Miracle-dz/UniFAR-FTUs huggingface dataset:
Upper and lower bound on signal-to-noise ratio gains for cooperative relay networks

Cooperative networking as a means of creating spatial diversity is used in order to mitigate the adverse effect of fading in a wireless channel and increase reliability of communications. We investigate signal-to-noise ratio (SNR) gain in wireless cooperative networks. We show that the differential SNR gain in the high data rate regime, which we refer to as SNR gain exponent ζ∞, is independent of the relaying strategy and only depends on the number of transmission phases used for communication. Furthermore, a straight-line upper and lower bound is derived based on geometric considerations. It is shown that the approximation error of the upper bound with respect to the exact SNR gain tends to zero for R → ∞. For the lower bound, the approximation error tends asymptotically to a constant factor δ for R → ∞. Both bounds are the best possible straight-line bounds with respect to absolute error.
Nitric Oxide-Sensitive Guanylyl Cyclase Is Differentially Regulated by Nuclear and Non-Nuclear Estrogen Pathways in Anterior Pituitary Gland

17β-estradiol (E2) regulates hormonal release as well as proliferation and cell death in the pituitary. The main nitric oxide receptor, nitric oxide sensitive- or soluble guanylyl cyclase (sGC), is a heterodimer composed of two subunits, α and β, that catalyses cGMP formation. α1β1 is the most abundant and widely expressed heterodimer, showing the greater activity. Previously we have shown that E2 decreased sGC activity but exerts opposite effects on sGC subunits increasing α1 and decreasing β1 mRNA and protein levels. In the present work we investigate the mechanisms by which E2 differentially regulates sGC subunits' expression on rat anterior pituitary gland. Experiments were performed on primary cultures of anterior pituitary cells from adult female Wistar rats at random stages of estrous cycle. After 6 h of E2 treatment, α1 mRNA and protein expression is increased while β1 levels are down-regulated. E2 effects on sGC expression are partially dependent on de novo transcription while de novo translation is fully required. E2 treatment decreased HuR mRNA stabilization factor and increased AUF1 p37 mRNA destabilization factor. E2-elicited β1 mRNA decrease correlates with a mRNA destabilization environment in the anterior pituitary gland. On the other hand, after 6 h of treatment, E2-BSA (1 nM) and E2-dendrimer conjugate (EDC, 1 nM) were unable to modify α1 or β1 mRNA levels, showing that nuclear receptor is involved in E2 actions. However, at earlier times (3 h), 1 nM EDC causes a transient decrease of α1 in a PI3k-dependent fashion. Our results show for the first time that E2 is able to exert opposite actions in the anterior pituitary gland, depending on the activation of classical or non-classical pathways. Thus, E2 can also modify sGC expression through membrane-initiated signals bringing to light a new point of regulation in NO/sGC pathway.
The joint MAP-ML criterion and its relation to ML and to extended least-squares

The joint maximum a posteriori-maximum likelihood (JMAP-ML) estimation criterion can serve as an alternative to the maximum likelihood (ML) criterion when estimating parameters from an observed data vector whenever another unobserved data vector is involved. Rather than maximize the probability of the observed data with respect to the parameters, JMAP-ML maximizes the joint probability of the observed and unobserved data with respect to both the unknown parameters and the unobserved data. In this paper, we characterize the relation between the ML and JMAP-ML estimates in the Gaussian case and provide insight into the apparent bias of JMAP-ML. Although JMAP-ML is an inconsistent estimator, we show that with short data records, it is often preferable to ML in terms of both bias and variance. We also identify JMAP-ML as a special case of the deterministic extended least squares (XLS) criterion. We indicate a general relation between a possible maximization algorithm for JMAP-ML and the well-known estimation-maximization (EM) algorithm.
Molecular Serotyping of Klebsiella Species Isolates by Restriction of the Amplified Capsular Antigen Gene Cluster

ABSTRACT The objective of the present work was to develop a molecular method that would enable determination of the capsular serotypes of Klebsiella isolates without the use of antiserum. PCR amplification of the capsular antigen gene cluster (cps) was followed by digestion with the restriction enzyme HincII (cps PCR-restriction fragment length polymorphism [RFLP] analysis). The profiles (C patterns) obtained for 224 strains representing the 77 known K serotypes showed 3 to 13 fragments ranging in size from 0.2 to 4.4 kb. A total of 97 distinct C patterns were obtained; 100% of 61 pairs of samples tested twice showed reproducible C patterns. The C patterns were K-type specific; i.e., the C pattern(s) of any K serotype was distinct from the C patterns of all other K serotypes, with the only exceptions being serotypes K22 and K37, which are known to cross-react. For 12 of 17 K types for which at least two strains were included, C-pattern variations were found among strains with the same K serotype. Therefore, cps PCR-RFLP analysis has a higher discriminatory power than classical K serotyping. C-pattern identity was observed among strains with a given K type that were collected many years apart and from distinct sources, indicating C-pattern stability. Only 4.5% of the strains were nontypeable, because of unsuccessful PCR amplification (whereas 8 to 23% are nontypeable by classical K serotyping). Three of four noncapsulated strains analyzed showed recognizable C patterns. The K serotypes of 18 (82%) of 22 recent Klebsiella pneumoniae clinical isolates could be deduced from their C patterns. In conclusion, cps PCR-RFLP analysis allows determination of the K serotype, while it is easier to perform and more discriminatory than classical serotyping.
Journal of Contemporary Ethnography / June 2000 Lowe / Art for Community Development Creating Community Art for Community Development

357 SEANA S. LOWE is a doctoral candidate in sociology at the University of Colorado at Boulder and program director of the International and National Voluntary Service Training Program. Her research and teaching interests include community sociology, sociology of education, environmental sociology, and social change. Her most recent publication is “The Interna tionalandNationalVoluntaryServiceTraining (IN VST) Program at the University of Colorado, Boulder” in Teaching for Justice: Concepts and Models for Ser vice-Learning in Peace Studies (Scarritt and Lowe 1999). She is currently working on her dissertation research, examining a community learning model and its effects on conscientization for participants. “ . community
Fault‐tolerant model predictive control within the hybrid systems framework: Application to sewer networks

In this paper, model predictive control (MPC) problem with fault‐tolerance capabilities is formulated within the hybrid systems framework. In particular, the mixed logical dynamic form to represent hybrid systems is considered. Using this approach, a hybrid model of the system to be controlled is obtained, which includes inherent hybrid phenomena and possible modes caused by faults occurrence. This allows to adapt the system model on‐line by taking into account the fault information provided by a fault diagnosis and isolation module. In this way, the controller can cope with the considered faults. Additionally, different implementation schemes and fault‐tolerance evaluation procedures for hybrid MPC (HMPC) considering fault‐tolerance capabilities are proposed and discussed. Finally and in order to exemplify the implementation of the proposed approach, it is applied to include actuator fault tolerance in the design of a HMPC controller for a portion of the sewer network of Barcelona. Copyright © 2008 John Wiley & Sons, Ltd.
Moral conflicts perceived by students of a project course

Regardless of the popularity of project courses in computing curricula, little is researched on moral issues in these courses. The aim of this study was to increase understanding in this area by determining what students on a project course in information systems (IS) perceived as moral conflicts. Data was gathered from diaries, drawings, interviews and questionnaires, and the analysis was inspired by phenomenography. The results show that the hardest moral conflicts were confronted when a student acted in a project manager's role, and that many originated in problems related to the group process. A two-dimensional structure of moral conflicts was found. The results are considered in the light of the existing literature, and implications for research and practice are presented.
The influence of acute stress on attention mechanisms and its electrophysiological correlates

For the selection of relevant information out of a continuous stream of information, which is a common definition of attention, two core mechanisms are assumed: a competition-based comparison of the neuronal activity in sensory areas and the top-down modulation of this competition by frontal executive control functions. Those control functions are thought to bias the processing of information toward the intended goals. Acute stress is thought to impair these frontal functions through the release of cortisol. In the present study, subjects had to detect a luminance change of a stimulus and ignore more salient but task irrelevant orientation changes. Before the execution of this task, subjects underwent a socially evaluated cold pressor test (SECPT) or a non-stressful control situation. The SECPT revealed reliable stress response with a significant increase of cortisol and alpha-amylase. Stressed subjects showed higher error rates than controls, particularly in conditions which require top-down control processing to bias the less salient target feature against the more salient and spatially separated distracter. By means of the EEG, subjects who got stressed showed a reduced allocation to the relevant luminance change apparent in a modulation of the N1pc. The following N2pc, which reflects a re-allocation of attentional resources, supports the error pattern. There was only an N2pc in conditions, which required to bias the less salient luminance change. Moreover, this N2pc was decreased as a consequence of the induced stress. These results allow the conclusion that acute stress impairs the intention-based attentional allocation and enhances the stimulus-driven selection, leading to a strong distractibility during attentional information selection.
Integrating text mining into the MGI biocuration workflow

A major challenge for functional and comparative genomics resource development is the extraction of data from the biomedical literature. Although text mining for biological data is an active research field, few applications have been integrated into production literature curation systems such as those of the model organism databases (MODs). Not only are most available biological natural language (bioNLP) and information retrieval and extraction solutions difficult to adapt to existing MOD curation workflows, but many also have high error rates or are unable to process documents available in those formats preferred by scientific journals. In September 2008, Mouse Genome Informatics (MGI) at The Jackson Laboratory initiated a search for dictionary-based text mining tools that we could integrate into our biocuration workflow. MGI has rigorous document triage and annotation procedures designed to identify appropriate articles about mouse genetics and genome biology. We currently screen ∼1000 journal articles a month for Gene Ontology terms, gene mapping, gene expression, phenotype data and other key biological information. Although we do not foresee that curation tasks will ever be fully automated, we are eager to implement named entity recognition (NER) tools for gene tagging that can help streamline our curation workflow and simplify gene indexing tasks within the MGI system. Gene indexing is an MGI-specific curation function that involves identifying which mouse genes are being studied in an article, then associating the appropriate gene symbols with the article reference number in the MGI database. Here, we discuss our search process, performance metrics and success criteria, and how we identified a short list of potential text mining tools for further evaluation. We provide an overview of our pilot projects with NCBO's Open Biomedical Annotator and Fraunhofer SCAI's ProMiner. In doing so, we prove the potential for the further incorporation of semi-automated processes into the curation of the biomedical literature.
Simultaneous Adversarial Multi-Robot Learning

Multi-robot learning faces all of the challenges of robot learning with all of the challenges of multiagent learning. There has been a great deal of recent research on multiagent reinforcement learning in stochastic games, which is the intuitive extension of MDPs to multiple agents. This recent work, although general, has only been applied to small games with at most hundreds of states. On the other hand robot tasks have continuous, and often complex, state and action spaces. Robot learning tasks demand approximation and generalization techniques, which have only received extensive attention in single-agent learning. In this paper we introduce GraWoLF, a general-purpose, scalable, multiagent learning algorithm. It combines gradient-based policy learning techniques with the WoLF ("Win or Learn Fast") variable learning rate. We apply this algorithm to an adversarial multi-robot task with simultaneous learning. We show results of learning both in simulation and on the real robots. These results demonstrate that GraWoLF can learn successful policies, overcoming the many challenges in multi-robot learning.
Structured Prediction of Network Response

We introduce the following network response problem: given a complex network and an action, predict the subnetwork that responds to action, that is, which nodes perform the action and which directed edges relay the action to the adjacent nodes. We approach the problem through max-margin structured learning, in which a compatibility score is learned between the actions and their activated subnetworks. Thus, unlike the most popular influence network approaches, our method, called SPIN, is context-sensitive, namely, the presence, the direction and the dynamics of influences depend on the properties of the actions. The inference problems of finding the highest scoring as well as the worst margin violating networks, are proven to be NP-hard. To solve the problems, we present an approximate inference method through a semi-definite programming relaxation (SDP), as well as a more scalable greedy heuristic algorithm. In our experiments, we demonstrate that taking advantage of the context given by the actions and the network structure leads SPIN to a markedly better predictive performance over competing methods.
Analysis of flow regime changes due to operation of large reservoirs on the Zambezi River

The Zambezi Basin, a semi-arid 1.4 Mio km2 catchment area spreading across eight countries, constitutes a highly complex system. With several large dams, namely Kariba, Cahora Bassa, Kafue Gorge and Itezhi-Tezhi, the basin’s hydrology is also influenced by vast wetlands with high ecological value such as the Barotse plains, the Mana Pools or the Kafue flats. The African DAms ProjecT (ADAPT) is an interdisciplinary research project aiming to develop an integrated set of methods that help assessing the ecological and socio-economic effects of dams.Acomprehensive evaluation and characterization of the flow regimes before and after the dam’s construction is a stepping stone towards this goal. The analysis is based on historical data, taking into account the evolution of existing reservoirs and hydropower plants. Three indicators are considered to describe the flow regimes in the basin. They allow quantifying the seasonal transfer of the water, the subweekly flow fluctuations and the intensity and frequency of the flow changes. In a further stage, a semi-distributed conceptual hydrological model will be built to simulate the flow regime with and without dams for actual and future hydrological scenarios.
A new MAC algorithm based on reservation and scheduling for energy-limited ad-hoc networks

A wireless ad-hoc network consists of wireless mobile terminals forming a temporary network without any deployed infrastructure or centralized administration. Energy efficiency is an important issue in wireless ad-hoc networks because the battery power of terminals is limited. We propose an energy efficient MAC algorithm based on reservation and scheduling for wireless ad-hoc networks. We evaluate the performance of our proposed MAC scheme with respect to the number, of delayed slots, the channel utilization, and the energy efficiency. Then, we derive the fair threshold energy level for the pseudo base station (PBS) reselection in the proposed MAC.
Image Analysis Using Mathematical Morphology

For the purposes of object or defect identification required in industrial vision applications, the operations of mathematical morphology are more useful than the convolution operations employed in signal processing because the morphological operators relate directly to shape. The tutorial provided in this paper reviews both binary morphology and gray scale morphology, covering the operations of dilation, erosion, opening, and closing and their relations. Examples are given for each morphological concept and explanations are given for many of their interrelationships.
Expanding Wikidata's Parenthood Information by 178%, or How To Mine Relation Cardinality Information

While so far automated knowledge base construction has largely focused on fully qualified facts, e.g., 〈Obama, hasChild, Malia〉, the Web contains also extensive amounts of existential information in the form of cardinality assertions, e.g., that someone has two children without giving their names. In this paper we argue that the extraction of such information could substantially increase the scope of knowledge bases. For the sample of the hasChild relation in Wikidata, we show that simple regular-expression based extraction from Wikipedia can increase the size of the relation by 178%. We also show how such cardinality information can be used to estimate the recall of knowledge bases.
Imaging the Cerebral Blood Flow With Enhanced Laser Speckle Contrast Analysis (eLASCA) by Monotonic Point Transformation

Laser speckle contrast analysis (LASCA) has been demonstrated as a full-field method for imaging the cerebral blood flow (CBF). However, conventional LASCA is limited to extremely low dynamic range because of the ambient background field, dark current, and other anomalies in the circuits of a charge-coupled device camera, which makes it difficult to analyze the spatiotemporal variabilities in CBF. In this study, we proposed an enhanced LASCA (eLASCA) method to improve the dynamic range of LASCA based on monotonic point transformation. In investigating the influence of moderate hypothermia (32plusmn0.5degC) on capillary CBF change, eLASCA presented much more significant decrease of relative CBF than LASCA (hypothermia: 189% versus 137%, postrewarming: 151% versus 119%). Statistically, eLASCA resulted in a higher confidence degree (p=0.009) of CBF change after the rewarming than LASCA (p=0.013). In addition, eLASCA greatly improves the CBF visualization, which is very helpful in demonstrating the details of CBF change.
Efficient Representation of Adhoc Constraints

In most sentiment analysis applications, the sentiment lexicon plays a key role. However, it is hard, if not impossible, to collect and maintain a universal sentiment lexicon for all application domains because different words may be used in different domains. The main existing technique extracts such sentiment words from a large domain corpus based on different conjunctions and the idea of sentiment coherency in a sentence. In this paper, we propose a novel propagation approach that exploits the relations between sentiment words and topics or product features that the sentiment words modify, and also sentiment words and product features themselves to extract new sentiment words. As the method propagates information through both sentiment words and features, we call it double propagation. The extraction rules are designed based on relations described in dependency trees. A new method is also proposed to assign polarities to newly discovered sentiment words in a domain. Experimental results show that our approach is able to extract a large number of new sentiment words. The polarity assignment method is also effective.
DERIVED SPOKEN LANGUAGE MARKERS FOR DETECTING MILD COGNITIVE IMPAIRMENT

Speech produced by subjects during neuropsychological exams can provide markers other than test performance, via spoken language characteristics that discriminate between subject groups. We present preliminary results on the utility of such markers, automatically derived from spoken responses to narrative recall tests, in discriminating between healthy elderly and subjects with Mild Cognitive Impairment (MCI). Given the audio and transcript of the retellings, a range of markers were automatically derived, including (among others) pause frequency and grammatical complexity. Certain spoken language derived markers, which do not measure the fidelity of the retelling to the original narrative, show statistically significant differences between the group means, when calculated either manually or automatically.
Inter-domain Opinion Phrase Extraction Based on Feature Augmentation

In this paper, a system for the extraction of key argument phrases – which make the opinion holder feel negative or positive towards a particular product – from product reviews is introduced. Since the necessary amount of training examples from any arbitrary product type (target domain) is not always available, the possible usage of domain adaptation in the task of opinion phrase extraction is also examined. Experimental results show that models relying on training examples mainly from a different domain can still yield results that are comparable to that of the intra-domain settings.
Generating Compact Classifier Systems Using a Simple Artificial Immune System

Current artificial immune system (AIS) classifiers have two major problems: 1) their populations of B-cells can grow to huge proportions, and 2) optimizing one B-cell (part of the classifier) at a time does not necessarily guarantee that the B-cell pool (the whole classifier) will be optimized. In this paper, the design of a new AIS algorithm and classifier system called simple AIS is described. It is different from traditional AIS classifiers in that it takes only one B-cell, instead of a B-cell pool, to represent the classifier. This approach ensures global optimization of the whole system, and in addition, no population control mechanism is needed. The classifier was tested on seven benchmark data sets using different classification techniques and was found to be very competitive when compared to other classifiers.