Miracle-dz/UniFAR-FTUs huggingface dataset:
Engenius - Environmental genome Informational Utility System

Short-insert shotgun sequencing approaches have been applied in recent years to environmental genomic libraries. In the case of complex multispecies microbial communities, there can be many sequence reads that are not incorporated into assemblies, and thus need to be annotated and accessible as single reads. Most existing annotation systems and genome databases accommodate assembled genomes containing contiguous gene-encoding sequences. Thus, a solution is required that can work effectively with environmental genomic annotation information to facilitate data analysis. The Environmental Genome Informational Utility System (EnGenIUS) is a comprehensive environmental genome (metagenome) research toolset that was specifically designed to accommodate the needs of large (> 250 K sequence reads) environmental genome sequencing efforts. The core EnGenIUS modules consist of a set of UNIX scripts and PHP programs used for data preprocessing, an annotation pipeline with accompanying analysis tools, two entity relational databases, and a graphical user interface. The annotation pipeline has a modular structure and can be customized to best fit input data set properties. The integrated entity relational databases store raw data and annotation analysis results. Access to the underlying databases and services is facilitated through a web-based graphical user interface. Users have the ability to browse, upload, download, and analyze preprocessed data, based on diverse search criteria. The EnGenIUS toolset was successfully tested using the Alvinella pompejana epibiont environmental genome data set, which comprises more than 300 K sequence reads. A fully browsable EnGenIUS portal is available at (http://ocean.dbi.udel.edu/) (access code: "guest"). The scope of this paper covers the implementation details and technical aspects of the EnGenIUS toolset.
The role of IGCC in the global energy markets: part II: economics and hedging risks with spreads

This is part II of the joint paper. In this part, the economics of IGCC deployment is analyzed, including capital , operation, and maintenance costs. In addition, the risks of adopting IGCC lie in the future carbon dioxide regulation in the US due to the higher capital costs of IGCC technologies compared with conventional coal based power plants. Spread options are proposed as a means of hedging the higher capital costs and numerical results are provided. Index-terms – IGCC, economics, capital coat, carbon dioxide, risk, hedging.
Mixed modeling and multi-resolution remote sensing of soil evaporation

Evapotranspiration (ET) and its two main components, soil evaporation and plant transpiration, play a crucial role in the hydrological cycle, especially over semi-arid regions. Monitoring soil evaporation at multiple scales is especially relevant to a multitude of agronomic, hydrologic, meteorological and climatic applications, where remote sensing-based approaches have great potential via two evaporation-related observable variables: the land surface temperature (LST) and the surface soil moisture (SM). The aim of this PhD was to try and improve the spatio-temporal representation of soil evaporation by developing a multi-scale modeling approach that makes use of the synergy between readily available remote sensing data. A new methodology was developed to estimate the soil evaporative efficiency or SEE (ratio of actual to potential soil evaporation) at 100 m to 40 km resolution by combining 40 km resolution microwave-derived SM, 100 m to 1 km resolution thermal-derived LST, 100 m to 1 km resolution reflectance-derived vegetation cover/surface albedo and available meteorological data. The approach was tested over three different semi-arid irrigated areas in Morocco, Mexico and Spain. In a first step, a soil energy balance model (EBsoil) was proposed to improve the determination of the dry/wet boundaries of a LST-based ET model. EBsoil outputs were validated against in situ measurements of dry/wet soil temperatures over the Moroccan site. ET estimates were subsequently derived either from remote sensing data solely or by using EBsoil and intercompared at 100 m and 1 km resolutions with a reference ET over the Mexican site. Results showed that integrating EBsoil improves ET estimates at a medium (1 km) resolution. The next step consisted in looking into the temporal dynamics of SEE, using the global modeling technique, approach used for a very small number of environmental contexts, with even fewer models obtained that are able to reproduce the original dynamics. For the first time ever, a four dimensional chaotic model was obtained for the daily cycle of SEE, able to reproduce the dynamics of the variable with a good approximation in the phase domain.
Color Image Clutter Metrics for Predicting Human Target Acquisition Performance

A method to quantify clutter in color images is presented. This method is based on a grayscale clutter metric, target structure similarity (TSSIM) metric, which showed the best correlation with human perception than the other available metrics. The proposed new metric is designed to measure the background clutter in the perceptually decorrelated quasi-uniform color space of CIELAB. The TSSIM metric is extended to include color by applying it to the individual dimensions of the CIELAB and combining the individual components together using weighted means. To evaluate the new metric, TSSIM and this new metric are both applied to the Search_2 dataset, a visible imagery set with perception experiment data. Experimental results show the new metric correlates perceptual data better than the TSSIM metric and point the way to various improvements.
Geographic variation in CKD prevalence and ESRD incidence in the United States: results from the reasons for geographic and racial differences in stroke (REGARDS) study.

BACKGROUND It is not known whether geographic differences in the prevalence of chronic kidney disease exist and are associated with end-stage renal disease (ESRD) incidence rates across the United States. STUDY DESIGN Cross-sectional and ecologic. SETTING & PARTICIPANTS White (n = 16,410) and black (n = 11,109) participants from across the continental United States in the population-based Reasons for Geographic and Racial Differences in Stroke (REGARDS) Study. PREDICTOR Geographic region, defined by the 18 networks of the US ESRD Network Program. OUTCOMES & MEASUREMENTS Albuminuria, defined as albumin-creatinine ratio ≥30 mg/g, and decreased estimated glomerular filtration rate (eGFR), defined as <60 mL/min/1.73 m(2), were measured in the REGARDS Study. ESRD incidence rates were obtained from the US Renal Data System. RESULTS For whites, the network-specific prevalence of albuminuria ranged from 8.4% (95% CI, 3.3%-13.5%) in Network 15 to 14.8% (95% CI, 8.0%-21.6%) in Network 3, and decreased eGFR ranged from 4.3% (95% CI, 2.0%-6.6%) in Network 4 to 16.7% (95% CI, 12.7%-20.7%) in Network 7. For blacks, the prevalence of albuminuria ranged from 12.1% (95% CI, 8.7%-15.5%) in Network 5 to 26.5% (95% CI, 16.7%-36.3%) in Network 4, and decreased eGFR ranged from 6.7% (95% CI, 5.0%-8.4%) in Network 17/18 to 13.4% (95% CI, 7.8%-19.1%) in Network 12. Spearman correlation coefficients for the prevalence of albuminuria and decreased eGFR with network-specific ESRD incidence rates were 0.49 and 0.24, respectively, for whites and 0.29 and 0.25, respectively, for blacks. LIMITATIONS There were few cases of albuminuria and decreased eGFR in some geographic regions. CONCLUSIONS In the United States, substantial geographic variations in the prevalence of albuminuria and decreased eGFR exist, but were correlated only modestly with ESRD incidence, suggesting the chronic kidney disease burden may not explain the geographic variation in ESRD incidence.
A Hybrid Monte Carlo Local Branching Algorithm for the Single Vehicle Routing Problem with Stochastic Demands

We present a new algorithm that uses both local branching and Monte Carlo sampling in a multidescent search strategy for solving 0-1 integer stochastic programming problems. This procedure is applied to the single-vehicle routing problem with stochastic demands. Computational results show the effectiveness of this new approach to solving hard instances of the problem. This paper was accepted by former Editor-in-Chief Hani Mahmassani.
Activity Rhythm and Breast Cancer in Pituitary-Isografted C and D8 Mice; Effect of Hypothalamus.∗

Summary A light-dark synchronized activity rhythm persists in pituitary-isografted C mice. The adrenal cycle probably persists in mice with a significantly altered estrous cycle (1,2). Multiple pituitary isografts are associated with adenocarcinomata and adenoacanthomata in virgin female mice of the D8 and C stocks. The carcinogenic effect of the pituitary isografts is statistically significant and not a result of trauma. Number of implants as well as presence or absence of mammary tumor agent also are significant variables, but neither variable is critical to carcinogenesis from pituitary isografts, under the conditions studied. Insertion of the hypothalamus at the time of pituitary implantation delays mammary carcinogenesis by mechanisms as yet unknown.
Performance estimation of AODV variant with trust mechanism

Several research effort in recent years has shown its significant interest in the development of security measure in the routing of Mobile Ad-hoc Network (MANET). Ad-hoc On-demand Distance Vector (AODV) routing protocol has been one of the basic protocol that is modified to cope with security demands. This paper present an analytical model to evaluate the performance of AODV variant that is developed to be secure with malicious node detection and trust mechanism. Since the performance of MANET is tightly depends on the underlying topology of the nodes, route establishment may fail due to node's movement or malicious activity. The analytical model aims to provide the basic performance insight in the form of the route establishment probability of the MANET with and without malicious nodes. The model is important for MANET designer to optimize the connectivity for all nodes.
The Neural Network Model for Wind Field Assessment Based on Particle Swarm Optimization Algorithm

Using the global search ability and optimize the network structure and connection power of artificial neural network at the same time by particle swarm optimization algorithm and a new training of BP neural network was going on, then a nonlinear artificial neural network model used to calculate the wind speed of wind farms was constructed, here the Mountain Darong in Guangxi wind farms would be calculated at this paper for example. The results showed that the calculation accuracy by the nonlinear ensemble model of neural network based on particle swarm optimization algorithm for wind field is significantly higher than the traditional multiple linear regression model. Thus in practical application the long time sequence data of wind could be calculate according to the short time sequence data of the observation through the new model, therefore this model is better practicability and popularize value for it provide the basis to research the exploitation wind resources.
Circuit reliability simulation using TMI2

Using simulation to assess the impacts of various reliability mechanisms to circuit performance has become prevail for advanced technologies due to smaller headroom (=Vdd-Vth) and less design margins. This paper reviews existing circuit aging simulation approaches with focus on TMI. The limitations of aging models are also discussed so that reliability simulations can be executed more correctly with the right expectation. An example circuit under multi-waveform and multi-temperature stress is presented to illustrate reliability simulation flow through TMI.
An Immersive Bidirectional System for Life-size 3D Communication

Telecommunication and video conferencing are an integral part of modern society with implications in many aspects of everyday life. However, compared to a meeting in person, the sense of presence is still limited in electronic communication. In this paper, we present a novel system for life-size 3D telecommunication. It is designed to create an immersive user experience by seamlessly embedding a remote conversation partner into the local environment. To achieve this, users are captured in 3D by hybrid (color+depth) sensors and displayed on a life-size transparent 3D display. We have built two instances of this system in Zurich and Singapore. They form a complete and fully functional prototype enabling bidirectional communication in real-time over a long distance. We further demonstrate alternative hardware setups, which make our system flexible and adaptable to different usage scenarios.
Nonparametric subspace analysis for face recognition

Linear discriminant analysis (LDA) is a popular face recognition technique. However, an inherent problem with this technique stems from the parametric nature of the scatter matrix, in which the sample distribution in each class is assumed to be normal distribution. So it tends to suffer in the case of non-normal distribution. In this paper a nonparametric scatter matrix is defined to replace the traditional parametric scatter matrix in order to overcome this problem. Two kinds of nonparametric subspace analysis (NSA): PNSA and NNSA are proposed for face recognition. The former is based on the principal space of intra-personal scatter matrix, while the latter is based on the null space. In addition, based on the complementary nature of PNSA and NNSA, we further develop a dual NSA-based classifier framework using Gabor images to further improve the recognition performance. Experiments achieve near perfect recognition accuracy (99.7%) on the XM2VTS database.
Complex Vascular Anatomy in Live Kidney Donation: Imaging and Consequences for Clinical Outcome

Background. Live donor kidneys with multiple arteries are associated with surgical complexity for removal and increased rate of recipient ureteral complications. We evaluated the outcome of vascular imaging and the clinical consequences of multiple arteries and veins. Methods. From 2001 to 2005 data of 288 live kidney donations and transplantations were prospectively collected. Vascular anatomy at operation was compared with vascular anatomy as imaged by magnetic resonance imaging (MRI) or subtraction angiography, and consequences of multiple vessels were investigated. Results. Simple renal anatomy with a solitary artery and vein was present in 208 (72%) kidneys. Sixty (21%) transplants had multiple arteries. Thirty (10%) transplants had multiple veins. Magnetic resonance imaging failed to predict arterial anatomy in 23 of 220 donors (10%) compared with 3 of 101 (3%) after angiography. The presence of multiple veins did not influence outcomes after nephrectomy in general. Multiple arteries did not affect clinical outcomes in open donor nephrectomy (n=103). In laparoscopic donor nephrectomy (n=185) multiple arteries were associated with longer operation times (245 vs. 221 min, P=0.023) and increased blood loss (225 vs. 220 mL, P=0.029). In general, neither multiple arteries nor vascular reconstructions influenced recipient creatinine clearance or ureteral complication rate. However, accessory arteries to the lower pole correlated with an increased rate of ureteral complications (47% vs. 14%, P=0.01). Conclusions. Multiple arteries may increase operation time. Accessory lower pole arteries are associated with a higher rate of recipient ureteral complications indicating the importance of arterial imaging. Currently, both magnetic resonance imaging and angiography provide suboptimal information on renal vascular anatomy.
Probabilistic Key Management Practical Concerns in Wireless Sensor Networks

The subject of Key Management in Wireless Sensor Networks has gained increased attention from the security community around the world in the last years. Several proposals were made concerning the peculiarities of resource constrains inherent to sensor devices. One of the most accepted proposals is based on random distribution of keys among the sensor nodes, which was followed by some variants in order to increase its security. In this paper we introduce the mathematical concepts behind this class of proposals through a step-by-step mathematical analysis. This leads to some practical concerns about its applicability to real world applications where the technological constrains strictly compromise the mathematical theoretical models. We demonstrate that the number of communication links needed to assure near 100% network connectivity, is in fact, impractical in nowadays applications.
Visual Odometry [Tutorial]

Visual odometry (VO) is the process of estimating the egomotion of an agent (e.g., vehicle, human, and robot) using only the input of a single or If multiple cameras attached to it. Application domains include robotics, wearable computing, augmented reality, and automotive. The term VO was coined in 2004 by Nister in his landmark paper. The term was chosen for its similarity to wheel odometry, which incrementally estimates the motion of a vehicle by integrating the number of turns of its wheels over time. Likewise, VO operates by incrementally estimating the pose of the vehicle through examination of the changes that motion induces on the images of its onboard cameras. For VO to work effectively, there should be sufficient illumination in the environment and a static scene with enough texture to allow apparent motion to be extracted. Furthermore, consecutive frames should be captured by ensuring that they have sufficient scene overlap.
A Bispectrum Technique to Subpixel Image Registration under Noisy Conditions

This paper proposes an effective higher-order statistics method to address subpixel image registration. Conventional power spectrum-based techniques employ second-order statistics to estimate subpixel translation between two images. They are, however, susceptible to noise, thereby leading to significant performance deterioration in low signal-to-noise (SNR) environments. In view of this, we propose a bispectrum-based approach to alleviate this difficulty. The new method utilizes the characteristics of bispectrum to suppress Gaussian noise. It develops the phase relationship between the image pair, and estimates the subpixel translation by solving a set of nonlinear equations derived from the bispectrum. Experimental results show that the proposed method is effective in identifying subpixel translations under different noise levels and environments
MS2PIP prediction server: compute and visualize MS2 peak intensity predictions for CID and HCD fragmentation

We present an MS2 peak intensity prediction server that computes MS2 charge 2+ and 3+ spectra from peptide sequences for the most common fragment ions. The server integrates the Unimod public domain post-translational modification database for modified peptides. The prediction model is an improvement of the previously published MS2PIP model for Orbitrap-LTQ CID spectra. Predicted MS2 spectra can be downloaded as a spectrum file and can be visualized in the browser for comparisons with observations. In addition, we added prediction models for HCD fragmentation (Q-Exactive Orbitrap) and show that these models compute accurate intensity predictions on par with CID performance. We also show that training prediction models for CID and HCD separately improves the accuracy for each fragmentation method. The MS2PIP prediction server is accessible from http://iomics.ugent. be/ms2pip.
Energy of acoustic signals in a borehole

In the present article, the dependencies of the acoustic signal total energy and the energies of the wave packets of different types of the waves on the elastic parameters and permeability of rocks have been studied. We have considered traditional logging tools containing acoustical monopole source. Calculations were performed in a frequency range of dozens of kilohertz, typical for acoustic well logging. It was shown that in a typical high‐velocity formation (vs > vf, where vs and vf are the velocities of the shear wave in the rock and of the compressional wave in the borehole fluid, respectively), the pseudo‐Rayleigh waves, whose elastic properties depend mainly on the shear modulus of the rock, contributed significant energy to the total signal energy in the borehole. The energies of different wave packets depend on the permeability in different ways. The greatest sensitivity to permeability change has been shown by the acoustic signal total energy and the energy of the low‐velocity part of the pseudo‐Rayleigh wave packet. The theoretical analysis was illustrated by real sonic log data.
Semantic annotations for conversational speech: From speech transcriptions to predicate argument structures

In this paper, we describe the semantic content, which can be automatically generated, for the design of advanced dialog systems. Since the latter will be based on machine learning approaches, we created training data by annotating a corpus with the needed content. Given a sentence of our transcribed corpus, domain concepts and other linguistic levels ranging from basic ones, i.e. part-of-speech tagging and constituent chunking level, to more advanced ones, i.e. syntactic and predicate argument structure (PAS) levels are annotated. In particular, the proposed PAS and taxonomy of dialog acts appear to be promising for the design of more complex dialog systems. Statistics about our semantic annotation are reported.
NAP and WCCN: Comparison of Approaches using MLLR-SVM Speaker Verification System

We compare two recently proposed techniques, within class covariance normalization (WCCN) (A. Hatch et al., 2006) and nuisance attribute projection (NAP) (A. Solomonoff et al., 2005), for intersession variability compensation in speaker verification. The comparison is performed using an MLLR-SVM speaker verification system. Both techniques model intersession variability using a within-speaker covariance matrix (WSCM). However, they manipulate eigenvectors of this matrix differently. We compare them on the 2005 and 2006 NIST speaker recognition evaluation (SRE) task. Results show that WCCN is more sensitive to the choice of background speakers and NAP is more sensitive to the choice of data for WSCM estimation. WCCN gives the best performance on 2005 SRE. On 2006 SRE, both techniques give similar performance under matched conditions. Further experiments with a simple combination of these techniques show slight improvements in the best performance of either technique. Overall results show that an MLLR-SVM system with either NAP or WCCN performs comparably to the best single systems in the 2006 NIST SRE.