ppxscal/embedding_network huggingface dataset:
Computational prediction of human proteins that can be secreted into the bloodstream
Authors: Juan Cui, Qi Liu, David Puett, Ying Xu
Venue: Bioinformatics (2008)

We present a novel computational method for predicting which proteins from highly and abnormally expressed genes in diseased human tissues, such as cancers, can be secreted into the bloodstream, suggesting possible marker proteins for follow-up serum proteomic studies. A main challenging issue in tackling this problem is that our understanding about the downstream localization after proteins are secreted outside the cells is very limited and not sufficient to provide useful hints about secretion to the bloodstream. To bypass this difficulty, we have taken a data mining approach by first collecting, through extensive literature searches, human proteins that are known to be secreted into the bloodstream due to various pathological conditions as detected by previous proteomic studies, and then asking the question: ‘what do these secreted proteins have in common in terms of their physical and chemical properties, amino acid sequence and structural features that can be used to predict them?’ We have identified a list of features, such as signal peptides, transmembrane domains, glycosylation sites, disordered regions, secondary structural content, hydrophobicity and polarity measures that show relevance to protein secretion. Using these features, we have trained a support vector machine-based classifier to predict protein secretion to the bloodstream. On a large test set containing 98 secretory proteins and 6601 non-secretory proteins of human, our classifier achieved ~90% prediction sensitivity and ~98% prediction specificity. Several additional datasets are used to further assess the performance of our classifier. On a set of 122 proteins that were found to be of abnormally high abundance in human blood due to various cancers, our program predicted 62 as blood-secreted proteins. By applying our program to abnormally highly expressed genes in gastric cancer and lung cancer tissues detected through microarray gene expression studies, we predicted 13 and 31 as blood secreted, respectively, suggesting that they could serve as potential biomarkers for these two cancers, respectively. Our study demonstrated that our method can provide highly useful information to link genomic and proteomic studies for disease biomarker discovery. Our software can be accessed at http://csbl1.bmb.uga.edu/cgi-bin/Secretion/secretion.cgi. Contact: xyn@bmb.uga.edu Supplementary information:Supplementary data are available at Bioinformatics online.
Clustera: an integrated computation and data management system
Authors: David J. DeWitt, Erik Paulson, Eric Robinson, Jeffrey Naughton, Joshua Royalty, Srinath Shankar, Andrew Krioukov
Venue: Proceedings of the VLDB Endowment (2008)

This paper introduces Clustera, an integrated computation and data management system. In contrast to traditional cluster-management systems that target specific types of workloads, Clustera is designed for extensibility, enabling the system to be easily extended to handle a wide variety of job types ranging from computationally-intensive, long-running jobs with minimal I/O requirements to complex SQL queries over massive relational tables. Another unique feature of Clustera is the way in which the system architecture exploits modern software building blocks including application servers and relational database systems in order to realize important performance, scalability, portability and usability benefits. Finally, experimental evaluation suggests that Clustera has good scale-up properties for SQL processing, that Clustera delivers performance comparable to Hadoop for MapReduce processing and that Clustera can support higher job throughput rates than previously published results for the Condor and CondorJ2 batch computing systems.
Modified Optimal Prediction and its Application to a Particle-Method Problem
Authors: Alina Chertock, David Gottlieb, Alex Solomonoff
Venue: Journal of Scientific Computing (2008)

The paper is concerned with system reduction by statistical methods and, in particular, by the optimal prediction method introduced in (Chorin, A.J., Hald, O.H., Kupferman, R., Optimal prediction with memory, Phys. D 166:239---257, 2002). The optimal prediction method deals with problems that possess large and small scales and uses the conditional expectation to model the influence of the small scales on the large ones.In the current paper, we develop a different variant of the optimal prediction method as well as introduce and compare several approximations of this method. We apply the original and modified optimal prediction methods to a system of ODEs obtained from a particle method discretization of a hyperbolic PDE and demonstrate their performance in a number of numerical experiments.
A principled design for an integrated computational environment
Authors: Andrea A. diSessa
Venue: Human-computer interaction (1985)

This paper aims at the principled design of a computational environment; it aims at being as explicit as possible about the space of possibilities and about the assumptions made in choosing from among them in the design process. The point is to develop a more systematic, if not yet scientific, basis for the design of complex but understandable artifacts. The particular object of design here is a simple but multifunctional system for naive and inexperienced users. We begin theoretically by elaborating the notion of understandability, the key characteristic for which we must design. We present various models people can make of computational systems, each with its own learning curve, advantages, and disadvantages. Then we propose a pragmatic framework for a particular system. The framework includes the principle of naive realism: that users should be able to pretend that they see the system itself in the display. It also includes the pervasive use of a spatial metaphor whereby users' commonsense spatial knowledge is used to make the system easy to understand. The theoretical and pragmatic levels are linked, in that a number of important decisions about issues (such as reference, scoping and the meaning of evaluation) are based on the theoretical modeling considerations.
Smoke modified environment for crop frost protection: a fuzzy logic approach
Authors: Mohammad Abdel Kareem Jaradat, Moh'd A. Al-Nimr, Moh'd Noor Alhamad
Venue: Computers and Electronics in Agriculture (2008)

During cold weather conditions, there is a high possibility that many crops or planted fields will be damaged due to frost, especially during cloudless cold nights. As it is well known the soil stores energy gained from the sun during the day light time. During cloudless cold nights the soil starts to loss this stored energy, and the soil surface temperature starts to drop less than the ambient temperature. When the soil surface temperature becomes around 0^oC the planted corps starts to freeze. This frost problem can highly damage the crops, or affect the crops growth process which leads to great financial losses for the agriculture sector. In this research a new intelligent agriculture controlled environment is presented based on a fuzzy logic system for frost protection in an open field environment by using a smoke generator or burner and a set of distributed wireless sensor nodes. The presented system can also be integrated with the available cell phone network in the farmer's region, so that the farmer can easily monitor and operate more than one field at a time from his home through his cell phone. The demonstrated simulation results over an examined field, have confirmed the ability of the new system to handle the frost problem at critical situations during cold weather condition by maintaining the soil surface temperature above the freezing point and saving the field crops from being damaged.
Effective versus measured correlation length for radar-based surface soil moisture retrieval
Authors: J. Alvarez-Mozos, M. Gonzalez-Audicana, J. Casali, A. Larranaga
Venue: International Journal of Remote Sensing (2008)

At present, radar-based surface soil moisture (SM) retrieval is hampered by the influence of surface roughness on the backscattering coefficient (σ0). Surface roughness is typically represented by two parameters, namely the standard deviation of surface heights (s) and the surface correlation length (l). The latter is a very problematic parameter, since it is extremely variable and very difficult to measure adequately. Therefore, several authors proposed calibrating it using backscattering models yielding optimum or effective l values. Baghdadi et al. found that those effective l were related to the parameter s and the configuration of the sensor, and proposed an approach to calculate it. The objective of this study is to evaluate the validity of that approach using data acquired on a complementary test site. RADARSAT-1 scenes acquired over an experimental watershed are used. Soil moisture and surface roughness parameters were measured in detail, coinciding with satellite overpasses. The effective l values calculated from the equations of Baghdadi et al. (2006) are used to perform forward and inverse simulations using the Integral Equation Model that are compared with radar observations and ground measurements of SM. The results obtained highlight the potential of the evaluated approach towards an operational radar based soil moisture estimation.
Proceedings of the 6th IEEE/ACM/IFIP international conference on Hardware/Software codesign and system synthesis
Venue: International Conference on Hardware Software Codesign (2008)

On behalf of the CODES+ISSS 2008 Technical Program Committees, we would like to welcome you to the 6th IEEE/ACM International Conference on Hardware/Software Co-Design and System Synthesis, part of ESWeek 2008 in Atlanta, Georgia. CODES+ISSS is the premier conference for system-level design, with a focus on the design of embedded systems hardware, software, and tools. This year we continue the tradition of maintaining a high-quality forum for active discussion of current and innovative topics in embedded systems that bring together the latest in academic and industrial research and development. We selected the best of submitted papers through a rigorous review process, and have assembled an exciting program with a mix of keynote, special sessions, and regular papers. This year we received 143 full paper submissions and selected 44 papers for publication, for an acceptance rate of 31%. The Program Committee carefully reviewed all papers, with an average of over 4.5 reviews per paper. The Committee then met face to face for a day just before DAC 2008, for detailed discussions on the papers. Final paper selections were based on overall quality and novelty; papers with the highest quality and novelty were compared across different topic areas to yield the absolute best set of papers overall. We trust you will find this year's program to be exciting and stimulating, representing important advances in theory and practice of system-level design and related areas. We complemented the regular paper sessions with accompanying posters with a keynote address on "Co-Design in the Wilderness", given by Dan Gale, CTO of Canadian Microsystems Corporation. In addition, two special sessions, on design and defect tolerance beyond CMOS, and on transaction-level verification and debug, complement the regular papers and will give attendees additional insight into future technologies and current industrial practice. CODES+ISSS is part of the growing Embedded Systems Week (ESWEEK) event that has become the premier international event in embedded systems research and development. CODES+ISSS is complemented by two other flagship sister conferences: EMSOFT (focused on embedded software), and CASES (focused on architecture and compilers). Registrants to ESWEEK are affiliated with a primary conference, but may attend sessions in any of the three conferences. Moreover, the technical programs of the three conferences, including keynotes, special sessions, panels, tutorials and workshops, have been carefully planned to avoid major overlaps and complement each other. ESWEEK blazes the embedded systems trail and is uniquely positioned to offer the best technical content for attendees in the areas of embedded systems design, tools, software, architecture and compilers.
Partial discharge diagnosis on GIS based on envelope detection
Authors: Li Li-Xue, Huang Cheng-Jun, Zeng Yi, Jiang Xiu-Chen
Venue: WSEAS TRANSACTIONS on SYSTEMS (2008)

In order to detect and identify the defects on GIS, the ultra-high frequency envelope detection circuit and high-speed data acquisition system was designed, and the tests of five typical defects for GIS were made in the laboratory and the envelope signal was acquired based on Ultra-high frequency (UHF) envelop detection circuit. And wavelet denoising technique was used to reduce the white noise of the measurement envelop signal. A large number of experiments show that time-domain characteristics of partial discharge signals generated by the same defect model are about the same, envelopes are nearly identical too. The time-domain characteristics of partial discharge signals generated by different defect models are different, and their envelope shapes are different too. A new method of pattern recognition was proposed which analyses the characteristic parameters of the envelope signal at time-domain with BP neural network. A large number of test data in Lab indicate that the method is effective.
Power signal classification using dynamic wavelet network
Authors: B. Biswal, P. K. Dash, B. K. Panigrahi, J. B. V. Reddy
Venue: Applied Soft Computing (2009)

A new approach to classification of non-stationary power signals based on dynamic wavelet has been considered. This paper proposes a model for non-stationary power signal disturbance classification using dynamic wavelet networks (DWN). A DWN is a combination of two sub-networks consisting of a wavelet layer and adaptive probabilistic network. The DWN has the capability of automatic adjustment of learning cycles for different classes of signals, for minimizing error. DWN models are specifically suitable for application in dynamic environments with time varying non-stationary power signals. The test results showed accurate classification, fast and adaptive learning mechanism, fast processing time and overall model effectiveness in classifying various non-stationary power signals. The classification result of the DWN has been compared with that of the probabilistic neural network (PNN).
Confidence intervals for quantile estimation using Jackknife techniques
Authors: Y. Román-Montoya, M. Rueda, A. Arcos
Venue: Computational Statistics (2008)

We consider the inference on quantiles, Q y (β), with jackknife techniques, in finite populations of a variable, Y, using the quantile information on an auxiliary variable, X. Jackknife techniques are applied to estimate quantiles and the behaviour of these estimators is analyzed. Their properties are studied for simple random sampling. We also examine the confidence intervals obtained with jackknife variances.
Incorporating topic transition in topic detection and tracking algorithms
Authors: Jianping Zeng, Shiyong Zhang
Venue: Expert Systems with Applications: An International Journal (2009)

Topics often transit among documents in a document collection. To improve the accuracy of the topic detection and tracking (TDT) algorithms in discovering topics or classifying documents, it is necessary to make full use of this kind of topic transition information. However, TDT algorithms usually find topics based on topic models, such as LDA, pLSI, etc., which are a kind of mixture model and make the topic transition difficult to be denoted and implemented. A topic transition model representation based on hidden Markov model is present, and learning the topic transition from documents is discussed. Based on the model, two TDT algorithms incorporating topic transition, i.e. topic discovering and document classifying, are provided to show the application of the proposed model. Experiments on two real-world document collections are done with the two algorithms, and performance comparison with other similar algorithm shows that the accuracy can achieve 93% for topic discovering in Reuters-21578, and 97.3% in document classifying. Furthermore, topic transition discovered by the algorithm on a dataset which was collected from a BBS website is consistent with the manual analysis results.
Remote sensing of contrasting tillage practices in the Texas Panhandle
Authors: Prasanna H. Gowda, T. A. Howell, S. R. Evett, J. L. Chavez, L. New
Venue: International Journal of Remote Sensing (2008)

Tillage information is crucial in environmental modelling as it has a direct impact on water holding capacity, evapotranspiration, carbon sequestration and water quality. In this study, a set of Landsat Thematic Mapper (TM)-based linear logistic models were developed for mapping tillage practices and verified with an independent dataset. For data collection purposes, 35 and 41 commercial fields were randomly selected in Moore and Ochiltree counties, respectively, in the Texas Panhandle. Tillage survey was planned and conducted to coincide with Landsat 5 satellite overpasses during the 2005 planting season and two TM scenes were acquired. Using the Moore County dataset, seven logistic regression models were developed and these were evaluated with the data collected from Ochiltree County. The overall classification accuracy of the models varied from 86% to 91% with the Moore County dataset. These models were evaluated against independent Ochiltree County dataset and resulted in somewhat less accurate (classification accuracy of 67-85%) but still useful results. Analysis of these results indicates that logistic regression models that have indices derived from the combination of TM band 5 with bands 4 or 6 may provide consistent and acceptably accurate results when they are applied in the same geographic region.
Creating augmented and immersive television experiences using a semantic framework
Authors: Sergio Goldenberg
Venue: Proceeding of the 1st international conference on Designing interactive user experiences for TV and video (2008)

This paper proposes a design framework for creating successful iTV products based on the experience obtained from the development and implementation of several interactive television projects. The concept of semantic connection is introduced as a way to measure the relationship between the broadcast and paired interactive applications. This concept is related to the maintenance of the immersion of the television experience through applications that focus on augmenting the main content: the television broadcast.
Sorting Algorithms in MOQA
Authors: Jacinta Townley, Joseph Manning, Michel Schellekens
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2009)

A high-level overview of the MOQA language is presented. The representation of its data structure, a labeled series-parallel partial order, is shown along with some of the functions allowed upon this data structure. The combination of MOQA's data structure and its functions capture the required calculus to statically obtain the average-case time of algorithms written in this language. The implementation of these concepts is discussed and algorithms written in this implementation are presented. A detailed analysis of one of these implemented algorithms, quicksort, critically compares its average-case time in MOQA against the average-case time of standard quicksort. While the asymptotic average of quicksort in MOQA remains unchanged, extra constant costs are incurred by the MOQA method. It is shown that these costs result from molding the algorithm around the MOQA data structure and functions versus the general approach of choosing the data structure and functions that best match the algorithm. This limitation is balanced against an approach that aims to obtain the average-case time of algorithms statically.
Semantically-Enhanced Model-Experiment-Evaluation Processes (SeMEEPs) within the Atmospheric Chemistry Community
Authors: Chris Martin, Mohammed H. Haji, Peter Dew, Mike Pilling, Peter Jimack
Venue: Provenance and Annotation of Data and Processes: Second International Provenance and Annotation Workshop, IPAW 2008, Salt Lake City, UT, USA, June 17-18, 2008. Revised Selected Papers (2008)

The scientific model development process is often documented in an ad-hoc unstructured manner leading to difficulty in attributing provenance to data products. This can cause issues when the data owner or other interested stakeholder seeks to interpret the data at a later date. In this paper we discuss the design, development and evaluation of a Semantically-enhanced Electronic Lab-Notebook to facilitate the capture of provenance for the model development process, within the atmospheric chemistry community. We then proceed to consider the value of semantically enhanced provenance within the wider community processes, Semantically-enhanced Model-Experiment Evaluation Processes (SeMEEPs), that leverage data generated by experiments and computational models to conduct evaluations.
An adaptive staircase procedure for the E-Prime programming environment
Authors: W. David Hairston, Joseph A. Maldjian
Venue: Computer Methods and Programs in Biomedicine (2009)

Many studies need to determine a subject's threshold for a given task. This can be achieved efficiently using an adaptive staircase procedure. While the logic and algorithms for staircases have been well established, the few pre-programmed routines currently available to researchers require at least moderate programming experience to integrate into new paradigms and experimental settings. Here, we describe a freely distributed routine developed for the E-Prime programming environment that can be easily integrated into any experimental protocol with only a basic understanding of E-Prime. An example experiment (visual temporal-order-judgment task) where subjects report the order of occurrence of two circles illustrates the behavior and consistency of the routine.
Bio-inspired multi-agent data harvesting in a proactive urban monitoring environment
Authors: Uichin Lee, Eugenio Magistretti, Mario Gerla, Paolo Bellavista, Pietro Lió, Kang-Won Lee
Venue: Ad Hoc Networks (2009)

Vehicular sensor networks (VSNs) enable brand new and promising sensing applications, such as traffic reporting, relief to environmental monitoring, and distributed surveillance. In our past work, we have designed and implemented MobEyes, a middleware solution to support VSN-based urban monitoring, where agent vehicles (e.g., police cars) move around and harvest meta-data about sensed information from regular VSN-enabled vehicles. In urban sensing operations, multiple agents typically collaborate in harvesting and searching for key meta-data in parallel. Thus, it is critical to effectively coordinate the harvesting operations of the agents in a decentralized and lightweight way. The paper presents a bio-inspired meta-data harvesting algorithm, called datataxis, whose primary goal is to effectively cover large urban areas datataxis alternate foraging behaviors inspired by Escherichia coli chemotaxis and by Levy flights to favor agent movements towards ''information patches'' where the concentration of meta-data is high. The proposed scheme avoids harvesting duplication by preventing superfluous concentration of agents in the same region at the same time using stigmergy. We have validated datataxis via extensive simulations that demonstrate how the proposed bio-inspired behavior of harvesting agents effectively coordinates their movements, thus outperforming other decentralized strategies. Our solution was shown to be robust and to work well under a wide range of operation parameters, thus making it easily and rapidly deployable for different urban sensing operations.