124,802 research outputs found
Geissmann, B M, 404334
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/387149Surname: GEISSMANN. Given Name(s) or Initials: B M. Military Service Number or Last Known Location: 404334. Missing, Wounded and Prisoner of War Enquiry Card Index Number: S9821.208919
Item: [2016.0049.19442] "Geissmann, B M, 404334
Datasets for Geissmann et al., 2022
No full text# Datasets for Geissmann et al., 2022.
This entry provides the datasets for the relevant manuscript figures (Figure 4, 5, 6, S2 and S4).
Data is provided as individual CSVs. Data is often shared between different sub-figures and, sometimes, figures.
Fields are described below.
* `fig_4ACE_meta.csv` -- describes the metadata for figure 4 A,C,E
* id -- the unique identifier of an experimental replicate
* device -- a unique identifier of a device (one sticky pi)
* start -- start time of the experiment
* end -- end time of the experiment
* condition -- experimental treatment DD or LD, see manuscript
* ref_datetime -- The origin time of the experiment. Time is to be expressed relative to this reference. This is the start of the first experimental (subjective) day
* `fig_4AC_data.csv` -- the data for figure 4 A,C.
* id -- matches the metadata id (see above)
* t -- the time, in second, using the experimental reference time as an origin (see above)
* N -- the number of captured insects (sub-figure A)
* dN -- the smoothed time derivative of N, by experiment, (subfigure C)
* `fig_4E_data.csv` -- the data for figure 4 E.
* id -- matches the metadata id (see above)
* period -- the period in seconds (X axis)
* power -- Autocorrelation value, i.e. ACF, (Y axis)
* `fig_4BDF_meta.csv` -- describes the metadata for figure 4 B,D,F. Fields descriptions same as for `fig_4AC_meta.csv`.
* `fig_4BD_data.csv` -- the data for figure 4 B,D. Fields descriptions same as for `fig_4AC_data.csv`
* `fig_4F_data.csv` -- the data for figure 4 E. Fields descriptions same as for `fig_4E_data.csv`
* `fig_5_meta.csv` -- describes the metadata for figure 5
* id -- the unique identifier of a replicate x taxon
* label_itc -- a numerical label from the Insect Tuboid Classifier for the taxon
* taxon -- the name of the taxon in the manuscript/figure
* device -- a unique identifier of a device (one sticky pi)
* start_datetime -- start time of the experiment
* end_datetime -- end time of the experiment
* vinegar_bait -- "Y" or "N", whether this replicate (week x device) was baited with apple cider vinegar (see manuscript)
*
* `fig_5_data.csv` -- the data for figure 5
* id -- matches the metadata id (see above)
* wt -- Warped time, in second since 1970-01-01 (epoch). Warped time is used to account for changes in day length (see manuscript)
* wzt -- Warped Zeitgeber time, in seconds. Warped time since the sunrise
* N -- The number of insect detected for this id (taxon x device x week)
* `fig_6A_S4_meta.csv` -- describes the metadata for figure 6A and S4 . Fields are identical to`fig_5_meta.csv` (minus `vinegar_bait`, which was only in figure 5)
* `fig_6A_S4_data.csv` -- the data for figures 6A and S4. Fields are identical to`fig_5_data.csv`
* `fig_6B_data.csv` -- the data for figures 6A and S4. Fields are identical to`fig_5_data.csv`
* D1 -- the first dimension (multidimensional scaling)
* D2 -- the second dimension
* taxon -- the name of the taxon
* rep -- the bootstrap replicate (see manuscript). The first replicate (rep = 1) is just the original data (not resampled)
* `fig_S2DE_data.csv` -- the data for figures S2 D and E. Each row is an instance (putative insect) from given validation file
* filename -- the validation file
* in_gt -- whether the instance is present in the ground truth (human-annotated) image
* in_im -- whether the same instance is present in the UID-predicted image
* area -- the area of the instance, in pixels
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Vocal Diversity of Kloss’s Gibbons (Hylobates Klossii) in the Mentawai Islands, Indonesia
No full textGibbons (family Hylobatidae) are generally described as monogamous, frugivorous, arboreal, and territorial apes and inhabit tropical and subtropical forests of South and Southeast Asia (Marshall and Sugardjito 1986; Leighton
1987; Chivers 2001; Geissmann 2003). All gibbon species are known to produce elaborate, loud, long, and stereotyped patterns of vocalization often referred to as ‘‘songs’’ (Marshall and Marshall 1976; Haimoff 1984; Geissmann 1993, 1995, 2002b, 2003). Generally, song bouts are produced in the early morning and last approximately 10–30 min. Species-specific song characteristics in gibbons are thought to have a strong genetic component (Brockelman and Schilling 1984; Geissmann 1984; Tenaza 1985; Marshall and Sugardjito 1986; Mather 1992; Geissmann 1993). It has previously been demonstrated that gibbon song characteristics are useful for assessing systematic relationships on the level
of the gibbon genus, species and local population, and for reconstructing gibbon phylogeny (Haimoff et al. 1982; Haimoff 1983; Creel and Preuschoft 1984; Haimoff et al. 1984; Marshall et al. 1984; Geissmann 1993, 2002a, b;
Konrad and Geissmann 2006; Dallmann and Geissmann this volume)
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Optimal Dislocation with Persistent Errors in Subquadratic Time
No full textWe study the problem of sorting N elements in the presence of persistent errors in comparisons: In this classical model, each comparison between two elements is wrong independently with some probability up to p, but repeating the same comparison gives always the same result. In this model, it is impossible to reliably compute a perfectly sorted permutation of the input elements. Rather, the quality of a sorting algorithm is often evaluated w.r.t. the maximum dislocation of the sequences it computes, namely, the maximum absolute difference between the position of an element in the returned sequence and the position of the same element in the perfectly sorted sequence. The best known algorithms for this problem have running time O(N2) and achieve, w.h.p., an optimal maximum dislocation of O(log N) for constant error probability p. Note that no algorithm can achieve maximum dislocation o(log N) w.h.p., regardless of its running time. In this work we present the first subquadratic time algorithm with optimal maximum dislocation. Our algorithm runs in O~ (N3 / 2) time and it guarantees O(log N) maximum dislocation with high probability for any p ≤ 1/16
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Optimal sorting with persistent comparison errors
No full textWe consider the problem of sorting n elements in the case of persistent comparison errors. In this problem, each comparison between two elements can be wrong with some fixed (small) probability p, and comparisons cannot be repeated (Braverman and Mossel, SODA’08). Sorting perfectly in this model is impossible, and the objective is to minimize the dislocation of each element in the output sequence, that is, the difference between its true rank and its position. Existing lower bounds for this problem show that no algorithm can guarantee, with high probability, maximum dislocation and total dislocation better than Ω(log n) and Ω(n), respectively, regardless of its running time. In this paper, we present the first O(n log n)-time sorting algorithm that guarantees both O(log n) maximum dislocation and O(n) total dislocation with high probability. This settles the time complexity of this problem and shows that comparison errors do not increase its computational difficulty: a sequence with the best possible dislocation can be obtained in O(n log n) time and, even without comparison errors, Ω(n log n) time is necessary to guarantee such dislocation bounds. In order to achieve this optimality result, we solve two sub-problems in the persistent error comparisons model, and the respective methods have their own merits for further application. One is how to locate a position in which to insert an element in an almost-sorted sequence having O(log n) maximum dislocation in such a way that the dislocation of the resulting sequence will still be O(log n). The other is how to simultaneously insert m elements into an almost sorted sequence of m different elements, such that the resulting sequence of 2m elements remains almost sorted
Optimal dislocation with persistent errors in subquadratic time
No full textWe study the problem of sorting N elements in presence of persistent errors in comparisons: In this classical model, each comparison between two elements is wrong independently with some probability p, but repeating the same comparison gives always the same result. The best known algorithms for this problem have running time O(N2) and achieve an optimal maximum dislocation of O(log N) for constant error probability. Note that no algorithm can achieve dislocation o(log N), regardless of its running time. In this work we present the first subquadratic time algorithm with optimal maximum dislocation: Our algorithm runs in O(N3/2) time and guarantees O(log N) maximum dislocation with high probability. Though the first version of our algorithm is randomized, it can be derandomized by extracting the necessary random bits from the results of the comparisons (errors)
Sorting with recurrent comparison errors
No full textWe present a sorting algorithm for the case of recurrent random comparison errors. The algorithm essentially achieves simultaneously good properties of previous algorithms for sorting n distinct elements in this model. In particular, it runs in O(n2) time, the maximum dislocation of the elements in the output is O(log n), while the total dislocation is O(n). These guarantees are the best possible since we prove that even randomized algorithms cannot achieve o(log n) maximum dislocation with high probability, or o(n) total dislocation in expectation, regardless of their running time
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