of the black ornaments of guppy males, which in the case of csf1ra might be mediated by xanthophore–melanophore interactions. Furthermore, we provide evidence that two temporally and genetically distinct melanophore populations contribute to the adult camouflage pattern expressed in both sexes: one early appearing and kita-dependent and the other late-developing and kita-independent. The identification of csf1ra and kita mutants provides the first molecular insights into pigment pattern formation in this important model species for ecological and evolutionary genetics.
Urban Computing: a challenging problem for Semantic Technologies,

In this paper we present the Urban Computing challenge and in particular we exemplify it in the context of traffic management. From our previous experiences in the field we draw requirements in terms of capacity to cope with heterogeneity in representation, semantics and defaults; with scale; with time-dependency of data; and with noisy, uncertain and inconsistent Data. Existing reasoning techniques fail to fulfill all those requirements once and for all.
A Friend Mechanism for Mobile Ad Hoc Networks

In the autonomous environment of mobile ad hoc network (MANET) where nodes are free to move around and depend on each other to initiate communication, cooperation among nodes is an essential component of a successful data transmission process. Since there is no central controller such as router to determine the communication paths in MANET, each node in the ad hoc network has to rely on each other in order to forward packets, thus highly cooperative nodes are required to ensure that the initiated data transmission process does not fail. However, it is hard to encourage cooperativeness among nodes for each node owns limited resources that need to be preserved. These particular nodes which are also known as selfish nodes refuse to help other nodes in forwarding packets due to the anxiety of having resource degradation such as exhausted battery power and limited processor capability. This problem has aroused several issues in MANET: routing, security, quality of service (QoS), resource management and auto-configuration. Hence, there is a need to provide a mechanism that can encourage cooperation among nodes without jeopardizing their resources. In this paper, we provide evidence through simulation experiments on how a friendship concept could be used to minimize the number of false alarms raised in MANET intrusion detection system (IDS). We also discuss on how the similar friendship concept could be used to address several challenges faced in MANET environment.
SIZING METHOD FOR STORMWATER HARVESTING TANKS USING DAILY RESOLUTION RAINFALL AND WATER DEMAND DATA SETS

This work presents a simplified method for rainwater harvesting (RWH) tank sizing using long day-resolution rainfall time series. This method considers heterogeneous contributing catchments and water demand flow rates. For the tank sizing, we proposed to take into account the probability to supply the water demand, as well as the most needed probable time step and their respective variabilities. The method was applied to a specific case study (Pontificia Universidad Javeriana, RHW project), with 73 years of daily-resolution rainfall information (between 1936-2010). For the analysis we used different time periods from data-set and the results were: (i) for the whole data-set 76 years: 395 m (28 days, probability: 78%); (ii) for the last ten years: 494 m (35 days, probability: 89%); (iii) for the last five years: 346 m (25 days, probability: 84%); (iv) for the last year: 155-198 m (11-14 days, probability: 89-90%). These results seem to be influenced by an evolution of rainfall depth in different selected periods, which will be studied in further researches.
Phonetic Set Indexing for Fast Lexical Access

A novel nonsequential indexing mechanism (termed phonetic set indexing) has been evaluated for the purpose of fast word pre-selection. Our approach to handling the lexical access problem stems from the primary observation that the set of phones which are present in the transcription of a word is sparsely distributed across the vocabulary, and is thus suitable as an indexing key for retrieving a short-list of word possibilities.
Mining of association patterns for language modeling

Language modeling using n-gram is popular for speech recognition and many other applications. The conventional ngram suffers from the insufficiencies of training data, domain knowledge and long distance language dependencies. This paper presents a new approach to mining long distance word associations and incorporating their mutual information into language models. We aim to discover the associations of multiple distant words from training corpus. An efficient algorithm is exploited to merge the frequent word subsets and construct the association patterns. The resulting association pattern n-gram is general with a special realization to trigger pair n-gram where only associations of two distant words are considered. To improve the modeling, we further compensate the weaknesses of sparse training data via parameter smoothing and domain mismatch via online adaptive learning. The proposed association pattern n-gram and several hybrid models are successfully applied for speech recognition. We also find that the incorporation of mutual information of association patterns can significantly reduce the perplexities of language models.
Study and Application of Digital Image Reconstruction

Digital image reconstruction is a sound method to reveal the underlying images hidden in fuzzy and noisy data. The main defiance of this method is its sensitivity to measure noise in the input data, which will result in large artifacts in the reconstructed image. So it has become very important that finding a robust method to remove the noise and recon- struct the image. For the purpose of acquiring a gray image without loosing information from it, a method of removing noise from a gray image and reconstructing this image was proposed in this paper. The proposed method mainly used the opening-by-reconstruction. In order to test the efficacy of this method, it is used to isolate the text out of the image of cal- culator keys. The experimental results showed that the method is efficient.
Ieee Transactions on Visualization and Computer Graphics 1 a Point Cloud Based Multi-view Stereo Algorithm for Free-viewpoint Video

—This paper presents a robust multi-view stereo (MVS) algorithm for free-viewpoint video. Our MVS scheme is totally point cloud based, and consists of three stages: point cloud extraction, merging and meshing. To guarantee reconstruction accuracy, point clouds are first extracted according to a stereo matching metric which is robust to noise, occlusion and lack of texture. Visual hull information, frontier points and implicit points are then detected and fused with point fidelity information in the merging and meshing steps. All aspects of our method are designed to counteract potential challenges in MVS datasets for accurate and complete model reconstruction. Experimental results demonstrate that, our technique produces the most competent performance among current algorithms under sparse viewpoint setups according to both static and motion MVS datasets.
Monetary cost of dietary energy is negatively associated with BMI and waist circumference, but not with other metabolic risk factors, in young Japanese women

Abstract Objective Little is known about the relationship of dietary cost to health status. The present cross-sectional study examined the association between the monetary cost of dietary energy (Japanese yen/4184 kJ) and several metabolic risk factors. Design Monetary cost of dietary energy was estimated based on dietary intake assessed by a self-administered diet history questionnaire and retail food prices. Body height and weight, from which BMI was derived, waist circumference and blood pressure were measured and fasting blood samples were collected for biochemical measurements. Setting A total of fifteen universities and colleges in Japan. Subjects A total of 1136 female Japanese dietetic students aged 18–22 years. Results After adjustment for potential confounding factors, monetary cost of dietary energy was significantly and negatively associated with BMI (P for trend = 0·0024). Monetary cost of dietary energy also showed a significant and negative association with waist circumference independently of potential confounding factors, including BMI (P for trend = 0·0003). No significant associations were observed for other metabolic risk factors examined (P for trend = 0·10–0·88). Conclusions The monetary cost of dietary energy was independently and negatively associated with both BMI and waist circumference, but not other metabolic risk factors, in a group of young Japanese women.
Cross-Domain Facial Expression Recognition Using Supervised Kernel Mean Matching

Even though facial expressions have universal meaning in communications, their appearances show a large amount of variation due to many factors, such as different image acquisition setups, different ages, genders, and cultural backgrounds etc. Collecting enough amounts of annotated samples for each target domain is impractical, this paper investigates the problem of facial expression recognition in the more challenging situation, where the training and testing samples are taken from different domains. To address this problem, after observing the fact of unsatisfactory performance of the Kernel Mean Matching (KMM) algorithm, we propose a supervised extension that matches the distributions in a class-to-class manner, called Supervised Kernel Mean Matching (SKMM). The new approach stands out by taking into consideration both matching the distributions and preserving the discriminative information between classes at the same time. The extensive experimental studies on four cross-dataset facial expression recognition tasks show promising improvements of the proposed method, in which a small number of labeled samples guide the matching process.
Pattern Detection with Rare Item-set Mining

The discovery of new and interesting patterns in large datasets, known as data mining, draws more and more interest as the quantities of available data are exploding. Data mining techniques may be applied to different domains and fields such as computer science, health sector, insurances, homeland security, banking and finance, etc. In this paper we are interested by the discovery of a specific category of patterns, known as rare and non-present patterns. We present a novel approach towards the discovery of non-present patterns using rare item-set mining.
A preliminary study of the effects of trigger timing on diffusion tensor imaging of the human spinal cord.

BACKGROUND AND PURPOSE Diffusion tensor and diffusion-weighted spinal cord imaging remain relatively unexplored techniques despite demonstrations that such images can be obtained and may yield clinically relevant findings. In this study, we examined the temporal dynamics of spinal cord motion and their impact on diffusion tensor image quality. METHODS Four healthy volunteers underwent phase contrast-based velocity mapping and segmented echo-planar diffusion tensor scans of the cervical spinal cord. Regions of interest in the cord were used to identify the temporal patterns of motion. The delay of data acquisition after the cardiac trigger was varied to correspond to either quiescence or motion of the cord. RESULTS The cervical spinal cord consistently displayed maximal velocities in the range of 0.5 cm/s and accelerations of up to 25 cm/s(2). In both these respects, the cervical cord values were greater than those of the medulla. Despite this pronounced motion, approximately 40% of the cardiac cycle can be described as relatively calm, with absolute velocities and accelerations less than 20% of the maximum values. Confining image acquisition to this window reduced ghosting artifacts and increased the consistency with which the dominant direction of diffusion was along the rostral-caudal axis in both gray and white matter of the spine. Preliminary clinical application and fiber tracking in pathologic cases was feasible, and alterations of the diffusion properties by multiple sclerosis lesions, tumor, and syringomyelia were seen. CONCLUSIONS Acquiring DTI data during the quiescent phase of spinal cord motion can reduce ghosting artifacts and improve fiber tracking.
Antisecretory factor peptide derivatives specifically inhibit [3H]-gamma-amino-butyric acid/36Cl- out-->in permeation across the isolated rabbit Deiters' neuronal membrane.

AIMS Antisecretory factor (AF) is a 41-kDa protein, its main function being the regulation of intestinal ion/water transport, but it also inhibits chloride and gamma-amino-butyric acid transport across nerve cell membranes. The present experiments were designed to evaluate whether the same AF peptide sequence mediates the permeability effects seen at the nerve cell membrane and in the rat small intestine. METHODS Four peptides were prepared by the solid phase technique with sequences derived from positions 1-51 of the full-length antisecretory factor AF and tested on nerve cell membranes isolated from rabbit Dieter cells. RESULTS AF peptides containing the active 36-51 peptide exerted a blocking effect of the out-->in permeation of 36Cl- as well as of [3H]-gamma-amino-butyric acid. The minimal dose causing inhibition, however, varied between 10(-11) m (AF10) and 10(-7) m (AF13). The most potent peptides have been shown previously to be active in inhibiting experimental diarrhoea in vivo in small intestinal ligated loops in rats. The non-active sequence AF23-32 did not inhibit any of the two permeation markers in vitro, a result which supports the lack of activity found also in vivo. CONCLUSION The results suggest that AF, or AF derivatives, counteract intestinal hypersecretion by blocking anion permeation across large anionic pores. Such a blocking effect could also influence the generation of action potentials in enteric nerve cells controlling the intestinal water and ion transport system.
CAWS: Criticality-aware warp scheduling for GPGPU workloads

The ability to perform fast context-switching and massive multi-threading is the forte of modern GPU architectures, which have emerged as an efficient alternative to traditional chip-multiprocessors for parallel workloads. One of the main benefits of such architecture is its latency-hiding capability. However, the efficacy of GPU's latency-hiding varies significantly across GPGPU applications. To investigate this, this paper first proposes a new algorithm that profiles execution behavior of GPGPU applications. We characterize latencies caused by various pipeline hazards, memory accesses, synchronization primitives, and the warp scheduler. Our results show that the current round-robin warp scheduler works well in overlapping various latency stalls with the execution of other available warps for only a few GPGPU applications. For other applications, there is an excessive latency stall that cannot be hidden by the scheduler effectively. With the latency characterization insight, we observe a significant execution time disparity for warps within the same thread block, which causes suboptimal performance, called the warp criticality problem. To tackle the warp criticality problem, we design a family of criticality-aware warp scheduling (CAWS) policies by scheduling the critical warp(s) more frequently than other warps. Our results on the breadth-first-search, B+tree search, two point angular correlation function, and K-means clustering show that, with oracle knowledge of warp criticality, our best-performing scheduling policy can improve GPGPU applications' performance by 17% on average. With our designed criticality predictor, the various scheduling policies can improve performance by 10–21% on breadth-first-search. To our knowledge, this is the first paper to characterize warp criticality and explore different criticality-aware warp scheduling policies for GPGPU workloads.
Divergence Filter for Saliency

Detection of regions with high visual attention from image has various applications including advertising design where ads are often associated with relevant semantic visual information. The salient regions in the image/video have to be identified in a consistent way, even if original objects or background are texture scene. This is achieved by solving combinatorial problem of down-sampling that searches for the optimal texture region map. The complexity of this solution makes it impractical. The problem becomes easy by a new approach for saliency detection. It is based on the spatial attention model that evaluates divergence of a given local region from its surrounding where objects and background can be texture scene. Our proposed solution is based on an adaptive version of the bilateral filter that searches for the divergence of a pixel with its local neighbors. The contributions of this work are new divergence estimation function which reduces potential global search into a simple local filter, and efficient convex-hull algorithm for creating saliency map. Experimental results show that the solution can deal with texture during analysis of visual attention, and saliency detection's performance is improved.
L1 Regularized Regression for Reranking and System Combination in Machine Translation

We use L1 regularized transductive regression to learn mappings between source and target features of the training sets derived for each test sentence and use these mappings to rerank translation outputs. We compare the effectiveness of L1 regularization techniques for regression to learn mappings between features given in a sparse feature matrix. The results show the effectiveness of using L1 regularization versus L2 used in ridge regression. We show that regression mapping is effective in reranking translation outputs and in selecting the best system combinations with encouraging results on different language pairs.
Map-Based Strategies for Robot Navigation in Unknown Environments

Robot path planning algorithms for finding a goal in a unknown environment focus on completeness rather than optimality. In this paper, we investigate several strategies for using map information, however incomplete or approximate, to reduce the cost of the robot’s traverse. The strategies are based on optimistic, pessimistic, and average value assumptions about the unknown portions of the robot’s environment. The strategies were compared us ing randomly-genera ted f rac ta l t e r ra in environments. We determined that average value approximations work best across small regions. In their absence, an optimistic strategy explores the environment, and a pessimistic strategy refines existing paths.
Document description: what works for images should also work for text?

The success of the bag-of-words approach for text has inspired the recent use of analogous strategies for global representation of images with local visual features. Many applications have been proposed for object detection, image annotation, queries-by-example, relevance feedback, automatic annotation, and clustering. In this paper, we investigate the validity of the bag-of-words analogy for image representation and, more specifically, local pattern selection for feature generation. We propose a generalized document representation framework and apply it to the evaluation of two pattern selection strategies for images: dense sampling and point-of-interest detection. We present empirical results that support our contention that text-based experimentation can provide useful insights into the effectiveness of image representations based on the bag-of-visual-words technique.
Efficient Parameter Estimation in the High-Dimensional Inverse Problem of Seismic Tomography

The focus of this dissertation is on efficient parameter estimation and uncertainty quantification in high dimensional seismic tomography within the Bayesian spatial modeling framework. Seismic tomography is an imaging technique in geophysics used to infer the three-dimensional seismic velocity structure of the earth's interior by assimilating data measured at the surface. The research within this dissertation consists of two pillars: (1) We present a Bayesian hierarchical model to estimate the joint distribution of earth structural and earthquake source correction parameters. We construct an ellipsoidal spatial prior which allows to accommodate the layered nature of the earth's mantle. With our efficient Markov chain Monte Carlo algorithm (MCMC) we sample from the posterior distribution for large-scale linear inverse problems and provide precise uncertainty quantification in terms of parameter distributions and credible intervals given the data. (2) We develop and implement a spatial dependency model of the earth's three-dimensional velocity structure based on a Gaussian Matern field approximation using the theory of stochastic partial differential equations (Lindgren et al. 2011). We carry out the uncertainty quantification of the high dimensional parameter space using the integrated nested Laplace approximation (INLA) (Rue et al., 2009). Both modeling approaches are applied to a full-fledged tomography problem. In particular the inversion for the upper mantle structure under western North America is facilitated. It involves more than 11,000 seismic velocity and source correction parameters using seismological data from the continental-scale USArray experiment. Our results based on the MCMC algorithm reveal major structures of the mantle beneath the western USA with novel uncertainty assessments. We compare both approaches and demonstrate that the INLA algorithm substantially improves previous work based on regular MCMC sampling. The outcome based on the INLA approach confirms the previous results while simultaneously capturing the spatial dependencies caused by the earthquake sources and the receiver stations. The statistical misfit is reduced by about 40% and the computing time shows a speedup of about 1.5 to 2 times.