A proposal for a First-Citation-Speed-Index Link Peer-reviewed author version

Size: px
Start display at page:

Download "A proposal for a First-Citation-Speed-Index Link Peer-reviewed author version"

Transcription

1 A proposal for a First-Citation-Speed-Index Link Peer-reviewed author version Made available by Hasselt University Library in Docuent Server@UHasselt Reference (Published version): EGGHE, Leo; Bornann, L. & Guns, R.(20) A proposal for a First-Citation-Speed-Index. In: JOURNAL OF INFORMETRICS, 5(). p DOI: 0.06/j.joi Handle:

2 A proposal for a First-Citation-Speed-Index by L. Egghe (*) Universiteit Hasselt (UHasselt), Capus Diepenbeek, Agoralaan, B-3590 Diepenbeek, Belgiu (**) and Universiteit Antwerpen (UA), Inforatie- en Bibliotheekwetenschap, Stadscapus, Venusstraat 35, B-2000 Antwerpen, Belgiu leo.egghe@uhasselt.be L. Bornann Professorship for Social Psychology and Research on Higher Education ETH Zurich Zähringerstr. 24, 8092 Zurich, Switzerland bornann@gess.ethz.ch R. Guns Universiteit Antwerpen (UA), Inforatie- en bibliotheekwetenschap, Stadscapus, Venusstraat 35, 2000 Antwerpen raf.guns@ua.ac.be ABSTRACT In this paper, we define a First-Citation-Speed-Index (FCSI) for a set of papers, based on their ties of publication and of first citation. The index is based on the definition of a h-index for increasing sequences. We show that the index has several good properties in the sense that the shorter the ties are between publication and first citation (in a global anner) the higher the FCSI is.

3 2 We present two case studies: a first-citation speed coparison of three journals in the field of psychology and a first-citation speed coparison of accepted and rejected, but published elsewhere anuscripts by the journal Angewandte Cheie International Edition. Both case studies indicate that our FCSI satisfies the intuitive feeling of what values a FCSI should have in these cases. (*) Corresponding author (**) Peranent address. Key words and phrases: First-Citation-Speed-Index, FCSI, h-index, increasing sequence.

4 3 I. Introduction If we have a set of articles (e.g. in a journal) we can deterine, for each article, the publication tie, denoted t P and (if it exists), the tie that this article received its first citation (denoted t C and of course we can assue that t C t P ). How t P and t C can be expressed (e.g; in onths or years) is not iportant at this oent, but we will spend soe coents on it in the discussion section at the end of this paper. So for all these papers, we can deterine t tc tp (), i.e. the tie between publication of a paper and first-citation to this paper. This is an iportant indicator since it expresses how fast ( t sall ) this paper changes its status of unused to used. Since we have a set of papers we, hence, also have a sequence of t - values. As is classical in such cases we can wonder if this sequence can be used to define a kind of First-Citation-Speed-Index (FCSI). Of course, we should discuss soe desired properties for such a speed index (this will, of course, be done in this paper). Deriving an indicator fro a sequence reinds us of the Hirsch-index or h -index (Hirsch, 2005). There, in its classical application, we have a decreasing sequence of nubers of citations to papers of e.g. an author. This is a situation where large is beautiful since the higher the nuber of citations to a paper are, the better and also: the higher the h -index, the better. In our case of t -values, however, it does not ake uch sense to arrange the in decreasing order. Here we have sall is beautiful since the saller the t -values, the better. So it is natural to arrange these t -values in increasing order. But it is not clear how to apply the well-known definition of the h -index to increasing sequences. In the next section we will propose a ethod to define an h -index for increasing sequences. With this h -index we are then able to define an indicator of first-citation-speed. Here, again large is beautiful since high speed indicates sall t -values. Details will be given. We give soe exaples but we also forulate soe logical good properties that a FCSI should

5 4 have. For instance, if all t -values are 0, the FCSI should attain its axiu. Conversely, if all t -values are very high (in the liit, ), the FCSI should go to its iniu. Also adding a constant a 0 to all t -values should decrease the value of the FCSI. Our indicator satisfies these good properties. Also we can show that ultiplying all t -values with a constant a leads to a decrease of our FCSI, another good property. In the next section practical exaples of journals in psychology and cheistry are given, illustrating the good properties of our FCSI. A first attept for defining such a FCSI was given in Bornann and Daniel (200). Avoiding the proble of defining a h -index for increasing sequences, they apply the h -index (for decreasing sequences) on the data of tie between the present tie and the tie of the firstcitation. They argue that the larger this nuber is, the ore the first-citation tie is to the past, hence the earlier the first-citation is received. The present paper tries to iprove this ethod by also taking into account the tie of publication since it is essential in calculating first-citation speed. The paper closes with soe concluding rearks and soe open probles (incl. the proble of how to deal with tie-units in FCSIs).

6 5 II. Definition of the First-Citation-Speed-Index (FCSI) and its good properties We assue that we have a set of papers (e.g. in a journal) and for each of the we have deterined the tie t P of publication and the tie t C at which the first citation is received and t tc tp. Let t ax t (2) where the axiu is over all papers. Since sall values of t are iportant (cf. the Introduction) we will define a h -index for increasing sequences as follows. Calculate t t for each paper and put the in decreasing order (we have added so that the case of one paper or the case of papers with equal t yields and not 0 ). On this decreasing sequence, the classical h -index can be calculated. We denote it by h. We then define the First-Citation-Speed-Index (FCSI), denoted F, as F t h (3) First we look at soe siple exaples which show that we are on the right track.. Set A has 3 articles with t -values:, 2,3. Hence t 3 and the t t values are 3, 2,, hence h 2 and hence F Set B has 3 articles with t -values:,2,4. Now t 4 and the t t values are 4,3,, hence h 2 and hence F It is logical that the FCSI of B is saller than that of A.

7 6 3. Set C has 3 articles with t -values:,2,6. Now t 6 and the t t values are 6,5,2, hence h 2 and F It is logical that this value of C is saller than the ones of A and B. Of course, as is also the case with the h -index, there is soe robustness. 4. Set D has 3 articles with t -values:,3,4. Now t 4 and the t t values are 4, 2,, hence h 2 and F 4 2 3, the sae as for B although the speed in D is a bit slower (a strictly higher value of F for case D would be a bad property which is not the case here). Let us now consider soe logical requireents for a FCSI and we will check if F satisfies these.. Let us have N papers all with t 0 (in practise, any papers have this as follows fro Bornann and Daniel (200) but it is also the case for the data of e.g. Egghe (see Egghe, L. in WOS)). Now t 0 and t t for all papers, hence h (as we can see clearly here, h is not a good speed easure). Now t h 0 and hence F, the highest possible value, as it should in this case since all t -values are 0. Note: if we want to avoid as highest value we can always apply a strictly increasing transforation such as 2 x Arctan x (4) (highest value is then the highest value while the lowest value 0 reains 0 ). 2. Let us have N papers with all t -values equal and very high (say ), the worst case. Now t, all values t t and hence h.

8 7 Now F 0 t h (5) (and 0 for t ), the lowest possible value for F. 3. Copare two cases 3.. N papers with the sae t -values Here t t, and all t t values are, hence h. So F t h t (6) 3.2. N papers with the sae t -values, naely t t. The sae arguent as above, with t replaced by t yields F t t (7) a logical fact. More general: if all t -values are equal to t a ( a 0 ) then F, (8) t a decreasing in a, which is a logical fact N papers with the sae t -values, naely t at where a. The sae arguent as above with t replaced by t yields F (8) at t a logical fact.

9 8 4. The next case shows a robustness property of F. Let N papers have equal t -values t th and the N paper has t - value t t. Then t t. In the table for calculating h we have that the first N papers have t t t t values and the t t value. If N is sufficiently large we have that h t t and F t h t t t th N paper has F t (as if the th N paper was not there): this is exactly the sae robustness as with the h -index where sall values do not count. We could have several papers with high t -values as long as the h -index is based on the top articles with t -values, t t. This robustness of the h -index is considered as a good property!

10 9 III. Case study: three journals fro subject category Psychology We will now investigate the FCSI in practice. To this end, we collected publication and citation data for the following three journals fro the ISI subject category Psychology : Discourse & Society, Psychological Methods, and Aerican Journal of Counity Psychology. All data were collected fro Thoson Reuters Web of Science on May 0, 200. The citations in the data refer to articles published in these journals in the year 2000, thus allowing for sufficient tie for all articles to gain (at least) a first citation. The data were obtained using a query like SO=(AMERICAN JOURNAL OF COMMUNITY PSYCHOLOGY) AND PY=2000 and siilarly for the other two journals and analyzed using the Citation Report feature. The tie units here are years; for instance, if t 0, the first citation was received in the year of publication. We note that, overall, there are only two articles one in Discourse & Society and one in Psychological Methods that have no citations and hence no tie of first citation t C or t. With t and t defined as in the previous section, Table contains the first citation data for Discourse & Society, Psychological Methods, and Aerican Journal of Counity Psychology. The two uncited articles are oitted, since there is no obvious rank where they should be positioned. In a way, uncited articles receive their first citation at t. It is now straightforward to deterine the h -index for each journal on the basis of the third colun for each journal. We first look at Discourse & Society and find h 7. The largest value for t is 8, hence t h 2 and F. Next, we turn to Psychological Methods. For 2 this journal, we find that h 6, and hence t h and F. The Aerican Journal of Counity Psychology has h 4. Thus, t h 0and F. This is a case soewhat siilar to the one discussed in requireent. 0

11 0 Generally, if the first t papers have t 0, then each of these papers has t t t, and h t. Hence, in this case t h 0 and F. This best case scenario can also be found in practice, as evidenced by Table. Indeed, the first ( t 4) papers received their first citation within less than a year, leading to the largest F possible. These results accord well with our intuition of how these three journals should copare regarding first citation speed. Although Discourse & Society has published less articles than Psychological Methods, its t is larger. The sae observation holds when coparing either to Aerican Journal of Counity Psychology. More iportantly, ore than one quarter of the articles in the latter journal have been cited within the sae year, which clearly exceeds the other two journals. Siilarly, a larger fraction of articles fro Psychological Methods has been cited after one year, copared to Discourse & Society. It is interesting to copare the FCSI to the edian of the t values. In principle, this also gives an indication of first citation speed (note, though, that, for the edian, saller values indicate higher speed). In this case study, however, the edian for each journal equals 2, which would lead us to conclude that these journals are siilar in first citation speed. This is clearly in contrast with the FCSI, which assigns a different score to each one. It sees that the FCSI paints a ore correct picture of their existing differences. The iediacy index II is the nuber of citations in the year of publication divided by the nuber of publications. Hence, it can also be considered an indicator of early interest in a given paper. For the three journals fro Table, we find iediacy index values of, respectively, 0.263, 0.85 and Just like for the FCSI, the Aerican Journal of Counity Psychology has the highest value. The ranking of the other two journals by II is, however, different fro the ranking by F. Closer exaination shows that this is due to the nuber of papers published in 2000; in fact, both journals receive 5 citations within the sae year, but Psychological Methods has published ore articles, leading to a lower iediacy index.

12 Table. First citation data for Discourse & Society, Psychological Methods and Aerican Journal of Counity Psychology Discourse & Society Psychological Methods Aerican Journal of Counity Psychology rank t t t + Rank t t t + rank t t t

13 2 IV. Case study: first-citation speed coparison of accepted and rejected, but published elsewhere anuscripts by Angewandte Cheie International Edition. For the second case study we used biblioetric data of Bornann and Daniel (200) for 899 anuscripts that were subitted to Angewandte Cheie International Edition (AC-IE). What the editors of AC-IE look for ost of all is excellence in cheical research. Manuscripts that reviewers dee to be of high quality are selected for publication. Manuscripts that do not eet the high standards are rejected. Of the 899 anuscripts that were reviewed by the AC- IE in the year 2000, 46% (n=878) were accepted for publication in AC-IE, and 54% (n=02) were rejected. A search in the literature databases Science Citation Index (SCI, Thoson Reuters) and Cheical Abstracts (CA, Cheical Abstracts Services, CAS, Colubus, OH) revealed that of the 02 rejected anuscripts, 959 (94%) were published later in 36 other (different) journals. For accepted and rejected (but published elsewhere) anuscripts, Bornann and Daniel (200) deterined in addition to the nuber of citations the nuber of onths since publication and the first tie the paper was cited. If t =0, the first citation was received within the publication onth. The searches were done using Web of Science (WoS, Thoson Reuters). For different values of t and t -t +, Table 2 shows the nuber of accepted and rejected, but published elsewhere anuscripts. There are, e.g., 8 accepted and rejected, but published elsewhere anuscripts with t =7 and t -t +=6. For both anuscript groups, the largest value for the difference between t c and t p is 77 (=t ). Siilar to the Aerican Journal of Counity Psychology in the previous section, we have a best case scenario in Table 2 for the accepted anuscripts: 86 anuscripts received their first citation within the publication onth (about 0% of all accepted anuscripts). This leads to the largest possible F. For the rejected, but published elsewhere anuscripts we find h=76. The largest value for t is 77 (=t ), hence t -h+=2 and F. 2

14 3 The difference in F between both anuscript groups point to a higher first-citation-speed for accepted than for rejected, but published elsewhere anuscripts. These findings are in accordance to the results of Bornann and Daniel (200). They found not only a higher h index (Hirsch, 2005) for accepted than for rejected, but published elsewhere anuscripts, but also a higher speed, defined there as a longer tie between first citation and date of search for the first citation. Thus, the FCSI results are convergently valid: they correspond to the results produced by other (speed) indicators and to the qualitative outcoe of the AC-IE peer review process. Table 2. First citation data for accepted or rejected, but published elsewhere anuscripts with t <8 t t -t + Accepted anuscripts Rejected anuscripts

15 4 V. Conclusions and suggestions for further research We presented a proposal for a First-Citation-Speed-Index (FCSI), hereby introducing an h - index for increasing sequences. Several good logical properties are shown and the practical exaples underline the good distinctive power of the FCSI. The FCSI takes values between 0 and but we indicate how the FCSI can be transfored into an FCSI with values in the interval 0, and with the sae good properties. Several probles reain. First of all, one should search for other good FCSIs. As in concentration theory (cf. Egghe (2005)) one has defined several concentration easures with good properties. So we should do the sae for FCSIs. All FCSIs should be based on the values t t C t P. Next there is the proble with the tie units. Of course, if we keep the sae tie unit, exaples can be copared. The proble with tie unit can be forulated in two different ways. Suppose we keep the sae tie unit but we apply the transforation t at where a. Then all tie periods (such as t ) are strictly larger and hence the FCSI should decrease. But we can also look at t at being a transforation where the tie unit is changed (e.g. going fro a year to a onth). The sae tie period is then ultiplied by a 2 and it is not clear how a FCSI should behave in this context. This proble is coparable (though not identical) with the principle of scale invariance for concentration easures (also called inequality easures). If a transforation r ar eans a change of the currency (e.g. fro $ to ) then the inequality should be identical: wealth or poverty is not changed when we change the currency! But if the transforation r ar (say for a ) eans that each person s incoe (or capital) is ultiplied by a (keeping the sae currency) then it is not at all clear that inequality reains the sae. The proble of the tie units in the fraework of FCSIs is left as an open proble.

16 5 References Bornann L., & Daniel H.-D. (200). The citation speed index: a useful biblioetric indicator to add to the h index. Journal of Inforetrics, 4(), Egghe L. (2005). Power Laws in the Inforation Production Process: Lotkaian Inforetrics., Oxford, UK: Elsevier. Hirsch J. (2005). An index to quantify an individual s scientific research output. Proceedings of the National Acadey of Sciences of the United States of Aerica 02(46),

OBJECTIVES INTRODUCTION

OBJECTIVES INTRODUCTION M7 Chapter 3 Section 1 OBJECTIVES Suarize data using easures of central tendency, such as the ean, edian, ode, and idrange. Describe data using the easures of variation, such as the range, variance, and

More information

PHY 171. Lecture 14. (February 16, 2012)

PHY 171. Lecture 14. (February 16, 2012) PHY 171 Lecture 14 (February 16, 212) In the last lecture, we looked at a quantitative connection between acroscopic and icroscopic quantities by deriving an expression for pressure based on the assuptions

More information

Measures of average are called measures of central tendency and include the mean, median, mode, and midrange.

Measures of average are called measures of central tendency and include the mean, median, mode, and midrange. CHAPTER 3 Data Description Objectives Suarize data using easures of central tendency, such as the ean, edian, ode, and idrange. Describe data using the easures of variation, such as the range, variance,

More information

A note on the multiplication of sparse matrices

A note on the multiplication of sparse matrices Cent. Eur. J. Cop. Sci. 41) 2014 1-11 DOI: 10.2478/s13537-014-0201-x Central European Journal of Coputer Science A note on the ultiplication of sparse atrices Research Article Keivan Borna 12, Sohrab Aboozarkhani

More information

COS 424: Interacting with Data. Written Exercises

COS 424: Interacting with Data. Written Exercises COS 424: Interacting with Data Hoework #4 Spring 2007 Regression Due: Wednesday, April 18 Written Exercises See the course website for iportant inforation about collaboration and late policies, as well

More information

Soft Computing Techniques Help Assign Weights to Different Factors in Vulnerability Analysis

Soft Computing Techniques Help Assign Weights to Different Factors in Vulnerability Analysis Soft Coputing Techniques Help Assign Weights to Different Factors in Vulnerability Analysis Beverly Rivera 1,2, Irbis Gallegos 1, and Vladik Kreinovich 2 1 Regional Cyber and Energy Security Center RCES

More information

Chapter 6: Economic Inequality

Chapter 6: Economic Inequality Chapter 6: Econoic Inequality We are interested in inequality ainly for two reasons: First, there are philosophical and ethical grounds for aversion to inequality per se. Second, even if we are not interested

More information

13.2 Fully Polynomial Randomized Approximation Scheme for Permanent of Random 0-1 Matrices

13.2 Fully Polynomial Randomized Approximation Scheme for Permanent of Random 0-1 Matrices CS71 Randoness & Coputation Spring 018 Instructor: Alistair Sinclair Lecture 13: February 7 Disclaier: These notes have not been subjected to the usual scrutiny accorded to foral publications. They ay

More information

Chapter 6 1-D Continuous Groups

Chapter 6 1-D Continuous Groups Chapter 6 1-D Continuous Groups Continuous groups consist of group eleents labelled by one or ore continuous variables, say a 1, a 2,, a r, where each variable has a well- defined range. This chapter explores:

More information

Mathematical derivation of the impact factor distribution Link Peer-reviewed author version

Mathematical derivation of the impact factor distribution Link Peer-reviewed author version Mathematical derivation of the impact factor distribution Link Peerreviewed author version Made available by Hasselt University Library in Document Server@UHasselt Reference (Published version: EGGHE,

More information

A Note on Scheduling Tall/Small Multiprocessor Tasks with Unit Processing Time to Minimize Maximum Tardiness

A Note on Scheduling Tall/Small Multiprocessor Tasks with Unit Processing Time to Minimize Maximum Tardiness A Note on Scheduling Tall/Sall Multiprocessor Tasks with Unit Processing Tie to Miniize Maxiu Tardiness Philippe Baptiste and Baruch Schieber IBM T.J. Watson Research Center P.O. Box 218, Yorktown Heights,

More information

Feature Extraction Techniques

Feature Extraction Techniques Feature Extraction Techniques Unsupervised Learning II Feature Extraction Unsupervised ethods can also be used to find features which can be useful for categorization. There are unsupervised ethods that

More information

Cosine similarity and the Borda rule

Cosine similarity and the Borda rule Cosine siilarity and the Borda rule Yoko Kawada Abstract Cosine siilarity is a coonly used siilarity easure in coputer science. We propose a voting rule based on cosine siilarity, naely, the cosine siilarity

More information

. The univariate situation. It is well-known for a long tie that denoinators of Pade approxiants can be considered as orthogonal polynoials with respe

. The univariate situation. It is well-known for a long tie that denoinators of Pade approxiants can be considered as orthogonal polynoials with respe PROPERTIES OF MULTIVARIATE HOMOGENEOUS ORTHOGONAL POLYNOMIALS Brahi Benouahane y Annie Cuyt? Keywords Abstract It is well-known that the denoinators of Pade approxiants can be considered as orthogonal

More information

Curious Bounds for Floor Function Sums

Curious Bounds for Floor Function Sums 1 47 6 11 Journal of Integer Sequences, Vol. 1 (018), Article 18.1.8 Curious Bounds for Floor Function Sus Thotsaporn Thanatipanonda and Elaine Wong 1 Science Division Mahidol University International

More information

In this chapter, we consider several graph-theoretic and probabilistic models

In this chapter, we consider several graph-theoretic and probabilistic models THREE ONE GRAPH-THEORETIC AND STATISTICAL MODELS 3.1 INTRODUCTION In this chapter, we consider several graph-theoretic and probabilistic odels for a social network, which we do under different assuptions

More information

LONG-TERM PREDICTIVE VALUE INTERVAL WITH THE FUZZY TIME SERIES

LONG-TERM PREDICTIVE VALUE INTERVAL WITH THE FUZZY TIME SERIES Journal of Marine Science and Technology, Vol 19, No 5, pp 509-513 (2011) 509 LONG-TERM PREDICTIVE VALUE INTERVAL WITH THE FUZZY TIME SERIES Ming-Tao Chou* Key words: fuzzy tie series, fuzzy forecasting,

More information

Physics 202H - Introductory Quantum Physics I Homework #12 - Solutions Fall 2004 Due 5:01 PM, Monday 2004/12/13

Physics 202H - Introductory Quantum Physics I Homework #12 - Solutions Fall 2004 Due 5:01 PM, Monday 2004/12/13 Physics 0H - Introctory Quantu Physics I Hoework # - Solutions Fall 004 Due 5:0 PM, Monday 004//3 [70 points total] Journal questions. Briefly share your thoughts on the following questions: What aspects

More information

ma x = -bv x + F rod.

ma x = -bv x + F rod. Notes on Dynaical Systes Dynaics is the study of change. The priary ingredients of a dynaical syste are its state and its rule of change (also soeties called the dynaic). Dynaical systes can be continuous

More information

A Better Algorithm For an Ancient Scheduling Problem. David R. Karger Steven J. Phillips Eric Torng. Department of Computer Science

A Better Algorithm For an Ancient Scheduling Problem. David R. Karger Steven J. Phillips Eric Torng. Department of Computer Science A Better Algorith For an Ancient Scheduling Proble David R. Karger Steven J. Phillips Eric Torng Departent of Coputer Science Stanford University Stanford, CA 9435-4 Abstract One of the oldest and siplest

More information

Probability Distributions

Probability Distributions Probability Distributions In Chapter, we ephasized the central role played by probability theory in the solution of pattern recognition probles. We turn now to an exploration of soe particular exaples

More information

Principal Components Analysis

Principal Components Analysis Principal Coponents Analysis Cheng Li, Bingyu Wang Noveber 3, 204 What s PCA Principal coponent analysis (PCA) is a statistical procedure that uses an orthogonal transforation to convert a set of observations

More information

New Slack-Monotonic Schedulability Analysis of Real-Time Tasks on Multiprocessors

New Slack-Monotonic Schedulability Analysis of Real-Time Tasks on Multiprocessors New Slack-Monotonic Schedulability Analysis of Real-Tie Tasks on Multiprocessors Risat Mahud Pathan and Jan Jonsson Chalers University of Technology SE-41 96, Göteborg, Sweden {risat, janjo}@chalers.se

More information

lecture 36: Linear Multistep Mehods: Zero Stability

lecture 36: Linear Multistep Mehods: Zero Stability 95 lecture 36: Linear Multistep Mehods: Zero Stability 5.6 Linear ultistep ethods: zero stability Does consistency iply convergence for linear ultistep ethods? This is always the case for one-step ethods,

More information

R. L. Ollerton University of Western Sydney, Penrith Campus DC1797, Australia

R. L. Ollerton University of Western Sydney, Penrith Campus DC1797, Australia FURTHER PROPERTIES OF GENERALIZED BINOMIAL COEFFICIENT k-extensions R. L. Ollerton University of Western Sydney, Penrith Capus DC1797, Australia A. G. Shannon KvB Institute of Technology, North Sydney

More information

e-companion ONLY AVAILABLE IN ELECTRONIC FORM

e-companion ONLY AVAILABLE IN ELECTRONIC FORM OPERATIONS RESEARCH doi 10.1287/opre.1070.0427ec pp. ec1 ec5 e-copanion ONLY AVAILABLE IN ELECTRONIC FORM infors 07 INFORMS Electronic Copanion A Learning Approach for Interactive Marketing to a Custoer

More information

Determining OWA Operator Weights by Mean Absolute Deviation Minimization

Determining OWA Operator Weights by Mean Absolute Deviation Minimization Deterining OWA Operator Weights by Mean Absolute Deviation Miniization Micha l Majdan 1,2 and W lodziierz Ogryczak 1 1 Institute of Control and Coputation Engineering, Warsaw University of Technology,

More information

A method to determine relative stroke detection efficiencies from multiplicity distributions

A method to determine relative stroke detection efficiencies from multiplicity distributions A ethod to deterine relative stroke detection eiciencies ro ultiplicity distributions Schulz W. and Cuins K. 2. Austrian Lightning Detection and Inoration Syste (ALDIS), Kahlenberger Str.2A, 90 Vienna,

More information

Estimation of the Population Mean Based on Extremes Ranked Set Sampling

Estimation of the Population Mean Based on Extremes Ranked Set Sampling Aerican Journal of Matheatics Statistics 05, 5(: 3-3 DOI: 0.593/j.ajs.05050.05 Estiation of the Population Mean Based on Extrees Ranked Set Sapling B. S. Biradar,*, Santosha C. D. Departent of Studies

More information

Ph 20.3 Numerical Solution of Ordinary Differential Equations

Ph 20.3 Numerical Solution of Ordinary Differential Equations Ph 20.3 Nuerical Solution of Ordinary Differential Equations Due: Week 5 -v20170314- This Assignent So far, your assignents have tried to failiarize you with the hardware and software in the Physics Coputing

More information

Lecture 21. Interior Point Methods Setup and Algorithm

Lecture 21. Interior Point Methods Setup and Algorithm Lecture 21 Interior Point Methods In 1984, Kararkar introduced a new weakly polynoial tie algorith for solving LPs [Kar84a], [Kar84b]. His algorith was theoretically faster than the ellipsoid ethod and

More information

On Poset Merging. 1 Introduction. Peter Chen Guoli Ding Steve Seiden. Keywords: Merging, Partial Order, Lower Bounds. AMS Classification: 68W40

On Poset Merging. 1 Introduction. Peter Chen Guoli Ding Steve Seiden. Keywords: Merging, Partial Order, Lower Bounds. AMS Classification: 68W40 On Poset Merging Peter Chen Guoli Ding Steve Seiden Abstract We consider the follow poset erging proble: Let X and Y be two subsets of a partially ordered set S. Given coplete inforation about the ordering

More information

Relations between the shape of a size-frequency distribution and the shape of a rank-frequency distribution Link Peer-reviewed author version

Relations between the shape of a size-frequency distribution and the shape of a rank-frequency distribution Link Peer-reviewed author version Relations between the shape of a size-frequency distribution and the shape of a rank-frequency distribution Link Peer-reviewed author version Made available by Hasselt University Library in Document Server@UHasselt

More information

13 Harmonic oscillator revisited: Dirac s approach and introduction to Second Quantization

13 Harmonic oscillator revisited: Dirac s approach and introduction to Second Quantization 3 Haronic oscillator revisited: Dirac s approach and introduction to Second Quantization. Dirac cae up with a ore elegant way to solve the haronic oscillator proble. We will now study this approach. The

More information

Gaussian Illuminants and Reflectances for Colour Signal Prediction

Gaussian Illuminants and Reflectances for Colour Signal Prediction Gaussian Illuinants and Reflectances for Colour Signal Prediction Haidreza Mirzaei, Brian Funt; Sion Fraser University; Vancouver, BC, Canada Abstract An alternative to the von Kries scaling underlying

More information

26 Impulse and Momentum

26 Impulse and Momentum 6 Ipulse and Moentu First, a Few More Words on Work and Energy, for Coparison Purposes Iagine a gigantic air hockey table with a whole bunch of pucks of various asses, none of which experiences any friction

More information

A Note on the Applied Use of MDL Approximations

A Note on the Applied Use of MDL Approximations A Note on the Applied Use of MDL Approxiations Daniel J. Navarro Departent of Psychology Ohio State University Abstract An applied proble is discussed in which two nested psychological odels of retention

More information

Polygonal Designs: Existence and Construction

Polygonal Designs: Existence and Construction Polygonal Designs: Existence and Construction John Hegean Departent of Matheatics, Stanford University, Stanford, CA 9405 Jeff Langford Departent of Matheatics, Drake University, Des Moines, IA 5011 G

More information

AN IMPROVED FUZZY TIME SERIES THEORY WITH APPLICATIONS IN THE SHANGHAI CONTAINERIZED FREIGHT INDEX

AN IMPROVED FUZZY TIME SERIES THEORY WITH APPLICATIONS IN THE SHANGHAI CONTAINERIZED FREIGHT INDEX Journal of Marine Science and Technology, Vol 5, No, pp 393-398 (01) 393 DOI: 106119/JMST-01-0313-1 AN IMPROVED FUZZY TIME SERIES THEORY WITH APPLICATIONS IN THE SHANGHAI CONTAINERIZED FREIGHT INDEX Ming-Tao

More information

E0 370 Statistical Learning Theory Lecture 6 (Aug 30, 2011) Margin Analysis

E0 370 Statistical Learning Theory Lecture 6 (Aug 30, 2011) Margin Analysis E0 370 tatistical Learning Theory Lecture 6 (Aug 30, 20) Margin Analysis Lecturer: hivani Agarwal cribe: Narasihan R Introduction In the last few lectures we have seen how to obtain high confidence bounds

More information

Keywords: Estimator, Bias, Mean-squared error, normality, generalized Pareto distribution

Keywords: Estimator, Bias, Mean-squared error, normality, generalized Pareto distribution Testing approxiate norality of an estiator using the estiated MSE and bias with an application to the shape paraeter of the generalized Pareto distribution J. Martin van Zyl Abstract In this work the norality

More information

SPECTRUM sensing is a core concept of cognitive radio

SPECTRUM sensing is a core concept of cognitive radio World Acadey of Science, Engineering and Technology International Journal of Electronics and Counication Engineering Vol:6, o:2, 202 Efficient Detection Using Sequential Probability Ratio Test in Mobile

More information

Name: Partner(s): Date: Angular Momentum

Name: Partner(s): Date: Angular Momentum Nae: Partner(s): Date: Angular Moentu 1. Purpose: In this lab, you will use the principle of conservation of angular oentu to easure the oent of inertia of various objects. Additionally, you develop a

More information

This model assumes that the probability of a gap has size i is proportional to 1/i. i.e., i log m e. j=1. E[gap size] = i P r(i) = N f t.

This model assumes that the probability of a gap has size i is proportional to 1/i. i.e., i log m e. j=1. E[gap size] = i P r(i) = N f t. CS 493: Algoriths for Massive Data Sets Feb 2, 2002 Local Models, Bloo Filter Scribe: Qin Lv Local Models In global odels, every inverted file entry is copressed with the sae odel. This work wells when

More information

IN modern society that various systems have become more

IN modern society that various systems have become more Developent of Reliability Function in -Coponent Standby Redundant Syste with Priority Based on Maxiu Entropy Principle Ryosuke Hirata, Ikuo Arizono, Ryosuke Toohiro, Satoshi Oigawa, and Yasuhiko Takeoto

More information

Midterm 1 Sample Solution

Midterm 1 Sample Solution Midter 1 Saple Solution NOTE: Throughout the exa a siple graph is an undirected, unweighted graph with no ultiple edges (i.e., no exact repeats of the sae edge) and no self-loops (i.e., no edges fro a

More information

About the definition of parameters and regimes of active two-port networks with variable loads on the basis of projective geometry

About the definition of parameters and regimes of active two-port networks with variable loads on the basis of projective geometry About the definition of paraeters and regies of active two-port networks with variable loads on the basis of projective geoetry PENN ALEXANDR nstitute of Electronic Engineering and Nanotechnologies "D

More information

CHAPTER ONE. Physics and the Life Sciences

CHAPTER ONE. Physics and the Life Sciences Solution anual for Physics for the Life Sciences 2nd Edition by Allang Link download full: http://testbankair.co/download/solution-anual-forphysics-for-the-life-sciences-2nd-edition-by-allang/ CHAPTER

More information

Non-Parametric Non-Line-of-Sight Identification 1

Non-Parametric Non-Line-of-Sight Identification 1 Non-Paraetric Non-Line-of-Sight Identification Sinan Gezici, Hisashi Kobayashi and H. Vincent Poor Departent of Electrical Engineering School of Engineering and Applied Science Princeton University, Princeton,

More information

When Short Runs Beat Long Runs

When Short Runs Beat Long Runs When Short Runs Beat Long Runs Sean Luke George Mason University http://www.cs.gu.edu/ sean/ Abstract What will yield the best results: doing one run n generations long or doing runs n/ generations long

More information

4 = (0.02) 3 13, = 0.25 because = 25. Simi-

4 = (0.02) 3 13, = 0.25 because = 25. Simi- Theore. Let b and be integers greater than. If = (. a a 2 a i ) b,then for any t N, in base (b + t), the fraction has the digital representation = (. a a 2 a i ) b+t, where a i = a i + tk i with k i =

More information

List Scheduling and LPT Oliver Braun (09/05/2017)

List Scheduling and LPT Oliver Braun (09/05/2017) List Scheduling and LPT Oliver Braun (09/05/207) We investigate the classical scheduling proble P ax where a set of n independent jobs has to be processed on 2 parallel and identical processors (achines)

More information

Estimation of Combined Wave and Storm Surge Overtopping at Earthen Levees

Estimation of Combined Wave and Storm Surge Overtopping at Earthen Levees May 008 Estiation of Cobined Wave and Stor Surge Overtopping at Earthen Levees by Steven A. Hughes PURPOSE: This Coastal and Hydraulics Engineering Technical Note (CHETN) provides epirical equations for

More information

Intelligent Systems: Reasoning and Recognition. Perceptrons and Support Vector Machines

Intelligent Systems: Reasoning and Recognition. Perceptrons and Support Vector Machines Intelligent Systes: Reasoning and Recognition Jaes L. Crowley osig 1 Winter Seester 2018 Lesson 6 27 February 2018 Outline Perceptrons and Support Vector achines Notation...2 Linear odels...3 Lines, Planes

More information

arxiv: v1 [stat.ot] 7 Jul 2010

arxiv: v1 [stat.ot] 7 Jul 2010 Hotelling s test for highly correlated data P. Bubeliny e-ail: bubeliny@karlin.ff.cuni.cz Charles University, Faculty of Matheatics and Physics, KPMS, Sokolovska 83, Prague, Czech Republic, 8675. arxiv:007.094v

More information

Model Fitting. CURM Background Material, Fall 2014 Dr. Doreen De Leon

Model Fitting. CURM Background Material, Fall 2014 Dr. Doreen De Leon Model Fitting CURM Background Material, Fall 014 Dr. Doreen De Leon 1 Introduction Given a set of data points, we often want to fit a selected odel or type to the data (e.g., we suspect an exponential

More information

Lean Walsh Transform

Lean Walsh Transform Lean Walsh Transfor Edo Liberty 5th March 007 inforal intro We show an orthogonal atrix A of size d log 4 3 d (α = log 4 3) which is applicable in tie O(d). By applying a rando sign change atrix S to the

More information

Introduction to Machine Learning. Recitation 11

Introduction to Machine Learning. Recitation 11 Introduction to Machine Learning Lecturer: Regev Schweiger Recitation Fall Seester Scribe: Regev Schweiger. Kernel Ridge Regression We now take on the task of kernel-izing ridge regression. Let x,...,

More information

Efficient Filter Banks And Interpolators

Efficient Filter Banks And Interpolators Efficient Filter Banks And Interpolators A. G. DEMPSTER AND N. P. MURPHY Departent of Electronic Systes University of Westinster 115 New Cavendish St, London W1M 8JS United Kingdo Abstract: - Graphical

More information

Figure 1: Equivalent electric (RC) circuit of a neurons membrane

Figure 1: Equivalent electric (RC) circuit of a neurons membrane Exercise: Leaky integrate and fire odel of neural spike generation This exercise investigates a siplified odel of how neurons spike in response to current inputs, one of the ost fundaental properties of

More information

The accelerated expansion of the universe is explained by quantum field theory.

The accelerated expansion of the universe is explained by quantum field theory. The accelerated expansion of the universe is explained by quantu field theory. Abstract. Forulas describing interactions, in fact, use the liiting speed of inforation transfer, and not the speed of light.

More information

Experimental Design For Model Discrimination And Precise Parameter Estimation In WDS Analysis

Experimental Design For Model Discrimination And Precise Parameter Estimation In WDS Analysis City University of New York (CUNY) CUNY Acadeic Works International Conference on Hydroinforatics 8-1-2014 Experiental Design For Model Discriination And Precise Paraeter Estiation In WDS Analysis Giovanna

More information

Estimation of Korean Monthly GDP with Mixed-Frequency Data using an Unobserved Component Error Correction Model

Estimation of Korean Monthly GDP with Mixed-Frequency Data using an Unobserved Component Error Correction Model 100Econoic Papers Vol.11 No.1 Estiation of Korean Monthly GDP with Mixed-Frequency Data using an Unobserved Coponent Error Correction Model Ki-Ho Ki* Abstract Since GDP is announced on a quarterly basis,

More information

The Weierstrass Approximation Theorem

The Weierstrass Approximation Theorem 36 The Weierstrass Approxiation Theore Recall that the fundaental idea underlying the construction of the real nubers is approxiation by the sipler rational nubers. Firstly, nubers are often deterined

More information

arxiv: v1 [math.nt] 14 Sep 2014

arxiv: v1 [math.nt] 14 Sep 2014 ROTATION REMAINDERS P. JAMESON GRABER, WASHINGTON AND LEE UNIVERSITY 08 arxiv:1409.411v1 [ath.nt] 14 Sep 014 Abstract. We study properties of an array of nubers, called the triangle, in which each row

More information

The Transactional Nature of Quantum Information

The Transactional Nature of Quantum Information The Transactional Nature of Quantu Inforation Subhash Kak Departent of Coputer Science Oklahoa State University Stillwater, OK 7478 ABSTRACT Inforation, in its counications sense, is a transactional property.

More information

[95/95] APPROACH FOR DESIGN LIMITS ANALYSIS IN VVER. Shishkov L., Tsyganov S. Russian Research Centre Kurchatov Institute Russian Federation, Moscow

[95/95] APPROACH FOR DESIGN LIMITS ANALYSIS IN VVER. Shishkov L., Tsyganov S. Russian Research Centre Kurchatov Institute Russian Federation, Moscow [95/95] APPROACH FOR DESIGN LIMITS ANALYSIS IN VVER Shishkov L., Tsyganov S. Russian Research Centre Kurchatov Institute Russian Federation, Moscow ABSTRACT The aer discusses a well-known condition [95%/95%],

More information

AN OPTIMAL SHRINKAGE FACTOR IN PREDICTION OF ORDERED RANDOM EFFECTS

AN OPTIMAL SHRINKAGE FACTOR IN PREDICTION OF ORDERED RANDOM EFFECTS Statistica Sinica 6 016, 1709-178 doi:http://dx.doi.org/10.5705/ss.0014.0034 AN OPTIMAL SHRINKAGE FACTOR IN PREDICTION OF ORDERED RANDOM EFFECTS Nilabja Guha 1, Anindya Roy, Yaakov Malinovsky and Gauri

More information

time time δ jobs jobs

time time δ jobs jobs Approxiating Total Flow Tie on Parallel Machines Stefano Leonardi Danny Raz y Abstract We consider the proble of optiizing the total ow tie of a strea of jobs that are released over tie in a ultiprocessor

More information

On Conditions for Linearity of Optimal Estimation

On Conditions for Linearity of Optimal Estimation On Conditions for Linearity of Optial Estiation Erah Akyol, Kuar Viswanatha and Kenneth Rose {eakyol, kuar, rose}@ece.ucsb.edu Departent of Electrical and Coputer Engineering University of California at

More information

Journal of Modern Physics, 2011, 2, doi: /jmp Published Online November 2011 (http://www.scirp.

Journal of Modern Physics, 2011, 2, doi: /jmp Published Online November 2011 (http://www.scirp. Journal of Modern Physics, 11,, 1331-1347 doi:1.436/jp.11.11165 Published Online Noveber 11 (http://www.scirp.org/journal/jp) Transforation of the Angular Power Spectru of the Cosic Microwave Background

More information

3.8 Three Types of Convergence

3.8 Three Types of Convergence 3.8 Three Types of Convergence 3.8 Three Types of Convergence 93 Suppose that we are given a sequence functions {f k } k N on a set X and another function f on X. What does it ean for f k to converge to

More information

Handwriting Detection Model Based on Four-Dimensional Vector Space Model

Handwriting Detection Model Based on Four-Dimensional Vector Space Model Journal of Matheatics Research; Vol. 10, No. 4; August 2018 ISSN 1916-9795 E-ISSN 1916-9809 Published by Canadian Center of Science and Education Handwriting Detection Model Based on Four-Diensional Vector

More information

Reducing Vibration and Providing Robustness with Multi-Input Shapers

Reducing Vibration and Providing Robustness with Multi-Input Shapers 29 Aerican Control Conference Hyatt Regency Riverfront, St. Louis, MO, USA June -2, 29 WeA6.4 Reducing Vibration and Providing Robustness with Multi-Input Shapers Joshua Vaughan and Willia Singhose Abstract

More information

Lecture #8-3 Oscillations, Simple Harmonic Motion

Lecture #8-3 Oscillations, Simple Harmonic Motion Lecture #8-3 Oscillations Siple Haronic Motion So far we have considered two basic types of otion: translation and rotation. But these are not the only two types of otion we can observe in every day life.

More information

Ocean 420 Physical Processes in the Ocean Project 1: Hydrostatic Balance, Advection and Diffusion Answers

Ocean 420 Physical Processes in the Ocean Project 1: Hydrostatic Balance, Advection and Diffusion Answers Ocean 40 Physical Processes in the Ocean Project 1: Hydrostatic Balance, Advection and Diffusion Answers 1. Hydrostatic Balance a) Set all of the levels on one of the coluns to the lowest possible density.

More information

Block designs and statistics

Block designs and statistics Bloc designs and statistics Notes for Math 447 May 3, 2011 The ain paraeters of a bloc design are nuber of varieties v, bloc size, nuber of blocs b. A design is built on a set of v eleents. Each eleent

More information

Analysis of Impulsive Natural Phenomena through Finite Difference Methods A MATLAB Computational Project-Based Learning

Analysis of Impulsive Natural Phenomena through Finite Difference Methods A MATLAB Computational Project-Based Learning Analysis of Ipulsive Natural Phenoena through Finite Difference Methods A MATLAB Coputational Project-Based Learning Nicholas Kuia, Christopher Chariah, Mechatronics Engineering, Vaughn College of Aeronautics

More information

ASSUME a source over an alphabet size m, from which a sequence of n independent samples are drawn. The classical

ASSUME a source over an alphabet size m, from which a sequence of n independent samples are drawn. The classical IEEE TRANSACTIONS ON INFORMATION THEORY Large Alphabet Source Coding using Independent Coponent Analysis Aichai Painsky, Meber, IEEE, Saharon Rosset and Meir Feder, Fellow, IEEE arxiv:67.7v [cs.it] Jul

More information

Designing for the Road User. Maximum Spiral Transition Lengths

Designing for the Road User. Maximum Spiral Transition Lengths IPENZ Transportation Conference 11 October 2006 Queenstown, New Zealand Designing for the Road User Maxiu Spiral Transition Lengths K H M Weale Northern Region Technical Developent Leader MWH New Zealand

More information

Bootstrapping Dependent Data

Bootstrapping Dependent Data Bootstrapping Dependent Data One of the key issues confronting bootstrap resapling approxiations is how to deal with dependent data. Consider a sequence fx t g n t= of dependent rando variables. Clearly

More information

AUTOMATIC DETECTION OF RWIS SENSOR MALFUNCTIONS (PHASE II) Northland Advanced Transportation Systems Research Laboratories Project B: Fiscal Year 2006

AUTOMATIC DETECTION OF RWIS SENSOR MALFUNCTIONS (PHASE II) Northland Advanced Transportation Systems Research Laboratories Project B: Fiscal Year 2006 AUTOMATIC DETECTION OF RWIS SENSOR MALFUNCTIONS (PHASE II) FINAL REPORT Northland Advanced Transportation Systes Research Laboratories Project B: Fiscal Year 2006 Carolyn J. Crouch Donald B. Crouch Richard

More information

Support Vector Machine Classification of Uncertain and Imbalanced data using Robust Optimization

Support Vector Machine Classification of Uncertain and Imbalanced data using Robust Optimization Recent Researches in Coputer Science Support Vector Machine Classification of Uncertain and Ibalanced data using Robust Optiization RAGHAV PAT, THEODORE B. TRAFALIS, KASH BARKER School of Industrial Engineering

More information

Lecture 16: Scattering States and the Step Potential. 1 The Step Potential 1. 4 Wavepackets in the step potential 6

Lecture 16: Scattering States and the Step Potential. 1 The Step Potential 1. 4 Wavepackets in the step potential 6 Lecture 16: Scattering States and the Step Potential B. Zwiebach April 19, 2016 Contents 1 The Step Potential 1 2 Step Potential with E>V 0 2 3 Step Potential with E

More information

IMPROVEMENTS IN DESCRIBING WAVE OVERTOPPING PROCESSES

IMPROVEMENTS IN DESCRIBING WAVE OVERTOPPING PROCESSES IMPROVEMENS IN DESCRIBING WAVE OVEROPPING PROCESSES Steven Hughes 1, Christopher hornton 2, Jentsje van der Meer 3, and Bryon Scholl 4 his paper presents a new epirical relation for the shape factor in

More information

A Generalized Permanent Estimator and its Application in Computing Multi- Homogeneous Bézout Number

A Generalized Permanent Estimator and its Application in Computing Multi- Homogeneous Bézout Number Research Journal of Applied Sciences, Engineering and Technology 4(23): 5206-52, 202 ISSN: 2040-7467 Maxwell Scientific Organization, 202 Subitted: April 25, 202 Accepted: May 3, 202 Published: Deceber

More information

Example A1: Preparation of a Calibration Standard

Example A1: Preparation of a Calibration Standard Suary Goal A calibration standard is prepared fro a high purity etal (cadiu) with a concentration of ca.1000 g l -1. Measureent procedure The surface of the high purity etal is cleaned to reove any etal-oxide

More information

Birthday Paradox Calculations and Approximation

Birthday Paradox Calculations and Approximation Birthday Paradox Calculations and Approxiation Joshua E. Hill InfoGard Laboratories -March- v. Birthday Proble In the birthday proble, we have a group of n randoly selected people. If we assue that birthdays

More information

Physics 2107 Oscillations using Springs Experiment 2

Physics 2107 Oscillations using Springs Experiment 2 PY07 Oscillations using Springs Experient Physics 07 Oscillations using Springs Experient Prelab Read the following bacground/setup and ensure you are failiar with the concepts and theory required for

More information

Beyond Mere Convergence

Beyond Mere Convergence Beyond Mere Convergence Jaes A. Sellers Departent of Matheatics The Pennsylvania State University 07 Whitore Laboratory University Park, PA 680 sellers@ath.psu.edu February 5, 00 REVISED Abstract In this

More information

Extension of CSRSM for the Parametric Study of the Face Stability of Pressurized Tunnels

Extension of CSRSM for the Parametric Study of the Face Stability of Pressurized Tunnels Extension of CSRSM for the Paraetric Study of the Face Stability of Pressurized Tunnels Guilhe Mollon 1, Daniel Dias 2, and Abdul-Haid Soubra 3, M.ASCE 1 LGCIE, INSA Lyon, Université de Lyon, Doaine scientifique

More information

USEFUL HINTS FOR SOLVING PHYSICS OLYMPIAD PROBLEMS. By: Ian Blokland, Augustana Campus, University of Alberta

USEFUL HINTS FOR SOLVING PHYSICS OLYMPIAD PROBLEMS. By: Ian Blokland, Augustana Campus, University of Alberta 1 USEFUL HINTS FOR SOLVING PHYSICS OLYMPIAD PROBLEMS By: Ian Bloland, Augustana Capus, University of Alberta For: Physics Olypiad Weeend, April 6, 008, UofA Introduction: Physicists often attept to solve

More information

1 Proof of learning bounds

1 Proof of learning bounds COS 511: Theoretical Machine Learning Lecturer: Rob Schapire Lecture #4 Scribe: Akshay Mittal February 13, 2013 1 Proof of learning bounds For intuition of the following theore, suppose there exists a

More information

A Simplified Analytical Approach for Efficiency Evaluation of the Weaving Machines with Automatic Filling Repair

A Simplified Analytical Approach for Efficiency Evaluation of the Weaving Machines with Automatic Filling Repair Proceedings of the 6th SEAS International Conference on Siulation, Modelling and Optiization, Lisbon, Portugal, Septeber -4, 006 0 A Siplified Analytical Approach for Efficiency Evaluation of the eaving

More information

THE CONSTRUCTION OF GOOD EXTENSIBLE RANK-1 LATTICES. 1. Introduction We are interested in approximating a high dimensional integral [0,1]

THE CONSTRUCTION OF GOOD EXTENSIBLE RANK-1 LATTICES. 1. Introduction We are interested in approximating a high dimensional integral [0,1] MATHEMATICS OF COMPUTATION Volue 00, Nuber 0, Pages 000 000 S 0025-578(XX)0000-0 THE CONSTRUCTION OF GOOD EXTENSIBLE RANK- LATTICES JOSEF DICK, FRIEDRICH PILLICHSHAMMER, AND BENJAMIN J. WATERHOUSE Abstract.

More information

2 Q 10. Likewise, in case of multiple particles, the corresponding density in 2 must be averaged over all

2 Q 10. Likewise, in case of multiple particles, the corresponding density in 2 must be averaged over all Lecture 6 Introduction to kinetic theory of plasa waves Introduction to kinetic theory So far we have been odeling plasa dynaics using fluid equations. The assuption has been that the pressure can be either

More information

Many-to-Many Matching Problem with Quotas

Many-to-Many Matching Problem with Quotas Many-to-Many Matching Proble with Quotas Mikhail Freer and Mariia Titova February 2015 Discussion Paper Interdisciplinary Center for Econoic Science 4400 University Drive, MSN 1B2, Fairfax, VA 22030 Tel:

More information

Distributed Subgradient Methods for Multi-agent Optimization

Distributed Subgradient Methods for Multi-agent Optimization 1 Distributed Subgradient Methods for Multi-agent Optiization Angelia Nedić and Asuan Ozdaglar October 29, 2007 Abstract We study a distributed coputation odel for optiizing a su of convex objective functions

More information

Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence

Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence Best Ar Identification: A Unified Approach to Fixed Budget and Fixed Confidence Victor Gabillon Mohaad Ghavazadeh Alessandro Lazaric INRIA Lille - Nord Europe, Tea SequeL {victor.gabillon,ohaad.ghavazadeh,alessandro.lazaric}@inria.fr

More information

Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence

Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence Best Ar Identification: A Unified Approach to Fixed Budget and Fixed Confidence Victor Gabillon, Mohaad Ghavazadeh, Alessandro Lazaric To cite this version: Victor Gabillon, Mohaad Ghavazadeh, Alessandro

More information