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1 er programs with significant improvements in running time.
2 ed HMMs in bounded memory without increasing running time.
3 or, while exhibiting comparable accuracy and running time.
4 takes advantage of multiprocessing to reduce running time.
5 30 min of hands-on time and 4.5 h of machine-running time.
6 ng accuracy, specificity and sensitivity and running time.
7  when given 64 GB of memory and 48 h maximum running time.
8  The fast screening algorithm reduced 98% of running time.
9 hile being the fastest in terms of the total running time.
10 ers with 52K redundant genes in 1.5 hours of running time.
11 r on the number of considered candidates and running time.
12 co-clustering based method has advantages in running time.
13 tion time, which is the dominant part of the running time.
14 an other state-of-the-art tools with shorter running time.
15 rnative gain measure is also given to reduce running time.
16 twork search, which dramatically reduces the running time.
17 greatly reduces computational complexity and running time.
18 its per query, significantly speeding up the running time.
19 er quality than competing methods in shorter running times.
20 rated high accuracy and significantly faster running times.
21 he-art shows reduced memory requirements and running times.
22 savings in cache misses reduce the empirical running times.
23 er experience with significant reductions in running time, ~3.5 min for the analysis of all five majo
24 progression model of tumor evolution, with a running time a fraction of that used in prior studies.
25 ignificantly outperforms existing methods in running time, accuracy, or both.
26  maintained high performance and competitive running time across all datasets.
27 ows subjects to train at a longer continuous running time and a more stable cycling training speed.
28  studied their performance, in terms of both running time and accuracy, on simulated as well as on bi
29 ased methods have performed well in terms of running time and accuracy, they tend to have reduced acc
30 there remains large room for improvements in running time and accuracy.
31 ity in mice, as evidenced by their increased running time and distance.
32 lary-to-fibre ratio (C/F), increased maximal running time and elevated basal expression of VEGF and m
33  of Metsky et al. shows clearly super-linear running time and fails to process even a subset of 17% o
34        The RLZ-Graph scales well in terms of running time and graph sizes with an increasing number o
35               At the same time, its moderate running time and low memory footprint allow metaBEETL to
36                                  We optimize running time and memory consumption by recycling memory
37 ey are computationally expensive in terms of running time and memory consumption due to the huge sear
38                                Additionally, running time and memory requirements are about constant
39 eck in assembly pipelines, and improving its running time and memory usage is an important problem.
40 cy of the predictions, we bench-marked HAP's running time and phasing accuracy against PHASE.
41 the performances of our approach in terms of running time and quality of the alignments using the BAl
42 w the efficiency of this package in reducing running time and RAM usage in large-scale EWAS.
43 notypes to analyse has little effect both on running time and required memory.
44 he correct network and compares favorably in running time and results with methods based on value of
45 We demonstrate an improved trade-off between running time and retrieval accuracy, controlled by the s
46 the methods using d-separable matrices while running time and solution size are comparable.
47                                          The running time and space requirement of all procedures is
48 ategy does not work due to rapid increase in running time and space usage.
49 e existing methods both in the computational running time and the metastability.
50          INRICH has wide applicability, fast running time and, most importantly, robustness to potent
51 -to-use graphical user interface (GUI), fast running times and automated parameter discovery.
52             Our experimentation compares the running times and distance efficiency of Lloyd's K-means
53                                              Running times and memory requirements are also discussed
54 ing all possible elements severely increases running times and more importantly the chance for false
55         We analyze the asymptotic worst-case running times and provide experimental results that demo
56 s' lengths are above 100 residues, excessive running times and sub-optimal energy functions.
57  species tree accuracy, dramatically reduces running time, and enables both ASTRAL-III and RAxML to c
58 fferences with respect to alignment quality, running time, and usability in general.
59 ino acid determination are desired to reduce running times, and the main factors involved in the rapi
60                             In addition, the running times are 30-40% faster for M-ZDOCK.
61                                       With a running time below 5 min, our method is applicable to la
62 unding its rate of progress, we decrease the running time by a factor of 100 without sacrificing accu
63                             For example, the running time can be reduced by 20-30% while achieving RO
64 lted in a further 326% increase in endurance running time compared with the performance level of mice
65 een described, but many are hampered by long running times, confounding of selection and recombinatio
66 erms of ease of use, equipment requirements, running time, cost per sample and sequencing quality.
67                                              Running times differ substantially between methods.
68  in runners were similar across quintiles of running time, distance, frequency, amount, and speed, co
69 e data, significant differences in the total running time, equilibrium moisture content, sorption hys
70  thus they may exhibit sharp fluctuations in running time, especially for large alphabets.
71 ential abundance testing methods in terms of running time, false discovery rate and power.
72  often not well-defined on categorical data; running time for computations using high dimensional dat
73 osed divide-and-conquer framework, has O(n5) running time for datasets with n species.
74 ther sacrifice optimality or trade increased running time for reduced memory.
75 rograms) to achieve an effectively quadratic running time for simultaneous pairwise alignment and fol
76  the Bayesian prior on node ages reduces the running time for this computation on the 349 taxa datase
77 arkov Models, we achieve drastically reduced running times for Bayesian inference using Forward-Backw
78 hark tool with VNTyper significantly reduced running time from 6-12 hours to 5-10 minutes per sample,
79 roved performance by >100-fold, reducing the running time from hours to mere minutes for typical jobs
80  is a complex computational problem in which running time grows exponentially with the number of mani
81 memory (at least 8 GB), but whose asymptotic running time has never been theoretically established.
82 ems: (i) high false-positive rate; (ii) long running time; (iii) work only for genomes in their datab
83                                          The running time increases with the size of the genetic data
84                                     ASTRAL's running time is [Formula: see text], and ASTRAL-II's run
85 time is [Formula: see text], and ASTRAL-II's running time is [Formula: see text], where n is the numb
86 ernative is qscore, a method whose empirical running time is approximately the same as FastSP when gi
87    DECOD uses a k-mer count table and so its running time is independent of the size of the input set
88                               Elsewhere, the running time is no greater than O(mn(m+n)).
89 m is based on fractional programming and its running time is O(n2log n).
90  the algorithm is output sensitive, i.e. its running time is quasi-linear to the size of the generate
91  is discussed theoretically, and the program running time is reported for various test examples.
92 spectroscopic method and show that the total running time maintains polynomial dependence on accuracy
93 lying generic tree distance measure and fast running time make MulRF useful for inferring phylogenies
94                    The linear scalability of running time makes scMDC a promising method for analyzin
95 led genomes and three sequencing datasets in running time, memory consumption, and hard disk occupati
96 th one exception, the algorithm has expected running time O(mn).
97 he recursive-cut exact kinship algorithm has running time O(s2m) where s is the number of individuals
98 e of the SEEDY algorithm is that it is fast, running time O[(E + V) log V] for V proteins and E inter
99              The exact kinship algorithm has running-time O(n2) for an n-individual pedigree.
100                The approximate algorithm has running-time O(nd) for an n-individual pedigree on which
101                                 With its low running time of 1 h 35 m per sample at eight threads, Lo
102                               We studied the running time of 47 problems generated from 17 data sets.
103 4-well plate can be achieved with unattended running time of 5.4h.
104                                      A total running time of 6 min was obtained, the faster comprehen
105 ion suggests that (i) our method reduced the running time of a single query on a database of around 3
106 ACompress program significantly improves the running time of all previous DNA compression programs.
107 parameter tuning and, surprisingly, quickest running time of all the tried methods.
108                                          The running time of ARCHes does not depend on the size of a
109 sting tools, PSAMM significantly reduced the running time of constraint-based analysis and enabled fl
110                           Unfortunately, the running time of EM is linear in the length of the input
111              This algorithm has a worst-case running time of O(n(2)) which is inferior to two previou
112 e present further improvements that make the running time of our algorithm practical.
113                                          The running time of Syotti shows linear scaling in practice,
114       We provide an analysis of the expected running time of the algorithm and demonstrate that STEME
115  problem in polynomial time, which means the running time of the algorithm is a polynomial function o
116                                          The running time of the algorithm is dependent on the scorin
117  motif consists of multiple elements and the running time of the algorithm is highly dependent on the
118                                Moreover, the running time of the cloud implementation for our method
119 y and locally related sequence sets, and the running time of the program is considerably improved.
120 y more than 50%, and this leads to a reduced running time of the quantification tool.
121                                          The running time of this algorithm scales algebraically with
122                                    We report running times of 127 s to find all SSRs of length 20 bp
123  we find strategies that previously required running times of days or more.
124 and empirical analysis demonstrated a linear running-time of the algorithm, which is the fastest appr
125 task, which can require hundreds of hours of running time on a single CPU even for the fastest known
126  less memory and disk while having a shorter running time on assembled genomes.
127  with other methods in terms of accuracy and running time on both simulated and real data, and our ex
128 n engineered carefully to achieve remarkable running times on regular PCs.
129 ate its performance in terms of accuracy and running times on two gold standard datasets: the UK Biob
130 exact algorithms with exponential worst-case running time or heuristics that do not guarantee optimal
131 th BTSC and MOTSC demonstrated a much faster running time over exhaustive search with the same accura
132  has comparable power to and a much improved running time over previous methods, especially in detect
133 s leads to improvements both in accuracy and running time over the alternative, which is to run a bin
134 our index achieves a significant speed-up in running time over the state-of-the-art methods such as C
135 rofile of Mood States post-BCT and in faster running time (P < 0.05) in volunteers reporting to BCT w
136 ater in older, male participants with slower running times (p < 0.05 for all).
137 ld gel electrophoresis (PFGE), is slow, with running times ranging from 10 hours to more than 200 hou
138   The highly reproducible data including the running time, real-time sample mass, target relative hum
139 tional optimization methods with the average running time reduced by as much as 80% and with optimali
140 pplication of a step stimulus, the bacterial running time relaxes to its pre-stimulus level.
141          The algorithm is iterative, and its running time requirements are proportional to T.N.(d.(lo
142 ore, results indicated a significantly lower running time (RT) on the treadmill [t((20)) = 4.84, p <
143  ChemWalker has a series of improvements, on running time, scalability and maintainability and is ava
144  an expectation-maximization algorithm whose running time scales linearly with the number of observed
145                                 CloudBurst's running time scales linearly with the number of reads ma
146                       In addition, DFBAlab's running time scales linearly with the number of species
147                        Unfortunately, MEME's running time scales poorly with the size of the dataset.
148 tially with the number of sub-chains; such a running time scaling is impractical for many application
149 ed various factors, including memory usages, running time, sequencing depth, and recovery of protein-
150                  Here we present the longest running time-series of DMS-water (DMS(W)) concentrations
151 , age, systolic blood pressure (SBP), 3000-m running time, serum triglycerides, serum uric acid and w
152 educes common artifacts associated with long running times, such as blurred bands and comingling of c
153 more input formats, larger inputs and longer running times than the motif-x web server.
154  to each sequencing run, while maintaining a running time that is within the range of practical use.
155 n these so-called Factorial HMMs has a naive running time that scales as the square of the number of
156                                  To optimize running time, the code uses clustering and multi-threadi
157 ) or its vehicle at 7 days prior to tests of running time to exhaustion were evaluated in 60-day-old
158 tion of striatal DA, produces a reduction in running time to exhaustion.
159                                 Finally, the running time to fit the model grows linearly in the numb
160 rformance comes at the expense of increasing running time to O(N(2)), rendering BP prohibitive for mo
161       MA resulted in significantly decreased running times to exhaustion compared to vehicle-treated
162                     This makes usability and running time two additional important features of our me
163 of grip strength, muscle mass, and treadmill running time, using 2 SDs below the mean of their young
164                                        Total running time was 11 min.
165                                     Two-mile running time was assessed post-BCT; mood was assessed by
166 ated metagenome data for 10 values of k, the running time was close to 10x faster compared to a class
167                                Pre-treatment running times were not significantly different between t
168 he FFAS server was also optimized for speed: running times were reduced by an order of magnitude.
169 wever, BnpC is more advantageous in terms of running time when cell number is high (> 1500).
170  only once, there is a significant saving in running time when compared with VQSR (4 versus 50 min ap
171  mapping methods need more memory and longer running time when larger maximum deletion size is chosen
172  Using a cache-oblivious kd-tree, we realize running times, which match the state-of-the-art.
173 hat, Phylovar outperforms SCIPhi in terms of running time while being more accurate than Monovar (whi
174               It notably exhibits sub-linear running times with sample size, provides highly accurate
175 return a tree and it has dramatically faster running time within the same divide-and-conquer framewor
176 ignificantly reducing training data size and running time without compromising performance.

 
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