Results 251 to 260 of about 6,086,701 (288)

Exploring the chemical space of transition-metal cluster thermodynamics via automated first-principles calculations and machine learning. [PDF]

Nat Commun
Li NZ, Wang ZY, Li ZY, Shi Q, Liu QY, He SG.   +5 more
europepmc   +1 more source

Assessing second‐price auctions for parcel exchanges in last‐mile logistics

International Transactions in Operational Research, EarlyView.
Abstract The rapid growth of e‐commerce has led to multiple carriers operating in the same regions, creating opportunities for collaboration. However, logistics companies typically operate independently, leading to inefficiencies. Horizontal cooperation, where carriers share resources and infrastructure, can improve efficiency and reduce costs.
Christian Truden, Margaretha Gansterer
wiley   +1 more source

Time-Aware and Power-Law Retention Gating Mechanisms in LSTMs for Irregularly Sampled Sensor Data: A Survey. [PDF]

Sensors (Basel)
Das D, Adams SD, Corva DM, Bucknall TK, Kouzani AZ.   +4 more
europepmc   +1 more source

Quantitative understanding of PDF fits and their uncertainties. [PDF]

Eur Phys J C Part Fields
Chiefa A, Del Debbio L, Kenway R.
europepmc   +1 more source

Scalable modeling of multi-spin ensembles in SABRE hyperpolarization: a symmetry-based framework for zero and ultralow fields. [PDF]

Magn Reson (Gott)
Markelov D, Snadin A, Kiryutin A, Barskiy D, Yurkovskaya A.   +4 more
europepmc   +1 more source

Drifting population dynamics with transient resets characterize sensorimotor transformation in the monkey superior colliculus. [PDF]

Commun Biol
Heusser MR, Jagadisan UK, Ayar EC, Bourrelly C, Gandhi NJ.   +4 more
europepmc   +1 more source

Free Energy Calculation Method Based on Enhanced Sampling of Diverse Protein Conformations Predicted by Artificial Intelligence. [PDF]

J Phys Chem Lett
Aoki T, Harada R.
europepmc   +1 more source

Fourier Methods for Estimating the Central Subspace and the Central Mean Subspace in Regression [PDF]

Journal of the American Statistical Association, 2006
In regression with a high-dimensional predictor vector, it is important to estimate the central and central mean subspaces that preserve sufficient information about the response and the mean response. Using the Fourier transform, we have derived the candidate matrices whose column spaces recover the central and central mean subspaces exhaustively ...
Peng Zeng
exaly   +3 more sources

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Central Mean Subspace in Time Series

Journal of Computational and Graphical Statistics, 2009
We propose a notion of central mean dimension reduction subspace for time series {xt} which does not require specification of a model but seeks to find a p×d matrix Φd, d≤p, so that the d×1 vector ΦdTXt−1, where Xt−1=(xt−1, …, xt−p)T for some p≥1, includes all the information about xt that is available from E(xt|Xt−1).
Xiangrong Yin, Jin-Hong Park, T N Sriram
exaly   +2 more sources

Learning Functions Varying along a Central Subspace

SIAM Journal on Mathematics of Data Science
Many functions of interest are in a high-dimensional space but exhibit low-dimensional structures. This paper studies regression of a $s$-Hölder function $f$ in $\mathbb{R}^D$ which varies along a central subspace of dimension $d$ while $d\ll D$. A direct approximation of $f$ in $\mathbb{R}^D$ with an $\varepsilon$ accuracy requires the number of ...
Wenjing Liao
exaly   +4 more sources