ライフサイエンスコーパス: speedup

1 for 10,920 individuals in a few hours (a 5 x speedup).

2 ific algorithms that still promise a quantum speedup.

3 er architecture, and contributes to GRASP2's speedup.

4 e question "how close can we get?" to linear speedup.

5 explain the observed net regional summer ice speedup.

6 rogramming model in order to explore further speedup.

7 cientNet-lite0 (0.902) while achieving a x47 speedup.

8 n mapping yeast real raw signals with a 4.4x speedup.

9 transient model with potential computational speedup.

10 ous decoders and demonstrating the predicted speedup.

11 been devoted to detect and quantify quantum speedup.

12 earlier methods but yields at least 10-fold speedup.

13 lly small sensing range suffices for extreme speedup.

14 ies, only a few acceleration attempts report speedup.

15 role that quantum effects play in providing speedup.

16 n of how to fairly assess and detect quantum speedup.

17 void pitfalls that might mask or fake such a speedup.

18 mance computing clusters providing efficient speedup.

19 providing nearly order-of-magnitude CPU time speedups.

20 n, resulting in 40- to 80-fold computational speedups.

21 teness, which allows the potential for large speedups.

22 n algorithmic component that enables quantum speedups.

23 ty of broadly applicable theoretical quantum speedups.

24 , melt-driven processes responsible for past speedups.

25 On the ice sheet, these data reveal summer speedups (50 to 100%) consistent with, but somewhat larg

26 FastRemap provides up to a 7.82x speedup (6.47x, on average) and uses as low as 61.7% (80

27 evious efforts have focused on computational speedups, a definitive and provable quantum advantage th

28 This work shows the speedup ability of quantum algorithm in dimension reduct

29 detailed evaluation demonstrates significant speedup achieved by ti[Formula: see text]-NN over the na

30 comparison to the sequential NCBI-BLAST, the speedups achieved by GPU-BLAST range mostly between 3 an

31 Speedups achieved by H-BLAST over sequential NCBI-BLASTP

32 Speedup across the Getz basin is linear, with speedup an

33 oth the price of ansatz and the price of any speedup advantages of VQCs: numerical results with datat

34 , and we theoretically quantify the expected speedup afforded by trellises.

35 on a classical computer and the exponential speedups afforded by quantum computers.

36 The computational speedup allowed us to conduct pairwise comparisons of 11

37 With other effects producing outlet-glacier speedups an order of magnitude larger, seasonal melt's i

38 rate that Eagle2 attains a approximately 20x speedup and approximately 10% increase in accuracy compa

39 The speedup and efficiency are evaluated by using test data

40 The effects of temperature on speedup and free-energy landscapes, which may differ sub

41 e, we show how to define and measure quantum speedup and how to avoid pitfalls that might mask or fak

42 ers of workstations, providing a substantial speedup and low execution times on large numbers of node

43 gap expansion process, both in terms of ST99 speedup and network queue occupancy.

44 Speedup and other related performance studies are also r

45 ade-off between the magnitude of the optimal speedup and the width of the parameter range over which

46 peedup across the Getz basin is linear, with speedup and thinning directly correlated confirming the

47 hile achieving an order of magnitude or more speedup and using a fraction of the memory footprint.

48 at the proposed QTT method achieves dramatic speedups and several orders of magnitude storage savings

49 old (0.7 h on the above 400 genomes, or ~36x speedup) and achieves higher accuracies when compared to

50 corresponding to a spontaneous emission rate speedup approximately 115 x, for antenna gap spacing, d

51 tic sets (107 versus 107), the corresponding speedups are 1.6x, 5.5x, 6x, and 6x.

52 r PME and GB are the same, the corresponding speedups are approximately onefold (small conformational

53 tional changes considered here, the combined speedups are approximately twofold, approximately 1- to

54 ups, it may be prudent to assume exponential speedups are not generically available for this problem.

55 ults demonstrate the two orders of magnitude speedup as compared to Branch-and-Cut.

56 number of reads mapped, and with near linear speedup as the number of processors increases.

57 rare long-range jumps can lead to a drastic speedup--as air-traffic-mediated epidemics show--it has

58 ession matrices, followed by qualitative and speedup assessment.

59 s approximately 2,500 x spontaneous emission speedup at d approximately 10 nm, proportional to 1/d(2)

60 In simulations, we observed a polynomial speedup between cubic and quartic over such alternatives

61 ity mapping while reducing time-to-solution (speedups between [Formula: see text] to [Formula: see te

62 tative GWAS, achieving a substantial runtime speedup by avoiding the need to exhaustively test all SN

63 It achieves significant speedup by exploiting hierarchical parallelism on single

64 ing any correct sequences, or gain 111 times speedup by filtering out 99.64% of spectra while missing

65 od that achieves several orders of magnitude speedup by using a quantized composite likelihood over c

66 Huxley neurons but that for other models the speedup can differ.

67 Further speedups can be accomplished by splitting data for paral

68 performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achiev

69 Our method yields a 5-fold speedup compared to BPS and makes it possible to learn p

70 It produces a significant speedup compared to direct stochastic simulations in a t

71 resulting new program, GRASPx, achieves >30X speedup compared to its predecessor GRASP.

72 coli sequences, achieving a two to threefold speedup compared to PGGB without increasing greenhouse g

73 ovides predictive efficiency with 1406 times speedup compared to physics-based numerical simulation.

74 ometry (MS), with an approximately 1000-fold speedup compared to the MatchMS reference implementation

75 At the same time, with a significant speedup compared to the physics-based model, the machine

76 opard features a hundredfold to thousandfold speedup compared with current many-to-one machine learni

77 cases, a three orders of magnitude execution speedup compared with MILP.

78 dic spaced seeds, which leads to significant speedup compared with the most efficient methods current

79 -implementation can achieve over a 1000-fold speedup compared with the original tool on reasonable ta

80 , implemented in OpenCL, can have up to 158x speedups compared to ASTRAL-III.

81 BLAST architecture and achieved significant speedup comparing with previously published architecture

82 ces in each case while achieving substantial speedups, completing analyses in minutes.

83 of magnitude for large datasets and that the speedup continues to increase with the number of sequenc

84 Our tests demonstrated very good speedup derived from the parallelization for up to appro

85 In this work, we propose a Moire speedup dispersion tuning method that enables a microres

86 ugh unfolded intermediates, thus showing the speedup envisioned in the fly-casting scenario for molec

87 Our analysis of the speedup explains that another order of magnitude speedup

88 ivalent to that of the sequential HMM with a speedup factor approximately equal to the number of inde

89 VFFVA allowed to gain a threefold speedup factor with coupled models and up to 100 with il

90 threaded muBLASTP achieves up to a 4.41-fold speedup for alignment stages, and up to a 1.75-fold end-

91 predicted to offer [Formula: see text](M(2)) speedup for an M-qubit system, compared to the state-of-

92 neural-network-based approach allows a major speedup for data processing making it usable for online

93 Our configuration provides a speedup for equivalent spectral signal-to-noise ratio (S

94 mentation for codon-based models and >8-fold speedup for nucleotide-based models.

95 r results do not rule out the possibility of speedup for other classes of problems and illustrate the

96 Realizing quantum speedup for practically relevant, computationally hard p

97 Quantum algorithms provide an exponential speedup for solving certain classes of linear systems, i

98 We also introduce a computational speedup for two random-effects models that makes this ap

99 to approximately 24 cores and a quasi-linear speedup for up to approximately 8 cores.

100 ithreaded muBLASTP achieves up to a 5.7-fold speedups for alignment stages, and up to a 4.56-fold end

101 Recent efforts to demonstrate quantum speedups have therefore focused on problems that are bot

102 isting exact simulators, and permits further speedup in approximate mode while retaining support for

103 the decoherence functional (with exponential speedup in both the number of qubits and the number of t

104 he largest dataset we observed over 6.6-fold speedup in computation time on a cluster of eight GPUs c

105 e solution also shows a considerable quantum speedup in computations.

106 We find a remarkable speedup in cooling of twisted bilayer graphene near the

107 Experimental results demonstrate up to 8.8X speedup in DRL training from our approach over previous

108 est graphs, we observe a superlinear quantum speedup in finding exact solutions in the deep circuit r

109 network, which implies an enormous possible speedup in human brains, which encode a high number of s

110 We show that data efficiency and speedup in learning new items are increased roughly prop

111 gies, and shear flows can generate a tenfold speedup in particle formation.

112 utilizing GPUs, we achieve around a 30-fold speedup in protein and protein-DNA simulations over the

113 riers, allowing successful prediction of the speedup in rates in the presence of CypA, which is in no

114 Quantum computers promise a qualitative speedup in solving a broad spectrum of practical optimiz

115 m noise reduction leads to a twofold quantum speedup in the determination of gas concentration, with

116 n within the frozen-error dynamics enables a speedup in the training convergence.

117 The speedups in conformational sampling for GB relative to P

118 Our work suggests speedups in heuristic methods via photonic implementatio

119 under the ROC curve, while achieving average speedups in kernel computation of ~100x and speedups of

120 Run time measurements suggest proportional speedups in overall search times.

121 e local averaging provide order-of-magnitude speedups in spatiotemporally demixing calcium video data

122 ects and make estimates of the corresponding speedups in the overall translocation process.

123 systems studied, 1) conformational sampling speedup increases as Langevin collision frequency (effec

124 t accelerated calving can explain the recent speedup, independent of the grounding-line, melt-driven

125 nce than the previous R implementation (2-5x speedup), interoperability with AnnData format, flexible

126 both space and time), a provable exponential speedup is achieved compared to the standard situation i

127 sions for the conditions under which optimal speedup is achieved: valley or plateau crossing by the s

128 is contradicted by the observation that the speedup is concentrated at nonsynonymous sites.

129 The past speedup is largely due to grounding-line retreat in resp

130 y) decreases; and 2) conformational sampling speedup is mainly due to reduction in solvent viscosity

131 We conclude that quantum speedup is most pronounced for finite-temperature simula

132 The baseline for these speedups is an implementation that has been hand-tuned S

133 d-state quantum chemistry through polynomial speedups, it may be prudent to assume exponential speedu

134 dup explains that another order of magnitude speedup must come from model accuracy rather than comput

135 Computational speedup now enables us to tackle even larger problems: w

136 Finally, our computational speedups now enable (i) efficient LR testing when the ba

137 r accelerator achieves an average throughput speedup of 10.05 x over the CPU-only implementation, an

138 leration with only 7.2% of the energy, and a speedup of 10.11 x compared to an existing FPGA accelera

139 lticore versions of our algorithms achieve a speedup of 23.2 on 24 cores.

140 , in amino acids, our tool achieves a median speedup of 23.9X.

141 2.86-14.88% points less space and provides a speedup of 3.13-20.1 times.

142 00 % accuracy, which translates to a maximum speedup of 37.5, 23.1 and 11.6-fold for MSV, SSV and P7V

143 simulation scenarios we are able to offer a speedup of 6x-46x.

144 2.7-41.66% points less space and provides a speedup of 70-167.16 times, 1.44-35.57 times and 1.3-55.

145 ng, we show the potential for classification speedup of at least one order of magnitude.

146 Combining GPU and HCP, resulted in a speedup of at most 1,860-fold for our largest molecular

147 PINN, that retains the factor of 100-to-1000 speedup of deep learning-based reconstruction while impr

148 This results in an [Formula: see text]-fold speedup of ET steps at heme junctions that would otherwi

149 hydraulics will limit the potential for the speedup of flow.

150 Learning-to-learn, a progressive speedup of learning while solving a series of similar pr

151 compression level which provides an expected speedup of more than an order of magnitude.

152 shows accuracy ranging from 1% to 4.5% and a speedup of one order of magnitude compared to convention

153 organic compound, demonstrating the parallel speedup of our method as well as its flexibility in appl

154 The relative speedup of outlet glaciers, however, is far smaller (<15

155 accuracy and computational cost providing a speedup of over N/p compared to high-fidelity models.

156 Speedup of Pine Island Glacier over the past several dec

157 his induced-switch mechanism provides robust speedup of protein-DNA binding rates, and appears to be

158 t shortcuts to adiabaticity provide a robust speedup of quantum protocols of wide applicability in qu

159 ical redundant signal effect (RSE; i.e., the speedup of response times in multisensory compared with

160 e benefit in multisensory behavior (here the speedup of response times) is largest when behavioral pe

161 ive growth of biological sequences calls for speedup of sequence alignment tools such as BLAST.

162 lence with waves, jets, and vortices, with a speedup of several orders of magnitude compared with dir

163 nstrated that these improvements result in a speedup of several orders of magnitude for large dataset

164 , our enhancements can achieve a significant speedup of the A*-based protein design algorithm by four

165 del to illustrate the accuracy and potential speedup of the algorithm when compared with exact stocha

166 CURC achieves 2-6-fold speedup of the compression with competitive compression

167 ipled way, we demonstrate more than 100-fold speedup of the search for complex motifs compared to pre

168 A motif search that allows for a significant speedup of the search of complex motifs that include pse

169 sed method using real-world problems shows a speedup of total simulation time by a factor of up to 4.

170 graphics processing unit results in dramatic speedup of two orders of magnitude, greatly increasing t

171 ins in a matter of microseconds, providing a speedup of up to 5 orders of magnitude when compared wit

172 show that our novel enhancements result in a speedup of up to a factor of more than 1000 when applied

173 way, (2) particularly, makes them achieve a speedup of up to about 100x on the protein data, and (3)

174 A speedup of up to two orders of magnitude is demonstrated

175 We determine the optimal speedup of valley or plateau crossing that can be gained

176 tase, and antisweetener antibody NC6.8, show speedups of 17, 35, and 39, respectively.

177 TRUFFLE takes 2.6 h, corresponding to IBIS speedups of 20.2-23.3x.

178 n two model proteins with orientations shows speedups of 2578 for one set of configurations and 3341

179 or 7.8 min with IBD2 functionality enabled: speedups of 805-946x including phasing time.

180 ) calculations while achieving computational speedups of several orders of magnitude, allowing the st

181 tate-of-the-art predictors, but also achieve speedups of several orders of magnitude.

182 lossless process (> 99.9%) led to alignment speedups of up to 270% across a variety of data sets, wh

183 speedups in kernel computation of ~100x and speedups of ~800x for large feature lengths.

184 We analytically derive the speedup offered by GNN, relating it to the number of out

185 The massive speedup offered by our method should enable researchers

186 The latter yielded a factor of 2000 speedup on a cohort of size 13 500.

187 p experiments and verified the 28 fold times speedup on a single MIC against the original CPU-based i

188 approach to obtain a two orders of magnitude speedup on an ESF study that focused on the discovery of

189 , RepeatScout, phRAIDER shows an average 10x speedup on any single human chromosome and has the abili

190 ssing unit architectures, with more distinct speedups on less powerful hardware.

191 nverse temperature, exceeding a million-fold speedup over an efficient CPU implementation.

192 nsus sequences while providing a significant speedup over another polishing tool, Nanopolish.

193 f quantum SAT solvers achieve any asymptotic speedup over classical brute-force search for smooth num

194 NISQ cannot achieve a Grover-like quadratic speedup over classical computers.

195 tion of a quantum computer with a well-known speedup over classical searches of an unsorted database.

196 le approach, yielding as much as a 2000-fold speedup over conventional simulation methods.

197 Our model achieves a 10,000x speedup over docking while ranking active compounds in t

198 of Scrooge achieves a 20.1x, 1.7x, and 2.1x speedup over KSW2, Edlib, and a CPU implementation of Ge

199 nome, mapquik achieves a [Formula: see text] speedup over minimap2, making mapquik the fastest mapper

200 ent stages, and up to a 4.56-fold end-to-end speedup over multithreaded NCBI BLAST.

201 ent stages, and up to a 1.75-fold end-to-end speedup over single-threaded NCBI BLAST.

202 ical methods, yielding a 100-fold or greater speedup over standard methodology.

203 l experiments, SubseqHash2 achieves a 10-50x speedup over SubseqHash while maintaining nearly identic

204 parameter optimization procedure with 8X-30X speedup over the basic approach.

205 the string length) while achieving a 3-fold speedup over the best previous algorithm (Gene Myers's b

206 m approach attains up to [Formula: see text] speedup over the classical approach on larger datasets,

207 ndria and 49 yeasts, and observe a >128-fold speedup over the CPU implementation for codon-based mode

208 chieves a 4.0x, 80.4x, 6.8x, 12.6x, and 5.9x speedup over the CPU version of Scrooge, KSW2, Edlib, Da

209 , our quantum algorithm achieves exponential speedup over the fastest known classical algorithm.

210 We show that our method enables a 613x speedup over the latency-optimized LoLa and achieves an

211 tinel 1A/B-derived velocity data show a >12% speedup over the past 3 years, coincident with a 19-km r

212 e CPU-only implementation, an average 1.81 x speedup over the state-of-art GPU acceleration with only

213 a commodity computer, which represents >100 speedup over the state-of-the-art alignment-based method

214 providing, on average, 90-fold and 130-fold speedup over the state-of-the-art software pre-alignment

215 eads, mapquik achieves a [Formula: see text] speedup over the state-of-the-art tool minimap2, and on

216 parts of the analysis, offering substantial speedup over the traditional central processing unit ver

217 computer experiments to give a factor-of-two speedup over the use of a purely heuristic approximation

218 tility, our algorithm achieves a significant speedup over these baselines.

219 units (GPUs) and result in many fold higher speedups over previous CPU implementations.

220 tion, our algorithm demonstrates significant speedups over top-performing CPU-based tools (BLASTP, SW

221 n 4.5 hours using 13 GB of RAM, with further speedups possible using multiple processors.

222 Further, we include two speedup preserving algorithms for a) the functional form

223 illustrate the subtle nature of the quantum speedup question.

224 These experiments highlight a notable speedup, reducing execution time compared to BLAT.

225 ch, an algorithm that achieved a ~20-90-fold speedup relative to BLAST while still achieving similar

226 d quantum systems, but also offers a quantum speedup relative to the classical counterpart.

227 ons PySB/cupSODA achieves order-of-magnitude speedups relative to a CPU-based ordinary differential e

228 While the debate over speedup remains inconclusive as of now, instead of attem

229 ever, the demonstration of quantum annealing speedups remains to this day an elusive albeit coveted g

230 nce algorithms immediately yields up to 400x speedups, requiring only a small change to the program c

231 ovement of 53.10, 16.87, 3.60 and 1.64 times speedup, respectively.

232 results indicate that ERA can significantly speedup RNA structure-structure alignments compared to o

233 e, we show that W1 OT solver achieves 25~45x speedup, scales better on high dimensional transportatio

234 : see text]-ep features exceptional parallel speedup, scaling efficiently when using up to thousands

235 hes the minimum, thus demonstrating that the speedup seen with increasing volume exclusivity at low t

236 To speedup sequence alignment, ProbeMatch uses gapped q-gra

237 their shear zones, which results in further speedup, shearing, and weakening, hence promoting additi

238 This empirical speedup, should it persist to larger scales, could ease

239 al and local residual learning with multiple speedup techniques included resulting in fast convergenc

240 achieving up to 27% better solution and ~ 2X speedup than conventional simulated annealing.

241 oll innermost loop to yield upto 2 to 3-fold speedup than static compilation but also enables dynamic

242 Notably, we found that Yuzu provides speedups that increase with the complexity of the convol

243 nd operation errors, and hence significantly speedup the communication rate.

244 FiCoS was able to dramatically speedup the computations up to 855x, showing to be a pro

245 can identify promising sequence segments and speedup the detection process.

246 For speedup the series is divided into blocks that are distr

247 s of many atomistic systems with significant speedup, they currently lack the inputs required for pre

248 PU implementation on a dual Xeon 5520 and 3X speedup versus BEAGLE's GPU implementation on a Tesla T1

249 relevant multigene signatures; (iv) analysis speedups via caching; (v) a new dataset download feature

250 lity analysis that is accelerated (up to 60x speedup) via solver warm-starts.

251 s in 9.3 min on a 16-core workstation (35.3x speedup vs. XHMM), 12.7 min using 10 executor cores on a

252 10 executor cores on a Spark cluster (18.8x speedup vs. XHMM), and 9.8 min using 32 executor cores o

253 rvables in the quantum algorithm reduces the speedup, we show that one can estimate all elements of t

254 This represents a ~40X speedup when compared with BEAGLE's CPU implementation o

255 re-filtering methods that elicit substantial speedup when coupled with existing tools.

256 a benchmark, we found no evidence of quantum speedup when the entire data set is considered and obtai

257 Our parallel algorithm achieves 35x speedup when utilizing 64 processing cores for large rea

258 ose regression algorithms, obtaining 50-450x speedups while providing as accurate solutions as existi

259 mization Algorithm and conclude that quantum speedup will not be attainable, at least for a represent

260 We anticipate that this massive speedup will significantly advance the development of no

261 ures, GPU-I-TASSER achieves on average a 10x speedup with comparable structure prediction accuracy co

262 The results show a 700-fold speedup with respect to a single CPU implementation.

263 arallel measurement of the energy provides a speedup with respect to purely in silico simulations sca

264 n scheme, GPSat offers a 504 x computational speedup, with less than 4 mm difference on the derived f

265 antum information processing offers dramatic speedups, yet is susceptible to decoherence, whereby qua