@article{Weiss2006, title = {Charge qubit entanglement in double quantum dots}, author = {S. Weiss and M. Thorwart and R. Egger}, journal = {EPL (Europhysics Letters)}, number = 5, pages = {905-911}, volume = 76, year = 2006, url = {http://stacks.iop.org/0295-5075/76/905}, abstract = {We study entanglement of charge qubits in a vertical tunnel-coupled double quantum dot containing two interacting electrons. Exact diagonalization is used to compute the negativity characterizing entanglement. We find that entanglement can be efficiently generated and controlled by sidegate voltages, and describe how it can be detected. For large enough tunnel coupling, the negativity shows a pronounced maximum at an intermediate interaction strength within the Wigner molecule regime.}, biburl = {http://www.bibsonomy.org/bibtex/2e641bf2318b557d603cb0046f9610047/simen}, keywords = {entanglement unread quantumdots} } @article{heiblum:84, title = {New Results Are Right on the Quantum Dot}, author = {Moty Heiblum}, journal = {Physics Today}, number = 8, pages = {84-84}, publisher = {AIP}, volume = 50, year = 1997, url = {http://link.aip.org/link/?PTO/50/84/1}, collaboration = {}, doi = {10.1063/1.881875}, description = {www.PhysicsToday.org}, biburl = {http://www.bibsonomy.org/bibtex/2f81168ee60ffa98628907aed1b667681/simen}, keywords = {unread quantumdots} } @article{Slater1929, title = {The Theory of Complex Spectra}, author = {J. C. Slater}, journal = {Phys. Rev.}, month = {Nov}, number = 10, pages = {1293--1322}, publisher = {American Physical Society}, volume = 34, year = 1929, numpages = {29}, doi = {10.1103/PhysRev.34.1293}, description = {Phys. Rev. 34 (1929): J. C. Slater - The Theory of Complex...}, biburl = {http://www.bibsonomy.org/bibtex/20f040db52c1826f1ddb5abf845e392b8/simen}, keywords = {slater_determinants unread} } @misc{Znojil2004, title = {PT-symmetry, ghosts, supersymmetry and Klein-Gordon equation}, author = {Miloslav Znojil}, year = 2004, url = {http://arxiv.org/abs/hep-th/0408081}, description = {PT-symmetry, ghosts, supersymmetry and Klein-Gordon equation}, abstract = { Parallels between the concepts of symmetry, supersymmetry and (recently introduced) PT-symmetry are reviewed and discussed, with particular emphasis on the new insight in quantum theory which is rendered possible by their combined use. Comment: Summary of the invited talk presented to the XI-th International Conference "Symmetry Methods in Physics" (June 21-24, 2004, Prague, Czech Republic, URL http://thsun1.jinr.ru/meetings/2004/) Submitted to proceedings}, biburl = {http://www.bibsonomy.org/bibtex/2264d040f76970c901b2af2ba6867cf00/simen}, keywords = {susy unread} } @misc{Rodrigues2002, title = {The Quantum Mechanics SUSY Algebra: An Introductory Review}, author = {R. de Lima Rodrigues}, year = 2002, url = {http://arxiv.org/abs/hep-th/0205017}, description = {The Quantum Mechanics SUSY Algebra: An Introductory Review}, abstract = { Starting with the Lagrangian formalism with N=2 supersymmetry in terms of two Grassmann variables in Classical Mechanics, the Dirac canonical quantization method is implemented. The N=2 supersymmetry algebra is associated to one-component and two-component eigenfunctions considered in the Schr\"odinger picture of Nonrelativistic Quantum Mechanics. Applications are contemplated. Comment: PUBLICATION: Monograph CBPF-03-01 (December/2001). Revtex, 49 pages, no figures. A new version with new sections will be submitted for publication}, biburl = {http://www.bibsonomy.org/bibtex/27c649ee828520901acdad068e07f0a21/simen}, keywords = {susy unread} } @article{keyhere, title = {Using kNN Model for Automatic Feature Selection}, author = {Gongde Guo and Daniel Neagu and Mark T.D. Cronin}, journal = {Pattern Recognition and Data Mining}, pages = {410--419}, year = 2005, url = {http://dx.doi.org/10.1007/11551188_44}, description = {SpringerLink - Book Chapter}, abstract = {This paper proposes a kNN model-based feature selection method aimed at improving the efficiency and effectiveness of the ReliefF method by: (1) using a kNN model as the starter selection, aimed at choosing a set of more meaningful representatives to replace the original data for feature selection; (2) integration of the Heterogeneous Value Difference Metric to handle heterogeneous applications – those with both ordinal and nominal features; and (3) presenting a simple method of difference function calculation based on inductive information in each representative obtained by kNN model. We have evaluated the performance of the proposed kNN model-based feature selection method on toxicity dataset Phenols with two different endpoints. Experimental results indicate that the proposed feature selection method has a significant improvement in the classification accuracy for the trial dataset. ER -}, biburl = {http://www.bibsonomy.org/bibtex/29a43a1255896104679f67db7333593ac/rgolombe}, keywords = {unread} } @article{10.1109/ICPR.2000.906092, title = {Automatic Feature Selection - A Hybrid Statistical Approach}, address = {Los Alamitos, CA, USA}, author = {Hong Guo and Yi Lu Murphey}, journal = {Pattern Recognition, International Conference on}, pages = 2382, publisher = {IEEE Computer Society}, volume = 2, year = 2000, isbn = {}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICPR.2000.906092}, description = {Digital Library}, biburl = {http://www.bibsonomy.org/bibtex/2532fd465c952b2c67c46f2f1c3f3c9d3/rgolombe}, keywords = {unread} } @misc{yang99learning, title = {Learning approaches for detecting and tracking news events}, author = {Yiming Yang and Jaime Carbonell and Ralf Brown and Tom Pierce and Brian T. Archibald and Xin Liu}, year = 1999, url = {citeseer.ist.psu.edu/yang99learning.html}, description = {Learning approaches for Detecting and Tracking News Events - Yang, Carbonell, Brown, Pierce, Archibald, Liu (ResearchIndex)}, biburl = {http://www.bibsonomy.org/bibtex/23861fef7943082e0c670bad9ca8933cd/utahell}, keywords = {seminarthema unread eventDetection} } @inproceedings{yang98, title = {A study on retrospective and on-line event detection}, address = {Melbourne, AU}, author = {Yiming Yang and Tom Pierce and Jaime Carbonell}, booktitle = {Proceedings of {SIGIR}-98, 21st {ACM} International Conference on Research and Development in Information Retrieval}, pages = {28--36}, year = 1998, url = {http://citeseer.ist.psu.edu/yang98study.html}, description = {A Study on Retrospective and On-Line Event Detection - Yang, Pierce, Carbonell (ResearchIndex)}, biburl = {http://www.bibsonomy.org/bibtex/2908d3084c54398162c08b9edaaedbe1c/utahell}, keywords = {unread eventDetection} } @article{JonF._Wilkins12012004, title = {{A Separation-of-Timescales Approach to the Coalescent in a Continuous Population}}, author = {Jon F. Wilkins}, journal = {Genetics}, number = 4, pages = {2227-2244}, volume = 168, year = 2004, url = {http://www.genetics.org/cgi/content/abstract/168/4/2227}, doi = {10.1534/genetics.103.022830}, eprint = {http://www.genetics.org/cgi/reprint/168/4/2227.pdf}, description = {A Separation-of-Timescales Approach to the Coalescent in a Continuous Population}, abstract = {This article presents an analysis of a model of isolation by distance in a continuous, two-dimensional habitat. An approximate expression is derived for the distribution of coalescence times for a pair of sequences sampled from specific locations in a rectangular habitat. Results are qualitatively similar to previous analyses of isolation by distance, but account explicitly for the location of samples relative to the habitat boundaries. A separation-of-timescales approach takes advantage of the fact that the sampling locations affect only the recent coalescent behavior. When the population size is larger than the number of generations required for a lineage to cross the habitat range, the long-term genealogical process is reasonably well described by Kingman's coalescent with time rescaled by the effective population size. This long-term effective population size is affected by the local dispersal behavior as well as the geometry of the habitat. When the population size is smaller than the time required to cross the habitat, deep branches in the genealogy are longer than would be expected under the standard neutral coalescent, similar to the pattern expected for a panmictic population whose population size was larger in the past. }, biburl = {http://www.bibsonomy.org/bibtex/2c95ca89b735d08c7aa82f4077ba2e700/peter.ralph}, keywords = {spatial_coalescent unread} } @article{journals/cacm/Crowcroft08, title = {Toward a network architecture that does everything.}, author = {Jon Crowcroft}, journal = {Commun. ACM}, number = 1, pages = {74-77}, volume = 51, year = 2008, url = {http://dblp.uni-trier.de/db/journals/cacm/cacm51.html#Crowcroft08}, ee = {http://doi.acm.org/10.1145/1327452.1327486}, date = {2008-01-17}, description = {dblp}, biburl = {http://www.bibsonomy.org/bibtex/2f54fb73bbd0af28d83751413d555b244/chesteve}, keywords = {unread} } @inproceedings{peramunetilleke2002, title = {Currency Exchange Rate Forecasting From News Headlines}, address = {Melbourne, Australia}, author = {Desh Peramunetilleke and Raymond K. Wong}, booktitle = {Thirteenth Australasian Database Conference (ADC2002)}, editor = {Xiaofang Zhou}, publisher = {ACS}, year = 2002, url = {http://citeseer.ist.psu.edu/peramunetilleke02currency.html}, description = {Currency Exchange Rate Forecasting from News Headlines - Peramunetilleke, Wong (ResearchIndex)}, biburl = {http://www.bibsonomy.org/bibtex/249a5c33dbcb2232a34bf62169aa308a3/utahell}, keywords = {timeseries news classification unread finance} } @techreport{gidofalvi2003, title = {Using News Articles to Predict Stock Price Movements}, author = {G. Gid\'o falvi and C. Elkan}, institution = {Department of Computer Science and Engineering, University of California}, year = 2003, location = {San Diego}, date = {2003}, tech = {Technical Report}, description = {Homepage of Gyozo Gidofalvi}, biburl = {http://www.bibsonomy.org/bibtex/26b10bfd7fa4dccf020447dffccf73a47/utahell}, keywords = {timeseries news classification unread finance} } @phdthesis{thomas-news, title = {News and Trading Rules}, author = {James D. Thomas}, year = 2003, url = {http://citeseer.ist.psu.edu/thomas03news.html}, description = {News and Trading Rules (ResearchIndex)}, biburl = {http://www.bibsonomy.org/bibtex/226378f96efe0351922b922b93d30ae6f/utahell}, keywords = {timeseries news unread finance} } @inproceedings{wuthrich1998, title = {Daily Prediction of Major Stock Indices from Textual {WWW} Data}, author = {Beat Wuthrich and D. Permunetilleke and S. Leung and Vincent Cho and J. Zhang and W. Lam}, booktitle = {Knowledge Discovery and Data Mining}, pages = {364-368}, year = 1998, url = {http://citeseer.ist.psu.edu/wuthrich98daily.html}, description = {Daily Stock Market Forecast from Textual Web Data - Wuthrich, Cho, Leung, Permunetilleke, Sankaran, Zhang (ResearchIndex)}, biburl = {http://www.bibsonomy.org/bibtex/224df9f2c73486c4b5479229d27498b46/utahell}, keywords = {timeseries news classification unread finance} } @misc{zhang2004, title = {A Probabilistic Model for Online Document Clustering with Application to Novelty Detection}, author = {Jian Zhang and Zoubin Ghahramani and Yiming Yang}, year = 2004, url = {http://citeseer.ist.psu.edu/article/zhang04probabilistic.html}, description = {A Probabilistic Model for Online Document Clustering with Application to Novelty Detection (ResearchIndex)}, biburl = {http://www.bibsonomy.org/bibtex/291f7e2edb54813101884add74ea4e404/utahell}, keywords = {unread eventDetection} } @misc{das2001, title = {Yahoo! for Amazon: Sentiment Parsing from Small Talk on the Web }, author = {Sanjiv Das and Mike Chen}, year = 2001, date = {000 messages}, description = {Yahoo! for Amazon: Sentiment Parsing from Small Talk on the Web}, abstract = {The internet has made it feasible to tap a continuous stream of public sentiment from the world wide web, quite literally permitting one to "feel the pulse" of any issue under consideration. We present a methodology for real time sentiment extraction in the domain of finance. With the advent of the web, there has been a sharp increase in the influence of individuals on the stock market via web-based trading and the posting of sentiment to stock message boards. While it is important to capture this"sentiment"of small investors, as yet, no index of sentiment has been compiled. This paper comprises(a)a technology for extracting small investor sentiment from web sources to create an index, and(b)illustrative applications of the methodology. We make use of computerized natural language and statistical algorithms for the automated classification of messages posted on the web. We design a suite of classification algorithms, each of different theoretical content, with a view to characterizing the sentiment of any single posting to a message board. The use of multiple methods allows imposition of voting rules in the classification process. It also enables elimination of"fuzzy"messages which are better off uninterpreted. A majority rule across algorithms vastly improves classification accuracy, but also leads to a natural increase in the number of messages classified as"fuzzy". The classifier achieves an accuracy of 62 %(versus a random classification accuracy of 33 %), and compares favorably against human agreement on message classification, which was 72 %. The technology is computationally efficient, allowing the access and interpretations of thousands of messages within minutes. Our illustrative applications show evidence of a strong link between market movements and sentiment for the last quarter of 2000, we found evidence that sentiment is based on stock movements.}, biburl = {http://www.bibsonomy.org/bibtex/2309ee6610c6b0ca7fdda5d1c90a3b935/utahell}, keywords = {sentiment forums unread finance} } @misc{antweiler2004, title = {Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards}, author = {Werner Antweiler and Murray Z. Frank}, institution = {Trading Volume, Internet Stock Message Boards}, year = 2004, tech = {Keywords: Volatility}, description = {SSRN-Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards}, abstract = {Literally millions of messages have been posted on internet stock message boards. Financial press reports claim that these postings can move markets. We study message posting on Yahoo! Finance Raging Bull for the firms that were in the Dow Jones Industrial Average the Dow Jones Internet Commerce Index during the year 2000. Using computional linguistics methods we measure the bullishness of the messages. Significant predictive content was found between message posting between message posting trading volume between the degree of bullishness of the messages volatility trading volume. These results were obtained even after taking news stories from the Wall Street Journal into account.}, biburl = {http://www.bibsonomy.org/bibtex/290653284d715722ea9952d1059a93e8a/utahell}, keywords = {sentiment forums unread finance} } @misc{antweiler2006, title = {Do US Stock Markets Typically Overreact to Corporate News Stories? }, author = {Werner Antweiler and Murray Z. Frank}, year = {2006 }, description = {Do US Stock Markets Typically Overreact to Corporate News Stories? by Werner Antweiler, Murray Frank}, abstract = {It is widely believed that once news is made public the information is fully reflected in prices within at most a day or two(the efficient market hypothesis). We test this idea using the set of 245, 429 Wall Street Journal corporate news stories fromUsing computational linguistics methods we classify the stories according to topic, and for each topic with a sufficient number of identified events, we run an event study. Our results differ from popular impressions in several ways.On average there is a reversal or overreaction, so that pre-event and post-event abnormal returns have the opposite sign.Statistically significant return momentum is observed for many days after publication.As a result, the inference to be drawn from an event study is often very sensitive to the assumed event window.The average news story has a bigger and more prolonged impact during a recession than during an expansion.}, biburl = {http://www.bibsonomy.org/bibtex/2c36aedc0124dfaa9e59d07480f2ec002/utahell}, keywords = {sentiment text unread finance} } @inproceedings{Ingvaldsen2006, title = {Financial News Mining: Monitoring Continuous Streams of Text}, author = {J.E. Ingvaldsen and J.A. Gulla and T. Laegreid and P.C. Sandal}, booktitle = {Web Intelligence, 2006. WI 2006. IEEE/WIC/ACM International Conference on}, pages = {321-324}, year = 2006, url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?tp=&arnumber=4061386&isnumber=4061322}, isbn = {0-7695-2747-7}, doi = {10.1109/WI.2006.80}, description = {Welcome to IEEE Xplore 2.0: Financial News Mining: Monitoring Continuous Streams of Text}, abstract = {This paper addresses the problem of extracting, analyzing and synthesizing valuable information from continuous text streams covering financial information. A text mining framework combining elements from information retrieval, information extraction and natural language processing has been implemented. The framework is utilized to extract information regarding key actors in the domain, how they relate to each other, and how these characteristics evolve over time}, biburl = {http://www.bibsonomy.org/bibtex/2868a51b509aebbba07deb23c1873d451/utahell}, keywords = {unread} }