Miracle-dz/UniFAR-FTUs huggingface dataset:
Projection-Based Model Reduction of Multi-Agent Systems Using Graph Partitions

In this paper, we establish a projection-based model reduction method for multiagent systems defined on a graph. Reduced order models are obtained by clustering the vertices (agents) of the underlying communication graph by means of suitable graph partitions. In the reduction process, the spatial structure of the network is preserved and the reduced order models can again be realized as multiagent systems defined on a graph. The agents are assumed to have single-integrator dynamics and the communication graph of the original system is weighted and undirected. The proposed model reduction technique reduces the number of vertices of the graph (which is equal to the dynamic order of the original multi-agent system) and yields a reduced order multiagent system defined on a new graph with a reduced number of vertices. This new graph is a weighted symmetric directed graph. It is shown that if the original multiagent system reaches consensus, then so does the reduced order model. For the case that the clusters are chosen using an almost equitable partition (AEP) of the graph, we obtain an explicit formula for the H2-norm of the error system obtained by comparing the input-output behaviors of the original model and the reduced order model. We also prove that the error obtained by taking an arbitrary partition of the graph is bounded from below by the error obtained using the largest AEP finer than the given partition. The proposed results are illustrated by means of a running example.
PERMANOVA-S: association test for microbial community composition that accommodates confounders and multiple distances

Motivation: Recent advances in sequencing technology have made it possible to obtain high-throughput data on the composition of microbial communities and to study the effects of dysbiosis on the human host. Analysis of pairwise intersample distances quantifies the association between the microbiome diversity and covariates of interest (e.g. environmental factors, clinical outcomes, treatment groups). In the design of these analyses, multiple choices for distance metrics are available. Most distance-based methods, however, use a single distance and are underpowered if the distance is poorly chosen. In addition, distance-based tests cannot flexibly handle confounding variables, which can result in excessive false-positive findings. Results: We derive presence-weighted UniFrac to complement the existing UniFrac distances for more powerful detection of the variation in species richness. We develop PERMANOVA-S, a new distance-based method that tests the association of microbiome composition with any covariates of interest. PERMANOVA-S improves the commonly-used Permutation Multivariate Analysis of Variance (PERMANOVA) test by allowing flexible confounder adjustments and ensembling multiple distances. We conducted extensive simulation studies to evaluate the performance of different distances under various patterns of association. Our simulation studies demonstrate that the power of the test relies on how well the selected distance captures the nature of the association. The PERMANOVA-S unified test combines multiple distances and achieves good power regardless of the patterns of the underlying association. We demonstrate the usefulness of our approach by reanalyzing several real microbiome datasets. Availability and Implementation: miProfile software is freely available at https://medschool.vanderbilt.edu/tang-lab/software/miProfile. Contact: z.tang@vanderbilt.edu or g.chen@vanderbilt.edu Supplementary information: Supplementary data are available at Bioinformatics online.
Robust speaker recognition using spectro-temporal autoregressive models

Speaker recognition in noisy environments is challenging when there is a mis-match in the data used for enrollment and verification. In this paper, we propose a robust feature extraction scheme based on spectro-temporal modulation filtering using two-dimensional (2-D) autoregressive (AR) models. The first step is the AR modeling of the sub-band temporal envelopes by the application of the linear prediction on the sub-band discrete cosine transform (DCT) components. These sub-band envelopes are stacked together and used for a second AR modeling step. The spectral envelope across the sub-bands is approximated in this AR model and cepstral features are derived which are used for speaker recognition. The use of AR models emphasizes the focus on the high energy regions which are relatively well preserved in the presence of noise. The degree of modulation filtering is controlled using AR model order parameter. Experiments are performed using noisy versions of NIST 2010 speaker recognition evaluation (SRE) data with a stateof-art speaker recognition system. In these experiments, the proposed features provide significant improvements compared to baseline features (relative improvements of 20% in terms of equal error rate (EER) and 35 % in terms of miss rate at 10 % false alarm).
Reconfigurable Hardware Architecture for Vision-Based Driving Systems

Vision-based driving systems are embedded systems that commonly have to consider real time constraints. Proposed systems use general purpose computers to execute designed algorithms based on their processing power and reduced designing time. These computers and other sensors are considerably power consuming and considerably increase the final cost of proposed solutions. Design of specific hardware systems that can be easily embed in autonomous vehicles without compromising power consumption, processing time and system final cost is an interesting solution. This work presents a hardware structure that can be used to increase the performance of autonomous vehicles visual navigation systems. Proposed structure was test with hardware version of image filters that are used in pre-processing steps of visual navigation algorithms. Results showed that proposed hardware achieved acceptable frame rate with a good image size compared to other proposed hardware systems.
Westerbork H I observations of high-velocity clouds near M 31 and M 33

We have undertaken high-resolution follow-up of a sample of high velocity H  clouds apparently associated with M 31. Our sample was chosen from the population of high-velocity clouds (HVCs) detected out to 50 kpc projected radius of the Andromeda Galaxy by Thilker et al. (2004, ApJ, 601, L39) with the Green Bank Telescope. Nine pointings were observed with the Westerbork Synthesis Radio Telescope to determine the physical parameters of these objects and to find clues to their origin. One additional pointing was directed at a similar object near M 33. At 2 � resolution we detect 16 individual HVCs around M 31 and 1 HVC near M 33 with typical H  masses of a few times 10 5 Mand sizes of the order of 1 kpc. Estimates of the dynamical and virial masses of some of the HVCs indicate that they are likely gravitationally dominated by additional mass components such as dark matter or ionised gas. Twelve of the clouds are concentrated in an area of only 1 ◦ × 1 ◦ at a projected separation of less than 15 kpc from the disk of M 31. This HVC complex has a rather complicated morphological and kinematical structure and partly overlaps with the giant stellar stream of M 31, suggesting a tidal origin. Another detected feature is in close proximity, in both position and velocity, with NGC 205, perhaps also indicative of tidal processes. Other HVCs in our survey are isolated and might represent primordial, dark-matter dominated clouds.
An Experiment on Bare-Metal BigData Provisioning

Many BigData customers use on-demand platforms in the cloud, where they can get a dedicated virtual cluster in a couple of minutes and pay only for the time they use. Increasingly, there is a demand for bare-metal bigdata solutions for applications that cannot tolerate the unpredictability and performance degradation of virtualized systems. Existing bare-metal solutions can introduce delays of 10s of minutes to provision a cluster by installing operating systems and applications on the local disks of servers. This has motivated recent research developing sophisticated mechanisms to optimize this installation. These approaches assume that using network mounted boot disks incur unacceptable run-time overhead. Our analysis suggest that while this assumption is true for application data, it is incorrect for operating systems and applications, and network mounting the boot disk and applications result in negligible run-time impact while leading to faster provisioning time.
Regression models for explaining and predicting concentrations of organochlorine pesticides in fish from streams in the United States

Empirical regression models were developed for estimating concentrations of dieldrin, total chlordane, and total DDT in whole fish from U.S. streams. Models were based on pesticide concentrations measured in whole fish at 648 stream sites nationwide (1992–2001) as part of the U.S. Geological Survey's National Water Quality Assessment Program. Explanatory variables included fish lipid content, estimates (or surrogates) representing historical agricultural and urban sources, watershed characteristics, and geographic location. Models were developed using Tobit regression methods appropriate for data with censoring. Typically, the models explain approximately 50 to 70% of the variability in pesticide concentrations measured in whole fish. The models were used to predict pesticide concentrations in whole fish for streams nationwide using the U.S. Environmental Protection Agency's River Reach File 1 and to estimate the probability that whole‐fish concentrations exceed benchmarks for protection of fish‐eating wildlife. Predicted concentrations were highest for dieldrin in the Corn Belt, Texas, and scattered urban areas; for total chlordane in the Corn Belt, Texas, the Southeast, and urbanized Northeast; and for total DDT in the Southeast, Texas, California, and urban areas nationwide. The probability of exceeding wildlife benchmarks for dieldrin and chlordane was predicted to be low for most U.S. streams. The probability of exceeding wildlife benchmarks for total DDT is higher but varies depending on the fish taxon and on the benchmark used. Because the models in the present study are based on fish data collected during the 1990s and organochlorine pesticide residues in the environment continue to decline decades after their uses were discontinued, these models may overestimate present‐day pesticide concentrations in fish.
Denoising neutral current of a power transformer measured during impulse test by framelet technique

Condition monitoring of the insulation of the windings of a power transformer depends on the accuracy with which the neutral current is measured during impulse test both at full and at reduced voltage. The most common problem faced while measuring neutral current is that it contains noise that causes ambiguous fault signatures. This paper presents an effective means for filtering out the noise from the neutral current using framelet technique. A fast and yet simple algorithm has been used here to calculate the framelet coefficients. Results presented are for an electromagnetic transient program (EMTP) simulated model of a 3 MVA transformer mostly used up to 33 kV. Studies have been carried out with both high and low frequency synthetic noises. Comparison of the results with that obtained from wavelet technique based method has also been reported in the paper.
Intonational Features of Local and Global Discourse Structure

We present results of a study of the relationship between intonational features including pitch range, timing, and amplitude and aspects of discourse structure defined in terms of Grosz and Sidner's (1986) model of discourse. We compare structural labelings of AP news text with prosodic/acoustic features examined from recordings of the same text read by a professional newscaster. We find significant correlations between prosodic/acoustic characteristics and both local and global aspects of discourse structure identified by our labelers. Our results have applications for speech synthesis and, potentially, for speech recognition.
Cryptic carbon and sulfur cycling between surface ocean plankton

Significance In the surface ocean, organic matter released by phytoplankton and degraded by heterotrophic bacteria is a key step in the carbon cycle. Compounds important in this trophic link are poorly known, in part because of the thousands of chemicals making up marine dissolved organic matter. We cocultured a Roseobacter clade bacterium with the diatom Thalassiosira pseudonana and used gene expression changes to assay for compounds passed to the bacterium. A C3-sulfonate with no previously known role in the microbial food web was identified and subsequently shown to be an abundant diatom metabolite and actively cycling compound in seawater. This work identifies a missing component of the marine carbon and sulfur cycles. About half the carbon fixed by phytoplankton in the ocean is taken up and metabolized by marine bacteria, a transfer that is mediated through the seawater dissolved organic carbon (DOC) pool. The chemical complexity of marine DOC, along with a poor understanding of which compounds form the basis of trophic interactions between bacteria and phytoplankton, have impeded efforts to identify key currencies of this carbon cycle link. Here, we used transcriptional patterns in a bacterial-diatom model system based on vitamin B12 auxotrophy as a sensitive assay for metabolite exchange between marine plankton. The most highly up-regulated genes (up to 374-fold) by a marine Roseobacter clade bacterium when cocultured with the diatom Thalassiosira pseudonana were those encoding the transport and catabolism of 2,3-dihydroxypropane-1-sulfonate (DHPS). This compound has no currently recognized role in the marine microbial food web. As the genes for DHPS catabolism have limited distribution among bacterial taxa, T. pseudonana may use this sulfonate for targeted feeding of beneficial associates. Indeed, DHPS was both a major component of the T. pseudonana cytosol and an abundant microbial metabolite in a diatom bloom in the eastern North Pacific Ocean. Moreover, transcript analysis of the North Pacific samples provided evidence of DHPS catabolism by Roseobacter populations. Other such biogeochemically important metabolites may be common in the ocean but difficult to discriminate against the complex chemical background of seawater. Bacterial transformation of this diatom-derived sulfonate represents a previously unidentified and likely sizeable link in both the marine carbon and sulfur cycles.
Some Groups with One End

Let p be a real number greater than one. In this paper we study the vanishing and nonvanishing of the first L-cohomology space of some groups that have one end. We also make a connection between the first Lcohomolgy space and the Floyd boundary of the Cayley graph of a group. We apply the result about Floyd boundaries to show that there exists a real number p such that the first L-cohomology space of a nonelementary hyperbolic group does not vanish.
Radon Transform of Distributions

: In the literature there are three apparently different definitions of the Radon transform on various spaces of distributions: Gelfand-Graev’s, Helgason’s and Ludwig’s. In this paper a new definition of the Radon transform on the space of the tempered distributions is given and it is proved that, properly understood, the earlier definitions are all equivalent to the new one. A constructive description (a characterization) of the space of test functions is given. A simple method for studying the range of the Radon transform on some spaces of distributions is described.
Aalborg Universitet Estimating Multiple Pitches Using Block Sparsity

We study the problem of estimating the fundamental frequencies of a signal containing multiple harmonically related sinusoidal signals using a novel block sparsity representation of the signal model. An efficient algorithm for solving the resulting optimization is devised exploiting an alternating directions method of multipliers (ADMM) formulation of the problem. The superiority of the proposed method, as compared to earlier methods, is demonstrated using both simulated and measured audio signals.
A Survey on Detection of Sinkhole Attack in Wireless Sensor Network

Wireless Sensor Network (WSN) consists of large number of low-cost, resource-constrained sensor nodes. The constraints of the wireless sensor node is their characteristics which include low memory, low computation power, they are deployed in hostile area and left unattended, small range of communication capability and low energy capabilities. Base on those characteristics makes this network vulnerable to several attacks, such as sinkhole attack. Sinkhole attack is a type of attack were compromised node tries to attract network traffic by advertise its fake routing update. One of the impacts of sinkhole attack is that, it can be used to launch other attacks like selective forwarding attack, acknowledge spoofing attack and drops or altered routing information. It can also used to send bogus information to base station. This paper is focus on exploring and analyzing the existing solutions which used to detect and identify sinkhole attack in wireless sensor network. The analysis is based on advantages and limitation of the proposed solutions.
Machines and Thought : Machines and thought Universal Turing Machine

Futurists agree that interposable symmetries are an interesting new topic in the field of theory, and information theorists concur. After years of extensive research into the transistor, we disconfirm the construction of IPv7, which embodies the extensive principles of evoting technology. Here we confirm not only that digital-to-analog converters and digital-toanalog converters are generally incompatible, but that the same is true for Web services.
Comparison of Fast Learning Large-Scale Multi-Class Classification

Recent progress in the development of techniques to optimize large-scale classification problems has extended the use of multi-class classification. Specifically the use of multi-class classification algorithms when the dataset is to large to fit into limited memory available of most computers. The most prominent algorithms used today solve the multi-class classification problem through an optimization approach based on coordinate decent. Two of the most recognized algorithms, Vowpal Wabbit and LIBLINEAR LibSVM have emerged as the most consistent options when solving for a multi-class problems. This paper proposes an analysis of these methods and tests the efficiency and performance of each algorithm. The results are recorded and comparisons are made. After analyzing the results, the conclusion made is that the Vowpal Wabbit algorithm is best suited for solving large-scale multi-class classification problems when computer memory is constrained.
Web wrapper generation using tree alignment and transfer learning

This paper studies the web wrapper generation for web pages of forum, blog and news web sites. While more and more web pages are dynamically generated using a common template populated with data from databases. This paper proposes a novel method that uses tree alignment and transfer learning method to generate the wrapper from this kind of web pages. We present a new tree alignment algorithm to find the best matching structure of the input web pages. A kind of linear regression method is employed to get the weight of different tag-matching. Based on the alignment, we merge the trees into one union tree whose nodes record the statistical information gotten from multiple web pages. We use a transfer learning method to find the most likely content block and use the alignment algorithm to detect the repeat patterns on the union tree. After that, we generate a wrapper to extract data from web pages. Experimental results show that the method can achieve high extraction accuracy and has steady performance.
In-vivo testing of digital cellular telephones in patients with implantable cardioverter-defibrillators.

AIMS To investigate the susceptibility of implantable cardioverter defibrillators to electromagnetic interference generated by digital cellular telephones, functioning in both international transmission technologies: the Global System for Mobile Communication (GSM) and the Digital Cellular System (DCS 1800). METHODS AND RESULTS In 36 patients with transvenous implantable cardioverter defibrillators from two manufacturers (Medtronic and Guidant/CPI), cellular telephones with different levels of minimal and maximal power output were tested in the transmitting and receiving mode. Evaluation was performed in activated implantable defibrillators during spontaneous cardiac activity and continuous VVI or DDD pacing to assess possible electromagnetic interference. In two patients, appropriateness of ventricular fibrillation detection and therapy was judged during telephone testing. There was no damage, reprogramming, inappropriate shock therapy or pacing inhibition during the tests. In seven pre-pectoral Medtronic implantable defibrillators, transient electromagnetic interference caused 19 erroneous sensing events, when the operating phone was held in close vicinity to the programmer head. These 'pseudo-oversensing' events, which did not result in logging of arrhythmia episodes in the device counter, were interpreted as an adverse interaction between the telephone and the programming device. CONCLUSION Digital cellular telephones do not represent a risk to Medtronic and Guidant/CPI recipients of the specific implantable defibrillator models herein tested.
Maintenance of host variation in tolerance to pathogens and parasites

Tolerance and resistance provide hosts with two distinct defense strategies against parasitism. In resistance the hosts “fight” the parasite directly, whereas in tolerance the hosts fight the disease by ameliorating the damage that infection causes. There is increasing recognition that the two mechanisms may exhibit very different evolutionary behaviors. Although empirical work has often noted considerable variance in tolerance within hosts, theory has predicted the fixation of tolerance due to positive frequency dependence through a feedback with disease prevalence. Here we reconcile these findings through a series of dynamic game theoretical models. We emphasize that there is a crucial distinction between tolerance to the effects of disease-induced mortality and tolerance to the effect of the disease-induced reductions in fecundity. Only mortality tolerance has a positive effect on parasite fitness, whereas sterility tolerance is neutral and may therefore result in polymorphisms. The nature of the costs to defense and their relationship to trade-offs between resistance and tolerance are crucial in determining the likelihood of variation, whereas the co-evolution of the parasite will not affect diversity. Our findings stress that it is important to measure the effects of different mechanisms on characteristics that affect the epidemiology of the parasite to completely understand the evolutionary dynamics of defense.