Miracle-dz/UniFAR-FTUs huggingface dataset:
Within-population genetic structure differs between two sympatric sister-species of ectomycorrhizal fungi, Rhizopogon vinicolor and R. vesiculosus

Using spatial autocorrelation analysis, we examined the within-population genetic structure of Rhizopogon vinicolor and R. vesiculosus, two hypogeous ectomycorrhizal (EM) species that are sympatric sister taxa known to differ in their clonal structure. We collected 121 sporocarps and 482 tuberculate EM of both species from a 20 ha forest stand dominated by Douglas-fir (Pseudotsuga menziesii). Field collections were identified to species with restriction fragment length polymorphism analysis of the nuclear ribosomal internal transcribed spacer. Five and six microsatellite markers were used to characterize the genetic diversity of EM and sporocarp samples from R. vesiculosus and R. vinicolor respectively. After correcting for genet structure, spatial autocorrelation analyses of the EM samples were used to test the null hypothesis that multilocus genotypes characterized from each species were randomly distributed within the study area. We detected positive and statistically significant fine-scale genetic structure up to 120 m within the R. vesiculosus sample. In contrast, no spatial genetic structure was evident for R. vinicolor, indicating that the genotypes characterized for this species were randomly distributed throughout the study area. Differences in statistical power or the nuclear count of basidiospores are unlikely agents of the genetic patterns observed. Our results suggest that differences in reproductive output or competitive ability may act individually or in combination to create clusters of similar genotypes for R. vesiculosus throughout the study area.
Barriers to Near-Optimal Equilibria

This paper explains when and how communication and computational lower bounds for algorithms for an optimization problem translate to lower bounds on the worst-case quality of equilibria in games derived from the problem. We give three families of lower bounds on the quality of equilibria, each motivated by a different set of problems: congestion, scheduling, and distributed welfare games, welfare-maximization in combinatorial auctions with "black-box" bidder valuations, and welfare-maximization in combinatorial auctions with succinctly described valuations. The most straightforward use of our lower bound framework is to harness an existing computational or communication lower bound to derive a lower bound on the worst-case price of anarchy (POA) in a class of games. This is a new approach to POA lower bounds, which relies on reductions in lieu of explicit constructions. More generally, the POA lower bounds implied by our framework apply to all classes of games that share the same underlying optimization problem, independent of the details of players' utility functions. For this reason, our lower bounds are particularly significant for problems of game design -- ranging from the design of simple combinatorial auctions to the computation of tolls for routing networks -- where the goal is to design a game that has only near-optimal equilibria. For example, our results imply that the simultaneous first-price auction format is optimal among all "simple combinatorial auctions" in several settings.
Probing the Mesolimbic Dopamine Reward System: A Common Neurocircuitry in Substance Abuse and Schizophrenia

Toluene is the main intoxicant in many of the products that are inhaled by school children. Although inhalant abuse is now being recognized as a worldwide problem, solvents are currently the least studied drugs of abuse. Therefore, we examined the central reward circuit, the mesolimbic dopaminergic system, using a dopamine receptor ligand, [(11)C]raclopride and microPET to measure alterations in toluene-induced dopamine synaptic function. In addition, metabolic activity was measured using (18)FDG, an analog of glucose, during a drug-seeking behavioral test to understand which regions of the brain are important for toluene reinforcement. Toluene caused a 13 % reduction in [(11)C]raclopride binding. Enhanced regional brain glucose activity correlated with an increase in the animal’s drug-seeking behavioral score. These studies provide support for the use of microPET as a tool for monitoring brain and behavior relationships.
Truck Drivers and Traffic Fatalities : Estimating the Value of a Statistical Life Using Panel Data

The value of a statistical life (VSL) literature primarily utilizes cross-sectional analyses that are susceptible to bias from unobserved time-invariant factors. Further, many occupational death data have considerable non-classical measurement error. This paper addresses these two shortcomings by using truck driver panel data from 1979 to 2002 to bound VSL estimates. Measurement error will attenuate Fixed Effects and First Differences estimates. It will also anti-attenuate Instrumental Variable (IV) estimates when another mismeasured risk measure is used as an instrument. Both strategies control for unobserved time-invariant factors, and examining wages within a competitive occupation controls for occupation-specific productivity shocks, worker risk tolerance and worker heterogeneity. This study also uses a time-aggregated cross-section estimator to investigate bias due to within-occupation aggregation. Risk measures are created from the National Highway Traffic Safety Administration—Fatality Analysis Reporting System (NHTSA-FARS), the census of all traffic fatalities on public U.S. roadways. The OLS-IV bounding strategy returns a VSL range of $0.4 million to $4.1 million. The timeaggregated cross-section estimator returns $8.2 million and $8.8 million, in the middle of the VSL literature’s between-occupation estimates. This suggests upward bias of at least $4.1 million when aggregating heterogeneous workers. Although not externally valid, this paper illustrates the need to control for time-invariant factors. When estimating a VSL, future research should reduce sample measurement error and control for time-invariant factors with panel data.
Switchgrass Genomic Diversity, Ploidy, and Evolution: Novel Insights from a Network-Based SNP Discovery Protocol

Switchgrass (Panicum virgatum L.) is a perennial grass that has been designated as an herbaceous model biofuel crop for the United States of America. To facilitate accelerated breeding programs of switchgrass, we developed both an association panel and linkage populations for genome-wide association study (GWAS) and genomic selection (GS). All of the 840 individuals were then genotyped using genotyping by sequencing (GBS), generating 350 GB of sequence in total. As a highly heterozygous polyploid (tetraploid and octoploid) species lacking a reference genome, switchgrass is highly intractable with earlier methodologies of single nucleotide polymorphism (SNP) discovery. To access the genetic diversity of species like switchgrass, we developed a SNP discovery pipeline based on a network approach called the Universal Network-Enabled Analysis Kit (UNEAK). Complexities that hinder single nucleotide polymorphism discovery, such as repeats, paralogs, and sequencing errors, are easily resolved with UNEAK. Here, 1.2 million putative SNPs were discovered in a diverse collection of primarily upland, northern-adapted switchgrass populations. Further analysis of this data set revealed the fundamentally diploid nature of tetraploid switchgrass. Taking advantage of the high conservation of genome structure between switchgrass and foxtail millet (Setaria italica (L.) P. Beauv. ), two parent-specific, synteny-based, ultra high-density linkage maps containing a total of 88,217 SNPs were constructed. Also, our results showed clear patterns of isolation-by-distance and isolation-by-ploidy in natural populations of switchgrass. Phylogenetic analysis supported a general south-to-north migration path of switchgrass. In addition, this analysis suggested that upland tetraploid arose from upland octoploid. All together, this study provides unparalleled insights into the diversity, genomic complexity, population structure, phylogeny, phylogeography, ploidy, and evolutionary dynamics of switchgrass.
Shortest Paths and Network Flow Algorithms for Electrostatic Discharge Analysis

Designers of integrated circuits have always been concerned about the damaging effects of ESD (Electrostatic Discharge). However, as their designs become increasingly more complex, the problem of analyzing the schemes they develop to protect against ESD is quickly becoming too difficult perform manually. Current VLSI (Very Large Scale Integration) designs exceed one hundred bondpads, through which the device communicates with the outside environment, and through which harmful amounts of current might enter the chip. A protection scheme for a complex device such as this might require a network of thousands of non-trivial connections between the bondpads to keep harmful amounts of current from the chip’s delicate core. In order to measure the robustness of a protection scheme designers would like to be able to take resistance measurements between bondpad pairs. On such large designs, this can be a monumental task. In this paper we focus on two things: the automation of strategies previously performed manually, and an in-depth development of (newer) more accurate techniques provided by network flow algorithms, each for gauging the degree of protection afforded by ESD Protection Schemes. The underlying algorithms we will discuss for automating this ESD Analysis are well understood. However, their application, and the introduction of more advanced simulation techniques make this discussion original. We would like to develop a tool which not only accurately models the protection scheme against an ESD event, but also helps to pinpoint its weaknesses. In many cases it is not obvious that the techniques we suggest can be applied or adapted to the simulation of electrical devices and networks. It is also interesting that such techniques make no direct use of Kirchoff’s Voltage or Current Laws (KVL/KCL) yet still produce meaningful and accurate results. This document is intended to summarize standard techniques for analysis of ESD protection schemes, as well as to introduce some clever variations on more complicated algorithms whose intent is to generate more meaningful results never before feasible manually.
Multiview 3D warps

Image registration and 3D reconstruction are fundamental computer vision and medical imaging problems. They are particularly challenging when the input data are images of a deforming body obtained by a single moving camera. We propose a new modelling framework, the multiview 3D warps. Existing models are twofold: they estimate inter-image warps which are often inconsistent between the different images and do not model the underlying 3D structure, or reconstruct just a sparse set of points. In contrast, our multiview 3D warps combine the advantages of both; they have an explicit 3D component and a set of 3D deformations combined with projection to 2D. They thus capture the dense deforming body's time-varying shape and camera pose. The advantages over the classical solutions are numerous: thanks to our feature-based estimation method for the multiview 3D warps, one can not only augment the original images but also retarget or clone the observed body's 3D deformations by changing the pose. Experimental results on simulated and real data are reported, confirming the advantages of our framework over existing methods.
Theorizing Black Community Online: Of Black Mind, Black Self, and Black Society

ii DEDICATION . iv ACKNOWLEDGEMENTS . v CONTRIBUTORS AND FUNDING SOURCES . vi NOMENCLATURE . vii TABLE OF CONTENTS . viii LIST OF FIGURES . x CHAPTER I THEORIZING THE MEDIATED SELF IN LIGHT OF MEDIATED COMMUNITIES: AN INTRODUCTION . 1 CHAPTER II THEORIZING SELF AND COMMUNITY ONLINE . 10 Race & Social Media. 17 CHAPTER III RACE AND INTERSECTIONALITY IN INTERNET RESEARCH (OR LACK THEREOF): A LITERATURE REVIEW . 27 Intersectional Representation . 27 Racial Formation Theory . 36 Colorblind Racism . 37 Ethnicity . 39 Systemic Racism: A Rejoinder . 41 CHAPTER IV ON THURSDAYS WE WATCH SCANDAL: COMMUNAL VIEWING AND BLACK TWITTER . 46 Literature Review: Community – Online/Offline . 48 Methods . 52 Results . 54 Discussion . 59 Conclusion . 63
Introduction to special issue on recommender systems

Recommender systems are changing the way people interact with the Web. From e-commerce sites like Amazon.com to news and information sites like digg and slashdot, recommenders help people choose among diverse products and complex information by providing a more personalized information access experience. For this special issue we seek to identify mature research that focuses not just on foundations and algorithms but also on novel applications of recommender systems technologies in emerging areas of Web technologies. Four articles have been selected as a result of a comprehensive peer-review process. In total 29 articles were submitted, each reviewed by 3 experts in this field of research. The remarkably strong response and the diverse topics of the submitted articles exemplifies the vitality of research in recommender systems today. Nearly every article had interesting and through-provoking insights, but only four could be accepted for the special issue. After the Netflix Challenge awarded one million dollars to the winning team, practitioners confessed that the fundamental approach used to win the prize—combining literally hundreds of individual algorithms with a powerful machine-learning framework—was impossible to use in practice because of its lack of scalability. The first article in this special issue, by Cacheda et al., examines the limitations of current collaborative filtering techniques in the context of the scalability demands of high-performance recommender systems. In particular, they provide a comprehensive comparative evaluation of a range of contemporary collaborative filtering algorithms using popular evaluation metrics and, in addition, propose a number of new metrics that seek to emphasize certain important recommendation properties. Moreover, the authors present a novel alternative collaborative filtering technique that focuses on the differences between users and items, rather than their similarities and, despite the simplicity of this approach, report competitive results especially in larger datasets. By and large, the success of a recommender system is critically dependent on the preference information that forms the basis of its user models or profiles. For example, collaborative filtering-style systems depend on ratings-based profiles constructed from the explicit ratings or transactional history of users. This data is often sparse, which ultimately limits recommendation opportunities, and, in practice, many systems are faced with the cold-start problem if profile data is limited. The work of Umyarov and Tuzhilin proposes the use of external aggregate rating information as one possible way of dealing with such challenges in order to improve recommendation performance. In particular, they describe how external aggregate ratings …
Resilient Overlays for IoT-Based Community Infrastructure Communications

This paper considers the reliable delivery of sensor data from Internet of Things (IoT) devices to distributed cloud service instances in the face of localized failures for event detection, community infrastructure management, and emergency response. We develop and explore the notion of GEOgraphically Correlated Resilient Overlay Networks (GeoCRON) to capture the localized nature of community IoT deployments and their impact/vulnerability in the context of small failures and massive disruptions such as that caused by a large seismic event. As a focus use case, we study the viability and utility of GeoCRON in two different community sensor networks. The first, a large participatory sensing-style network of small inexpensive seismic sensors, reports ground motion in homes and businesses to a cloud service for detecting and characterizing earthquakes. The second, a smaller infrastructure-based network of wireless devices embedded in the water distribution network, reports water pressure changes at pipe junctions that may be indicative of leaks that require human intervention. Using realistic topologies derived from experiences with real world community scale deployments, we study the impact of geo-correlated failures on the combined network infrastructure and evaluate a range of GeoCRON heuristics to improve commnunications resilience in this context. We validate the promise of the proposed GeoCRON-based approach using extensive simulations and an initial prototype system implementation.
New semantic and serial clustering indices for the California Verbal Learning Test–Second Edition: Background, rationale, and formulae

The original California Verbal Learning Test (CVLT) employed a semantic clustering index that used the words recalled during a given trial as the baseline for calculating expected values of chance clustering (recall-based expectancy). Although commonly used in cognitive psychology, clustering indices that use recall-based calculations of expectancy are implied by the assumption that organizational processes do not occur until after words are retrieved from memory. This assumption contradicts the generally held assumptions among neuropsychologists that (1) organization is an antecedent to recall, and (2) increases in the use of organizational strategies will result in better recall performance. After reviewing a brief history of clustering metrics, we used Monte Carlo simulations, informative examples, and patient data to examine clustering indices that use the word list as a baseline for calculating expectancy and propose these list-based expectancy measures as a refinement of the clustering indices used on the original CVLT. These indices are used on the recently published CVLT–II. (JINS, 2002, 8, 425–435.)
Comprehensive analysis of voltage balancing techniques for 5L-NPC converters

The reduced capability of the 5L-NPC Inverter to balance the voltages of the four DC-bus capacitors makes this converter unattractive for real power applications. This is especially true if the load demands active power. The Back-to-Back (B2B) configuration of two 5L-NPC converters and the use of a Space Vector Modulation (SVM) that exploits the voltage balancing capabilities of the redundant switching vectors, extend the operating conditions range in which a proper voltage balance can be achieved. However, if practical modulation restrictions are considered (limitation of voltage steps, dead times, switching losses, etc.) the voltage balance cannot be achieved for all operation conditions. This paper introduces the main restrictions that should be considered for the 5L-NPC modulation strategy. The voltage balancing limits of the proposed SVM scheme are shown and additional considerations to improve the voltage balancing capability are proposed and evaluated.
Modeling of a Pulsed Plasma Thruster; Simple Design, Complex Matter

Experiments conducted at the Surrey Space Centre on a breadboard pulsed plasma thruster (PPT) have led to greater insights in to the underlying principles of electrode erosion during plasma formation. It has been shown that during a PPT discharge a significant proportion of the plasma that is produced is from direct erosion of the electrodes to the extent that traditional Teflon propellant bars were removed from the PPT and it remained operational. The removal of the propellant bar allowed a direct relationship between the plasma mass and the eroded mass to be constructed. Research from the metal film deposition and cathodic plasma fields was then drawn upon to create a model for the eroded mass. This model was then expanded and used to predict the current profile of a discharging PPT and compared to experimental current profiles using a rogowski coil.
Industrial motion control applications using linux RTAI

RTAI extension to Linux operating system makes it possible to perform Hard Real-Time (HRT) controls using a commercial PC and utilizing open-source code. This paper details a software installation process, along with the suitable hardware chosen for realizing motion controls using Linux-RTAI. Advantages offered by the possibility of designing and developing motion controls using the powerful tools of high level object-languages (i.e. Simulink) are described. It is proven that the combination of Simulink as a development environment and Linux-RTAI PC as a controller platform brings advantages that can be greatly appreciated in industrial motion control applications, such as: very reduced time for development, diagnostics, design of controls; adequate control performance; cost reductions offered by the open-source status of the operating system.
Algorithms of constructing models for compensating navigation systems of unmanned aerial vehicles

The problem of compensating autonomous navigation systems with predictive algorithms was studied. For the purpose of high-precision compensation, the method of constructing nonlinear models, i.e. modified self-organization algorithm with redundant trends, was proposed with the help of identification of basic functions. The results of mathematical modeling of inertial navigation system errors with predictive algorithms demonstrated the effectiveness and high-accuracy of proposed algorithms for constructing models.
A hierarchical approach for better estimation of unseen event likelihood in speech recognition

The backoff hierarchical class n-gram language models (LMs) are a generalization of the common backoff word n-gram LMs. Compared to the traditional backoff word n-gram LMs that uses (n-1)-gram to estimate the likelihood of an unseen n-gram event, backoff hierarchical class n-gram LMs uses a class hierarchy to define an appropriate context. We study the impact of the hierarchy depth on the performance of the approach. Performance is evaluated on several databases such us switchboard, call-home and wall street journal (WSJ). Results show that better improvement is achieved when a shallow word (few levels) tree is used. Experiments show up to 26% improvement on the unseen events perplexity and up to 12% improvement in the word error rate (WER).
No . 43 / November 2004 Volume and Skewness in International Equity Markets

We examine the relation between trading volume and skewness in 6 international stock markets Britain, France, Germany, Italy, Japan and the United States using daily data from February 1978 to December 2001. We construct both single equation and VAR models of the relation between the first three moments of market returns and trading volumes. Our results show hitherto unrecognised channels of influence, and are supportive of the investor heterogeneity approach to explaining return asymmetries.
Learning to Suggest Questions in Online Forums

Online forums contain interactive and semantically related discussions on various questions. Extracted question-answer archive is invaluable knowledge, which can be used to improve Question Answering services. In this paper, we address the problem of Question Suggestion, which targets at suggesting questions that are semantically related to a queried question. Existing bag-of-words approaches suffer from the shortcoming that they could not bridge the lexical chasm between semantically related questions. Therefore, we present a new framework to suggest questions, and propose the Topicenhanced Translation-based Language Model (TopicTRLM) which fuses both the lexical and latent semantic knowledge. Extensive experiments have been conducted with a large real world data set. Experimental results indicate our approach is very effective and outperforms other popular methods in several metrics.