Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Color Constancy to Advantage in Color Gamut Calculations

The human color constancy uses spatial comparisons. The relationships among neighboring pixels are far more important than the absolute differences between the colorimetric values of an original and its gamut-limited reproduction. If all the pixels in an image have a reproduction error in the same direction (red, green, blue, lightness, hue, chroma), then our color constancy mechanism helps to make large errors appear small. However, if all the errors are randomly distributed, then small errors appear large. This paper will describe experiments using constant errors to produce variable apparent errors and describe a technique of calculating the best appearance image using spatial comparisons. This calculation will be applied to colorgamut problems.
Weighted proportional allocation

We consider a weighted proportional allocation of resources that allows providers to discriminate usage of resources by users. This framework is a generalization of well-known proportional allocation by accommodating allocation of resources proportional to weighted bids or proportional to submitted bids but with weighted payments. We study a competition game where everyone is selfish: providers choose user discrimination weights aiming at maximizing their individual revenues while users choose their bids aiming at maximizing their individual payoffs. We analyze revenue and social welfare of this game. We find that the revenue is lower bounded by k/(k+1) times the revenue under standard price discrimination scheme, where a set of k users is excluded. For users with linear utility functions, we find that the social welfare is at least 1/(1+2/√3) of the maximum social welfare (approx. 46%) and that this bound is tight. We extend this efficiency result to a broad class of utility functions and multiple competing providers. We also describe an algorithm for adjusting discrimination weights by providers without a prior knowledge of user utility functions and establish convergence to equilibrium points of the competition game. Our results show that, in many cases, weighted proportional sharing achieves competitive revenue and social welfare, despite the fact that everyone is selfish.
Mobile Devices to Interact With Lighting

INTRODUCTION Advances in Light Emitting Diode (LED) technology provide a wide range of new opportunities for interaction with light. In this workshop paper, I will focus on the role mobile phones (and devices) have played in this lighting revolution. Mobile phones have been: 1) used as means to remotely control lighting, 2) fitted with LEDs to allow people to directly control lighting, and 3) enhanced with pico projectors, which provide new possibilities to cast light and shadows. Finally, I explore the idea of using space as cue to interact with lighting using mobile phones.
An image matrix compression based supervised locality preserving projections for face recognition

Recently, a new manifold learning algorithm named locality preserving projections (LPP) that aims at finding an embedding that preserves local information has been proposed and used for face recognition. In this paper, an image matrix compression based supervised locality preserving projections is proposed for face representation and recognition. In this new scheme, a bilateral-projection-based 2DPCA (B2DPCA) for image matrix compression is performed before supervised locality preserving projections. The bilateral-projection-based DPCA algorithm is used to obtain the meaningful low dimensional structure of the data space in this new method. Experiments based on the ORL face database demonstrate the effectiveness and efficiency of the new. Results show that the new algorithm outperforms the Laplacian faces which uses the locality preserving projections (LPP) and achieve a much higher accurate recognition rate.
Spatial Weighting and Iterative Projection Methods for EOFs

Often there is a need to consider spatial weighting in methods for finding spatial patterns in climate data. The focus of this paper is on techniques that maximize variance, such as empirical orthogonal functions (EOFs). A weighting matrix is introduced into a generalized framework for dealing with spatial weighting. One basic principal in the design of the weighting matrix is that the resulting spatial patterns are independent of the grid used to represent the data. A weighting matrix can also be used for other purposes, such as to compensate for the neglect of unrepresented subgrid-scale variance or, in the form of a prewhitening filter, to maximize the signal-to-noise ratio of EOFs. The new methodology is applicable to other types of climate pattern analysis, such as extended EOF analysis and maximum covariance analysis. The increasing availability of large datasets of three-dimensional gridded variables (e.g., reanalysis products and model output) raises special issues for data-reduction methods such as EOFs. Fast, memory-efficient methods are required in order to extract leading EOFs from such large datasets. This study proposes one such approach based on a simple iteration of successive projections of the data onto time series and spatial maps. It is also demonstrated that spatial weighting can be combined with the iterative methods. Throughout the paper, multivariate statistics notation is used, simplifying implementation as matrix commands in high-level computing languages.
Empirical study on overlapping community detection in question and answer sites

In many social networks, people interact based on their interests. Community detection algorithms are then useful to reveal the sub-structures of a network and help us find interest groups. Identifying these social communities can bring benefit to understanding and predicting users behaviors. However, for some kind of online community sites such as question-and-answer (Q&A) sites or forums, there is no friendship based social network structure, which means people are not aware who they are in contact with. Therefore, many traditional community detection techniques do not apply directly. In this paper, we propose an empirical approach for extracting data from Q&A sites suitable to apply community detection methods. Then we compare three kinds of community detection methods we applied on a dataset extracted from the popular Q&A site StackOverflow. We analyze and comment the results of each method.
Distributed scheduling for mobile ad hoc networks - a novel approach

Spatial reuse TDMA is a collision-free access scheme for ad hoc networks. The idea is to let spatially separated radio terminals reuse the same time slot when the resulting interferences are not too severe. In this paper we first describe the properties a distributed STDMA algorithm must have in order to be efficient. No existing algorithm fulfills all of these properties. Second we focus on how to efficiently use distributed information and describe an algorithm that can handle different amount of information. Furthermore, we evaluate this algorithm for different information and show that it can give the same capacity as a centralized reference algorithm.
BOUNDING THE GENERALIZATION ERROR OF NEURALNETWORKS AND COMBINED CLASSIFIERSVladimir

Recently, several authors developed a new approach to bounding the generalization error of complex classi-ers (of large or even innnite VC-dimension) obtained by combining simpler classiiers. The new bounds are in terms of the distributions of the margin of combined classiiers and they provide some theoretical explanation of generalization performance of large neu-We obtained new probabilistic bounds on generalization error of complex classiiers that further improve the previous results in this direction. The bounds are based on the methods of the theory of Gaussian and empirical processes, in particular , on various comparison inequalities, symmetrization techniques and concentration inequalities.
Indirect Evidence Link PCB Dehalogenation with Geobacteraceae in Anaerobic Sediment-Free Microcosms

Although polychlorinated biphenyls (PCBs) production was brought to a halt 30 years ago, recalcitrance to degradation makes them a major environmental pollutant at a global scale. Previous studies confirmed that organohalide-respiring bacteria (OHRB) were capable of utilizing chlorinated congeners as electron acceptor. OHRB belonging to the Phyla Chloroflexi and Firmicutes are nowadays considered as the main PCB-dechlorinating organisms. In this study, we aimed at exploring the involvement of other taxa in PCB dechlorination using sediment-free microcosms (SFMs) and the Delor PCB mixture. High rates of congener dehalogenation (up to 96%) were attained in long-term incubations of up to 692 days. Bacterial communities were dominated by Chloroflexi, Proteobacteria, and Firmicutes, among strictly simplified community structures composed of 12 major phyla only. In a first batch of SFMs, Dehalococcoides mccartyi closely affiliated with strains CG4 and CBDB1 was considered as the main actor associated with congener dehalogenation. Addition of 2-bromoethanesulfonate (BES), a known inhibitor of methanogenic activity in a second batch of SFMs had an adverse effect on the abundance of Dehalococcoides sp. Only two sequences affiliated to this Genus could be detected in two (out of six) BES-treated SFMs, contributing to a mere 0.04% of the communities. BES-treated SFMs showed very different community structures, especially in the contributions of organisms involved in fermentation and syntrophic activities. Indirect evidence provided by both statistical and phylogenetic analysis validated the implication of a new cluster of actors, distantly affiliated with the Family Geobacteraceae (Phylum δ-Proteobacteria), in the dehalogenation of low chlorinated PCB congeners. Members of this Family are known already for their dehalogenation capacity of chlorinated solvents. As a result, the present study widens the knowledge for the phylogenetic reservoir of indigenous PCB dechlorinating taxa.
Bayesian Sparse Partial Least Squares

Partial least squares (PLS) is a class of methods that makes use of a set of latent or unobserved variables to model the relation between (typically) two sets of input and output variables, respectively. Several flavors, depending on how the latent variables or components are computed, have been developed over the last years. In this letter, we propose a Bayesian formulation of PLS along with some extensions. In a nutshell, we provide sparsity at the input space level and an automatic estimation of the optimal number of latent components. We follow the variational approach to infer the parameter distributions. We have successfully tested the proposed methods on a synthetic data benchmark and on electrocorticogram data associated with several motor outputs in monkeys.
Genetic Variations in ADIPOQ Gene Are Associated with Chronic Obstructive Pulmonary Disease

Background Adiponectin is reported to be related to the development of chronic obstructive pulmonary disease (COPD). Genetic variants in the gene encoding adiponectin (ADIPOQ) have been reported to be associated with adiponectin level in several genome–wide linkage and association studies. However, relatively little is known about the effects of ADIPOQ gene variants on COPD susceptibility. We determined the frequencies of single-nucleotide polymorphisms (SNPs) in ADIPOQ in a Chinese Han population and their possible association with COPD susceptibility. Methods We conducted a case–control study of 279 COPD patients and 367 age- and gender-distribution-matched control subjects. Seven tagging SNPs in ADIPOQ, including rs710445, rs16861205, rs822396, rs7627128, rs1501299, rs3821799 and rs1063537 were genotyped by SNaPshot. Association analysis of genotypes/alleles and haplotypes constructed from these loci with COPD was conducted under different genetic models. Results The alleles or genotypes of rs1501299 distributed significantly differently in COPD patients and controls (allele: P = 0.002, OR = 1.43 and 95%CI = 1.14–1.79; genotype: P = 0.008). The allele A at rs1501299 was potentially associated with an increased risk of COPD in all dominant model analysis (P = 0.009; OR: 1.54; 95%CI: 1.11–2.13), recessive model analyses (P = 0.015; OR: 1.75; 95% CI: 1.11–2.75) and additive model analyses (P = 0.003; OR: 2.11; 95% CI: 1.29–3.47). In haplotype analysis, we observed haplotypes AAAAACT and GGACCTC had protective effects, while haplotypes AGAACTC, AGGCCTC, GGAACTC, GGACACT and GGGCCTC were significantly associated with the increased risk of COPD. Conclusions We conducted the first investigation of the association between the SNPs in ADIPOQ and COPD risk. Our current findings suggest that ADIPOQ may be a potential risk gene for COPD. Further studies in larger groups are warranted to confirm our results.
Physiology and topography of neurons with multipeaked tuning curves in cat primary auditory cortex.

1. The physiology and topography of single neuron responses along the isofrequency domain of the middle- and high-frequency portions [characteristic frequencies (CFs) greater than 4 kHz] of the primary auditory cortex (AI) were investigated in the barbiturate-anesthetized cat. Single neurons were recorded at several locations along the extent of isofrequency contours, defined from initial multiple-unit mapping. For each neuron a high-resolution excitatory tuning curve was determined, and for some neurons high-resolution two-tone tuning curves were recorded to measure inhibitory/suppressive areas. 2. A physiologically distinct population of neurons was found in the dorsal part of cat AI. These neurons exhibited two or three distinct excitatory frequency ranges, whereas most neurons in AI responded with excitation to a single narrow frequency range. These were called multipeaked neurons because of the shape of their tuning curves. At frequencies between the excitatory regions, the multipeaked neurons were inhibited or unresponsive. 3. Multipeaked neurons exhibited several distinct threshold minima in their frequency tuning curves. Most of the multipeaked neurons (88%) displayed two frequency minima, whereas the rest exhibited three minima. 4. The frequency separation between threshold minima was less than 1 octave in 71% of the double-peaked neurons recorded. Occasionally, the frequency peaks of these neurons closely corresponded to a response to second and third harmonics without a response to the fundamental frequency. 5. Multipeaked neurons exhibited a wide range of total bandwidths (highest excitatory frequency minus lowest excitatory frequency expressed in octaves). Bandwidths of the isolated peaks within the same neuron were also quite variable. 6. Response latencies to tones with frequencies within each peak of a multipeaked neuron could vary considerably. In 71% (17) of the neurons, tones corresponding to the high-frequency peak (CFh) elicited a longer response latency (greater than 4 ms) than those corresponding to the low-frequency peak (CF1). 7. Inhibitory/suppressive bands, as demonstrated with a two-tone paradigm, were often present between the peaks. Typically, neurons with excitatory peaks of similar response latencies showed an inhibitory band located between the peaks. 8. Ninety percent of the topographically localized multipeaked neurons were in the dorsal part of AI (greater than 1 mm dorsal to the maximum in the sharpness-of-tuning map). Although these neurons were restricted to dorsal AI, only 35% of neurons in this region were multipeaked. 9. Multipeaked neurons could show decreased response latencies and thresholds to two-tone combinations. (ABSTRACT TRUNCATED AT 400 WORDS)
Text independent speaker recognition using speaker dependent word spotting

This paper is motivated by the fact that text dependent speaker recognition is inherently more accurate than text independent speaker recognition. In this work we assign models to frequent words spoken by a speaker and spot them in a test call. In this way, text-dependent speaker recognition technology can be used for text independent tasks. The approach we take is to use DTW (Dynamic Time Warp) word spotting to find words in the test that resemble words in the train set. Results on the SPIDRE corpus show that using a combined DTW spotter based system and a GMM system improves performance significantly. For very low false acceptance rate (0.1%) misdetection was reduced from 32.2% to 23.3% (28% reduction). For low false acceptance rate (1%) misdetection was reduced from 28.9% to 21.1% (27% reduction).
What seems to be the problem officer: A look at burnout and depression among Icelandic police officers.

Depression and burnout are prevalent problems among police officers since their repeated exposure to critical incidents can put them at increased risk of developing these disorders. In this study a questionnaire, comprised of several subscales that measure depression, burnout, perceived stress, social support, as well as resilience, was sent to all working police officers working in Iceland. The purpose of this study was to identify risk factors as well as protective factors for burnout and depression among Icelandic police officers. A total of 301 police officers finished the questionnaire, of those 49 were female. The prevalence of depression in the sample was 17.8% and the prevalence of burnout was 34.0%. Perceived stress had a high positive correlation as well as a high effect on both burnout and depression. Resilience was found to have a buffering effect on perceived stress for both burnout and depression while social support did not have a buffering effect. More research is needed to identify more potential factors that can influence the development of these disorders, and to confirm this buffering effect.
Inducing Classification Rules for Public Health Data

Analyzing data with the intent of inducing classification rules typically proceeds from a set of training data in which classifications are known. In the event classifications are unknown, algorithms exist for performing unsupervised learning to determine concept classes inherent in the data. In this paper, we describe experiments applying multiple learning strategies for classifying unlabeled data. Specifically, three unsupervised learning algorithms were applied to a large set of public health data in order to determine likely concept classes for the data based on the inherent features in the data. After inducing the concept classes, the data were processed by a decision tree algorithm in order to determine more efficient classification rules under the assumption that the concepts induced during unsupervised learning were correct.
Oedipus—Myth and Complex

Spend your few moment to read a book even only few pages. Reading book is not obligation and force for everybody. When you don't want to read, you can get punishment from the publisher. Read a book becomes a choice of your different characteristics. Many people with reading habit will always be enjoyable to read, or on the contrary. For some reasons, this oedipus myth and complex tends to be the representative book in this website.
A Novel Edge Detection Method Based on PCA1

By analyzing the direction characteristic of principal component analysis (PCA), we propose an edge detection method based on PCA. Using Karhunen–Loeve transform, PCA transforms the original dataset into lower-dimensional feature data. The transform has directivity both on energy accumulation and data selection. The author points out and proves the two direction characteristics. In this paper, authors proposed a method of edge detection based on PCA. Authors used twice PCA operations, called TPCA, first by process the image by PCA and second by process the transpose of the image by PCA. Experimental results are shown by using some typical images.
Multi-region Segmentation of CT Images Based on Information Fusion

In order to make both the edges and the multi-regions of a CT image more clearly, a new multi-region segmentation method based on information fusion, in which clustering segmentation algorithm is combined with edge detection, is proposed in this paper. Firstly the CT image is segmented by the K-means clustering algorithm and Canny edge detection operator to obtain two images with clear regions and distinct edges respectively. Secondly these two images are synthesized to form a new one by the information fusion method based on features. The advantage of the method is that value K and the initial clustering centroids are determined automatically according to the image histogram and no need for the rectification of images before the images are fused. Experimental results show that the images are clearly segmented into multi-regions and the distinct image edges are preserved simultaneously as well
The reconstruction of large three-dimensional meshes

Surface reconstruction is the process of creating virtual three-dimensional representations of real-world objects using data obtained from 3D scanners. The traditional challenges of surface reconstruction arise from the uncertain nature of input data. Inaccuracies in scanning devices create noisy data. Point sampling is often non-uniform. And, accessibility constraints during the scanning process may leave some regions of the surface devoid of data. Robustly constructing a surface in the presence of these data anomalies is a difficult problem. In addition, surface reconstruction methods have recently encountered a new challenge resulting from developments in 3D scanning techniques. New scanning technologies have driven a dramatic increase in the size of datasets available for surface reconstruction, with datasets now exceeding one billion point samples. As a result, space and time efficiency have become critical in the development of effective reconstruction algorithms, and the design of streaming and parallel techniques has become indispensable. In this dissertation we describe a new technique for surface reconstruction, based on the solution to a Poisson equation. Our approach is designed to meet the multiple challenges of modern datasets. The method is robust to the types of noise found in real-world data, allowing the reconstruction of high quality surfaces. Despite formulating surface reconstruction as a global problem, we also show that our method can be implemented using only local updates which allows extremely large reconstruction problems to be solved in a streaming manner. We also exploit current industry trends towards multi-core and parallel computing by presenting a parallel implementation of our method that is able to dramatically reduce the time taken to produce highly detailed reconstructions. We demonstrate the practicality of our method on several of the largest reconstruction datasets available to date.
Polyglutamine Genes Interact to Modulate the Severity and Progression of Neurodegeneration in Drosophila

The expansion of polyglutamine tracts in a variety of proteins causes devastating, dominantly inherited neurodegenerative diseases, including six forms of spinal cerebellar ataxia (SCA). Although a polyglutamine expansion encoded in a single allele of each of the responsible genes is sufficient for the onset of each disease, clinical observations suggest that interactions between these genes may affect disease progression. In a screen for modifiers of neurodegeneration due to SCA3 in Drosophila, we isolated atx2, the fly ortholog of the human gene that causes a related ataxia, SCA2. We show that the normal activity of Ataxin-2 (Atx2) is critical for SCA3 degeneration and that Atx2 activity hastens the onset of nuclear inclusions associated with SCA3. These activities depend on a conserved protein interaction domain of Atx2, the PAM2 motif, which mediates binding of cytoplasmic poly(A)-binding protein (PABP). We show here that PABP also influences SCA3-associated neurodegeneration. These studies indicate that the toxicity of one polyglutamine disease protein can be dramatically modulated by the normal activity of another. We propose that functional links between these genes are critical to disease severity and progression, such that therapeutics for one disease may be applicable to others.